librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-04 06:29:44	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 550 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-04 06:26:08	card stringlengths 11 1.01M
om-ashish-soni/output	om-ashish-soni	2024-03-08T04:27:13Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:om-ashish-soni/shiv-mahapuran-ai", "base_model:finetune:om-ashish-soni/shiv-mahapuran-ai", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T15:34:24Z	--- base_model: om-ashish-soni/shiv-mahapuran-ai tags: - generated_from_trainer model-index: - name: output results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output This model is a fine-tuned version of [om-ashish-soni/shiv-mahapuran-ai](https://huggingface.co/om-ashish-soni/shiv-mahapuran-ai) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
om-ashish-soni/shripad-charitramrutam-lm-v2	om-ashish-soni	2024-03-08T04:27:07Z	5	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T04:26:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
okeanos/uptimeai-8273	okeanos	2024-03-08T04:26:46Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Phind/Phind-CodeLlama-34B-v2", "base_model:merge:Phind/Phind-CodeLlama-34B-v2", "base_model:codellama/CodeLlama-34b-Instruct-hf", "base_model:merge:codellama/CodeLlama-34b-Instruct-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T18:56:55Z	--- base_model: - codellama/CodeLlama-34b-Instruct-hf - Phind/Phind-CodeLlama-34B-v2 library_name: transformers tags: - mergekit - merge --- # merge-legacy This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) as a base. ### Models Merged The following models were included in the merge: * [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: codellama/CodeLlama-34b-Instruct-hf parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: Phind/Phind-CodeLlama-34B-v2 parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient merge_method: dare_ties base_model: codellama/CodeLlama-34b-Instruct-hf parameters: normalize: true int8_mask: true dtype: float16 ```
ahmedgongi10/mistral_instruct_devops11	ahmedgongi10	2024-03-08T04:23:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T04:23:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
saqidr/pegasus-samsum	saqidr	2024-03-08T04:18:13Z	71	0	transformers	[ "transformers", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-cnn_dailymail", "base_model:finetune:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-08T03:54:21Z	--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4849 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6267 \| 0.54 \| 500 \| 1.4849 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
frankenmerger/delta-4b-notso-base	frankenmerger	2024-03-08T04:04:06Z	66	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T18:41:10Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - conversational --- ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "gmonsoon/Delta-4B-notso-base" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Lemunite/vietinbank-vistral-7b-chat_merged	Lemunite	2024-03-08T03:56:29Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T03:52:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Sumail/Silver_Waves04_2b	Sumail	2024-03-08T03:56:12Z	3	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:deepnetguy/gemma-55", "base_model:merge:deepnetguy/gemma-55", "base_model:tomaszki/gemma-31", "base_model:merge:tomaszki/gemma-31", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T03:47:28Z	--- base_model: - deepnetguy/gemma-55 - 0x0dad0/nous_nb20_plus - tomaszki/gemma-31 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [tomaszki/gemma-31](https://huggingface.co/tomaszki/gemma-31) as a base. ### Models Merged The following models were included in the merge: * [deepnetguy/gemma-55](https://huggingface.co/deepnetguy/gemma-55) * [0x0dad0/nous_nb20_plus](https://huggingface.co/0x0dad0/nous_nb20_plus) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: tomaszki/gemma-31 # no parameters necessary for base model - model: deepnetguy/gemma-55 parameters: density: 0.5 weight: 0.4 - model: 0x0dad0/nous_nb20_plus parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: tomaszki/gemma-31 parameters: normalize: true dtype: bfloat16 ```
Neela/layoutlm-funsd	Neela	2024-03-08T03:53:17Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "layoutlm", "token-classification", "generated_from_trainer", "dataset:funsd", "base_model:microsoft/layoutlm-base-uncased", "base_model:finetune:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-07T17:07:05Z	--- license: mit base_model: microsoft/layoutlm-base-uncased tags: - generated_from_trainer datasets: - funsd model-index: - name: layoutlm-funsd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 1.1243 - Answer: {'precision': 0.40076335877862596, 'recall': 0.519159456118665, 'f1': 0.4523424878836834, 'number': 809} - Header: {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} - Question: {'precision': 0.5280065897858319, 'recall': 0.6018779342723005, 'f1': 0.5625274243089073, 'number': 1065} - Overall Precision: 0.4616 - Overall Recall: 0.5459 - Overall F1: 0.5002 - Overall Accuracy: 0.6215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Answer \| Header \| Question \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------------------------------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------------:\|:------------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 1.7728 \| 1.0 \| 10 \| 1.5441 \| {'precision': 0.04580152671755725, 'recall': 0.059332509270704575, 'f1': 0.05169628432956382, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.20335429769392033, 'recall': 0.18215962441314554, 'f1': 0.19217434373452202, 'number': 1065} \| 0.1209 \| 0.1214 \| 0.1212 \| 0.3719 \| \| 1.4551 \| 2.0 \| 20 \| 1.3517 \| {'precision': 0.20478234212139793, 'recall': 0.41285537700865266, 'f1': 0.27377049180327867, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.26090225563909775, 'recall': 0.32582159624413143, 'f1': 0.28977035490605424, 'number': 1065} \| 0.2297 \| 0.3417 \| 0.2747 \| 0.4263 \| \| 1.295 \| 3.0 \| 30 \| 1.2465 \| {'precision': 0.26224426534407935, 'recall': 0.522867737948084, 'f1': 0.34929810074318746, 'number': 809} \| {'precision': 0.058823529411764705, 'recall': 0.01680672268907563, 'f1': 0.026143790849673203, 'number': 119} \| {'precision': 0.3458528951486698, 'recall': 0.41502347417840374, 'f1': 0.37729406743491256, 'number': 1065} \| 0.2964 \| 0.4350 \| 0.3526 \| 0.4803 \| \| 1.1635 \| 4.0 \| 40 \| 1.1449 \| {'precision': 0.28778467908902694, 'recall': 0.515451174289246, 'f1': 0.3693534100974314, 'number': 809} \| {'precision': 0.2638888888888889, 'recall': 0.15966386554621848, 'f1': 0.19895287958115182, 'number': 119} \| {'precision': 0.412396694214876, 'recall': 0.46854460093896716, 'f1': 0.4386813186813187, 'number': 1065} \| 0.3424 \| 0.4691 \| 0.3959 \| 0.5521 \| \| 1.0456 \| 5.0 \| 50 \| 1.0703 \| {'precision': 0.3060240963855422, 'recall': 0.47095179233621753, 'f1': 0.37098344693281404, 'number': 809} \| {'precision': 0.3472222222222222, 'recall': 0.21008403361344538, 'f1': 0.2617801047120419, 'number': 119} \| {'precision': 0.40298507462686567, 'recall': 0.5830985915492958, 'f1': 0.476592478894858, 'number': 1065} \| 0.3593 \| 0.5153 \| 0.4234 \| 0.5797 \| \| 0.9601 \| 6.0 \| 60 \| 1.2304 \| {'precision': 0.30907920154539603, 'recall': 0.5933250927070457, 'f1': 0.40643522438611346, 'number': 809} \| {'precision': 0.3333333333333333, 'recall': 0.16806722689075632, 'f1': 0.223463687150838, 'number': 119} \| {'precision': 0.4642857142857143, 'recall': 0.4394366197183099, 'f1': 0.4515195369030391, 'number': 1065} \| 0.3693 \| 0.4857 \| 0.4196 \| 0.5479 \| \| 0.9153 \| 7.0 \| 70 \| 1.1091 \| {'precision': 0.35518157661647476, 'recall': 0.4956736711990111, 'f1': 0.41382868937048506, 'number': 809} \| {'precision': 0.3125, 'recall': 0.21008403361344538, 'f1': 0.25125628140703515, 'number': 119} \| {'precision': 0.5262645914396887, 'recall': 0.507981220657277, 'f1': 0.5169612995699953, 'number': 1065} \| 0.4323 \| 0.4852 \| 0.4572 \| 0.6011 \| \| 0.8346 \| 8.0 \| 80 \| 1.0632 \| {'precision': 0.35597826086956524, 'recall': 0.4857849196538937, 'f1': 0.4108729743857816, 'number': 809} \| {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} \| {'precision': 0.46401799100449775, 'recall': 0.5812206572769953, 'f1': 0.516048353480617, 'number': 1065} \| 0.4102 \| 0.5213 \| 0.4591 \| 0.6103 \| \| 0.7789 \| 9.0 \| 90 \| 1.0955 \| {'precision': 0.3817062445030783, 'recall': 0.5364647713226205, 'f1': 0.44604316546762585, 'number': 809} \| {'precision': 0.26, 'recall': 0.2184873949579832, 'f1': 0.23744292237442924, 'number': 119} \| {'precision': 0.5137693631669535, 'recall': 0.5605633802816902, 'f1': 0.5361472833408173, 'number': 1065} \| 0.4406 \| 0.5304 \| 0.4813 \| 0.6082 \| \| 0.7751 \| 10.0 \| 100 \| 1.1232 \| {'precision': 0.38474434199497065, 'recall': 0.5673671199011124, 'f1': 0.45854145854145856, 'number': 809} \| {'precision': 0.3010752688172043, 'recall': 0.23529411764705882, 'f1': 0.2641509433962264, 'number': 119} \| {'precision': 0.5040358744394619, 'recall': 0.5276995305164319, 'f1': 0.5155963302752293, 'number': 1065} \| 0.4369 \| 0.5263 \| 0.4775 \| 0.6032 \| \| 0.6875 \| 11.0 \| 110 \| 1.1092 \| {'precision': 0.39342723004694835, 'recall': 0.5179233621755254, 'f1': 0.44717182497331914, 'number': 809} \| {'precision': 0.34146341463414637, 'recall': 0.23529411764705882, 'f1': 0.27860696517412936, 'number': 119} \| {'precision': 0.5076305220883535, 'recall': 0.5934272300469483, 'f1': 0.5471861471861472, 'number': 1065} \| 0.4511 \| 0.5414 \| 0.4921 \| 0.6233 \| \| 0.6808 \| 12.0 \| 120 \| 1.1286 \| {'precision': 0.40641158221303, 'recall': 0.4857849196538937, 'f1': 0.44256756756756754, 'number': 809} \| {'precision': 0.24561403508771928, 'recall': 0.23529411764705882, 'f1': 0.24034334763948498, 'number': 119} \| {'precision': 0.49772036474164133, 'recall': 0.6150234741784038, 'f1': 0.5501889962200757, 'number': 1065} \| 0.4489 \| 0.5399 \| 0.4902 \| 0.6159 \| \| 0.656 \| 13.0 \| 130 \| 1.1237 \| {'precision': 0.39822134387351776, 'recall': 0.49814585908529047, 'f1': 0.442613948380011, 'number': 809} \| {'precision': 0.2967032967032967, 'recall': 0.226890756302521, 'f1': 0.2571428571428572, 'number': 119} \| {'precision': 0.5141732283464567, 'recall': 0.6131455399061033, 'f1': 0.5593147751605996, 'number': 1065} \| 0.4564 \| 0.5434 \| 0.4961 \| 0.6179 \| \| 0.6359 \| 14.0 \| 140 \| 1.1296 \| {'precision': 0.3996399639963996, 'recall': 0.5488257107540173, 'f1': 0.46249999999999997, 'number': 809} \| {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119} \| {'precision': 0.5376712328767124, 'recall': 0.5896713615023474, 'f1': 0.5624720107478729, 'number': 1065} \| 0.4655 \| 0.5514 \| 0.5048 \| 0.6173 \| \| 0.6117 \| 15.0 \| 150 \| 1.1243 \| {'precision': 0.40076335877862596, 'recall': 0.519159456118665, 'f1': 0.4523424878836834, 'number': 809} \| {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} \| {'precision': 0.5280065897858319, 'recall': 0.6018779342723005, 'f1': 0.5625274243089073, 'number': 1065} \| 0.4616 \| 0.5459 \| 0.5002 \| 0.6215 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
OwOOwO/eacc_contTrain_m6_2	OwOOwO	2024-03-08T03:52:45Z	89	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T03:50:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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debasishdas/llama2-7b-chat-finetuned-legal	debasishdas	2024-03-08T03:36:52Z	0	0	null	[ "safetensors", "generated_from_trainer", "region:us" ]	null	2024-03-07T08:55:42Z	--- tags: - generated_from_trainer model-index: - name: llama2-7b-chat-finetuned-legal results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama2-7b-chat-finetuned-legal This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Sumail/Silver_Waves03_2b	Sumail	2024-03-08T03:31:47Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergewss]", "mergekit", "lazymergekit", "tomaszki/gemma-31", "0x0dad0/nous_nb20_plus", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T03:19:41Z	--- license: apache-2.0 tags: - mergewss] - mergekit - lazymergekit - tomaszki/gemma-31 - 0x0dad0/nous_nb20_plus --- # Silver_Waves03_2b Silver_Waves03_2b is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [tomaszki/gemma-31](https://huggingface.co/tomaszki/gemma-31) * [0x0dad0/nous_nb20_plus](https://huggingface.co/0x0dad0/nous_nb20_plus) ## 🧩 Configuration ```yaml models: - model: deepnetguy/gemma-55 # no parameters necessary for base model - model: tomaszki/gemma-31 parameters: density: 0.5 weight: 0.3 - model: 0x0dad0/nous_nb20_plus parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: deepnetguy/gemma-55 parameters: normalize: true dtype: bfloat16 ```
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_64_0.05_4_0.0002	ferrazzipietro	2024-03-08T03:30:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T03:29:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Aayush232730/Unit1	Aayush232730	2024-03-08T03:30:45Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-07T23:25:26Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.74 +/- 21.90 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
KhangSimple/output	KhangSimple	2024-03-08T03:27:55Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-08T02:29:51Z	--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_trainer model-index: - name: output results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
dbvenkat/code-search-net-tokenizer	dbvenkat	2024-03-08T03:17:33Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T03:17:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Jayfeather1024/alpaca_struq	Jayfeather1024	2024-03-08T03:11:52Z	20	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2402.06363", "license:unknown", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T16:04:40Z	--- license: unknown --- Unofficial checkpoint for the StruQ defense method against prompt injection attack. The base model is https://huggingface.co/chavinlo/alpaca-native. StruQ: Defending Against Prompt Injection with Structured Queries (https://arxiv.org/abs/2402.06363)
Kudod/hoa-1b4_model_kaggle_format	Kudod	2024-03-08T03:09:38Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:vlsp-2023-vllm/hoa-1b4", "base_model:adapter:vlsp-2023-vllm/hoa-1b4", "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2024-03-08T02:55:15Z	--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: vlsp-2023-vllm/hoa-1b4 model-index: - name: hoa-1b4_model_kaggle_format results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hoa-1b4_model_kaggle_format This model is a fine-tuned version of [vlsp-2023-vllm/hoa-1b4](https://huggingface.co/vlsp-2023-vllm/hoa-1b4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 65 \| 2.6363 \| \| No log \| 2.0 \| 130 \| 1.8356 \| \| No log \| 3.0 \| 195 \| 1.3984 \| \| No log \| 4.0 \| 260 \| 1.1658 \| \| No log \| 5.0 \| 325 \| 0.9857 \| \| No log \| 6.0 \| 390 \| 0.8724 \| \| No log \| 7.0 \| 455 \| 0.8085 \| \| 1.4171 \| 8.0 \| 520 \| 0.7400 \| \| 1.4171 \| 9.0 \| 585 \| 0.6925 \| \| 1.4171 \| 10.0 \| 650 \| 0.6654 \| \| 1.4171 \| 11.0 \| 715 \| 0.6383 \| \| 1.4171 \| 12.0 \| 780 \| 0.6341 \| \| 1.4171 \| 13.0 \| 845 \| 0.6148 \| \| 1.4171 \| 14.0 \| 910 \| 0.5979 \| \| 1.4171 \| 15.0 \| 975 \| 0.6061 \| \| 0.2596 \| 16.0 \| 1040 \| 0.5960 \| \| 0.2596 \| 17.0 \| 1105 \| 0.5810 \| \| 0.2596 \| 18.0 \| 1170 \| 0.5812 \| \| 0.2596 \| 19.0 \| 1235 \| 0.5761 \| \| 0.2596 \| 20.0 \| 1300 \| 0.5724 \| \| 0.2596 \| 21.0 \| 1365 \| 0.5600 \| \| 0.2596 \| 22.0 \| 1430 \| 0.5927 \| \| 0.2596 \| 23.0 \| 1495 \| 0.5627 \| \| 0.1245 \| 24.0 \| 1560 \| 0.5500 \| \| 0.1245 \| 25.0 \| 1625 \| 0.5706 \| \| 0.1245 \| 26.0 \| 1690 \| 0.5551 \| \| 0.1245 \| 27.0 \| 1755 \| 0.5548 \| \| 0.1245 \| 28.0 \| 1820 \| 0.5573 \| \| 0.1245 \| 29.0 \| 1885 \| 0.5642 \| \| 0.1245 \| 30.0 \| 1950 \| 0.5712 \| \| 0.0896 \| 31.0 \| 2015 \| 0.5524 \| \| 0.0896 \| 32.0 \| 2080 \| 0.5644 \| \| 0.0896 \| 33.0 \| 2145 \| 0.5511 \| \| 0.0896 \| 34.0 \| 2210 \| 0.5648 \| \| 0.0896 \| 35.0 \| 2275 \| 0.5722 \| \| 0.0896 \| 36.0 \| 2340 \| 0.5619 \| \| 0.0896 \| 37.0 \| 2405 \| 0.5632 \| \| 0.0896 \| 38.0 \| 2470 \| 0.5628 \| \| 0.0746 \| 39.0 \| 2535 \| 0.5593 \| \| 0.0746 \| 40.0 \| 2600 \| 0.5624 \| \| 0.0746 \| 41.0 \| 2665 \| 0.5744 \| \| 0.0746 \| 42.0 \| 2730 \| 0.5525 \| \| 0.0746 \| 43.0 \| 2795 \| 0.5858 \| \| 0.0746 \| 44.0 \| 2860 \| 0.5615 \| \| 0.0746 \| 45.0 \| 2925 \| 0.5614 \| \| 0.0746 \| 46.0 \| 2990 \| 0.5678 \| \| 0.0696 \| 47.0 \| 3055 \| 0.5735 \| \| 0.0696 \| 48.0 \| 3120 \| 0.5674 \| \| 0.0696 \| 49.0 \| 3185 \| 0.5637 \| \| 0.0696 \| 50.0 \| 3250 \| 0.5623 \| \| 0.0696 \| 51.0 \| 3315 \| 0.5668 \| \| 0.0696 \| 52.0 \| 3380 \| 0.5625 \| \| 0.0696 \| 53.0 \| 3445 \| 0.5630 \| \| 0.0636 \| 54.0 \| 3510 \| 0.5675 \| \| 0.0636 \| 55.0 \| 3575 \| 0.5646 \| \| 0.0636 \| 56.0 \| 3640 \| 0.5702 \| \| 0.0636 \| 57.0 \| 3705 \| 0.5729 \| \| 0.0636 \| 58.0 \| 3770 \| 0.5745 \| \| 0.0636 \| 59.0 \| 3835 \| 0.5737 \| \| 0.0636 \| 60.0 \| 3900 \| 0.5724 \| \| 0.0636 \| 61.0 \| 3965 \| 0.5658 \| \| 0.0579 \| 62.0 \| 4030 \| 0.5759 \| \| 0.0579 \| 63.0 \| 4095 \| 0.5777 \| \| 0.0579 \| 64.0 \| 4160 \| 0.5722 \| \| 0.0579 \| 65.0 \| 4225 \| 0.5721 \| \| 0.0579 \| 66.0 \| 4290 \| 0.5772 \| \| 0.0579 \| 67.0 \| 4355 \| 0.5747 \| \| 0.0579 \| 68.0 \| 4420 \| 0.5800 \| \| 0.0579 \| 69.0 \| 4485 \| 0.5814 \| \| 0.0557 \| 70.0 \| 4550 \| 0.5777 \| \| 0.0557 \| 71.0 \| 4615 \| 0.5765 \| \| 0.0557 \| 72.0 \| 4680 \| 0.5790 \| \| 0.0557 \| 73.0 \| 4745 \| 0.5845 \| \| 0.0557 \| 74.0 \| 4810 \| 0.5788 \| \| 0.0557 \| 75.0 \| 4875 \| 0.5836 \| \| 0.0557 \| 76.0 \| 4940 \| 0.5911 \| \| 0.052 \| 77.0 \| 5005 \| 0.5841 \| \| 0.052 \| 78.0 \| 5070 \| 0.5822 \| \| 0.052 \| 79.0 \| 5135 \| 0.5828 \| \| 0.052 \| 80.0 \| 5200 \| 0.5868 \| \| 0.052 \| 81.0 \| 5265 \| 0.5858 \| \| 0.052 \| 82.0 \| 5330 \| 0.5899 \| \| 0.052 \| 83.0 \| 5395 \| 0.5888 \| \| 0.052 \| 84.0 \| 5460 \| 0.5871 \| \| 0.0478 \| 85.0 \| 5525 \| 0.5867 \| \| 0.0478 \| 86.0 \| 5590 \| 0.5894 \| \| 0.0478 \| 87.0 \| 5655 \| 0.5899 \| \| 0.0478 \| 88.0 \| 5720 \| 0.5899 \| \| 0.0478 \| 89.0 \| 5785 \| 0.5915 \| \| 0.0478 \| 90.0 \| 5850 \| 0.5901 \| \| 0.0478 \| 91.0 \| 5915 \| 0.5919 \| \| 0.0478 \| 92.0 \| 5980 \| 0.5919 \| \| 0.0458 \| 93.0 \| 6045 \| 0.5916 \| \| 0.0458 \| 94.0 \| 6110 \| 0.5914 \| \| 0.0458 \| 95.0 \| 6175 \| 0.5929 \| \| 0.0458 \| 96.0 \| 6240 \| 0.5920 \| \| 0.0458 \| 97.0 \| 6305 \| 0.5922 \| \| 0.0458 \| 98.0 \| 6370 \| 0.5922 \| \| 0.0458 \| 99.0 \| 6435 \| 0.5924 \| \| 0.0425 \| 100.0 \| 6500 \| 0.5927 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
core-3/kuno-royale-v3-7b	core-3	2024-03-08T03:01:36Z	57	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "SanjiWatsuki/Kunoichi-DPO-v2-7B", "eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3", "base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:merge:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3", "base_model:merge:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3", "license:cc-by-nc-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T14:55:59Z	--- license: cc-by-nc-2.0 tags: - merge - mergekit - lazymergekit - SanjiWatsuki/Kunoichi-DPO-v2-7B - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3 base_model: - SanjiWatsuki/Kunoichi-DPO-v2-7B - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3 model-index: - name: kuno-royale-v3-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.23 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 71.13 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.81 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard --- # kuno-royale-v3-7b Another experimental combination of eren23's ongo-monarch-jaskier merges and Kunoichi-DPO-v2-7B. Untested. kuno-royale-v3-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3) ## 🧩 Configuration ```yaml slices: - sources: - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [0, 32] - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3 layer_range: [0, 32] merge_method: slerp base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "core-3/kuno-royale-v3-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_core-3__kuno-royale-v3-7b) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|74.88\| \|AI2 Reasoning Challenge (25-Shot)\|71.76\| \|HellaSwag (10-Shot) \|88.23\| \|MMLU (5-Shot) \|65.06\| \|TruthfulQA (0-shot) \|71.13\| \|Winogrande (5-shot) \|82.32\| \|GSM8k (5-shot) \|70.81\|
madroid/qwen1.5-0.5B-4bit-new	madroid	2024-03-08T02:57:06Z	5	0	mlx	[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "license:other", "region:us" ]	text-generation	2024-03-08T02:56:16Z	--- language: - en license: other tags: - chat - mlx - mlx license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE pipeline_tag: text-generation --- # madroid/Qwen1.5-0.5B-4bit-new This model was converted to MLX format from [`mlx-community/Qwen1.5-0.5B-Chat-4bit`](). Refer to the [original model card](https://huggingface.co/mlx-community/Qwen1.5-0.5B-Chat-4bit) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("madroid/Qwen1.5-0.5B-4bit-new") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.01_8_0.0002	ferrazzipietro	2024-03-08T02:51:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T02:50:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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denyslinkov/sentiment-lora-dpo	denyslinkov	2024-03-08T02:47:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T02:40:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OwOOwO/eacc_bm_sl3	OwOOwO	2024-03-08T02:35:23Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-07T01:29:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.05_16_0.0002	ferrazzipietro	2024-03-08T02:11:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T02:10:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EricValen/ppo-LunarLander-v2-CleanRL	EricValen	2024-03-08T02:11:33Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-03-08T02:10:51Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -10.07 +/- 84.91 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.0001 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'EricValen/ppo-LunarLander-v2-CleanRL' 'batch_size': 512 'minibatch_size': 128} ```
OwOOwO/eacc_contTrain_m2_55_ori2	OwOOwO	2024-03-08T02:09:24Z	88	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T02:06:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.05_8_0.0002	ferrazzipietro	2024-03-08T01:52:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T01:51:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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automerger/Experiment27Inex12-7B	automerger	2024-03-08T01:45:29Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "base_model:MSL7/INEX12-7b", "base_model:merge:MSL7/INEX12-7b", "base_model:yam-peleg/Experiment27-7B", "base_model:merge:yam-peleg/Experiment27-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T01:44:27Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit base_model: - yam-peleg/Experiment27-7B - MSL7/INEX12-7b --- # Experiment27Inex12-7B Experiment27Inex12-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [yam-peleg/Experiment27-7B](https://huggingface.co/yam-peleg/Experiment27-7B) * [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: yam-peleg/Experiment27-7B layer_range: [0, 32] - model: MSL7/INEX12-7b layer_range: [0, 32] merge_method: slerp base_model: yam-peleg/Experiment27-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment27Inex12-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
AyeSee/roberta-large-lora-token-classification_v1	AyeSee	2024-03-08T01:35:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T13:48:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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abhishekchoudhary0509/distilbert-base-uncased-lora-text-classification	abhishekchoudhary0509	2024-03-08T01:32:52Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-03-08T01:32:46Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-lora-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2642 - Accuracy: {'accuracy': 0.902} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\| \| No log \| 1.0 \| 63 \| 0.2566 \| {'accuracy': 0.902} \| \| No log \| 2.0 \| 126 \| 0.2642 \| {'accuracy': 0.902} \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.05_4_0.0002	ferrazzipietro	2024-03-08T01:32:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T01:31:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
daze-unlv/axolotl-medmcqa-2-epoch	daze-unlv	2024-03-08T01:28:30Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-07T22:54:28Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: lora-out/medmcqa-2-epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: daze-unlv/medmcqa_axolotl type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0 output_dir: ./lora-out/medmcqa-2-epoch eval_sample_packing: false adapter: lora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 2 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: false sdp_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # lora-out/medmcqa-2-epoch This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
farid1088/RoBERTa-legal-de-cased_German_legal_SQuAD_1000	farid1088	2024-03-08T01:27:10Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2024-03-05T20:54:11Z	--- tags: - generated_from_trainer model-index: - name: RoBERTa-legal-de-cased_German_legal_SQuAD_1000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RoBERTa-legal-de-cased_German_legal_SQuAD_1000 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 160 - eval_batch_size: 40 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 1.0 \| 2 \| 6.2392 \| \| No log \| 2.0 \| 4 \| 6.3606 \| \| No log \| 3.0 \| 6 \| 6.3302 \| \| No log \| 4.0 \| 8 \| 6.2556 \| \| No log \| 5.0 \| 10 \| 5.9838 \| \| No log \| 6.0 \| 12 \| 5.6434 \| \| No log \| 7.0 \| 14 \| 5.4625 \| \| No log \| 8.0 \| 16 \| 5.3216 \| \| No log \| 9.0 \| 18 \| 5.1803 \| \| No log \| 10.0 \| 20 \| 5.0230 \| \| No log \| 11.0 \| 22 \| 4.8791 \| \| No log \| 12.0 \| 24 \| 4.8112 \| \| No log \| 13.0 \| 26 \| 4.6359 \| \| No log \| 14.0 \| 28 \| 4.4133 \| \| No log \| 15.0 \| 30 \| 4.2477 \| \| No log \| 16.0 \| 32 \| 4.0479 \| \| No log \| 17.0 \| 34 \| 3.8281 \| \| No log \| 18.0 \| 36 \| 3.6850 \| \| No log \| 19.0 \| 38 \| 3.5521 \| \| No log \| 20.0 \| 40 \| 3.3836 \| \| No log \| 21.0 \| 42 \| 3.2738 \| \| No log \| 22.0 \| 44 \| 3.1723 \| \| No log \| 23.0 \| 46 \| 3.1062 \| \| No log \| 24.0 \| 48 \| 3.0506 \| \| No log \| 25.0 \| 50 \| 2.9974 \| \| No log \| 26.0 \| 52 \| 2.8952 \| \| No log \| 27.0 \| 54 \| 2.8692 \| \| No log \| 28.0 \| 56 \| 2.8122 \| \| No log \| 29.0 \| 58 \| 2.7477 \| \| No log \| 30.0 \| 60 \| 2.7818 \| \| No log \| 31.0 \| 62 \| 2.7222 \| \| No log \| 32.0 \| 64 \| 2.6513 \| \| No log \| 33.0 \| 66 \| 2.5553 \| \| No log \| 34.0 \| 68 \| 2.4697 \| \| No log \| 35.0 \| 70 \| 2.5147 \| \| No log \| 36.0 \| 72 \| 2.4701 \| \| No log \| 37.0 \| 74 \| 2.3817 \| \| No log \| 38.0 \| 76 \| 2.3397 \| \| No log \| 39.0 \| 78 \| 2.3285 \| \| No log \| 40.0 \| 80 \| 2.3427 \| \| No log \| 41.0 \| 82 \| 2.1274 \| \| No log \| 42.0 \| 84 \| 2.0858 \| \| No log \| 43.0 \| 86 \| 2.0831 \| \| No log \| 44.0 \| 88 \| 1.9282 \| \| No log \| 45.0 \| 90 \| 1.9103 \| \| No log \| 46.0 \| 92 \| 1.8713 \| \| No log \| 47.0 \| 94 \| 1.7713 \| \| No log \| 48.0 \| 96 \| 1.7105 \| \| No log \| 49.0 \| 98 \| 1.6483 \| \| No log \| 50.0 \| 100 \| 1.6115 \| \| No log \| 51.0 \| 102 \| 1.5694 \| \| No log \| 52.0 \| 104 \| 1.5768 \| \| No log \| 53.0 \| 106 \| 1.4820 \| \| No log \| 54.0 \| 108 \| 1.4422 \| \| No log \| 55.0 \| 110 \| 1.4515 \| \| No log \| 56.0 \| 112 \| 1.3668 \| \| No log \| 57.0 \| 114 \| 1.4229 \| \| No log \| 58.0 \| 116 \| 1.3764 \| \| No log \| 59.0 \| 118 \| 1.3159 \| \| No log \| 60.0 \| 120 \| 1.3684 \| \| No log \| 61.0 \| 122 \| 1.4024 \| \| No log \| 62.0 \| 124 \| 1.4022 \| \| No log \| 63.0 \| 126 \| 1.4163 \| \| No log \| 64.0 \| 128 \| 1.3030 \| \| No log \| 65.0 \| 130 \| 1.3511 \| \| No log \| 66.0 \| 132 \| 1.4307 \| \| No log \| 67.0 \| 134 \| 1.3482 \| \| No log \| 68.0 \| 136 \| 1.2050 \| \| No log \| 69.0 \| 138 \| 1.2197 \| \| No log \| 70.0 \| 140 \| 1.2955 \| \| No log \| 71.0 \| 142 \| 1.2603 \| \| No log \| 72.0 \| 144 \| 1.2188 \| \| No log \| 73.0 \| 146 \| 1.2209 \| \| No log \| 74.0 \| 148 \| 1.3233 \| \| No log \| 75.0 \| 150 \| 1.3907 \| \| No log \| 76.0 \| 152 \| 1.2892 \| \| No log \| 77.0 \| 154 \| 1.2385 \| \| No log \| 78.0 \| 156 \| 1.2649 \| \| No log \| 79.0 \| 158 \| 1.2912 \| \| No log \| 80.0 \| 160 \| 1.2787 \| \| No log \| 81.0 \| 162 \| 1.2894 \| \| No log \| 82.0 \| 164 \| 1.2219 \| \| No log \| 83.0 \| 166 \| 1.2526 \| \| No log \| 84.0 \| 168 \| 1.3134 \| \| No log \| 85.0 \| 170 \| 1.2738 \| \| No log \| 86.0 \| 172 \| 1.1862 \| \| No log \| 87.0 \| 174 \| 1.1754 \| \| No log \| 88.0 \| 176 \| 1.1856 \| \| No log \| 89.0 \| 178 \| 1.1411 \| \| No log \| 90.0 \| 180 \| 1.1468 \| \| No log \| 91.0 \| 182 \| 1.2219 \| \| No log \| 92.0 \| 184 \| 1.2348 \| \| No log \| 93.0 \| 186 \| 1.2539 \| \| No log \| 94.0 \| 188 \| 1.3142 \| \| No log \| 95.0 \| 190 \| 1.3426 \| \| No log \| 96.0 \| 192 \| 1.2950 \| \| No log \| 97.0 \| 194 \| 1.1578 \| \| No log \| 98.0 \| 196 \| 1.1482 \| \| No log \| 99.0 \| 198 \| 1.2214 \| \| No log \| 100.0 \| 200 \| 1.2621 \| \| No log \| 101.0 \| 202 \| 1.2568 \| \| No log \| 102.0 \| 204 \| 1.2313 \| \| No log \| 103.0 \| 206 \| 1.1385 \| \| No log \| 104.0 \| 208 \| 1.1336 \| \| No log \| 105.0 \| 210 \| 1.1972 \| \| No log \| 106.0 \| 212 \| 1.2738 \| \| No log \| 107.0 \| 214 \| 1.2061 \| \| No log \| 108.0 \| 216 \| 1.1259 \| \| No log \| 109.0 \| 218 \| 1.1295 \| \| No log \| 110.0 \| 220 \| 1.1641 \| \| No log \| 111.0 \| 222 \| 1.1672 \| \| No log \| 112.0 \| 224 \| 1.2211 \| \| No log \| 113.0 \| 226 \| 1.2340 \| \| No log \| 114.0 \| 228 \| 1.2608 \| \| No log \| 115.0 \| 230 \| 1.2590 \| \| No log \| 116.0 \| 232 \| 1.2412 \| \| No log \| 117.0 \| 234 \| 1.2275 \| \| No log \| 118.0 \| 236 \| 1.2798 \| \| No log \| 119.0 \| 238 \| 1.3240 \| \| No log \| 120.0 \| 240 \| 1.2910 \| \| No log \| 121.0 \| 242 \| 1.2228 \| \| No log \| 122.0 \| 244 \| 1.1676 \| \| No log \| 123.0 \| 246 \| 1.2019 \| \| No log \| 124.0 \| 248 \| 1.2762 \| \| No log \| 125.0 \| 250 \| 1.3170 \| \| No log \| 126.0 \| 252 \| 1.2557 \| \| No log \| 127.0 \| 254 \| 1.2017 \| \| No log \| 128.0 \| 256 \| 1.2145 \| \| No log \| 129.0 \| 258 \| 1.3000 \| \| No log \| 130.0 \| 260 \| 1.3371 \| \| No log \| 131.0 \| 262 \| 1.3282 \| \| No log \| 132.0 \| 264 \| 1.2549 \| \| No log \| 133.0 \| 266 \| 1.2636 \| \| No log \| 134.0 \| 268 \| 1.3543 \| \| No log \| 135.0 \| 270 \| 1.3776 \| \| No log \| 136.0 \| 272 \| 1.3820 \| \| No log \| 137.0 \| 274 \| 1.3624 \| \| No log \| 138.0 \| 276 \| 1.3286 \| \| No log \| 139.0 \| 278 \| 1.3389 \| \| No log \| 140.0 \| 280 \| 1.3843 \| \| No log \| 141.0 \| 282 \| 1.4119 \| \| No log \| 142.0 \| 284 \| 1.3404 \| \| No log \| 143.0 \| 286 \| 1.2233 \| \| No log \| 144.0 \| 288 \| 1.1634 \| \| No log \| 145.0 \| 290 \| 1.1743 \| \| No log \| 146.0 \| 292 \| 1.2216 \| \| No log \| 147.0 \| 294 \| 1.2615 \| \| No log \| 148.0 \| 296 \| 1.2698 \| \| No log \| 149.0 \| 298 \| 1.2574 \| \| No log \| 150.0 \| 300 \| 1.2013 \| \| No log \| 151.0 \| 302 \| 1.1782 \| \| No log \| 152.0 \| 304 \| 1.1868 \| \| No log \| 153.0 \| 306 \| 1.2209 \| \| No log \| 154.0 \| 308 \| 1.2650 \| \| No log \| 155.0 \| 310 \| 1.2678 \| \| No log \| 156.0 \| 312 \| 1.2483 \| \| No log \| 157.0 \| 314 \| 1.2249 \| \| No log \| 158.0 \| 316 \| 1.2192 \| \| No log \| 159.0 \| 318 \| 1.2685 \| \| No log \| 160.0 \| 320 \| 1.3042 \| \| No log \| 161.0 \| 322 \| 1.3329 \| \| No log \| 162.0 \| 324 \| 1.3820 \| \| No log \| 163.0 \| 326 \| 1.3776 \| \| No log \| 164.0 \| 328 \| 1.3062 \| \| No log \| 165.0 \| 330 \| 1.2287 \| \| No log \| 166.0 \| 332 \| 1.1804 \| \| No log \| 167.0 \| 334 \| 1.1878 \| \| No log \| 168.0 \| 336 \| 1.2288 \| \| No log \| 169.0 \| 338 \| 1.2620 \| \| No log \| 170.0 \| 340 \| 1.2738 \| \| No log \| 171.0 \| 342 \| 1.2856 \| \| No log \| 172.0 \| 344 \| 1.3189 \| \| No log \| 173.0 \| 346 \| 1.2971 \| \| No log \| 174.0 \| 348 \| 1.2227 \| \| No log \| 175.0 \| 350 \| 1.2113 \| \| No log \| 176.0 \| 352 \| 1.2372 \| \| No log \| 177.0 \| 354 \| 1.2345 \| \| No log \| 178.0 \| 356 \| 1.2357 \| \| No log \| 179.0 \| 358 \| 1.2578 \| \| No log \| 180.0 \| 360 \| 1.2575 \| \| No log \| 181.0 \| 362 \| 1.2438 \| \| No log \| 182.0 \| 364 \| 1.2362 \| \| No log \| 183.0 \| 366 \| 1.2906 \| \| No log \| 184.0 \| 368 \| 1.3564 \| \| No log \| 185.0 \| 370 \| 1.3361 \| \| No log \| 186.0 \| 372 \| 1.3235 \| \| No log \| 187.0 \| 374 \| 1.3131 \| \| No log \| 188.0 \| 376 \| 1.3451 \| \| No log \| 189.0 \| 378 \| 1.3708 \| \| No log \| 190.0 \| 380 \| 1.3735 \| \| No log \| 191.0 \| 382 \| 1.3659 \| \| No log \| 192.0 \| 384 \| 1.3499 \| \| No log \| 193.0 \| 386 \| 1.3248 \| \| No log \| 194.0 \| 388 \| 1.2972 \| \| No log \| 195.0 \| 390 \| 1.3089 \| \| No log \| 196.0 \| 392 \| 1.3088 \| \| No log \| 197.0 \| 394 \| 1.3057 \| \| No log \| 198.0 \| 396 \| 1.2836 \| \| No log \| 199.0 \| 398 \| 1.2748 \| \| No log \| 200.0 \| 400 \| 1.2783 \| \| No log \| 201.0 \| 402 \| 1.3234 \| \| No log \| 202.0 \| 404 \| 1.3851 \| \| No log \| 203.0 \| 406 \| 1.4287 \| \| No log \| 204.0 \| 408 \| 1.3798 \| \| No log \| 205.0 \| 410 \| 1.2660 \| \| No log \| 206.0 \| 412 \| 1.2068 \| \| No log \| 207.0 \| 414 \| 1.2213 \| \| No log \| 208.0 \| 416 \| 1.2811 \| \| No log \| 209.0 \| 418 \| 1.3142 \| \| No log \| 210.0 \| 420 \| 1.3317 \| \| No log \| 211.0 \| 422 \| 1.3334 \| \| No log \| 212.0 \| 424 \| 1.3037 \| \| No log \| 213.0 \| 426 \| 1.2620 \| \| No log \| 214.0 \| 428 \| 1.2192 \| \| No log \| 215.0 \| 430 \| 1.2268 \| \| No log \| 216.0 \| 432 \| 1.2740 \| \| No log \| 217.0 \| 434 \| 1.3298 \| \| No log \| 218.0 \| 436 \| 1.3930 \| \| No log \| 219.0 \| 438 \| 1.4287 \| \| No log \| 220.0 \| 440 \| 1.4227 \| \| No log \| 221.0 \| 442 \| 1.3803 \| \| No log \| 222.0 \| 444 \| 1.3389 \| \| No log \| 223.0 \| 446 \| 1.3402 \| \| No log \| 224.0 \| 448 \| 1.3458 \| \| No log \| 225.0 \| 450 \| 1.3779 \| \| No log \| 226.0 \| 452 \| 1.4241 \| \| No log \| 227.0 \| 454 \| 1.4453 \| \| No log \| 228.0 \| 456 \| 1.4269 \| \| No log \| 229.0 \| 458 \| 1.3875 \| \| No log \| 230.0 \| 460 \| 1.3527 \| \| No log \| 231.0 \| 462 \| 1.3338 \| \| No log \| 232.0 \| 464 \| 1.3420 \| \| No log \| 233.0 \| 466 \| 1.3536 \| \| No log \| 234.0 \| 468 \| 1.3931 \| \| No log \| 235.0 \| 470 \| 1.4257 \| \| No log \| 236.0 \| 472 \| 1.4281 \| \| No log \| 237.0 \| 474 \| 1.4027 \| \| No log \| 238.0 \| 476 \| 1.3635 \| \| No log \| 239.0 \| 478 \| 1.3048 \| \| No log \| 240.0 \| 480 \| 1.2874 \| \| No log \| 241.0 \| 482 \| 1.3135 \| \| No log \| 242.0 \| 484 \| 1.3534 \| \| No log \| 243.0 \| 486 \| 1.3877 \| \| No log \| 244.0 \| 488 \| 1.4125 \| \| No log \| 245.0 \| 490 \| 1.4280 \| \| No log \| 246.0 \| 492 \| 1.4330 \| \| No log \| 247.0 \| 494 \| 1.4254 \| \| No log \| 248.0 \| 496 \| 1.4343 \| \| No log \| 249.0 \| 498 \| 1.3983 \| \| 0.4984 \| 250.0 \| 500 \| 1.3501 \| \| 0.4984 \| 251.0 \| 502 \| 1.3319 \| \| 0.4984 \| 252.0 \| 504 \| 1.3261 \| \| 0.4984 \| 253.0 \| 506 \| 1.3543 \| \| 0.4984 \| 254.0 \| 508 \| 1.3817 \| \| 0.4984 \| 255.0 \| 510 \| 1.4107 \| \| 0.4984 \| 256.0 \| 512 \| 1.4216 \| \| 0.4984 \| 257.0 \| 514 \| 1.3670 \| \| 0.4984 \| 258.0 \| 516 \| 1.3489 \| \| 0.4984 \| 259.0 \| 518 \| 1.3245 \| \| 0.4984 \| 260.0 \| 520 \| 1.3046 \| \| 0.4984 \| 261.0 \| 522 \| 1.3024 \| \| 0.4984 \| 262.0 \| 524 \| 1.2989 \| \| 0.4984 \| 263.0 \| 526 \| 1.3072 \| \| 0.4984 \| 264.0 \| 528 \| 1.3100 \| \| 0.4984 \| 265.0 \| 530 \| 1.3296 \| \| 0.4984 \| 266.0 \| 532 \| 1.3444 \| \| 0.4984 \| 267.0 \| 534 \| 1.3580 \| \| 0.4984 \| 268.0 \| 536 \| 1.3623 \| \| 0.4984 \| 269.0 \| 538 \| 1.3863 \| \| 0.4984 \| 270.0 \| 540 \| 1.4010 \| \| 0.4984 \| 271.0 \| 542 \| 1.4060 \| \| 0.4984 \| 272.0 \| 544 \| 1.4048 \| \| 0.4984 \| 273.0 \| 546 \| 1.4001 \| \| 0.4984 \| 274.0 \| 548 \| 1.3804 \| \| 0.4984 \| 275.0 \| 550 \| 1.3607 \| \| 0.4984 \| 276.0 \| 552 \| 1.3414 \| \| 0.4984 \| 277.0 \| 554 \| 1.3338 \| \| 0.4984 \| 278.0 \| 556 \| 1.3401 \| \| 0.4984 \| 279.0 \| 558 \| 1.3512 \| \| 0.4984 \| 280.0 \| 560 \| 1.3606 \| \| 0.4984 \| 281.0 \| 562 \| 1.3636 \| \| 0.4984 \| 282.0 \| 564 \| 1.3589 \| \| 0.4984 \| 283.0 \| 566 \| 1.3478 \| \| 0.4984 \| 284.0 \| 568 \| 1.3387 \| \| 0.4984 \| 285.0 \| 570 \| 1.3533 \| \| 0.4984 \| 286.0 \| 572 \| 1.3818 \| \| 0.4984 \| 287.0 \| 574 \| 1.4216 \| \| 0.4984 \| 288.0 \| 576 \| 1.4690 \| \| 0.4984 \| 289.0 \| 578 \| 1.4980 \| \| 0.4984 \| 290.0 \| 580 \| 1.5126 \| \| 0.4984 \| 291.0 \| 582 \| 1.5328 \| \| 0.4984 \| 292.0 \| 584 \| 1.5507 \| \| 0.4984 \| 293.0 \| 586 \| 1.5507 \| \| 0.4984 \| 294.0 \| 588 \| 1.5699 \| \| 0.4984 \| 295.0 \| 590 \| 1.5493 \| \| 0.4984 \| 296.0 \| 592 \| 1.5112 \| \| 0.4984 \| 297.0 \| 594 \| 1.4635 \| \| 0.4984 \| 298.0 \| 596 \| 1.4157 \| \| 0.4984 \| 299.0 \| 598 \| 1.3829 \| \| 0.4984 \| 300.0 \| 600 \| 1.3594 \| \| 0.4984 \| 301.0 \| 602 \| 1.3757 \| \| 0.4984 \| 302.0 \| 604 \| 1.4016 \| \| 0.4984 \| 303.0 \| 606 \| 1.4373 \| \| 0.4984 \| 304.0 \| 608 \| 1.4400 \| \| 0.4984 \| 305.0 \| 610 \| 1.4478 \| \| 0.4984 \| 306.0 \| 612 \| 1.4511 \| \| 0.4984 \| 307.0 \| 614 \| 1.4484 \| \| 0.4984 \| 308.0 \| 616 \| 1.4229 \| \| 0.4984 \| 309.0 \| 618 \| 1.3912 \| \| 0.4984 \| 310.0 \| 620 \| 1.3733 \| \| 0.4984 \| 311.0 \| 622 \| 1.3450 \| \| 0.4984 \| 312.0 \| 624 \| 1.3264 \| \| 0.4984 \| 313.0 \| 626 \| 1.3251 \| \| 0.4984 \| 314.0 \| 628 \| 1.3312 \| \| 0.4984 \| 315.0 \| 630 \| 1.3335 \| \| 0.4984 \| 316.0 \| 632 \| 1.3298 \| \| 0.4984 \| 317.0 \| 634 \| 1.3226 \| \| 0.4984 \| 318.0 \| 636 \| 1.3150 \| \| 0.4984 \| 319.0 \| 638 \| 1.3055 \| \| 0.4984 \| 320.0 \| 640 \| 1.2983 \| \| 0.4984 \| 321.0 \| 642 \| 1.2899 \| \| 0.4984 \| 322.0 \| 644 \| 1.2646 \| \| 0.4984 \| 323.0 \| 646 \| 1.2413 \| \| 0.4984 \| 324.0 \| 648 \| 1.2316 \| \| 0.4984 \| 325.0 \| 650 \| 1.2295 \| \| 0.4984 \| 326.0 \| 652 \| 1.2300 \| \| 0.4984 \| 327.0 \| 654 \| 1.2594 \| \| 0.4984 \| 328.0 \| 656 \| 1.2869 \| \| 0.4984 \| 329.0 \| 658 \| 1.2923 \| \| 0.4984 \| 330.0 \| 660 \| 1.3231 \| \| 0.4984 \| 331.0 \| 662 \| 1.3421 \| \| 0.4984 \| 332.0 \| 664 \| 1.3503 \| \| 0.4984 \| 333.0 \| 666 \| 1.3452 \| \| 0.4984 \| 334.0 \| 668 \| 1.3347 \| \| 0.4984 \| 335.0 \| 670 \| 1.3203 \| \| 0.4984 \| 336.0 \| 672 \| 1.3098 \| \| 0.4984 \| 337.0 \| 674 \| 1.3021 \| \| 0.4984 \| 338.0 \| 676 \| 1.3016 \| \| 0.4984 \| 339.0 \| 678 \| 1.3007 \| \| 0.4984 \| 340.0 \| 680 \| 1.3001 \| \| 0.4984 \| 341.0 \| 682 \| 1.3070 \| \| 0.4984 \| 342.0 \| 684 \| 1.3475 \| \| 0.4984 \| 343.0 \| 686 \| 1.3788 \| \| 0.4984 \| 344.0 \| 688 \| 1.3991 \| \| 0.4984 \| 345.0 \| 690 \| 1.4028 \| \| 0.4984 \| 346.0 \| 692 \| 1.4028 \| \| 0.4984 \| 347.0 \| 694 \| 1.3971 \| \| 0.4984 \| 348.0 \| 696 \| 1.3793 \| \| 0.4984 \| 349.0 \| 698 \| 1.3543 \| \| 0.4984 \| 350.0 \| 700 \| 1.3296 \| \| 0.4984 \| 351.0 \| 702 \| 1.3322 \| \| 0.4984 \| 352.0 \| 704 \| 1.3556 \| \| 0.4984 \| 353.0 \| 706 \| 1.3936 \| \| 0.4984 \| 354.0 \| 708 \| 1.4202 \| \| 0.4984 \| 355.0 \| 710 \| 1.4235 \| \| 0.4984 \| 356.0 \| 712 \| 1.3934 \| \| 0.4984 \| 357.0 \| 714 \| 1.3511 \| \| 0.4984 \| 358.0 \| 716 \| 1.2957 \| \| 0.4984 \| 359.0 \| 718 \| 1.2690 \| \| 0.4984 \| 360.0 \| 720 \| 1.2670 \| \| 0.4984 \| 361.0 \| 722 \| 1.2906 \| \| 0.4984 \| 362.0 \| 724 \| 1.3083 \| \| 0.4984 \| 363.0 \| 726 \| 1.3239 \| \| 0.4984 \| 364.0 \| 728 \| 1.3353 \| \| 0.4984 \| 365.0 \| 730 \| 1.3442 \| \| 0.4984 \| 366.0 \| 732 \| 1.3308 \| \| 0.4984 \| 367.0 \| 734 \| 1.3172 \| \| 0.4984 \| 368.0 \| 736 \| 1.3009 \| \| 0.4984 \| 369.0 \| 738 \| 1.2826 \| \| 0.4984 \| 370.0 \| 740 \| 1.2781 \| \| 0.4984 \| 371.0 \| 742 \| 1.2796 \| \| 0.4984 \| 372.0 \| 744 \| 1.2815 \| \| 0.4984 \| 373.0 \| 746 \| 1.3100 \| \| 0.4984 \| 374.0 \| 748 \| 1.3447 \| \| 0.4984 \| 375.0 \| 750 \| 1.3591 \| \| 0.4984 \| 376.0 \| 752 \| 1.3892 \| \| 0.4984 \| 377.0 \| 754 \| 1.4185 \| \| 0.4984 \| 378.0 \| 756 \| 1.4329 \| \| 0.4984 \| 379.0 \| 758 \| 1.4273 \| \| 0.4984 \| 380.0 \| 760 \| 1.4074 \| \| 0.4984 \| 381.0 \| 762 \| 1.3999 \| \| 0.4984 \| 382.0 \| 764 \| 1.3906 \| \| 0.4984 \| 383.0 \| 766 \| 1.3857 \| \| 0.4984 \| 384.0 \| 768 \| 1.3740 \| \| 0.4984 \| 385.0 \| 770 \| 1.3637 \| \| 0.4984 \| 386.0 \| 772 \| 1.3600 \| \| 0.4984 \| 387.0 \| 774 \| 1.3614 \| \| 0.4984 \| 388.0 \| 776 \| 1.3720 \| \| 0.4984 \| 389.0 \| 778 \| 1.3822 \| \| 0.4984 \| 390.0 \| 780 \| 1.3862 \| \| 0.4984 \| 391.0 \| 782 \| 1.3850 \| \| 0.4984 \| 392.0 \| 784 \| 1.3857 \| \| 0.4984 \| 393.0 \| 786 \| 1.3859 \| \| 0.4984 \| 394.0 \| 788 \| 1.3968 \| \| 0.4984 \| 395.0 \| 790 \| 1.4054 \| \| 0.4984 \| 396.0 \| 792 \| 1.4105 \| \| 0.4984 \| 397.0 \| 794 \| 1.4135 \| \| 0.4984 \| 398.0 \| 796 \| 1.4122 \| \| 0.4984 \| 399.0 \| 798 \| 1.3965 \| \| 0.4984 \| 400.0 \| 800 \| 1.3806 \| \| 0.4984 \| 401.0 \| 802 \| 1.3833 \| \| 0.4984 \| 402.0 \| 804 \| 1.3848 \| \| 0.4984 \| 403.0 \| 806 \| 1.3755 \| \| 0.4984 \| 404.0 \| 808 \| 1.3663 \| \| 0.4984 \| 405.0 \| 810 \| 1.3541 \| \| 0.4984 \| 406.0 \| 812 \| 1.3481 \| \| 0.4984 \| 407.0 \| 814 \| 1.3484 \| \| 0.4984 \| 408.0 \| 816 \| 1.3506 \| \| 0.4984 \| 409.0 \| 818 \| 1.3486 \| \| 0.4984 \| 410.0 \| 820 \| 1.3474 \| \| 0.4984 \| 411.0 \| 822 \| 1.3512 \| \| 0.4984 \| 412.0 \| 824 \| 1.3562 \| \| 0.4984 \| 413.0 \| 826 \| 1.3683 \| \| 0.4984 \| 414.0 \| 828 \| 1.3778 \| \| 0.4984 \| 415.0 \| 830 \| 1.3839 \| \| 0.4984 \| 416.0 \| 832 \| 1.3879 \| \| 0.4984 \| 417.0 \| 834 \| 1.3888 \| \| 0.4984 \| 418.0 \| 836 \| 1.3952 \| \| 0.4984 \| 419.0 \| 838 \| 1.4006 \| \| 0.4984 \| 420.0 \| 840 \| 1.3990 \| \| 0.4984 \| 421.0 \| 842 \| 1.3698 \| \| 0.4984 \| 422.0 \| 844 \| 1.3452 \| \| 0.4984 \| 423.0 \| 846 \| 1.3087 \| \| 0.4984 \| 424.0 \| 848 \| 1.2798 \| \| 0.4984 \| 425.0 \| 850 \| 1.2656 \| \| 0.4984 \| 426.0 \| 852 \| 1.2812 \| \| 0.4984 \| 427.0 \| 854 \| 1.2965 \| \| 0.4984 \| 428.0 \| 856 \| 1.3184 \| \| 0.4984 \| 429.0 \| 858 \| 1.3456 \| \| 0.4984 \| 430.0 \| 860 \| 1.3730 \| \| 0.4984 \| 431.0 \| 862 \| 1.3882 \| \| 0.4984 \| 432.0 \| 864 \| 1.3960 \| \| 0.4984 \| 433.0 \| 866 \| 1.3961 \| \| 0.4984 \| 434.0 \| 868 \| 1.3904 \| \| 0.4984 \| 435.0 \| 870 \| 1.3826 \| \| 0.4984 \| 436.0 \| 872 \| 1.3876 \| \| 0.4984 \| 437.0 \| 874 \| 1.3942 \| \| 0.4984 \| 438.0 \| 876 \| 1.3903 \| \| 0.4984 \| 439.0 \| 878 \| 1.4131 \| \| 0.4984 \| 440.0 \| 880 \| 1.4386 \| \| 0.4984 \| 441.0 \| 882 \| 1.4533 \| \| 0.4984 \| 442.0 \| 884 \| 1.4633 \| \| 0.4984 \| 443.0 \| 886 \| 1.4364 \| \| 0.4984 \| 444.0 \| 888 \| 1.3961 \| \| 0.4984 \| 445.0 \| 890 \| 1.3603 \| \| 0.4984 \| 446.0 \| 892 \| 1.3205 \| \| 0.4984 \| 447.0 \| 894 \| 1.2876 \| \| 0.4984 \| 448.0 \| 896 \| 1.2629 \| \| 0.4984 \| 449.0 \| 898 \| 1.2929 \| \| 0.4984 \| 450.0 \| 900 \| 1.3158 \| \| 0.4984 \| 451.0 \| 902 \| 1.3561 \| \| 0.4984 \| 452.0 \| 904 \| 1.4016 \| \| 0.4984 \| 453.0 \| 906 \| 1.4331 \| \| 0.4984 \| 454.0 \| 908 \| 1.4514 \| \| 0.4984 \| 455.0 \| 910 \| 1.4568 \| \| 0.4984 \| 456.0 \| 912 \| 1.4481 \| \| 0.4984 \| 457.0 \| 914 \| 1.4331 \| \| 0.4984 \| 458.0 \| 916 \| 1.4101 \| \| 0.4984 \| 459.0 \| 918 \| 1.4124 \| \| 0.4984 \| 460.0 \| 920 \| 1.4035 \| \| 0.4984 \| 461.0 \| 922 \| 1.3846 \| \| 0.4984 \| 462.0 \| 924 \| 1.3591 \| \| 0.4984 \| 463.0 \| 926 \| 1.3337 \| \| 0.4984 \| 464.0 \| 928 \| 1.3211 \| \| 0.4984 \| 465.0 \| 930 \| 1.3289 \| \| 0.4984 \| 466.0 \| 932 \| 1.3686 \| \| 0.4984 \| 467.0 \| 934 \| 1.4247 \| \| 0.4984 \| 468.0 \| 936 \| 1.4679 \| \| 0.4984 \| 469.0 \| 938 \| 1.4892 \| \| 0.4984 \| 470.0 \| 940 \| 1.5036 \| \| 0.4984 \| 471.0 \| 942 \| 1.5144 \| \| 0.4984 \| 472.0 \| 944 \| 1.5118 \| \| 0.4984 \| 473.0 \| 946 \| 1.4974 \| \| 0.4984 \| 474.0 \| 948 \| 1.4768 \| \| 0.4984 \| 475.0 \| 950 \| 1.4562 \| \| 0.4984 \| 476.0 \| 952 \| 1.4385 \| \| 0.4984 \| 477.0 \| 954 \| 1.4229 \| \| 0.4984 \| 478.0 \| 956 \| 1.4084 \| \| 0.4984 \| 479.0 \| 958 \| 1.4004 \| \| 0.4984 \| 480.0 \| 960 \| 1.4004 \| \| 0.4984 \| 481.0 \| 962 \| 1.3982 \| \| 0.4984 \| 482.0 \| 964 \| 1.3999 \| \| 0.4984 \| 483.0 \| 966 \| 1.4041 \| \| 0.4984 \| 484.0 \| 968 \| 1.4065 \| \| 0.4984 \| 485.0 \| 970 \| 1.4074 \| \| 0.4984 \| 486.0 \| 972 \| 1.3975 \| \| 0.4984 \| 487.0 \| 974 \| 1.4100 \| \| 0.4984 \| 488.0 \| 976 \| 1.4375 \| \| 0.4984 \| 489.0 \| 978 \| 1.4597 \| \| 0.4984 \| 490.0 \| 980 \| 1.4732 \| \| 0.4984 \| 491.0 \| 982 \| 1.4704 \| \| 0.4984 \| 492.0 \| 984 \| 1.4610 \| \| 0.4984 \| 493.0 \| 986 \| 1.4437 \| \| 0.4984 \| 494.0 \| 988 \| 1.4284 \| \| 0.4984 \| 495.0 \| 990 \| 1.4139 \| \| 0.4984 \| 496.0 \| 992 \| 1.4026 \| \| 0.4984 \| 497.0 \| 994 \| 1.3938 \| \| 0.4984 \| 498.0 \| 996 \| 1.4228 \| \| 0.4984 \| 499.0 \| 998 \| 1.4441 \| \| 0.0013 \| 500.0 \| 1000 \| 1.4600 \| \| 0.0013 \| 501.0 \| 1002 \| 1.4651 \| \| 0.0013 \| 502.0 \| 1004 \| 1.4571 \| \| 0.0013 \| 503.0 \| 1006 \| 1.4481 \| \| 0.0013 \| 504.0 \| 1008 \| 1.4398 \| \| 0.0013 \| 505.0 \| 1010 \| 1.4303 \| \| 0.0013 \| 506.0 \| 1012 \| 1.4208 \| \| 0.0013 \| 507.0 \| 1014 \| 1.4074 \| \| 0.0013 \| 508.0 \| 1016 \| 1.3926 \| \| 0.0013 \| 509.0 \| 1018 \| 1.3814 \| \| 0.0013 \| 510.0 \| 1020 \| 1.3729 \| \| 0.0013 \| 511.0 \| 1022 \| 1.3687 \| \| 0.0013 \| 512.0 \| 1024 \| 1.3629 \| \| 0.0013 \| 513.0 \| 1026 \| 1.3900 \| \| 0.0013 \| 514.0 \| 1028 \| 1.4067 \| \| 0.0013 \| 515.0 \| 1030 \| 1.3830 \| \| 0.0013 \| 516.0 \| 1032 \| 1.3642 \| \| 0.0013 \| 517.0 \| 1034 \| 1.3945 \| \| 0.0013 \| 518.0 \| 1036 \| 1.4173 \| \| 0.0013 \| 519.0 \| 1038 \| 1.4311 \| \| 0.0013 \| 520.0 \| 1040 \| 1.4405 \| \| 0.0013 \| 521.0 \| 1042 \| 1.4485 \| \| 0.0013 \| 522.0 \| 1044 \| 1.4568 \| \| 0.0013 \| 523.0 \| 1046 \| 1.4552 \| \| 0.0013 \| 524.0 \| 1048 \| 1.4257 \| \| 0.0013 \| 525.0 \| 1050 \| 1.3988 \| \| 0.0013 \| 526.0 \| 1052 \| 1.3722 \| \| 0.0013 \| 527.0 \| 1054 \| 1.3477 \| \| 0.0013 \| 528.0 \| 1056 \| 1.3285 \| \| 0.0013 \| 529.0 \| 1058 \| 1.3126 \| \| 0.0013 \| 530.0 \| 1060 \| 1.2998 \| \| 0.0013 \| 531.0 \| 1062 \| 1.2948 \| \| 0.0013 \| 532.0 \| 1064 \| 1.2972 \| \| 0.0013 \| 533.0 \| 1066 \| 1.2976 \| \| 0.0013 \| 534.0 \| 1068 \| 1.2979 \| \| 0.0013 \| 535.0 \| 1070 \| 1.3181 \| \| 0.0013 \| 536.0 \| 1072 \| 1.3510 \| \| 0.0013 \| 537.0 \| 1074 \| 1.3788 \| \| 0.0013 \| 538.0 \| 1076 \| 1.3992 \| \| 0.0013 \| 539.0 \| 1078 \| 1.4265 \| \| 0.0013 \| 540.0 \| 1080 \| 1.4463 \| \| 0.0013 \| 541.0 \| 1082 \| 1.4578 \| \| 0.0013 \| 542.0 \| 1084 \| 1.4586 \| \| 0.0013 \| 543.0 \| 1086 \| 1.4551 \| \| 0.0013 \| 544.0 \| 1088 \| 1.4510 \| \| 0.0013 \| 545.0 \| 1090 \| 1.4462 \| \| 0.0013 \| 546.0 \| 1092 \| 1.4394 \| \| 0.0013 \| 547.0 \| 1094 \| 1.4334 \| \| 0.0013 \| 548.0 \| 1096 \| 1.4384 \| \| 0.0013 \| 549.0 \| 1098 \| 1.4397 \| \| 0.0013 \| 550.0 \| 1100 \| 1.4445 \| \| 0.0013 \| 551.0 \| 1102 \| 1.4514 \| \| 0.0013 \| 552.0 \| 1104 \| 1.4554 \| \| 0.0013 \| 553.0 \| 1106 \| 1.4576 \| \| 0.0013 \| 554.0 \| 1108 \| 1.4583 \| \| 0.0013 \| 555.0 \| 1110 \| 1.4601 \| \| 0.0013 \| 556.0 \| 1112 \| 1.4597 \| \| 0.0013 \| 557.0 \| 1114 \| 1.4596 \| \| 0.0013 \| 558.0 \| 1116 \| 1.4577 \| \| 0.0013 \| 559.0 \| 1118 \| 1.4520 \| \| 0.0013 \| 560.0 \| 1120 \| 1.4491 \| \| 0.0013 \| 561.0 \| 1122 \| 1.4455 \| \| 0.0013 \| 562.0 \| 1124 \| 1.4424 \| \| 0.0013 \| 563.0 \| 1126 \| 1.4388 \| \| 0.0013 \| 564.0 \| 1128 \| 1.4303 \| \| 0.0013 \| 565.0 \| 1130 \| 1.4266 \| \| 0.0013 \| 566.0 \| 1132 \| 1.4235 \| \| 0.0013 \| 567.0 \| 1134 \| 1.4207 \| \| 0.0013 \| 568.0 \| 1136 \| 1.4185 \| \| 0.0013 \| 569.0 \| 1138 \| 1.4172 \| \| 0.0013 \| 570.0 \| 1140 \| 1.4145 \| \| 0.0013 \| 571.0 \| 1142 \| 1.4177 \| \| 0.0013 \| 572.0 \| 1144 \| 1.4230 \| \| 0.0013 \| 573.0 \| 1146 \| 1.4247 \| \| 0.0013 \| 574.0 \| 1148 \| 1.4152 \| \| 0.0013 \| 575.0 \| 1150 \| 1.4082 \| \| 0.0013 \| 576.0 \| 1152 \| 1.4027 \| \| 0.0013 \| 577.0 \| 1154 \| 1.4000 \| \| 0.0013 \| 578.0 \| 1156 \| 1.3985 \| \| 0.0013 \| 579.0 \| 1158 \| 1.4005 \| \| 0.0013 \| 580.0 \| 1160 \| 1.4054 \| \| 0.0013 \| 581.0 \| 1162 \| 1.4075 \| \| 0.0013 \| 582.0 \| 1164 \| 1.4120 \| \| 0.0013 \| 583.0 \| 1166 \| 1.4161 \| \| 0.0013 \| 584.0 \| 1168 \| 1.4199 \| \| 0.0013 \| 585.0 \| 1170 \| 1.4222 \| \| 0.0013 \| 586.0 \| 1172 \| 1.4239 \| \| 0.0013 \| 587.0 \| 1174 \| 1.4254 \| \| 0.0013 \| 588.0 \| 1176 \| 1.4162 \| \| 0.0013 \| 589.0 \| 1178 \| 1.4203 \| \| 0.0013 \| 590.0 \| 1180 \| 1.4341 \| \| 0.0013 \| 591.0 \| 1182 \| 1.4659 \| \| 0.0013 \| 592.0 \| 1184 \| 1.4891 \| \| 0.0013 \| 593.0 \| 1186 \| 1.5046 \| \| 0.0013 \| 594.0 \| 1188 \| 1.5110 \| \| 0.0013 \| 595.0 \| 1190 \| 1.5053 \| \| 0.0013 \| 596.0 \| 1192 \| 1.5001 \| \| 0.0013 \| 597.0 \| 1194 \| 1.4795 \| \| 0.0013 \| 598.0 \| 1196 \| 1.4530 \| \| 0.0013 \| 599.0 \| 1198 \| 1.4300 \| \| 0.0013 \| 600.0 \| 1200 \| 1.4101 \| \| 0.0013 \| 601.0 \| 1202 \| 1.3887 \| \| 0.0013 \| 602.0 \| 1204 \| 1.3722 \| \| 0.0013 \| 603.0 \| 1206 \| 1.3588 \| \| 0.0013 \| 604.0 \| 1208 \| 1.3521 \| \| 0.0013 \| 605.0 \| 1210 \| 1.3470 \| \| 0.0013 \| 606.0 \| 1212 \| 1.3519 \| \| 0.0013 \| 607.0 \| 1214 \| 1.3647 \| \| 0.0013 \| 608.0 \| 1216 \| 1.3756 \| \| 0.0013 \| 609.0 \| 1218 \| 1.3838 \| \| 0.0013 \| 610.0 \| 1220 \| 1.3876 \| \| 0.0013 \| 611.0 \| 1222 \| 1.3876 \| \| 0.0013 \| 612.0 \| 1224 \| 1.3871 \| \| 0.0013 \| 613.0 \| 1226 \| 1.3861 \| \| 0.0013 \| 614.0 \| 1228 \| 1.3932 \| \| 0.0013 \| 615.0 \| 1230 \| 1.4157 \| \| 0.0013 \| 616.0 \| 1232 \| 1.4386 \| \| 0.0013 \| 617.0 \| 1234 \| 1.4567 \| \| 0.0013 \| 618.0 \| 1236 \| 1.4693 \| \| 0.0013 \| 619.0 \| 1238 \| 1.4772 \| \| 0.0013 \| 620.0 \| 1240 \| 1.4793 \| \| 0.0013 \| 621.0 \| 1242 \| 1.4671 \| \| 0.0013 \| 622.0 \| 1244 \| 1.4450 \| \| 0.0013 \| 623.0 \| 1246 \| 1.4167 \| \| 0.0013 \| 624.0 \| 1248 \| 1.3841 \| \| 0.0013 \| 625.0 \| 1250 \| 1.3548 \| \| 0.0013 \| 626.0 \| 1252 \| 1.3333 \| \| 0.0013 \| 627.0 \| 1254 \| 1.3233 \| \| 0.0013 \| 628.0 \| 1256 \| 1.3179 \| \| 0.0013 \| 629.0 \| 1258 \| 1.3158 \| \| 0.0013 \| 630.0 \| 1260 \| 1.3153 \| \| 0.0013 \| 631.0 \| 1262 \| 1.3201 \| \| 0.0013 \| 632.0 \| 1264 \| 1.3260 \| \| 0.0013 \| 633.0 \| 1266 \| 1.3341 \| \| 0.0013 \| 634.0 \| 1268 \| 1.3430 \| \| 0.0013 \| 635.0 \| 1270 \| 1.3519 \| \| 0.0013 \| 636.0 \| 1272 \| 1.3612 \| \| 0.0013 \| 637.0 \| 1274 \| 1.3718 \| \| 0.0013 \| 638.0 \| 1276 \| 1.3815 \| \| 0.0013 \| 639.0 \| 1278 \| 1.3941 \| \| 0.0013 \| 640.0 \| 1280 \| 1.4047 \| \| 0.0013 \| 641.0 \| 1282 \| 1.4108 \| \| 0.0013 \| 642.0 \| 1284 \| 1.4149 \| \| 0.0013 \| 643.0 \| 1286 \| 1.4114 \| \| 0.0013 \| 644.0 \| 1288 \| 1.4072 \| \| 0.0013 \| 645.0 \| 1290 \| 1.4023 \| \| 0.0013 \| 646.0 \| 1292 \| 1.3963 \| \| 0.0013 \| 647.0 \| 1294 \| 1.3909 \| \| 0.0013 \| 648.0 \| 1296 \| 1.3862 \| \| 0.0013 \| 649.0 \| 1298 \| 1.3821 \| \| 0.0013 \| 650.0 \| 1300 \| 1.3786 \| \| 0.0013 \| 651.0 \| 1302 \| 1.3785 \| \| 0.0013 \| 652.0 \| 1304 \| 1.3798 \| \| 0.0013 \| 653.0 \| 1306 \| 1.3825 \| \| 0.0013 \| 654.0 \| 1308 \| 1.3856 \| \| 0.0013 \| 655.0 \| 1310 \| 1.3837 \| \| 0.0013 \| 656.0 \| 1312 \| 1.3796 \| \| 0.0013 \| 657.0 \| 1314 \| 1.3739 \| \| 0.0013 \| 658.0 \| 1316 \| 1.3675 \| \| 0.0013 \| 659.0 \| 1318 \| 1.3617 \| \| 0.0013 \| 660.0 \| 1320 \| 1.3569 \| \| 0.0013 \| 661.0 \| 1322 \| 1.3516 \| \| 0.0013 \| 662.0 \| 1324 \| 1.3562 \| \| 0.0013 \| 663.0 \| 1326 \| 1.3711 \| \| 0.0013 \| 664.0 \| 1328 \| 1.3824 \| \| 0.0013 \| 665.0 \| 1330 \| 1.3873 \| \| 0.0013 \| 666.0 \| 1332 \| 1.3901 \| \| 0.0013 \| 667.0 \| 1334 \| 1.3912 \| \| 0.0013 \| 668.0 \| 1336 \| 1.3906 \| \| 0.0013 \| 669.0 \| 1338 \| 1.3892 \| \| 0.0013 \| 670.0 \| 1340 \| 1.3863 \| \| 0.0013 \| 671.0 \| 1342 \| 1.3834 \| \| 0.0013 \| 672.0 \| 1344 \| 1.3811 \| \| 0.0013 \| 673.0 \| 1346 \| 1.3789 \| \| 0.0013 \| 674.0 \| 1348 \| 1.3783 \| \| 0.0013 \| 675.0 \| 1350 \| 1.3775 \| \| 0.0013 \| 676.0 \| 1352 \| 1.3765 \| \| 0.0013 \| 677.0 \| 1354 \| 1.3750 \| \| 0.0013 \| 678.0 \| 1356 \| 1.3732 \| \| 0.0013 \| 679.0 \| 1358 \| 1.3714 \| \| 0.0013 \| 680.0 \| 1360 \| 1.3701 \| \| 0.0013 \| 681.0 \| 1362 \| 1.3690 \| \| 0.0013 \| 682.0 \| 1364 \| 1.3669 \| \| 0.0013 \| 683.0 \| 1366 \| 1.3650 \| \| 0.0013 \| 684.0 \| 1368 \| 1.3652 \| \| 0.0013 \| 685.0 \| 1370 \| 1.3661 \| \| 0.0013 \| 686.0 \| 1372 \| 1.3711 \| \| 0.0013 \| 687.0 \| 1374 \| 1.3762 \| \| 0.0013 \| 688.0 \| 1376 \| 1.3815 \| \| 0.0013 \| 689.0 \| 1378 \| 1.3849 \| \| 0.0013 \| 690.0 \| 1380 \| 1.3866 \| \| 0.0013 \| 691.0 \| 1382 \| 1.3856 \| \| 0.0013 \| 692.0 \| 1384 \| 1.3827 \| \| 0.0013 \| 693.0 \| 1386 \| 1.3785 \| \| 0.0013 \| 694.0 \| 1388 \| 1.3752 \| \| 0.0013 \| 695.0 \| 1390 \| 1.3722 \| \| 0.0013 \| 696.0 \| 1392 \| 1.3719 \| \| 0.0013 \| 697.0 \| 1394 \| 1.3713 \| \| 0.0013 \| 698.0 \| 1396 \| 1.3706 \| \| 0.0013 \| 699.0 \| 1398 \| 1.3682 \| \| 0.0013 \| 700.0 \| 1400 \| 1.3655 \| \| 0.0013 \| 701.0 \| 1402 \| 1.3735 \| \| 0.0013 \| 702.0 \| 1404 \| 1.3824 \| \| 0.0013 \| 703.0 \| 1406 \| 1.3917 \| \| 0.0013 \| 704.0 \| 1408 \| 1.3977 \| \| 0.0013 \| 705.0 \| 1410 \| 1.4018 \| \| 0.0013 \| 706.0 \| 1412 \| 1.4048 \| \| 0.0013 \| 707.0 \| 1414 \| 1.4069 \| \| 0.0013 \| 708.0 \| 1416 \| 1.4071 \| \| 0.0013 \| 709.0 \| 1418 \| 1.4056 \| \| 0.0013 \| 710.0 \| 1420 \| 1.4038 \| \| 0.0013 \| 711.0 \| 1422 \| 1.4027 \| \| 0.0013 \| 712.0 \| 1424 \| 1.3999 \| \| 0.0013 \| 713.0 \| 1426 \| 1.3940 \| \| 0.0013 \| 714.0 \| 1428 \| 1.3880 \| \| 0.0013 \| 715.0 \| 1430 \| 1.3814 \| \| 0.0013 \| 716.0 \| 1432 \| 1.3756 \| \| 0.0013 \| 717.0 \| 1434 \| 1.3708 \| \| 0.0013 \| 718.0 \| 1436 \| 1.3658 \| \| 0.0013 \| 719.0 \| 1438 \| 1.3619 \| \| 0.0013 \| 720.0 \| 1440 \| 1.3605 \| \| 0.0013 \| 721.0 \| 1442 \| 1.3587 \| \| 0.0013 \| 722.0 \| 1444 \| 1.3685 \| \| 0.0013 \| 723.0 \| 1446 \| 1.3823 \| \| 0.0013 \| 724.0 \| 1448 \| 1.3939 \| \| 0.0013 \| 725.0 \| 1450 \| 1.4022 \| \| 0.0013 \| 726.0 \| 1452 \| 1.4089 \| \| 0.0013 \| 727.0 \| 1454 \| 1.4147 \| \| 0.0013 \| 728.0 \| 1456 \| 1.4190 \| \| 0.0013 \| 729.0 \| 1458 \| 1.4273 \| \| 0.0013 \| 730.0 \| 1460 \| 1.4373 \| \| 0.0013 \| 731.0 \| 1462 \| 1.4448 \| \| 0.0013 \| 732.0 \| 1464 \| 1.4494 \| \| 0.0013 \| 733.0 \| 1466 \| 1.4507 \| \| 0.0013 \| 734.0 \| 1468 \| 1.4513 \| \| 0.0013 \| 735.0 \| 1470 \| 1.4585 \| \| 0.0013 \| 736.0 \| 1472 \| 1.4685 \| \| 0.0013 \| 737.0 \| 1474 \| 1.4767 \| \| 0.0013 \| 738.0 \| 1476 \| 1.4740 \| \| 0.0013 \| 739.0 \| 1478 \| 1.4713 \| \| 0.0013 \| 740.0 \| 1480 \| 1.4689 \| \| 0.0013 \| 741.0 \| 1482 \| 1.4668 \| \| 0.0013 \| 742.0 \| 1484 \| 1.4648 \| \| 0.0013 \| 743.0 \| 1486 \| 1.4631 \| \| 0.0013 \| 744.0 \| 1488 \| 1.4613 \| \| 0.0013 \| 745.0 \| 1490 \| 1.4588 \| \| 0.0013 \| 746.0 \| 1492 \| 1.4550 \| \| 0.0013 \| 747.0 \| 1494 \| 1.4507 \| \| 0.0013 \| 748.0 \| 1496 \| 1.4456 \| \| 0.0013 \| 749.0 \| 1498 \| 1.4401 \| \| 0.0003 \| 750.0 \| 1500 \| 1.4360 \| \| 0.0003 \| 751.0 \| 1502 \| 1.4327 \| \| 0.0003 \| 752.0 \| 1504 \| 1.4302 \| \| 0.0003 \| 753.0 \| 1506 \| 1.4290 \| \| 0.0003 \| 754.0 \| 1508 \| 1.4285 \| \| 0.0003 \| 755.0 \| 1510 \| 1.4290 \| \| 0.0003 \| 756.0 \| 1512 \| 1.4267 \| \| 0.0003 \| 757.0 \| 1514 \| 1.4248 \| \| 0.0003 \| 758.0 \| 1516 \| 1.4228 \| \| 0.0003 \| 759.0 \| 1518 \| 1.4206 \| \| 0.0003 \| 760.0 \| 1520 \| 1.4184 \| \| 0.0003 \| 761.0 \| 1522 \| 1.4166 \| \| 0.0003 \| 762.0 \| 1524 \| 1.4148 \| \| 0.0003 \| 763.0 \| 1526 \| 1.4132 \| \| 0.0003 \| 764.0 \| 1528 \| 1.4126 \| \| 0.0003 \| 765.0 \| 1530 \| 1.4171 \| \| 0.0003 \| 766.0 \| 1532 \| 1.4209 \| \| 0.0003 \| 767.0 \| 1534 \| 1.4356 \| \| 0.0003 \| 768.0 \| 1536 \| 1.4466 \| \| 0.0003 \| 769.0 \| 1538 \| 1.4545 \| \| 0.0003 \| 770.0 \| 1540 \| 1.4605 \| \| 0.0003 \| 771.0 \| 1542 \| 1.4648 \| \| 0.0003 \| 772.0 \| 1544 \| 1.4678 \| \| 0.0003 \| 773.0 \| 1546 \| 1.4697 \| \| 0.0003 \| 774.0 \| 1548 \| 1.4707 \| \| 0.0003 \| 775.0 \| 1550 \| 1.4709 \| \| 0.0003 \| 776.0 \| 1552 \| 1.4680 \| \| 0.0003 \| 777.0 \| 1554 \| 1.4634 \| \| 0.0003 \| 778.0 \| 1556 \| 1.4592 \| \| 0.0003 \| 779.0 \| 1558 \| 1.4550 \| \| 0.0003 \| 780.0 \| 1560 \| 1.4512 \| \| 0.0003 \| 781.0 \| 1562 \| 1.4479 \| \| 0.0003 \| 782.0 \| 1564 \| 1.4652 \| \| 0.0003 \| 783.0 \| 1566 \| 1.4978 \| \| 0.0003 \| 784.0 \| 1568 \| 1.5235 \| \| 0.0003 \| 785.0 \| 1570 \| 1.5399 \| \| 0.0003 \| 786.0 \| 1572 \| 1.5518 \| \| 0.0003 \| 787.0 \| 1574 \| 1.5597 \| \| 0.0003 \| 788.0 \| 1576 \| 1.5629 \| \| 0.0003 \| 789.0 \| 1578 \| 1.5628 \| \| 0.0003 \| 790.0 \| 1580 \| 1.5599 \| \| 0.0003 \| 791.0 \| 1582 \| 1.5538 \| \| 0.0003 \| 792.0 \| 1584 \| 1.5479 \| \| 0.0003 \| 793.0 \| 1586 \| 1.5405 \| \| 0.0003 \| 794.0 \| 1588 \| 1.5318 \| \| 0.0003 \| 795.0 \| 1590 \| 1.5236 \| \| 0.0003 \| 796.0 \| 1592 \| 1.5222 \| \| 0.0003 \| 797.0 \| 1594 \| 1.5259 \| \| 0.0003 \| 798.0 \| 1596 \| 1.5279 \| \| 0.0003 \| 799.0 \| 1598 \| 1.5291 \| \| 0.0003 \| 800.0 \| 1600 \| 1.5242 \| \| 0.0003 \| 801.0 \| 1602 \| 1.5197 \| \| 0.0003 \| 802.0 \| 1604 \| 1.5153 \| \| 0.0003 \| 803.0 \| 1606 \| 1.5091 \| \| 0.0003 \| 804.0 \| 1608 \| 1.5018 \| \| 0.0003 \| 805.0 \| 1610 \| 1.4950 \| \| 0.0003 \| 806.0 \| 1612 \| 1.4887 \| \| 0.0003 \| 807.0 \| 1614 \| 1.4833 \| \| 0.0003 \| 808.0 \| 1616 \| 1.4786 \| \| 0.0003 \| 809.0 \| 1618 \| 1.4726 \| \| 0.0003 \| 810.0 \| 1620 \| 1.4676 \| \| 0.0003 \| 811.0 \| 1622 \| 1.4762 \| \| 0.0003 \| 812.0 \| 1624 \| 1.4831 \| \| 0.0003 \| 813.0 \| 1626 \| 1.4911 \| \| 0.0003 \| 814.0 \| 1628 \| 1.5145 \| \| 0.0003 \| 815.0 \| 1630 \| 1.5310 \| \| 0.0003 \| 816.0 \| 1632 \| 1.5441 \| \| 0.0003 \| 817.0 \| 1634 \| 1.5537 \| \| 0.0003 \| 818.0 \| 1636 \| 1.5606 \| \| 0.0003 \| 819.0 \| 1638 \| 1.5644 \| \| 0.0003 \| 820.0 \| 1640 \| 1.5652 \| \| 0.0003 \| 821.0 \| 1642 \| 1.5639 \| \| 0.0003 \| 822.0 \| 1644 \| 1.5595 \| \| 0.0003 \| 823.0 \| 1646 \| 1.5473 \| \| 0.0003 \| 824.0 \| 1648 \| 1.5360 \| \| 0.0003 \| 825.0 \| 1650 \| 1.5237 \| \| 0.0003 \| 826.0 \| 1652 \| 1.5143 \| \| 0.0003 \| 827.0 \| 1654 \| 1.5092 \| \| 0.0003 \| 828.0 \| 1656 \| 1.4986 \| \| 0.0003 \| 829.0 \| 1658 \| 1.4837 \| \| 0.0003 \| 830.0 \| 1660 \| 1.4722 \| \| 0.0003 \| 831.0 \| 1662 \| 1.4626 \| \| 0.0003 \| 832.0 \| 1664 \| 1.4545 \| \| 0.0003 \| 833.0 \| 1666 \| 1.4480 \| \| 0.0003 \| 834.0 \| 1668 \| 1.4345 \| \| 0.0003 \| 835.0 \| 1670 \| 1.4235 \| \| 0.0003 \| 836.0 \| 1672 \| 1.4138 \| \| 0.0003 \| 837.0 \| 1674 \| 1.4071 \| \| 0.0003 \| 838.0 \| 1676 \| 1.4051 \| \| 0.0003 \| 839.0 \| 1678 \| 1.4036 \| \| 0.0003 \| 840.0 \| 1680 \| 1.4020 \| \| 0.0003 \| 841.0 \| 1682 \| 1.3985 \| \| 0.0003 \| 842.0 \| 1684 \| 1.3947 \| \| 0.0003 \| 843.0 \| 1686 \| 1.3917 \| \| 0.0003 \| 844.0 \| 1688 \| 1.3896 \| \| 0.0003 \| 845.0 \| 1690 \| 1.3882 \| \| 0.0003 \| 846.0 \| 1692 \| 1.3870 \| \| 0.0003 \| 847.0 \| 1694 \| 1.4005 \| \| 0.0003 \| 848.0 \| 1696 \| 1.4152 \| \| 0.0003 \| 849.0 \| 1698 \| 1.4301 \| \| 0.0003 \| 850.0 \| 1700 \| 1.4422 \| \| 0.0003 \| 851.0 \| 1702 \| 1.4517 \| \| 0.0003 \| 852.0 \| 1704 \| 1.4587 \| \| 0.0003 \| 853.0 \| 1706 \| 1.4637 \| \| 0.0003 \| 854.0 \| 1708 \| 1.4669 \| \| 0.0003 \| 855.0 \| 1710 \| 1.4685 \| \| 0.0003 \| 856.0 \| 1712 \| 1.4689 \| \| 0.0003 \| 857.0 \| 1714 \| 1.4679 \| \| 0.0003 \| 858.0 \| 1716 \| 1.4595 \| \| 0.0003 \| 859.0 \| 1718 \| 1.4518 \| \| 0.0003 \| 860.0 \| 1720 \| 1.4440 \| \| 0.0003 \| 861.0 \| 1722 \| 1.4372 \| \| 0.0003 \| 862.0 \| 1724 \| 1.4310 \| \| 0.0003 \| 863.0 \| 1726 \| 1.4251 \| \| 0.0003 \| 864.0 \| 1728 \| 1.4212 \| \| 0.0003 \| 865.0 \| 1730 \| 1.4181 \| \| 0.0003 \| 866.0 \| 1732 \| 1.4154 \| \| 0.0003 \| 867.0 \| 1734 \| 1.4129 \| \| 0.0003 \| 868.0 \| 1736 \| 1.4109 \| \| 0.0003 \| 869.0 \| 1738 \| 1.4092 \| \| 0.0003 \| 870.0 \| 1740 \| 1.4077 \| \| 0.0003 \| 871.0 \| 1742 \| 1.4063 \| \| 0.0003 \| 872.0 \| 1744 \| 1.4045 \| \| 0.0003 \| 873.0 \| 1746 \| 1.4027 \| \| 0.0003 \| 874.0 \| 1748 \| 1.4011 \| \| 0.0003 \| 875.0 \| 1750 \| 1.3993 \| \| 0.0003 \| 876.0 \| 1752 \| 1.4034 \| \| 0.0003 \| 877.0 \| 1754 \| 1.4118 \| \| 0.0003 \| 878.0 \| 1756 \| 1.4173 \| \| 0.0003 \| 879.0 \| 1758 \| 1.4212 \| \| 0.0003 \| 880.0 \| 1760 \| 1.4245 \| \| 0.0003 \| 881.0 \| 1762 \| 1.4271 \| \| 0.0003 \| 882.0 \| 1764 \| 1.4292 \| \| 0.0003 \| 883.0 \| 1766 \| 1.4308 \| \| 0.0003 \| 884.0 \| 1768 \| 1.4316 \| \| 0.0003 \| 885.0 \| 1770 \| 1.4318 \| \| 0.0003 \| 886.0 \| 1772 \| 1.4317 \| \| 0.0003 \| 887.0 \| 1774 \| 1.4315 \| \| 0.0003 \| 888.0 \| 1776 \| 1.4311 \| \| 0.0003 \| 889.0 \| 1778 \| 1.4301 \| \| 0.0003 \| 890.0 \| 1780 \| 1.4281 \| \| 0.0003 \| 891.0 \| 1782 \| 1.4265 \| \| 0.0003 \| 892.0 \| 1784 \| 1.4248 \| \| 0.0003 \| 893.0 \| 1786 \| 1.4226 \| \| 0.0003 \| 894.0 \| 1788 \| 1.4189 \| \| 0.0003 \| 895.0 \| 1790 \| 1.4158 \| \| 0.0003 \| 896.0 \| 1792 \| 1.4134 \| \| 0.0003 \| 897.0 \| 1794 \| 1.4114 \| \| 0.0003 \| 898.0 \| 1796 \| 1.4095 \| \| 0.0003 \| 899.0 \| 1798 \| 1.4070 \| \| 0.0003 \| 900.0 \| 1800 \| 1.4048 \| \| 0.0003 \| 901.0 \| 1802 \| 1.4032 \| \| 0.0003 \| 902.0 \| 1804 \| 1.4020 \| \| 0.0003 \| 903.0 \| 1806 \| 1.4013 \| \| 0.0003 \| 904.0 \| 1808 \| 1.4006 \| \| 0.0003 \| 905.0 \| 1810 \| 1.4000 \| \| 0.0003 \| 906.0 \| 1812 \| 1.3997 \| \| 0.0003 \| 907.0 \| 1814 \| 1.3994 \| \| 0.0003 \| 908.0 \| 1816 \| 1.3990 \| \| 0.0003 \| 909.0 \| 1818 \| 1.3983 \| \| 0.0003 \| 910.0 \| 1820 \| 1.3979 \| \| 0.0003 \| 911.0 \| 1822 \| 1.3978 \| \| 0.0003 \| 912.0 \| 1824 \| 1.3986 \| \| 0.0003 \| 913.0 \| 1826 \| 1.3978 \| \| 0.0003 \| 914.0 \| 1828 \| 1.3970 \| \| 0.0003 \| 915.0 \| 1830 \| 1.3964 \| \| 0.0003 \| 916.0 \| 1832 \| 1.3958 \| \| 0.0003 \| 917.0 \| 1834 \| 1.3953 \| \| 0.0003 \| 918.0 \| 1836 \| 1.3945 \| \| 0.0003 \| 919.0 \| 1838 \| 1.3944 \| \| 0.0003 \| 920.0 \| 1840 \| 1.3942 \| \| 0.0003 \| 921.0 \| 1842 \| 1.3940 \| \| 0.0003 \| 922.0 \| 1844 \| 1.3935 \| \| 0.0003 \| 923.0 \| 1846 \| 1.3932 \| \| 0.0003 \| 924.0 \| 1848 \| 1.3927 \| \| 0.0003 \| 925.0 \| 1850 \| 1.3925 \| \| 0.0003 \| 926.0 \| 1852 \| 1.3925 \| \| 0.0003 \| 927.0 \| 1854 \| 1.3926 \| \| 0.0003 \| 928.0 \| 1856 \| 1.3928 \| \| 0.0003 \| 929.0 \| 1858 \| 1.3928 \| \| 0.0003 \| 930.0 \| 1860 \| 1.3903 \| \| 0.0003 \| 931.0 \| 1862 \| 1.3883 \| \| 0.0003 \| 932.0 \| 1864 \| 1.3866 \| \| 0.0003 \| 933.0 \| 1866 \| 1.3853 \| \| 0.0003 \| 934.0 \| 1868 \| 1.3842 \| \| 0.0003 \| 935.0 \| 1870 \| 1.3834 \| \| 0.0003 \| 936.0 \| 1872 \| 1.3826 \| \| 0.0003 \| 937.0 \| 1874 \| 1.3818 \| \| 0.0003 \| 938.0 \| 1876 \| 1.3803 \| \| 0.0003 \| 939.0 \| 1878 \| 1.3791 \| \| 0.0003 \| 940.0 \| 1880 \| 1.3782 \| \| 0.0003 \| 941.0 \| 1882 \| 1.3776 \| \| 0.0003 \| 942.0 \| 1884 \| 1.3770 \| \| 0.0003 \| 943.0 \| 1886 \| 1.3764 \| \| 0.0003 \| 944.0 \| 1888 \| 1.3758 \| \| 0.0003 \| 945.0 \| 1890 \| 1.3760 \| \| 0.0003 \| 946.0 \| 1892 \| 1.3763 \| \| 0.0003 \| 947.0 \| 1894 \| 1.3766 \| \| 0.0003 \| 948.0 \| 1896 \| 1.3770 \| \| 0.0003 \| 949.0 \| 1898 \| 1.3773 \| \| 0.0003 \| 950.0 \| 1900 \| 1.3776 \| \| 0.0003 \| 951.0 \| 1902 \| 1.3778 \| \| 0.0003 \| 952.0 \| 1904 \| 1.3780 \| \| 0.0003 \| 953.0 \| 1906 \| 1.3796 \| \| 0.0003 \| 954.0 \| 1908 \| 1.3821 \| \| 0.0003 \| 955.0 \| 1910 \| 1.3841 \| \| 0.0003 \| 956.0 \| 1912 \| 1.3858 \| \| 0.0003 \| 957.0 \| 1914 \| 1.3859 \| \| 0.0003 \| 958.0 \| 1916 \| 1.3858 \| \| 0.0003 \| 959.0 \| 1918 \| 1.3859 \| \| 0.0003 \| 960.0 \| 1920 \| 1.3857 \| \| 0.0003 \| 961.0 \| 1922 \| 1.3853 \| \| 0.0003 \| 962.0 \| 1924 \| 1.3850 \| \| 0.0003 \| 963.0 \| 1926 \| 1.3848 \| \| 0.0003 \| 964.0 \| 1928 \| 1.3847 \| \| 0.0003 \| 965.0 \| 1930 \| 1.3845 \| \| 0.0003 \| 966.0 \| 1932 \| 1.3843 \| \| 0.0003 \| 967.0 \| 1934 \| 1.3841 \| \| 0.0003 \| 968.0 \| 1936 \| 1.3839 \| \| 0.0003 \| 969.0 \| 1938 \| 1.3837 \| \| 0.0003 \| 970.0 \| 1940 \| 1.3836 \| \| 0.0003 \| 971.0 \| 1942 \| 1.3836 \| \| 0.0003 \| 972.0 \| 1944 \| 1.3836 \| \| 0.0003 \| 973.0 \| 1946 \| 1.3835 \| \| 0.0003 \| 974.0 \| 1948 \| 1.3838 \| \| 0.0003 \| 975.0 \| 1950 \| 1.3843 \| \| 0.0003 \| 976.0 \| 1952 \| 1.3847 \| \| 0.0003 \| 977.0 \| 1954 \| 1.3850 \| \| 0.0003 \| 978.0 \| 1956 \| 1.3852 \| \| 0.0003 \| 979.0 \| 1958 \| 1.3853 \| \| 0.0003 \| 980.0 \| 1960 \| 1.3854 \| \| 0.0003 \| 981.0 \| 1962 \| 1.3855 \| \| 0.0003 \| 982.0 \| 1964 \| 1.3855 \| \| 0.0003 \| 983.0 \| 1966 \| 1.3854 \| \| 0.0003 \| 984.0 \| 1968 \| 1.3854 \| \| 0.0003 \| 985.0 \| 1970 \| 1.3855 \| \| 0.0003 \| 986.0 \| 1972 \| 1.3857 \| \| 0.0003 \| 987.0 \| 1974 \| 1.3858 \| \| 0.0003 \| 988.0 \| 1976 \| 1.3859 \| \| 0.0003 \| 989.0 \| 1978 \| 1.3860 \| \| 0.0003 \| 990.0 \| 1980 \| 1.3860 \| \| 0.0003 \| 991.0 \| 1982 \| 1.3861 \| \| 0.0003 \| 992.0 \| 1984 \| 1.3860 \| \| 0.0003 \| 993.0 \| 1986 \| 1.3860 \| \| 0.0003 \| 994.0 \| 1988 \| 1.3860 \| \| 0.0003 \| 995.0 \| 1990 \| 1.3860 \| \| 0.0003 \| 996.0 \| 1992 \| 1.3860 \| \| 0.0003 \| 997.0 \| 1994 \| 1.3859 \| \| 0.0003 \| 998.0 \| 1996 \| 1.3859 \| \| 0.0003 \| 999.0 \| 1998 \| 1.3859 \| \| 0.0002 \| 1000.0 \| 2000 \| 1.3859 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.7 - Tokenizers 0.15.0
daze-unlv/axolotl-medmcqa-4-epoch	daze-unlv	2024-03-08T01:23:40Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-07T23:04:09Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: lora-out/medmcqa-4-epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: daze-unlv/medmcqa_axolotl type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0 output_dir: ./lora-out/medmcqa-4-epoch eval_sample_packing: false adapter: lora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 4 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: false sdp_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # lora-out/medmcqa-4-epoch This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
Maqqq/OpenHermes-2.5-Mistral-7B-16	Maqqq	2024-03-08T01:17:04Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T00:56:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_64_0.01_8_0.0002	ferrazzipietro	2024-03-08T00:53:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T00:52:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
keanurefresh/73981	keanurefresh	2024-03-08T00:44:46Z	0	0	null	[ "region:us" ]	null	2024-03-08T00:00:03Z	Include in your prompt <lora:facialized:1>, cum, facial.You might want to include in your negative prompt cum on breasts, cum on body. The model works best with low steps and CFG, I get good results with 10 steps and a CFG of 3 or 4.
not-lain/BaseModelWithConfigAndNamedParameter	not-lain	2024-03-08T00:41:29Z	5	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-03-08T00:29:16Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using ****: - Repo: [More Information Needed] - Docs: [More Information Needed]
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_64_0.01_4_0.0002	ferrazzipietro	2024-03-08T00:34:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T00:33:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
not-lain/BaseModelWithJustConfig	not-lain	2024-03-08T00:25:52Z	5	0	transformers	[ "transformers", "pytorch", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-03-08T00:24:23Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using ****: - Repo: [More Information Needed] - Docs: [More Information Needed]
farid1088/GQA_BERT_legal_SQuAD_complete_augmented_2000	farid1088	2024-03-08T00:22:50Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2024-03-07T21:47:56Z	--- tags: - generated_from_trainer model-index: - name: GQA_BERT_legal_SQuAD_complete_augmented_2000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GQA_BERT_legal_SQuAD_complete_augmented_2000 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 160 - eval_batch_size: 40 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 1.0 \| 3 \| 5.1193 \| \| No log \| 2.0 \| 6 \| 4.5794 \| \| No log \| 3.0 \| 9 \| 3.9562 \| \| No log \| 4.0 \| 12 \| 3.6226 \| \| No log \| 5.0 \| 15 \| 3.1767 \| \| No log \| 6.0 \| 18 \| 2.8026 \| \| No log \| 7.0 \| 21 \| 2.5106 \| \| No log \| 8.0 \| 24 \| 2.2343 \| \| No log \| 9.0 \| 27 \| 2.0290 \| \| No log \| 10.0 \| 30 \| 1.8059 \| \| No log \| 11.0 \| 33 \| 1.6448 \| \| No log \| 12.0 \| 36 \| 1.4814 \| \| No log \| 13.0 \| 39 \| 1.3270 \| \| No log \| 14.0 \| 42 \| 1.2522 \| \| No log \| 15.0 \| 45 \| 1.1957 \| \| No log \| 16.0 \| 48 \| 1.1489 \| \| No log \| 17.0 \| 51 \| 1.1251 \| \| No log \| 18.0 \| 54 \| 1.1000 \| \| No log \| 19.0 \| 57 \| 1.0762 \| \| No log \| 20.0 \| 60 \| 1.0465 \| \| No log \| 21.0 \| 63 \| 1.0398 \| \| No log \| 22.0 \| 66 \| 1.0363 \| \| No log \| 23.0 \| 69 \| 1.0388 \| \| No log \| 24.0 \| 72 \| 1.0330 \| \| No log \| 25.0 \| 75 \| 1.0242 \| \| No log \| 26.0 \| 78 \| 1.0188 \| \| No log \| 27.0 \| 81 \| 1.0227 \| \| No log \| 28.0 \| 84 \| 1.0281 \| \| No log \| 29.0 \| 87 \| 1.0362 \| \| No log \| 30.0 \| 90 \| 1.0278 \| \| No log \| 31.0 \| 93 \| 1.0463 \| \| No log \| 32.0 \| 96 \| 1.0733 \| \| No log \| 33.0 \| 99 \| 1.0895 \| \| No log \| 34.0 \| 102 \| 1.0818 \| \| No log \| 35.0 \| 105 \| 1.0836 \| \| No log \| 36.0 \| 108 \| 1.0664 \| \| No log \| 37.0 \| 111 \| 1.0578 \| \| No log \| 38.0 \| 114 \| 1.0792 \| \| No log \| 39.0 \| 117 \| 1.0465 \| \| No log \| 40.0 \| 120 \| 1.0288 \| \| No log \| 41.0 \| 123 \| 1.0609 \| \| No log \| 42.0 \| 126 \| 1.0676 \| \| No log \| 43.0 \| 129 \| 1.0343 \| \| No log \| 44.0 \| 132 \| 1.0653 \| \| No log \| 45.0 \| 135 \| 1.1017 \| \| No log \| 46.0 \| 138 \| 1.0780 \| \| No log \| 47.0 \| 141 \| 1.0841 \| \| No log \| 48.0 \| 144 \| 1.0921 \| \| No log \| 49.0 \| 147 \| 1.0919 \| \| No log \| 50.0 \| 150 \| 1.1088 \| \| No log \| 51.0 \| 153 \| 1.0983 \| \| No log \| 52.0 \| 156 \| 1.0897 \| \| No log \| 53.0 \| 159 \| 1.0991 \| \| No log \| 54.0 \| 162 \| 1.1124 \| \| No log \| 55.0 \| 165 \| 1.0800 \| \| No log \| 56.0 \| 168 \| 1.1173 \| \| No log \| 57.0 \| 171 \| 1.1244 \| \| No log \| 58.0 \| 174 \| 1.1127 \| \| No log \| 59.0 \| 177 \| 1.1290 \| \| No log \| 60.0 \| 180 \| 1.1127 \| \| No log \| 61.0 \| 183 \| 1.1141 \| \| No log \| 62.0 \| 186 \| 1.1494 \| \| No log \| 63.0 \| 189 \| 1.1185 \| \| No log \| 64.0 \| 192 \| 1.1394 \| \| No log \| 65.0 \| 195 \| 1.1624 \| \| No log \| 66.0 \| 198 \| 1.1620 \| \| No log \| 67.0 \| 201 \| 1.1518 \| \| No log \| 68.0 \| 204 \| 1.1353 \| \| No log \| 69.0 \| 207 \| 1.2165 \| \| No log \| 70.0 \| 210 \| 1.1765 \| \| No log \| 71.0 \| 213 \| 1.1964 \| \| No log \| 72.0 \| 216 \| 1.2078 \| \| No log \| 73.0 \| 219 \| 1.1245 \| \| No log \| 74.0 \| 222 \| 1.1631 \| \| No log \| 75.0 \| 225 \| 1.1314 \| \| No log \| 76.0 \| 228 \| 1.0521 \| \| No log \| 77.0 \| 231 \| 1.1047 \| \| No log \| 78.0 \| 234 \| 1.1412 \| \| No log \| 79.0 \| 237 \| 1.1133 \| \| No log \| 80.0 \| 240 \| 1.1257 \| \| No log \| 81.0 \| 243 \| 1.1375 \| \| No log \| 82.0 \| 246 \| 1.0486 \| \| No log \| 83.0 \| 249 \| 1.1223 \| \| No log \| 84.0 \| 252 \| 1.1664 \| \| No log \| 85.0 \| 255 \| 1.0748 \| \| No log \| 86.0 \| 258 \| 1.1151 \| \| No log \| 87.0 \| 261 \| 1.1358 \| \| No log \| 88.0 \| 264 \| 1.0981 \| \| No log \| 89.0 \| 267 \| 1.2120 \| \| No log \| 90.0 \| 270 \| 1.1805 \| \| No log \| 91.0 \| 273 \| 1.1296 \| \| No log \| 92.0 \| 276 \| 1.3029 \| \| No log \| 93.0 \| 279 \| 1.2570 \| \| No log \| 94.0 \| 282 \| 1.1256 \| \| No log \| 95.0 \| 285 \| 1.1910 \| \| No log \| 96.0 \| 288 \| 1.2814 \| \| No log \| 97.0 \| 291 \| 1.1195 \| \| No log \| 98.0 \| 294 \| 1.0572 \| \| No log \| 99.0 \| 297 \| 1.1948 \| \| No log \| 100.0 \| 300 \| 1.1649 \| \| No log \| 101.0 \| 303 \| 1.0716 \| \| No log \| 102.0 \| 306 \| 1.1648 \| \| No log \| 103.0 \| 309 \| 1.1558 \| \| No log \| 104.0 \| 312 \| 1.1381 \| \| No log \| 105.0 \| 315 \| 1.2201 \| \| No log \| 106.0 \| 318 \| 1.2335 \| \| No log \| 107.0 \| 321 \| 1.0798 \| \| No log \| 108.0 \| 324 \| 1.1202 \| \| No log \| 109.0 \| 327 \| 1.2209 \| \| No log \| 110.0 \| 330 \| 1.2331 \| \| No log \| 111.0 \| 333 \| 1.1878 \| \| No log \| 112.0 \| 336 \| 1.2108 \| \| No log \| 113.0 \| 339 \| 1.2244 \| \| No log \| 114.0 \| 342 \| 1.1712 \| \| No log \| 115.0 \| 345 \| 1.1699 \| \| No log \| 116.0 \| 348 \| 1.2039 \| \| No log \| 117.0 \| 351 \| 1.0968 \| \| No log \| 118.0 \| 354 \| 1.1880 \| \| No log \| 119.0 \| 357 \| 1.1514 \| \| No log \| 120.0 \| 360 \| 1.0878 \| \| No log \| 121.0 \| 363 \| 1.1416 \| \| No log \| 122.0 \| 366 \| 1.1696 \| \| No log \| 123.0 \| 369 \| 1.1387 \| \| No log \| 124.0 \| 372 \| 1.1488 \| \| No log \| 125.0 \| 375 \| 1.1840 \| \| No log \| 126.0 \| 378 \| 1.1501 \| \| No log \| 127.0 \| 381 \| 1.1900 \| \| No log \| 128.0 \| 384 \| 1.1478 \| \| No log \| 129.0 \| 387 \| 1.2309 \| \| No log \| 130.0 \| 390 \| 1.3350 \| \| No log \| 131.0 \| 393 \| 1.2147 \| \| No log \| 132.0 \| 396 \| 1.1993 \| \| No log \| 133.0 \| 399 \| 1.2747 \| \| No log \| 134.0 \| 402 \| 1.2372 \| \| No log \| 135.0 \| 405 \| 1.2479 \| \| No log \| 136.0 \| 408 \| 1.2942 \| \| No log \| 137.0 \| 411 \| 1.2322 \| \| No log \| 138.0 \| 414 \| 1.2148 \| \| No log \| 139.0 \| 417 \| 1.2922 \| \| No log \| 140.0 \| 420 \| 1.3430 \| \| No log \| 141.0 \| 423 \| 1.3824 \| \| No log \| 142.0 \| 426 \| 1.2082 \| \| No log \| 143.0 \| 429 \| 1.1967 \| \| No log \| 144.0 \| 432 \| 1.2483 \| \| No log \| 145.0 \| 435 \| 1.1599 \| \| No log \| 146.0 \| 438 \| 1.0864 \| \| No log \| 147.0 \| 441 \| 1.1238 \| \| No log \| 148.0 \| 444 \| 1.2074 \| \| No log \| 149.0 \| 447 \| 1.1902 \| \| No log \| 150.0 \| 450 \| 1.1397 \| \| No log \| 151.0 \| 453 \| 1.1546 \| \| No log \| 152.0 \| 456 \| 1.2126 \| \| No log \| 153.0 \| 459 \| 1.2443 \| \| No log \| 154.0 \| 462 \| 1.2378 \| \| No log \| 155.0 \| 465 \| 1.2335 \| \| No log \| 156.0 \| 468 \| 1.1798 \| \| No log \| 157.0 \| 471 \| 1.1297 \| \| No log \| 158.0 \| 474 \| 1.1737 \| \| No log \| 159.0 \| 477 \| 1.0970 \| \| No log \| 160.0 \| 480 \| 1.1708 \| \| No log \| 161.0 \| 483 \| 1.1551 \| \| No log \| 162.0 \| 486 \| 1.1848 \| \| No log \| 163.0 \| 489 \| 1.1971 \| \| No log \| 164.0 \| 492 \| 1.1720 \| \| No log \| 165.0 \| 495 \| 1.1960 \| \| No log \| 166.0 \| 498 \| 1.2754 \| \| 1.0047 \| 167.0 \| 501 \| 1.2083 \| \| 1.0047 \| 168.0 \| 504 \| 1.0888 \| \| 1.0047 \| 169.0 \| 507 \| 1.2684 \| \| 1.0047 \| 170.0 \| 510 \| 1.3395 \| \| 1.0047 \| 171.0 \| 513 \| 1.2508 \| \| 1.0047 \| 172.0 \| 516 \| 1.1460 \| \| 1.0047 \| 173.0 \| 519 \| 1.2464 \| \| 1.0047 \| 174.0 \| 522 \| 1.2131 \| \| 1.0047 \| 175.0 \| 525 \| 1.1181 \| \| 1.0047 \| 176.0 \| 528 \| 1.2012 \| \| 1.0047 \| 177.0 \| 531 \| 1.2957 \| \| 1.0047 \| 178.0 \| 534 \| 1.1890 \| \| 1.0047 \| 179.0 \| 537 \| 1.1628 \| \| 1.0047 \| 180.0 \| 540 \| 1.1929 \| \| 1.0047 \| 181.0 \| 543 \| 1.2900 \| \| 1.0047 \| 182.0 \| 546 \| 1.3240 \| \| 1.0047 \| 183.0 \| 549 \| 1.2145 \| \| 1.0047 \| 184.0 \| 552 \| 1.2942 \| \| 1.0047 \| 185.0 \| 555 \| 1.3425 \| \| 1.0047 \| 186.0 \| 558 \| 1.1772 \| \| 1.0047 \| 187.0 \| 561 \| 1.2255 \| \| 1.0047 \| 188.0 \| 564 \| 1.4528 \| \| 1.0047 \| 189.0 \| 567 \| 1.3898 \| \| 1.0047 \| 190.0 \| 570 \| 1.1862 \| \| 1.0047 \| 191.0 \| 573 \| 1.1700 \| \| 1.0047 \| 192.0 \| 576 \| 1.2801 \| \| 1.0047 \| 193.0 \| 579 \| 1.2571 \| \| 1.0047 \| 194.0 \| 582 \| 1.1962 \| \| 1.0047 \| 195.0 \| 585 \| 1.2228 \| \| 1.0047 \| 196.0 \| 588 \| 1.2153 \| \| 1.0047 \| 197.0 \| 591 \| 1.1498 \| \| 1.0047 \| 198.0 \| 594 \| 1.1130 \| \| 1.0047 \| 199.0 \| 597 \| 1.1537 \| \| 1.0047 \| 200.0 \| 600 \| 1.2239 \| \| 1.0047 \| 201.0 \| 603 \| 1.1742 \| \| 1.0047 \| 202.0 \| 606 \| 1.1292 \| \| 1.0047 \| 203.0 \| 609 \| 1.1688 \| \| 1.0047 \| 204.0 \| 612 \| 1.1844 \| \| 1.0047 \| 205.0 \| 615 \| 1.1928 \| \| 1.0047 \| 206.0 \| 618 \| 1.2253 \| \| 1.0047 \| 207.0 \| 621 \| 1.2585 \| \| 1.0047 \| 208.0 \| 624 \| 1.3174 \| \| 1.0047 \| 209.0 \| 627 \| 1.3660 \| \| 1.0047 \| 210.0 \| 630 \| 1.2523 \| \| 1.0047 \| 211.0 \| 633 \| 1.2249 \| \| 1.0047 \| 212.0 \| 636 \| 1.4178 \| \| 1.0047 \| 213.0 \| 639 \| 1.3895 \| \| 1.0047 \| 214.0 \| 642 \| 1.2523 \| \| 1.0047 \| 215.0 \| 645 \| 1.1921 \| \| 1.0047 \| 216.0 \| 648 \| 1.2245 \| \| 1.0047 \| 217.0 \| 651 \| 1.3426 \| \| 1.0047 \| 218.0 \| 654 \| 1.3673 \| \| 1.0047 \| 219.0 \| 657 \| 1.1933 \| \| 1.0047 \| 220.0 \| 660 \| 1.1469 \| \| 1.0047 \| 221.0 \| 663 \| 1.2684 \| \| 1.0047 \| 222.0 \| 666 \| 1.4222 \| \| 1.0047 \| 223.0 \| 669 \| 1.4067 \| \| 1.0047 \| 224.0 \| 672 \| 1.3425 \| \| 1.0047 \| 225.0 \| 675 \| 1.3358 \| \| 1.0047 \| 226.0 \| 678 \| 1.4246 \| \| 1.0047 \| 227.0 \| 681 \| 1.3301 \| \| 1.0047 \| 228.0 \| 684 \| 1.1915 \| \| 1.0047 \| 229.0 \| 687 \| 1.2654 \| \| 1.0047 \| 230.0 \| 690 \| 1.4043 \| \| 1.0047 \| 231.0 \| 693 \| 1.3357 \| \| 1.0047 \| 232.0 \| 696 \| 1.2512 \| \| 1.0047 \| 233.0 \| 699 \| 1.2383 \| \| 1.0047 \| 234.0 \| 702 \| 1.1516 \| \| 1.0047 \| 235.0 \| 705 \| 1.1382 \| \| 1.0047 \| 236.0 \| 708 \| 1.2749 \| \| 1.0047 \| 237.0 \| 711 \| 1.3747 \| \| 1.0047 \| 238.0 \| 714 \| 1.1791 \| \| 1.0047 \| 239.0 \| 717 \| 1.1527 \| \| 1.0047 \| 240.0 \| 720 \| 1.2194 \| \| 1.0047 \| 241.0 \| 723 \| 1.2754 \| \| 1.0047 \| 242.0 \| 726 \| 1.3448 \| \| 1.0047 \| 243.0 \| 729 \| 1.3382 \| \| 1.0047 \| 244.0 \| 732 \| 1.2932 \| \| 1.0047 \| 245.0 \| 735 \| 1.3135 \| \| 1.0047 \| 246.0 \| 738 \| 1.3671 \| \| 1.0047 \| 247.0 \| 741 \| 1.3735 \| \| 1.0047 \| 248.0 \| 744 \| 1.4142 \| \| 1.0047 \| 249.0 \| 747 \| 1.4000 \| \| 1.0047 \| 250.0 \| 750 \| 1.2954 \| \| 1.0047 \| 251.0 \| 753 \| 1.2629 \| \| 1.0047 \| 252.0 \| 756 \| 1.2982 \| \| 1.0047 \| 253.0 \| 759 \| 1.2750 \| \| 1.0047 \| 254.0 \| 762 \| 1.2273 \| \| 1.0047 \| 255.0 \| 765 \| 1.2209 \| \| 1.0047 \| 256.0 \| 768 \| 1.2359 \| \| 1.0047 \| 257.0 \| 771 \| 1.2626 \| \| 1.0047 \| 258.0 \| 774 \| 1.1799 \| \| 1.0047 \| 259.0 \| 777 \| 1.1506 \| \| 1.0047 \| 260.0 \| 780 \| 1.1846 \| \| 1.0047 \| 261.0 \| 783 \| 1.2278 \| \| 1.0047 \| 262.0 \| 786 \| 1.2040 \| \| 1.0047 \| 263.0 \| 789 \| 1.1920 \| \| 1.0047 \| 264.0 \| 792 \| 1.1921 \| \| 1.0047 \| 265.0 \| 795 \| 1.2421 \| \| 1.0047 \| 266.0 \| 798 \| 1.2557 \| \| 1.0047 \| 267.0 \| 801 \| 1.2245 \| \| 1.0047 \| 268.0 \| 804 \| 1.2240 \| \| 1.0047 \| 269.0 \| 807 \| 1.3193 \| \| 1.0047 \| 270.0 \| 810 \| 1.3523 \| \| 1.0047 \| 271.0 \| 813 \| 1.3143 \| \| 1.0047 \| 272.0 \| 816 \| 1.2657 \| \| 1.0047 \| 273.0 \| 819 \| 1.3099 \| \| 1.0047 \| 274.0 \| 822 \| 1.2485 \| \| 1.0047 \| 275.0 \| 825 \| 1.1617 \| \| 1.0047 \| 276.0 \| 828 \| 1.2186 \| \| 1.0047 \| 277.0 \| 831 \| 1.2683 \| \| 1.0047 \| 278.0 \| 834 \| 1.2432 \| \| 1.0047 \| 279.0 \| 837 \| 1.3252 \| \| 1.0047 \| 280.0 \| 840 \| 1.4173 \| \| 1.0047 \| 281.0 \| 843 \| 1.3807 \| \| 1.0047 \| 282.0 \| 846 \| 1.3895 \| \| 1.0047 \| 283.0 \| 849 \| 1.3531 \| \| 1.0047 \| 284.0 \| 852 \| 1.2847 \| \| 1.0047 \| 285.0 \| 855 \| 1.2734 \| \| 1.0047 \| 286.0 \| 858 \| 1.2917 \| \| 1.0047 \| 287.0 \| 861 \| 1.3048 \| \| 1.0047 \| 288.0 \| 864 \| 1.3169 \| \| 1.0047 \| 289.0 \| 867 \| 1.3620 \| \| 1.0047 \| 290.0 \| 870 \| 1.4486 \| \| 1.0047 \| 291.0 \| 873 \| 1.3860 \| \| 1.0047 \| 292.0 \| 876 \| 1.3026 \| \| 1.0047 \| 293.0 \| 879 \| 1.2993 \| \| 1.0047 \| 294.0 \| 882 \| 1.2825 \| \| 1.0047 \| 295.0 \| 885 \| 1.2764 \| \| 1.0047 \| 296.0 \| 888 \| 1.3134 \| \| 1.0047 \| 297.0 \| 891 \| 1.3452 \| \| 1.0047 \| 298.0 \| 894 \| 1.3714 \| \| 1.0047 \| 299.0 \| 897 \| 1.3125 \| \| 1.0047 \| 300.0 \| 900 \| 1.2099 \| \| 1.0047 \| 301.0 \| 903 \| 1.2298 \| \| 1.0047 \| 302.0 \| 906 \| 1.3122 \| \| 1.0047 \| 303.0 \| 909 \| 1.3047 \| \| 1.0047 \| 304.0 \| 912 \| 1.2591 \| \| 1.0047 \| 305.0 \| 915 \| 1.2820 \| \| 1.0047 \| 306.0 \| 918 \| 1.2770 \| \| 1.0047 \| 307.0 \| 921 \| 1.2783 \| \| 1.0047 \| 308.0 \| 924 \| 1.3475 \| \| 1.0047 \| 309.0 \| 927 \| 1.3819 \| \| 1.0047 \| 310.0 \| 930 \| 1.2759 \| \| 1.0047 \| 311.0 \| 933 \| 1.1658 \| \| 1.0047 \| 312.0 \| 936 \| 1.1919 \| \| 1.0047 \| 313.0 \| 939 \| 1.3712 \| \| 1.0047 \| 314.0 \| 942 \| 1.4586 \| \| 1.0047 \| 315.0 \| 945 \| 1.4405 \| \| 1.0047 \| 316.0 \| 948 \| 1.2275 \| \| 1.0047 \| 317.0 \| 951 \| 1.2043 \| \| 1.0047 \| 318.0 \| 954 \| 1.3147 \| \| 1.0047 \| 319.0 \| 957 \| 1.4305 \| \| 1.0047 \| 320.0 \| 960 \| 1.3858 \| \| 1.0047 \| 321.0 \| 963 \| 1.2997 \| \| 1.0047 \| 322.0 \| 966 \| 1.2348 \| \| 1.0047 \| 323.0 \| 969 \| 1.2264 \| \| 1.0047 \| 324.0 \| 972 \| 1.2819 \| \| 1.0047 \| 325.0 \| 975 \| 1.3146 \| \| 1.0047 \| 326.0 \| 978 \| 1.3341 \| \| 1.0047 \| 327.0 \| 981 \| 1.3511 \| \| 1.0047 \| 328.0 \| 984 \| 1.3223 \| \| 1.0047 \| 329.0 \| 987 \| 1.3236 \| \| 1.0047 \| 330.0 \| 990 \| 1.3429 \| \| 1.0047 \| 331.0 \| 993 \| 1.2715 \| \| 1.0047 \| 332.0 \| 996 \| 1.2452 \| \| 1.0047 \| 333.0 \| 999 \| 1.2350 \| \| 0.5933 \| 334.0 \| 1002 \| 1.1789 \| \| 0.5933 \| 335.0 \| 1005 \| 1.2327 \| \| 0.5933 \| 336.0 \| 1008 \| 1.2986 \| \| 0.5933 \| 337.0 \| 1011 \| 1.2372 \| \| 0.5933 \| 338.0 \| 1014 \| 1.1142 \| \| 0.5933 \| 339.0 \| 1017 \| 1.1219 \| \| 0.5933 \| 340.0 \| 1020 \| 1.2149 \| \| 0.5933 \| 341.0 \| 1023 \| 1.3215 \| \| 0.5933 \| 342.0 \| 1026 \| 1.3930 \| \| 0.5933 \| 343.0 \| 1029 \| 1.3952 \| \| 0.5933 \| 344.0 \| 1032 \| 1.3798 \| \| 0.5933 \| 345.0 \| 1035 \| 1.3870 \| \| 0.5933 \| 346.0 \| 1038 \| 1.3835 \| \| 0.5933 \| 347.0 \| 1041 \| 1.2778 \| \| 0.5933 \| 348.0 \| 1044 \| 1.2079 \| \| 0.5933 \| 349.0 \| 1047 \| 1.2545 \| \| 0.5933 \| 350.0 \| 1050 \| 1.3546 \| \| 0.5933 \| 351.0 \| 1053 \| 1.3485 \| \| 0.5933 \| 352.0 \| 1056 \| 1.2388 \| \| 0.5933 \| 353.0 \| 1059 \| 1.1877 \| \| 0.5933 \| 354.0 \| 1062 \| 1.1707 \| \| 0.5933 \| 355.0 \| 1065 \| 1.3036 \| \| 0.5933 \| 356.0 \| 1068 \| 1.4033 \| \| 0.5933 \| 357.0 \| 1071 \| 1.3046 \| \| 0.5933 \| 358.0 \| 1074 \| 1.1871 \| \| 0.5933 \| 359.0 \| 1077 \| 1.2303 \| \| 0.5933 \| 360.0 \| 1080 \| 1.4086 \| \| 0.5933 \| 361.0 \| 1083 \| 1.3546 \| \| 0.5933 \| 362.0 \| 1086 \| 1.1697 \| \| 0.5933 \| 363.0 \| 1089 \| 1.1320 \| \| 0.5933 \| 364.0 \| 1092 \| 1.1799 \| \| 0.5933 \| 365.0 \| 1095 \| 1.2172 \| \| 0.5933 \| 366.0 \| 1098 \| 1.3199 \| \| 0.5933 \| 367.0 \| 1101 \| 1.3302 \| \| 0.5933 \| 368.0 \| 1104 \| 1.3020 \| \| 0.5933 \| 369.0 \| 1107 \| 1.2652 \| \| 0.5933 \| 370.0 \| 1110 \| 1.3420 \| \| 0.5933 \| 371.0 \| 1113 \| 1.3486 \| \| 0.5933 \| 372.0 \| 1116 \| 1.2853 \| \| 0.5933 \| 373.0 \| 1119 \| 1.2203 \| \| 0.5933 \| 374.0 \| 1122 \| 1.1671 \| \| 0.5933 \| 375.0 \| 1125 \| 1.3050 \| \| 0.5933 \| 376.0 \| 1128 \| 1.4090 \| \| 0.5933 \| 377.0 \| 1131 \| 1.3682 \| \| 0.5933 \| 378.0 \| 1134 \| 1.2919 \| \| 0.5933 \| 379.0 \| 1137 \| 1.2611 \| \| 0.5933 \| 380.0 \| 1140 \| 1.2714 \| \| 0.5933 \| 381.0 \| 1143 \| 1.3204 \| \| 0.5933 \| 382.0 \| 1146 \| 1.3206 \| \| 0.5933 \| 383.0 \| 1149 \| 1.2592 \| \| 0.5933 \| 384.0 \| 1152 \| 1.1575 \| \| 0.5933 \| 385.0 \| 1155 \| 1.1801 \| \| 0.5933 \| 386.0 \| 1158 \| 1.2966 \| \| 0.5933 \| 387.0 \| 1161 \| 1.3092 \| \| 0.5933 \| 388.0 \| 1164 \| 1.3284 \| \| 0.5933 \| 389.0 \| 1167 \| 1.3397 \| \| 0.5933 \| 390.0 \| 1170 \| 1.3137 \| \| 0.5933 \| 391.0 \| 1173 \| 1.2775 \| \| 0.5933 \| 392.0 \| 1176 \| 1.1970 \| \| 0.5933 \| 393.0 \| 1179 \| 1.1671 \| \| 0.5933 \| 394.0 \| 1182 \| 1.3037 \| \| 0.5933 \| 395.0 \| 1185 \| 1.3400 \| \| 0.5933 \| 396.0 \| 1188 \| 1.2243 \| \| 0.5933 \| 397.0 \| 1191 \| 1.2322 \| \| 0.5933 \| 398.0 \| 1194 \| 1.3279 \| \| 0.5933 \| 399.0 \| 1197 \| 1.3577 \| \| 0.5933 \| 400.0 \| 1200 \| 1.3690 \| \| 0.5933 \| 401.0 \| 1203 \| 1.3068 \| \| 0.5933 \| 402.0 \| 1206 \| 1.2011 \| \| 0.5933 \| 403.0 \| 1209 \| 1.2389 \| \| 0.5933 \| 404.0 \| 1212 \| 1.3540 \| \| 0.5933 \| 405.0 \| 1215 \| 1.3858 \| \| 0.5933 \| 406.0 \| 1218 \| 1.3326 \| \| 0.5933 \| 407.0 \| 1221 \| 1.2234 \| \| 0.5933 \| 408.0 \| 1224 \| 1.1657 \| \| 0.5933 \| 409.0 \| 1227 \| 1.1664 \| \| 0.5933 \| 410.0 \| 1230 \| 1.2766 \| \| 0.5933 \| 411.0 \| 1233 \| 1.3610 \| \| 0.5933 \| 412.0 \| 1236 \| 1.3622 \| \| 0.5933 \| 413.0 \| 1239 \| 1.3024 \| \| 0.5933 \| 414.0 \| 1242 \| 1.2516 \| \| 0.5933 \| 415.0 \| 1245 \| 1.2160 \| \| 0.5933 \| 416.0 \| 1248 \| 1.1839 \| \| 0.5933 \| 417.0 \| 1251 \| 1.1225 \| \| 0.5933 \| 418.0 \| 1254 \| 1.1113 \| \| 0.5933 \| 419.0 \| 1257 \| 1.1720 \| \| 0.5933 \| 420.0 \| 1260 \| 1.3755 \| \| 0.5933 \| 421.0 \| 1263 \| 1.3626 \| \| 0.5933 \| 422.0 \| 1266 \| 1.2200 \| \| 0.5933 \| 423.0 \| 1269 \| 1.2175 \| \| 0.5933 \| 424.0 \| 1272 \| 1.3046 \| \| 0.5933 \| 425.0 \| 1275 \| 1.3120 \| \| 0.5933 \| 426.0 \| 1278 \| 1.3499 \| \| 0.5933 \| 427.0 \| 1281 \| 1.3850 \| \| 0.5933 \| 428.0 \| 1284 \| 1.3673 \| \| 0.5933 \| 429.0 \| 1287 \| 1.3124 \| \| 0.5933 \| 430.0 \| 1290 \| 1.2314 \| \| 0.5933 \| 431.0 \| 1293 \| 1.1724 \| \| 0.5933 \| 432.0 \| 1296 \| 1.2057 \| \| 0.5933 \| 433.0 \| 1299 \| 1.3040 \| \| 0.5933 \| 434.0 \| 1302 \| 1.3551 \| \| 0.5933 \| 435.0 \| 1305 \| 1.3777 \| \| 0.5933 \| 436.0 \| 1308 \| 1.3375 \| \| 0.5933 \| 437.0 \| 1311 \| 1.2963 \| \| 0.5933 \| 438.0 \| 1314 \| 1.3388 \| \| 0.5933 \| 439.0 \| 1317 \| 1.3685 \| \| 0.5933 \| 440.0 \| 1320 \| 1.3634 \| \| 0.5933 \| 441.0 \| 1323 \| 1.3484 \| \| 0.5933 \| 442.0 \| 1326 \| 1.3536 \| \| 0.5933 \| 443.0 \| 1329 \| 1.3584 \| \| 0.5933 \| 444.0 \| 1332 \| 1.3452 \| \| 0.5933 \| 445.0 \| 1335 \| 1.3379 \| \| 0.5933 \| 446.0 \| 1338 \| 1.3434 \| \| 0.5933 \| 447.0 \| 1341 \| 1.3378 \| \| 0.5933 \| 448.0 \| 1344 \| 1.3451 \| \| 0.5933 \| 449.0 \| 1347 \| 1.3583 \| \| 0.5933 \| 450.0 \| 1350 \| 1.3498 \| \| 0.5933 \| 451.0 \| 1353 \| 1.3202 \| \| 0.5933 \| 452.0 \| 1356 \| 1.3219 \| \| 0.5933 \| 453.0 \| 1359 \| 1.3534 \| \| 0.5933 \| 454.0 \| 1362 \| 1.3738 \| \| 0.5933 \| 455.0 \| 1365 \| 1.3947 \| \| 0.5933 \| 456.0 \| 1368 \| 1.3863 \| \| 0.5933 \| 457.0 \| 1371 \| 1.3747 \| \| 0.5933 \| 458.0 \| 1374 \| 1.3685 \| \| 0.5933 \| 459.0 \| 1377 \| 1.3519 \| \| 0.5933 \| 460.0 \| 1380 \| 1.3706 \| \| 0.5933 \| 461.0 \| 1383 \| 1.3956 \| \| 0.5933 \| 462.0 \| 1386 \| 1.3628 \| \| 0.5933 \| 463.0 \| 1389 \| 1.3669 \| \| 0.5933 \| 464.0 \| 1392 \| 1.3338 \| \| 0.5933 \| 465.0 \| 1395 \| 1.3316 \| \| 0.5933 \| 466.0 \| 1398 \| 1.3641 \| \| 0.5933 \| 467.0 \| 1401 \| 1.3980 \| \| 0.5933 \| 468.0 \| 1404 \| 1.4046 \| \| 0.5933 \| 469.0 \| 1407 \| 1.3757 \| \| 0.5933 \| 470.0 \| 1410 \| 1.3437 \| \| 0.5933 \| 471.0 \| 1413 \| 1.3552 \| \| 0.5933 \| 472.0 \| 1416 \| 1.3930 \| \| 0.5933 \| 473.0 \| 1419 \| 1.3926 \| \| 0.5933 \| 474.0 \| 1422 \| 1.3316 \| \| 0.5933 \| 475.0 \| 1425 \| 1.2435 \| \| 0.5933 \| 476.0 \| 1428 \| 1.2005 \| \| 0.5933 \| 477.0 \| 1431 \| 1.2154 \| \| 0.5933 \| 478.0 \| 1434 \| 1.2495 \| \| 0.5933 \| 479.0 \| 1437 \| 1.2615 \| \| 0.5933 \| 480.0 \| 1440 \| 1.2665 \| \| 0.5933 \| 481.0 \| 1443 \| 1.2593 \| \| 0.5933 \| 482.0 \| 1446 \| 1.2442 \| \| 0.5933 \| 483.0 \| 1449 \| 1.2603 \| \| 0.5933 \| 484.0 \| 1452 \| 1.2821 \| \| 0.5933 \| 485.0 \| 1455 \| 1.2940 \| \| 0.5933 \| 486.0 \| 1458 \| 1.2904 \| \| 0.5933 \| 487.0 \| 1461 \| 1.2815 \| \| 0.5933 \| 488.0 \| 1464 \| 1.2719 \| \| 0.5933 \| 489.0 \| 1467 \| 1.2950 \| \| 0.5933 \| 490.0 \| 1470 \| 1.3589 \| \| 0.5933 \| 491.0 \| 1473 \| 1.4231 \| \| 0.5933 \| 492.0 \| 1476 \| 1.4325 \| \| 0.5933 \| 493.0 \| 1479 \| 1.3372 \| \| 0.5933 \| 494.0 \| 1482 \| 1.2722 \| \| 0.5933 \| 495.0 \| 1485 \| 1.3250 \| \| 0.5933 \| 496.0 \| 1488 \| 1.4279 \| \| 0.5933 \| 497.0 \| 1491 \| 1.4185 \| \| 0.5933 \| 498.0 \| 1494 \| 1.3254 \| \| 0.5933 \| 499.0 \| 1497 \| 1.2996 \| \| 0.5698 \| 500.0 \| 1500 \| 1.2436 \| \| 0.5698 \| 501.0 \| 1503 \| 1.2112 \| \| 0.5698 \| 502.0 \| 1506 \| 1.2390 \| \| 0.5698 \| 503.0 \| 1509 \| 1.2883 \| \| 0.5698 \| 504.0 \| 1512 \| 1.3407 \| \| 0.5698 \| 505.0 \| 1515 \| 1.3793 \| \| 0.5698 \| 506.0 \| 1518 \| 1.4309 \| \| 0.5698 \| 507.0 \| 1521 \| 1.4088 \| \| 0.5698 \| 508.0 \| 1524 \| 1.3966 \| \| 0.5698 \| 509.0 \| 1527 \| 1.4082 \| \| 0.5698 \| 510.0 \| 1530 \| 1.3814 \| \| 0.5698 \| 511.0 \| 1533 \| 1.3396 \| \| 0.5698 \| 512.0 \| 1536 \| 1.3387 \| \| 0.5698 \| 513.0 \| 1539 \| 1.3057 \| \| 0.5698 \| 514.0 \| 1542 \| 1.2687 \| \| 0.5698 \| 515.0 \| 1545 \| 1.2707 \| \| 0.5698 \| 516.0 \| 1548 \| 1.4157 \| \| 0.5698 \| 517.0 \| 1551 \| 1.4618 \| \| 0.5698 \| 518.0 \| 1554 \| 1.4597 \| \| 0.5698 \| 519.0 \| 1557 \| 1.4605 \| \| 0.5698 \| 520.0 \| 1560 \| 1.4481 \| \| 0.5698 \| 521.0 \| 1563 \| 1.4423 \| \| 0.5698 \| 522.0 \| 1566 \| 1.4312 \| \| 0.5698 \| 523.0 \| 1569 \| 1.4020 \| \| 0.5698 \| 524.0 \| 1572 \| 1.3645 \| \| 0.5698 \| 525.0 \| 1575 \| 1.3438 \| \| 0.5698 \| 526.0 \| 1578 \| 1.3205 \| \| 0.5698 \| 527.0 \| 1581 \| 1.3053 \| \| 0.5698 \| 528.0 \| 1584 \| 1.2944 \| \| 0.5698 \| 529.0 \| 1587 \| 1.3649 \| \| 0.5698 \| 530.0 \| 1590 \| 1.4252 \| \| 0.5698 \| 531.0 \| 1593 \| 1.4653 \| \| 0.5698 \| 532.0 \| 1596 \| 1.4664 \| \| 0.5698 \| 533.0 \| 1599 \| 1.4386 \| \| 0.5698 \| 534.0 \| 1602 \| 1.3703 \| \| 0.5698 \| 535.0 \| 1605 \| 1.3156 \| \| 0.5698 \| 536.0 \| 1608 \| 1.3263 \| \| 0.5698 \| 537.0 \| 1611 \| 1.3055 \| \| 0.5698 \| 538.0 \| 1614 \| 1.3066 \| \| 0.5698 \| 539.0 \| 1617 \| 1.3549 \| \| 0.5698 \| 540.0 \| 1620 \| 1.4445 \| \| 0.5698 \| 541.0 \| 1623 \| 1.4701 \| \| 0.5698 \| 542.0 \| 1626 \| 1.4265 \| \| 0.5698 \| 543.0 \| 1629 \| 1.3599 \| \| 0.5698 \| 544.0 \| 1632 \| 1.3451 \| \| 0.5698 \| 545.0 \| 1635 \| 1.3428 \| \| 0.5698 \| 546.0 \| 1638 \| 1.3231 \| \| 0.5698 \| 547.0 \| 1641 \| 1.3266 \| \| 0.5698 \| 548.0 \| 1644 \| 1.3216 \| \| 0.5698 \| 549.0 \| 1647 \| 1.2599 \| \| 0.5698 \| 550.0 \| 1650 \| 1.2338 \| \| 0.5698 \| 551.0 \| 1653 \| 1.2140 \| \| 0.5698 \| 552.0 \| 1656 \| 1.2297 \| \| 0.5698 \| 553.0 \| 1659 \| 1.2842 \| \| 0.5698 \| 554.0 \| 1662 \| 1.3357 \| \| 0.5698 \| 555.0 \| 1665 \| 1.3797 \| \| 0.5698 \| 556.0 \| 1668 \| 1.3690 \| \| 0.5698 \| 557.0 \| 1671 \| 1.3163 \| \| 0.5698 \| 558.0 \| 1674 \| 1.2510 \| \| 0.5698 \| 559.0 \| 1677 \| 1.2714 \| \| 0.5698 \| 560.0 \| 1680 \| 1.3403 \| \| 0.5698 \| 561.0 \| 1683 \| 1.4387 \| \| 0.5698 \| 562.0 \| 1686 \| 1.4697 \| \| 0.5698 \| 563.0 \| 1689 \| 1.4641 \| \| 0.5698 \| 564.0 \| 1692 \| 1.4123 \| \| 0.5698 \| 565.0 \| 1695 \| 1.3808 \| \| 0.5698 \| 566.0 \| 1698 \| 1.3325 \| \| 0.5698 \| 567.0 \| 1701 \| 1.3470 \| \| 0.5698 \| 568.0 \| 1704 \| 1.3301 \| \| 0.5698 \| 569.0 \| 1707 \| 1.3255 \| \| 0.5698 \| 570.0 \| 1710 \| 1.3614 \| \| 0.5698 \| 571.0 \| 1713 \| 1.4034 \| \| 0.5698 \| 572.0 \| 1716 \| 1.4201 \| \| 0.5698 \| 573.0 \| 1719 \| 1.4221 \| \| 0.5698 \| 574.0 \| 1722 \| 1.4100 \| \| 0.5698 \| 575.0 \| 1725 \| 1.3791 \| \| 0.5698 \| 576.0 \| 1728 \| 1.3478 \| \| 0.5698 \| 577.0 \| 1731 \| 1.3398 \| \| 0.5698 \| 578.0 \| 1734 \| 1.3408 \| \| 0.5698 \| 579.0 \| 1737 \| 1.3577 \| \| 0.5698 \| 580.0 \| 1740 \| 1.3780 \| \| 0.5698 \| 581.0 \| 1743 \| 1.3871 \| \| 0.5698 \| 582.0 \| 1746 \| 1.3754 \| \| 0.5698 \| 583.0 \| 1749 \| 1.3487 \| \| 0.5698 \| 584.0 \| 1752 \| 1.3299 \| \| 0.5698 \| 585.0 \| 1755 \| 1.3215 \| \| 0.5698 \| 586.0 \| 1758 \| 1.3004 \| \| 0.5698 \| 587.0 \| 1761 \| 1.2819 \| \| 0.5698 \| 588.0 \| 1764 \| 1.2804 \| \| 0.5698 \| 589.0 \| 1767 \| 1.2724 \| \| 0.5698 \| 590.0 \| 1770 \| 1.2975 \| \| 0.5698 \| 591.0 \| 1773 \| 1.3615 \| \| 0.5698 \| 592.0 \| 1776 \| 1.4006 \| \| 0.5698 \| 593.0 \| 1779 \| 1.4037 \| \| 0.5698 \| 594.0 \| 1782 \| 1.3882 \| \| 0.5698 \| 595.0 \| 1785 \| 1.3919 \| \| 0.5698 \| 596.0 \| 1788 \| 1.3759 \| \| 0.5698 \| 597.0 \| 1791 \| 1.3215 \| \| 0.5698 \| 598.0 \| 1794 \| 1.3130 \| \| 0.5698 \| 599.0 \| 1797 \| 1.3547 \| \| 0.5698 \| 600.0 \| 1800 \| 1.3832 \| \| 0.5698 \| 601.0 \| 1803 \| 1.3755 \| \| 0.5698 \| 602.0 \| 1806 \| 1.3555 \| \| 0.5698 \| 603.0 \| 1809 \| 1.3085 \| \| 0.5698 \| 604.0 \| 1812 \| 1.3235 \| \| 0.5698 \| 605.0 \| 1815 \| 1.3616 \| \| 0.5698 \| 606.0 \| 1818 \| 1.4128 \| \| 0.5698 \| 607.0 \| 1821 \| 1.4333 \| \| 0.5698 \| 608.0 \| 1824 \| 1.4124 \| \| 0.5698 \| 609.0 \| 1827 \| 1.3622 \| \| 0.5698 \| 610.0 \| 1830 \| 1.2583 \| \| 0.5698 \| 611.0 \| 1833 \| 1.2334 \| \| 0.5698 \| 612.0 \| 1836 \| 1.2316 \| \| 0.5698 \| 613.0 \| 1839 \| 1.2430 \| \| 0.5698 \| 614.0 \| 1842 \| 1.2659 \| \| 0.5698 \| 615.0 \| 1845 \| 1.2801 \| \| 0.5698 \| 616.0 \| 1848 \| 1.3092 \| \| 0.5698 \| 617.0 \| 1851 \| 1.3340 \| \| 0.5698 \| 618.0 \| 1854 \| 1.3543 \| \| 0.5698 \| 619.0 \| 1857 \| 1.3771 \| \| 0.5698 \| 620.0 \| 1860 \| 1.3764 \| \| 0.5698 \| 621.0 \| 1863 \| 1.3577 \| \| 0.5698 \| 622.0 \| 1866 \| 1.3255 \| \| 0.5698 \| 623.0 \| 1869 \| 1.2972 \| \| 0.5698 \| 624.0 \| 1872 \| 1.2877 \| \| 0.5698 \| 625.0 \| 1875 \| 1.3092 \| \| 0.5698 \| 626.0 \| 1878 \| 1.3348 \| \| 0.5698 \| 627.0 \| 1881 \| 1.3486 \| \| 0.5698 \| 628.0 \| 1884 \| 1.3543 \| \| 0.5698 \| 629.0 \| 1887 \| 1.3504 \| \| 0.5698 \| 630.0 \| 1890 \| 1.3544 \| \| 0.5698 \| 631.0 \| 1893 \| 1.3419 \| \| 0.5698 \| 632.0 \| 1896 \| 1.3093 \| \| 0.5698 \| 633.0 \| 1899 \| 1.2775 \| \| 0.5698 \| 634.0 \| 1902 \| 1.2783 \| \| 0.5698 \| 635.0 \| 1905 \| 1.2753 \| \| 0.5698 \| 636.0 \| 1908 \| 1.2506 \| \| 0.5698 \| 637.0 \| 1911 \| 1.2332 \| \| 0.5698 \| 638.0 \| 1914 \| 1.2763 \| \| 0.5698 \| 639.0 \| 1917 \| 1.3084 \| \| 0.5698 \| 640.0 \| 1920 \| 1.3237 \| \| 0.5698 \| 641.0 \| 1923 \| 1.3340 \| \| 0.5698 \| 642.0 \| 1926 \| 1.3339 \| \| 0.5698 \| 643.0 \| 1929 \| 1.3103 \| \| 0.5698 \| 644.0 \| 1932 \| 1.2959 \| \| 0.5698 \| 645.0 \| 1935 \| 1.2915 \| \| 0.5698 \| 646.0 \| 1938 \| 1.3321 \| \| 0.5698 \| 647.0 \| 1941 \| 1.3656 \| \| 0.5698 \| 648.0 \| 1944 \| 1.3728 \| \| 0.5698 \| 649.0 \| 1947 \| 1.3629 \| \| 0.5698 \| 650.0 \| 1950 \| 1.3502 \| \| 0.5698 \| 651.0 \| 1953 \| 1.3297 \| \| 0.5698 \| 652.0 \| 1956 \| 1.3057 \| \| 0.5698 \| 653.0 \| 1959 \| 1.3008 \| \| 0.5698 \| 654.0 \| 1962 \| 1.2932 \| \| 0.5698 \| 655.0 \| 1965 \| 1.2945 \| \| 0.5698 \| 656.0 \| 1968 \| 1.2929 \| \| 0.5698 \| 657.0 \| 1971 \| 1.3073 \| \| 0.5698 \| 658.0 \| 1974 \| 1.3311 \| \| 0.5698 \| 659.0 \| 1977 \| 1.3472 \| \| 0.5698 \| 660.0 \| 1980 \| 1.3409 \| \| 0.5698 \| 661.0 \| 1983 \| 1.3315 \| \| 0.5698 \| 662.0 \| 1986 \| 1.3154 \| \| 0.5698 \| 663.0 \| 1989 \| 1.3030 \| \| 0.5698 \| 664.0 \| 1992 \| 1.3006 \| \| 0.5698 \| 665.0 \| 1995 \| 1.2968 \| \| 0.5698 \| 666.0 \| 1998 \| 1.3045 \| \| 0.5609 \| 667.0 \| 2001 \| 1.3166 \| \| 0.5609 \| 668.0 \| 2004 \| 1.3430 \| \| 0.5609 \| 669.0 \| 2007 \| 1.3718 \| \| 0.5609 \| 670.0 \| 2010 \| 1.3945 \| \| 0.5609 \| 671.0 \| 2013 \| 1.3919 \| \| 0.5609 \| 672.0 \| 2016 \| 1.3895 \| \| 0.5609 \| 673.0 \| 2019 \| 1.3659 \| \| 0.5609 \| 674.0 \| 2022 \| 1.3276 \| \| 0.5609 \| 675.0 \| 2025 \| 1.3060 \| \| 0.5609 \| 676.0 \| 2028 \| 1.2941 \| \| 0.5609 \| 677.0 \| 2031 \| 1.2893 \| \| 0.5609 \| 678.0 \| 2034 \| 1.2937 \| \| 0.5609 \| 679.0 \| 2037 \| 1.3019 \| \| 0.5609 \| 680.0 \| 2040 \| 1.3119 \| \| 0.5609 \| 681.0 \| 2043 \| 1.3222 \| \| 0.5609 \| 682.0 \| 2046 \| 1.3238 \| \| 0.5609 \| 683.0 \| 2049 \| 1.3280 \| \| 0.5609 \| 684.0 \| 2052 \| 1.3324 \| \| 0.5609 \| 685.0 \| 2055 \| 1.3401 \| \| 0.5609 \| 686.0 \| 2058 \| 1.3452 \| \| 0.5609 \| 687.0 \| 2061 \| 1.3752 \| \| 0.5609 \| 688.0 \| 2064 \| 1.3987 \| \| 0.5609 \| 689.0 \| 2067 \| 1.4118 \| \| 0.5609 \| 690.0 \| 2070 \| 1.4179 \| \| 0.5609 \| 691.0 \| 2073 \| 1.4122 \| \| 0.5609 \| 692.0 \| 2076 \| 1.3909 \| \| 0.5609 \| 693.0 \| 2079 \| 1.3439 \| \| 0.5609 \| 694.0 \| 2082 \| 1.3072 \| \| 0.5609 \| 695.0 \| 2085 \| 1.2981 \| \| 0.5609 \| 696.0 \| 2088 \| 1.3195 \| \| 0.5609 \| 697.0 \| 2091 \| 1.3502 \| \| 0.5609 \| 698.0 \| 2094 \| 1.3783 \| \| 0.5609 \| 699.0 \| 2097 \| 1.3925 \| \| 0.5609 \| 700.0 \| 2100 \| 1.4000 \| \| 0.5609 \| 701.0 \| 2103 \| 1.3797 \| \| 0.5609 \| 702.0 \| 2106 \| 1.3620 \| \| 0.5609 \| 703.0 \| 2109 \| 1.3533 \| \| 0.5609 \| 704.0 \| 2112 \| 1.3492 \| \| 0.5609 \| 705.0 \| 2115 \| 1.3400 \| \| 0.5609 \| 706.0 \| 2118 \| 1.3346 \| \| 0.5609 \| 707.0 \| 2121 \| 1.3254 \| \| 0.5609 \| 708.0 \| 2124 \| 1.3290 \| \| 0.5609 \| 709.0 \| 2127 \| 1.3406 \| \| 0.5609 \| 710.0 \| 2130 \| 1.3619 \| \| 0.5609 \| 711.0 \| 2133 \| 1.3898 \| \| 0.5609 \| 712.0 \| 2136 \| 1.3945 \| \| 0.5609 \| 713.0 \| 2139 \| 1.3817 \| \| 0.5609 \| 714.0 \| 2142 \| 1.3686 \| \| 0.5609 \| 715.0 \| 2145 \| 1.3627 \| \| 0.5609 \| 716.0 \| 2148 \| 1.3617 \| \| 0.5609 \| 717.0 \| 2151 \| 1.3548 \| \| 0.5609 \| 718.0 \| 2154 \| 1.3464 \| \| 0.5609 \| 719.0 \| 2157 \| 1.3368 \| \| 0.5609 \| 720.0 \| 2160 \| 1.3138 \| \| 0.5609 \| 721.0 \| 2163 \| 1.3073 \| \| 0.5609 \| 722.0 \| 2166 \| 1.3203 \| \| 0.5609 \| 723.0 \| 2169 \| 1.3342 \| \| 0.5609 \| 724.0 \| 2172 \| 1.3562 \| \| 0.5609 \| 725.0 \| 2175 \| 1.3725 \| \| 0.5609 \| 726.0 \| 2178 \| 1.3748 \| \| 0.5609 \| 727.0 \| 2181 \| 1.3711 \| \| 0.5609 \| 728.0 \| 2184 \| 1.3717 \| \| 0.5609 \| 729.0 \| 2187 \| 1.3627 \| \| 0.5609 \| 730.0 \| 2190 \| 1.3515 \| \| 0.5609 \| 731.0 \| 2193 \| 1.3373 \| \| 0.5609 \| 732.0 \| 2196 \| 1.3160 \| \| 0.5609 \| 733.0 \| 2199 \| 1.3125 \| \| 0.5609 \| 734.0 \| 2202 \| 1.3301 \| \| 0.5609 \| 735.0 \| 2205 \| 1.3197 \| \| 0.5609 \| 736.0 \| 2208 \| 1.3125 \| \| 0.5609 \| 737.0 \| 2211 \| 1.3072 \| \| 0.5609 \| 738.0 \| 2214 \| 1.2798 \| \| 0.5609 \| 739.0 \| 2217 \| 1.2672 \| \| 0.5609 \| 740.0 \| 2220 \| 1.2533 \| \| 0.5609 \| 741.0 \| 2223 \| 1.2383 \| \| 0.5609 \| 742.0 \| 2226 \| 1.2450 \| \| 0.5609 \| 743.0 \| 2229 \| 1.2557 \| \| 0.5609 \| 744.0 \| 2232 \| 1.2751 \| \| 0.5609 \| 745.0 \| 2235 \| 1.3235 \| \| 0.5609 \| 746.0 \| 2238 \| 1.3708 \| \| 0.5609 \| 747.0 \| 2241 \| 1.3867 \| \| 0.5609 \| 748.0 \| 2244 \| 1.3686 \| \| 0.5609 \| 749.0 \| 2247 \| 1.3309 \| \| 0.5609 \| 750.0 \| 2250 \| 1.2811 \| \| 0.5609 \| 751.0 \| 2253 \| 1.2294 \| \| 0.5609 \| 752.0 \| 2256 \| 1.1340 \| \| 0.5609 \| 753.0 \| 2259 \| 1.1346 \| \| 0.5609 \| 754.0 \| 2262 \| 1.2078 \| \| 0.5609 \| 755.0 \| 2265 \| 1.2462 \| \| 0.5609 \| 756.0 \| 2268 \| 1.2557 \| \| 0.5609 \| 757.0 \| 2271 \| 1.2358 \| \| 0.5609 \| 758.0 \| 2274 \| 1.2225 \| \| 0.5609 \| 759.0 \| 2277 \| 1.2298 \| \| 0.5609 \| 760.0 \| 2280 \| 1.2561 \| \| 0.5609 \| 761.0 \| 2283 \| 1.2861 \| \| 0.5609 \| 762.0 \| 2286 \| 1.3017 \| \| 0.5609 \| 763.0 \| 2289 \| 1.3228 \| \| 0.5609 \| 764.0 \| 2292 \| 1.3235 \| \| 0.5609 \| 765.0 \| 2295 \| 1.3232 \| \| 0.5609 \| 766.0 \| 2298 \| 1.3236 \| \| 0.5609 \| 767.0 \| 2301 \| 1.3289 \| \| 0.5609 \| 768.0 \| 2304 \| 1.3324 \| \| 0.5609 \| 769.0 \| 2307 \| 1.3325 \| \| 0.5609 \| 770.0 \| 2310 \| 1.3282 \| \| 0.5609 \| 771.0 \| 2313 \| 1.3176 \| \| 0.5609 \| 772.0 \| 2316 \| 1.2927 \| \| 0.5609 \| 773.0 \| 2319 \| 1.2773 \| \| 0.5609 \| 774.0 \| 2322 \| 1.2617 \| \| 0.5609 \| 775.0 \| 2325 \| 1.2578 \| \| 0.5609 \| 776.0 \| 2328 \| 1.2454 \| \| 0.5609 \| 777.0 \| 2331 \| 1.2212 \| \| 0.5609 \| 778.0 \| 2334 \| 1.2459 \| \| 0.5609 \| 779.0 \| 2337 \| 1.3040 \| \| 0.5609 \| 780.0 \| 2340 \| 1.3453 \| \| 0.5609 \| 781.0 \| 2343 \| 1.3773 \| \| 0.5609 \| 782.0 \| 2346 \| 1.3942 \| \| 0.5609 \| 783.0 \| 2349 \| 1.3854 \| \| 0.5609 \| 784.0 \| 2352 \| 1.3637 \| \| 0.5609 \| 785.0 \| 2355 \| 1.3213 \| \| 0.5609 \| 786.0 \| 2358 \| 1.2795 \| \| 0.5609 \| 787.0 \| 2361 \| 1.2844 \| \| 0.5609 \| 788.0 \| 2364 \| 1.3058 \| \| 0.5609 \| 789.0 \| 2367 \| 1.3198 \| \| 0.5609 \| 790.0 \| 2370 \| 1.3251 \| \| 0.5609 \| 791.0 \| 2373 \| 1.3193 \| \| 0.5609 \| 792.0 \| 2376 \| 1.3021 \| \| 0.5609 \| 793.0 \| 2379 \| 1.3105 \| \| 0.5609 \| 794.0 \| 2382 \| 1.3310 \| \| 0.5609 \| 795.0 \| 2385 \| 1.3574 \| \| 0.5609 \| 796.0 \| 2388 \| 1.3642 \| \| 0.5609 \| 797.0 \| 2391 \| 1.3580 \| \| 0.5609 \| 798.0 \| 2394 \| 1.3255 \| \| 0.5609 \| 799.0 \| 2397 \| 1.2785 \| \| 0.5609 \| 800.0 \| 2400 \| 1.2199 \| \| 0.5609 \| 801.0 \| 2403 \| 1.1221 \| \| 0.5609 \| 802.0 \| 2406 \| 1.1233 \| \| 0.5609 \| 803.0 \| 2409 \| 1.1873 \| \| 0.5609 \| 804.0 \| 2412 \| 1.3435 \| \| 0.5609 \| 805.0 \| 2415 \| 1.3522 \| \| 0.5609 \| 806.0 \| 2418 \| 1.3800 \| \| 0.5609 \| 807.0 \| 2421 \| 1.3976 \| \| 0.5609 \| 808.0 \| 2424 \| 1.3899 \| \| 0.5609 \| 809.0 \| 2427 \| 1.3480 \| \| 0.5609 \| 810.0 \| 2430 \| 1.1934 \| \| 0.5609 \| 811.0 \| 2433 \| 1.1259 \| \| 0.5609 \| 812.0 \| 2436 \| 1.1836 \| \| 0.5609 \| 813.0 \| 2439 \| 1.2207 \| \| 0.5609 \| 814.0 \| 2442 \| 1.3393 \| \| 0.5609 \| 815.0 \| 2445 \| 1.4465 \| \| 0.5609 \| 816.0 \| 2448 \| 1.4166 \| \| 0.5609 \| 817.0 \| 2451 \| 1.3814 \| \| 0.5609 \| 818.0 \| 2454 \| 1.3636 \| \| 0.5609 \| 819.0 \| 2457 \| 1.3334 \| \| 0.5609 \| 820.0 \| 2460 \| 1.2854 \| \| 0.5609 \| 821.0 \| 2463 \| 1.2674 \| \| 0.5609 \| 822.0 \| 2466 \| 1.2533 \| \| 0.5609 \| 823.0 \| 2469 \| 1.2967 \| \| 0.5609 \| 824.0 \| 2472 \| 1.3504 \| \| 0.5609 \| 825.0 \| 2475 \| 1.3052 \| \| 0.5609 \| 826.0 \| 2478 \| 1.2894 \| \| 0.5609 \| 827.0 \| 2481 \| 1.3342 \| \| 0.5609 \| 828.0 \| 2484 \| 1.4139 \| \| 0.5609 \| 829.0 \| 2487 \| 1.4048 \| \| 0.5609 \| 830.0 \| 2490 \| 1.3678 \| \| 0.5609 \| 831.0 \| 2493 \| 1.3604 \| \| 0.5609 \| 832.0 \| 2496 \| 1.3533 \| \| 0.5609 \| 833.0 \| 2499 \| 1.3609 \| \| 0.5608 \| 834.0 \| 2502 \| 1.3909 \| \| 0.5608 \| 835.0 \| 2505 \| 1.4105 \| \| 0.5608 \| 836.0 \| 2508 \| 1.4294 \| \| 0.5608 \| 837.0 \| 2511 \| 1.4313 \| \| 0.5608 \| 838.0 \| 2514 \| 1.4112 \| \| 0.5608 \| 839.0 \| 2517 \| 1.3844 \| \| 0.5608 \| 840.0 \| 2520 \| 1.3769 \| \| 0.5608 \| 841.0 \| 2523 \| 1.3679 \| \| 0.5608 \| 842.0 \| 2526 \| 1.3449 \| \| 0.5608 \| 843.0 \| 2529 \| 1.3389 \| \| 0.5608 \| 844.0 \| 2532 \| 1.3366 \| \| 0.5608 \| 845.0 \| 2535 \| 1.3453 \| \| 0.5608 \| 846.0 \| 2538 \| 1.3726 \| \| 0.5608 \| 847.0 \| 2541 \| 1.3670 \| \| 0.5608 \| 848.0 \| 2544 \| 1.3503 \| \| 0.5608 \| 849.0 \| 2547 \| 1.3262 \| \| 0.5608 \| 850.0 \| 2550 \| 1.3017 \| \| 0.5608 \| 851.0 \| 2553 \| 1.2902 \| \| 0.5608 \| 852.0 \| 2556 \| 1.2662 \| \| 0.5608 \| 853.0 \| 2559 \| 1.2408 \| \| 0.5608 \| 854.0 \| 2562 \| 1.2208 \| \| 0.5608 \| 855.0 \| 2565 \| 1.2003 \| \| 0.5608 \| 856.0 \| 2568 \| 1.2038 \| \| 0.5608 \| 857.0 \| 2571 \| 1.2344 \| \| 0.5608 \| 858.0 \| 2574 \| 1.2968 \| \| 0.5608 \| 859.0 \| 2577 \| 1.3401 \| \| 0.5608 \| 860.0 \| 2580 \| 1.3674 \| \| 0.5608 \| 861.0 \| 2583 \| 1.3837 \| \| 0.5608 \| 862.0 \| 2586 \| 1.3753 \| \| 0.5608 \| 863.0 \| 2589 \| 1.3121 \| \| 0.5608 \| 864.0 \| 2592 \| 1.2480 \| \| 0.5608 \| 865.0 \| 2595 \| 1.2293 \| \| 0.5608 \| 866.0 \| 2598 \| 1.2000 \| \| 0.5608 \| 867.0 \| 2601 \| 1.2027 \| \| 0.5608 \| 868.0 \| 2604 \| 1.2281 \| \| 0.5608 \| 869.0 \| 2607 \| 1.2710 \| \| 0.5608 \| 870.0 \| 2610 \| 1.3535 \| \| 0.5608 \| 871.0 \| 2613 \| 1.3937 \| \| 0.5608 \| 872.0 \| 2616 \| 1.4003 \| \| 0.5608 \| 873.0 \| 2619 \| 1.3758 \| \| 0.5608 \| 874.0 \| 2622 \| 1.3253 \| \| 0.5608 \| 875.0 \| 2625 \| 1.2449 \| \| 0.5608 \| 876.0 \| 2628 \| 1.1745 \| \| 0.5608 \| 877.0 \| 2631 \| 1.1366 \| \| 0.5608 \| 878.0 \| 2634 \| 1.1655 \| \| 0.5608 \| 879.0 \| 2637 \| 1.2965 \| \| 0.5608 \| 880.0 \| 2640 \| 1.3166 \| \| 0.5608 \| 881.0 \| 2643 \| 1.3225 \| \| 0.5608 \| 882.0 \| 2646 \| 1.3141 \| \| 0.5608 \| 883.0 \| 2649 \| 1.2992 \| \| 0.5608 \| 884.0 \| 2652 \| 1.2834 \| \| 0.5608 \| 885.0 \| 2655 \| 1.2698 \| \| 0.5608 \| 886.0 \| 2658 \| 1.2829 \| \| 0.5608 \| 887.0 \| 2661 \| 1.3100 \| \| 0.5608 \| 888.0 \| 2664 \| 1.3314 \| \| 0.5608 \| 889.0 \| 2667 \| 1.3393 \| \| 0.5608 \| 890.0 \| 2670 \| 1.3354 \| \| 0.5608 \| 891.0 \| 2673 \| 1.3278 \| \| 0.5608 \| 892.0 \| 2676 \| 1.3333 \| \| 0.5608 \| 893.0 \| 2679 \| 1.3443 \| \| 0.5608 \| 894.0 \| 2682 \| 1.3343 \| \| 0.5608 \| 895.0 \| 2685 \| 1.3148 \| \| 0.5608 \| 896.0 \| 2688 \| 1.2858 \| \| 0.5608 \| 897.0 \| 2691 \| 1.2698 \| \| 0.5608 \| 898.0 \| 2694 \| 1.2777 \| \| 0.5608 \| 899.0 \| 2697 \| 1.2901 \| \| 0.5608 \| 900.0 \| 2700 \| 1.3008 \| \| 0.5608 \| 901.0 \| 2703 \| 1.3260 \| \| 0.5608 \| 902.0 \| 2706 \| 1.3440 \| \| 0.5608 \| 903.0 \| 2709 \| 1.3438 \| \| 0.5608 \| 904.0 \| 2712 \| 1.3380 \| \| 0.5608 \| 905.0 \| 2715 \| 1.3237 \| \| 0.5608 \| 906.0 \| 2718 \| 1.3145 \| \| 0.5608 \| 907.0 \| 2721 \| 1.3022 \| \| 0.5608 \| 908.0 \| 2724 \| 1.2902 \| \| 0.5608 \| 909.0 \| 2727 \| 1.2793 \| \| 0.5608 \| 910.0 \| 2730 \| 1.2909 \| \| 0.5608 \| 911.0 \| 2733 \| 1.3084 \| \| 0.5608 \| 912.0 \| 2736 \| 1.3185 \| \| 0.5608 \| 913.0 \| 2739 \| 1.3250 \| \| 0.5608 \| 914.0 \| 2742 \| 1.3412 \| \| 0.5608 \| 915.0 \| 2745 \| 1.3491 \| \| 0.5608 \| 916.0 \| 2748 \| 1.3561 \| \| 0.5608 \| 917.0 \| 2751 \| 1.3675 \| \| 0.5608 \| 918.0 \| 2754 \| 1.3759 \| \| 0.5608 \| 919.0 \| 2757 \| 1.3829 \| \| 0.5608 \| 920.0 \| 2760 \| 1.3805 \| \| 0.5608 \| 921.0 \| 2763 \| 1.3669 \| \| 0.5608 \| 922.0 \| 2766 \| 1.3605 \| \| 0.5608 \| 923.0 \| 2769 \| 1.3455 \| \| 0.5608 \| 924.0 \| 2772 \| 1.3373 \| \| 0.5608 \| 925.0 \| 2775 \| 1.3440 \| \| 0.5608 \| 926.0 \| 2778 \| 1.3408 \| \| 0.5608 \| 927.0 \| 2781 \| 1.3424 \| \| 0.5608 \| 928.0 \| 2784 \| 1.3414 \| \| 0.5608 \| 929.0 \| 2787 \| 1.3383 \| \| 0.5608 \| 930.0 \| 2790 \| 1.3371 \| \| 0.5608 \| 931.0 \| 2793 \| 1.3406 \| \| 0.5608 \| 932.0 \| 2796 \| 1.3432 \| \| 0.5608 \| 933.0 \| 2799 \| 1.3564 \| \| 0.5608 \| 934.0 \| 2802 \| 1.3773 \| \| 0.5608 \| 935.0 \| 2805 \| 1.3931 \| \| 0.5608 \| 936.0 \| 2808 \| 1.4030 \| \| 0.5608 \| 937.0 \| 2811 \| 1.3998 \| \| 0.5608 \| 938.0 \| 2814 \| 1.3955 \| \| 0.5608 \| 939.0 \| 2817 \| 1.3937 \| \| 0.5608 \| 940.0 \| 2820 \| 1.3801 \| \| 0.5608 \| 941.0 \| 2823 \| 1.3729 \| \| 0.5608 \| 942.0 \| 2826 \| 1.3679 \| \| 0.5608 \| 943.0 \| 2829 \| 1.3550 \| \| 0.5608 \| 944.0 \| 2832 \| 1.3437 \| \| 0.5608 \| 945.0 \| 2835 \| 1.3347 \| \| 0.5608 \| 946.0 \| 2838 \| 1.3220 \| \| 0.5608 \| 947.0 \| 2841 \| 1.2968 \| \| 0.5608 \| 948.0 \| 2844 \| 1.2799 \| \| 0.5608 \| 949.0 \| 2847 \| 1.2549 \| \| 0.5608 \| 950.0 \| 2850 \| 1.2459 \| \| 0.5608 \| 951.0 \| 2853 \| 1.2461 \| \| 0.5608 \| 952.0 \| 2856 \| 1.2299 \| \| 0.5608 \| 953.0 \| 2859 \| 1.2177 \| \| 0.5608 \| 954.0 \| 2862 \| 1.2640 \| \| 0.5608 \| 955.0 \| 2865 \| 1.2997 \| \| 0.5608 \| 956.0 \| 2868 \| 1.2971 \| \| 0.5608 \| 957.0 \| 2871 \| 1.2788 \| \| 0.5608 \| 958.0 \| 2874 \| 1.2858 \| \| 0.5608 \| 959.0 \| 2877 \| 1.2694 \| \| 0.5608 \| 960.0 \| 2880 \| 1.2542 \| \| 0.5608 \| 961.0 \| 2883 \| 1.2733 \| \| 0.5608 \| 962.0 \| 2886 \| 1.3086 \| \| 0.5608 \| 963.0 \| 2889 \| 1.3123 \| \| 0.5608 \| 964.0 \| 2892 \| 1.3039 \| \| 0.5608 \| 965.0 \| 2895 \| 1.2834 \| \| 0.5608 \| 966.0 \| 2898 \| 1.2809 \| \| 0.5608 \| 967.0 \| 2901 \| 1.2696 \| \| 0.5608 \| 968.0 \| 2904 \| 1.2567 \| \| 0.5608 \| 969.0 \| 2907 \| 1.2497 \| \| 0.5608 \| 970.0 \| 2910 \| 1.2639 \| \| 0.5608 \| 971.0 \| 2913 \| 1.2809 \| \| 0.5608 \| 972.0 \| 2916 \| 1.2881 \| \| 0.5608 \| 973.0 \| 2919 \| 1.3082 \| \| 0.5608 \| 974.0 \| 2922 \| 1.3283 \| \| 0.5608 \| 975.0 \| 2925 \| 1.3331 \| \| 0.5608 \| 976.0 \| 2928 \| 1.3384 \| \| 0.5608 \| 977.0 \| 2931 \| 1.3405 \| \| 0.5608 \| 978.0 \| 2934 \| 1.3515 \| \| 0.5608 \| 979.0 \| 2937 \| 1.3734 \| \| 0.5608 \| 980.0 \| 2940 \| 1.3875 \| \| 0.5608 \| 981.0 \| 2943 \| 1.3766 \| \| 0.5608 \| 982.0 \| 2946 \| 1.3530 \| \| 0.5608 \| 983.0 \| 2949 \| 1.3309 \| \| 0.5608 \| 984.0 \| 2952 \| 1.3178 \| \| 0.5608 \| 985.0 \| 2955 \| 1.2963 \| \| 0.5608 \| 986.0 \| 2958 \| 1.2672 \| \| 0.5608 \| 987.0 \| 2961 \| 1.2697 \| \| 0.5608 \| 988.0 \| 2964 \| 1.2620 \| \| 0.5608 \| 989.0 \| 2967 \| 1.2438 \| \| 0.5608 \| 990.0 \| 2970 \| 1.2488 \| \| 0.5608 \| 991.0 \| 2973 \| 1.2630 \| \| 0.5608 \| 992.0 \| 2976 \| 1.2496 \| \| 0.5608 \| 993.0 \| 2979 \| 1.2646 \| \| 0.5608 \| 994.0 \| 2982 \| 1.3051 \| \| 0.5608 \| 995.0 \| 2985 \| 1.3445 \| \| 0.5608 \| 996.0 \| 2988 \| 1.3551 \| \| 0.5608 \| 997.0 \| 2991 \| 1.3600 \| \| 0.5608 \| 998.0 \| 2994 \| 1.3566 \| \| 0.5608 \| 999.0 \| 2997 \| 1.3485 \| \| 0.5596 \| 1000.0 \| 3000 \| 1.3403 \| \| 0.5596 \| 1001.0 \| 3003 \| 1.3328 \| \| 0.5596 \| 1002.0 \| 3006 \| 1.3367 \| \| 0.5596 \| 1003.0 \| 3009 \| 1.3306 \| \| 0.5596 \| 1004.0 \| 3012 \| 1.3026 \| \| 0.5596 \| 1005.0 \| 3015 \| 1.2606 \| \| 0.5596 \| 1006.0 \| 3018 \| 1.2459 \| \| 0.5596 \| 1007.0 \| 3021 \| 1.2332 \| \| 0.5596 \| 1008.0 \| 3024 \| 1.2062 \| \| 0.5596 \| 1009.0 \| 3027 \| 1.1985 \| \| 0.5596 \| 1010.0 \| 3030 \| 1.1937 \| \| 0.5596 \| 1011.0 \| 3033 \| 1.1920 \| \| 0.5596 \| 1012.0 \| 3036 \| 1.1953 \| \| 0.5596 \| 1013.0 \| 3039 \| 1.1919 \| \| 0.5596 \| 1014.0 \| 3042 \| 1.1809 \| \| 0.5596 \| 1015.0 \| 3045 \| 1.1649 \| \| 0.5596 \| 1016.0 \| 3048 \| 1.1612 \| \| 0.5596 \| 1017.0 \| 3051 \| 1.1667 \| \| 0.5596 \| 1018.0 \| 3054 \| 1.1732 \| \| 0.5596 \| 1019.0 \| 3057 \| 1.1847 \| \| 0.5596 \| 1020.0 \| 3060 \| 1.1990 \| \| 0.5596 \| 1021.0 \| 3063 \| 1.2160 \| \| 0.5596 \| 1022.0 \| 3066 \| 1.2672 \| \| 0.5596 \| 1023.0 \| 3069 \| 1.3042 \| \| 0.5596 \| 1024.0 \| 3072 \| 1.3417 \| \| 0.5596 \| 1025.0 \| 3075 \| 1.3652 \| \| 0.5596 \| 1026.0 \| 3078 \| 1.3665 \| \| 0.5596 \| 1027.0 \| 3081 \| 1.3571 \| \| 0.5596 \| 1028.0 \| 3084 \| 1.3403 \| \| 0.5596 \| 1029.0 \| 3087 \| 1.3310 \| \| 0.5596 \| 1030.0 \| 3090 \| 1.3274 \| \| 0.5596 \| 1031.0 \| 3093 \| 1.3228 \| \| 0.5596 \| 1032.0 \| 3096 \| 1.2960 \| \| 0.5596 \| 1033.0 \| 3099 \| 1.2831 \| \| 0.5596 \| 1034.0 \| 3102 \| 1.2817 \| \| 0.5596 \| 1035.0 \| 3105 \| 1.2808 \| \| 0.5596 \| 1036.0 \| 3108 \| 1.2747 \| \| 0.5596 \| 1037.0 \| 3111 \| 1.2732 \| \| 0.5596 \| 1038.0 \| 3114 \| 1.2738 \| \| 0.5596 \| 1039.0 \| 3117 \| 1.2797 \| \| 0.5596 \| 1040.0 \| 3120 \| 1.2912 \| \| 0.5596 \| 1041.0 \| 3123 \| 1.3257 \| \| 0.5596 \| 1042.0 \| 3126 \| 1.3495 \| \| 0.5596 \| 1043.0 \| 3129 \| 1.3620 \| \| 0.5596 \| 1044.0 \| 3132 \| 1.3673 \| \| 0.5596 \| 1045.0 \| 3135 \| 1.3723 \| \| 0.5596 \| 1046.0 \| 3138 \| 1.3709 \| \| 0.5596 \| 1047.0 \| 3141 \| 1.3701 \| \| 0.5596 \| 1048.0 \| 3144 \| 1.3690 \| \| 0.5596 \| 1049.0 \| 3147 \| 1.3811 \| \| 0.5596 \| 1050.0 \| 3150 \| 1.3936 \| \| 0.5596 \| 1051.0 \| 3153 \| 1.3898 \| \| 0.5596 \| 1052.0 \| 3156 \| 1.3976 \| \| 0.5596 \| 1053.0 \| 3159 \| 1.3920 \| \| 0.5596 \| 1054.0 \| 3162 \| 1.3665 \| \| 0.5596 \| 1055.0 \| 3165 \| 1.3330 \| \| 0.5596 \| 1056.0 \| 3168 \| 1.3195 \| \| 0.5596 \| 1057.0 \| 3171 \| 1.3350 \| \| 0.5596 \| 1058.0 \| 3174 \| 1.3444 \| \| 0.5596 \| 1059.0 \| 3177 \| 1.3567 \| \| 0.5596 \| 1060.0 \| 3180 \| 1.3821 \| \| 0.5596 \| 1061.0 \| 3183 \| 1.3965 \| \| 0.5596 \| 1062.0 \| 3186 \| 1.4039 \| \| 0.5596 \| 1063.0 \| 3189 \| 1.4126 \| \| 0.5596 \| 1064.0 \| 3192 \| 1.4127 \| \| 0.5596 \| 1065.0 \| 3195 \| 1.4188 \| \| 0.5596 \| 1066.0 \| 3198 \| 1.4220 \| \| 0.5596 \| 1067.0 \| 3201 \| 1.4240 \| \| 0.5596 \| 1068.0 \| 3204 \| 1.4197 \| \| 0.5596 \| 1069.0 \| 3207 \| 1.4138 \| \| 0.5596 \| 1070.0 \| 3210 \| 1.4155 \| \| 0.5596 \| 1071.0 \| 3213 \| 1.4155 \| \| 0.5596 \| 1072.0 \| 3216 \| 1.4227 \| \| 0.5596 \| 1073.0 \| 3219 \| 1.4209 \| \| 0.5596 \| 1074.0 \| 3222 \| 1.4186 \| \| 0.5596 \| 1075.0 \| 3225 \| 1.4118 \| \| 0.5596 \| 1076.0 \| 3228 \| 1.3992 \| \| 0.5596 \| 1077.0 \| 3231 \| 1.3924 \| \| 0.5596 \| 1078.0 \| 3234 \| 1.3884 \| \| 0.5596 \| 1079.0 \| 3237 \| 1.3913 \| \| 0.5596 \| 1080.0 \| 3240 \| 1.3882 \| \| 0.5596 \| 1081.0 \| 3243 \| 1.3765 \| \| 0.5596 \| 1082.0 \| 3246 \| 1.3725 \| \| 0.5596 \| 1083.0 \| 3249 \| 1.3893 \| \| 0.5596 \| 1084.0 \| 3252 \| 1.3933 \| \| 0.5596 \| 1085.0 \| 3255 \| 1.4005 \| \| 0.5596 \| 1086.0 \| 3258 \| 1.4017 \| \| 0.5596 \| 1087.0 \| 3261 \| 1.4086 \| \| 0.5596 \| 1088.0 \| 3264 \| 1.4195 \| \| 0.5596 \| 1089.0 \| 3267 \| 1.4274 \| \| 0.5596 \| 1090.0 \| 3270 \| 1.4258 \| \| 0.5596 \| 1091.0 \| 3273 \| 1.4179 \| \| 0.5596 \| 1092.0 \| 3276 \| 1.4090 \| \| 0.5596 \| 1093.0 \| 3279 \| 1.3901 \| \| 0.5596 \| 1094.0 \| 3282 \| 1.3714 \| \| 0.5596 \| 1095.0 \| 3285 \| 1.3512 \| \| 0.5596 \| 1096.0 \| 3288 \| 1.3355 \| \| 0.5596 \| 1097.0 \| 3291 \| 1.3368 \| \| 0.5596 \| 1098.0 \| 3294 \| 1.3421 \| \| 0.5596 \| 1099.0 \| 3297 \| 1.3195 \| \| 0.5596 \| 1100.0 \| 3300 \| 1.2919 \| \| 0.5596 \| 1101.0 \| 3303 \| 1.2551 \| \| 0.5596 \| 1102.0 \| 3306 \| 1.2370 \| \| 0.5596 \| 1103.0 \| 3309 \| 1.2445 \| \| 0.5596 \| 1104.0 \| 3312 \| 1.2213 \| \| 0.5596 \| 1105.0 \| 3315 \| 1.2361 \| \| 0.5596 \| 1106.0 \| 3318 \| 1.3104 \| \| 0.5596 \| 1107.0 \| 3321 \| 1.3632 \| \| 0.5596 \| 1108.0 \| 3324 \| 1.3822 \| \| 0.5596 \| 1109.0 \| 3327 \| 1.3887 \| \| 0.5596 \| 1110.0 \| 3330 \| 1.3920 \| \| 0.5596 \| 1111.0 \| 3333 \| 1.3876 \| \| 0.5596 \| 1112.0 \| 3336 \| 1.3874 \| \| 0.5596 \| 1113.0 \| 3339 \| 1.3850 \| \| 0.5596 \| 1114.0 \| 3342 \| 1.3685 \| \| 0.5596 \| 1115.0 \| 3345 \| 1.3439 \| \| 0.5596 \| 1116.0 \| 3348 \| 1.3327 \| \| 0.5596 \| 1117.0 \| 3351 \| 1.3158 \| \| 0.5596 \| 1118.0 \| 3354 \| 1.3046 \| \| 0.5596 \| 1119.0 \| 3357 \| 1.2996 \| \| 0.5596 \| 1120.0 \| 3360 \| 1.2958 \| \| 0.5596 \| 1121.0 \| 3363 \| 1.2871 \| \| 0.5596 \| 1122.0 \| 3366 \| 1.2576 \| \| 0.5596 \| 1123.0 \| 3369 \| 1.2534 \| \| 0.5596 \| 1124.0 \| 3372 \| 1.2344 \| \| 0.5596 \| 1125.0 \| 3375 \| 1.2290 \| \| 0.5596 \| 1126.0 \| 3378 \| 1.2363 \| \| 0.5596 \| 1127.0 \| 3381 \| 1.2271 \| \| 0.5596 \| 1128.0 \| 3384 \| 1.2219 \| \| 0.5596 \| 1129.0 \| 3387 \| 1.2365 \| \| 0.5596 \| 1130.0 \| 3390 \| 1.2537 \| \| 0.5596 \| 1131.0 \| 3393 \| 1.2754 \| \| 0.5596 \| 1132.0 \| 3396 \| 1.2962 \| \| 0.5596 \| 1133.0 \| 3399 \| 1.3161 \| \| 0.5596 \| 1134.0 \| 3402 \| 1.3244 \| \| 0.5596 \| 1135.0 \| 3405 \| 1.3309 \| \| 0.5596 \| 1136.0 \| 3408 \| 1.3317 \| \| 0.5596 \| 1137.0 \| 3411 \| 1.3369 \| \| 0.5596 \| 1138.0 \| 3414 \| 1.3336 \| \| 0.5596 \| 1139.0 \| 3417 \| 1.3099 \| \| 0.5596 \| 1140.0 \| 3420 \| 1.2747 \| \| 0.5596 \| 1141.0 \| 3423 \| 1.2515 \| \| 0.5596 \| 1142.0 \| 3426 \| 1.2653 \| \| 0.5596 \| 1143.0 \| 3429 \| 1.2975 \| \| 0.5596 \| 1144.0 \| 3432 \| 1.3184 \| \| 0.5596 \| 1145.0 \| 3435 \| 1.3373 \| \| 0.5596 \| 1146.0 \| 3438 \| 1.3265 \| \| 0.5596 \| 1147.0 \| 3441 \| 1.3195 \| \| 0.5596 \| 1148.0 \| 3444 \| 1.3177 \| \| 0.5596 \| 1149.0 \| 3447 \| 1.3045 \| \| 0.5596 \| 1150.0 \| 3450 \| 1.3045 \| \| 0.5596 \| 1151.0 \| 3453 \| 1.3020 \| \| 0.5596 \| 1152.0 \| 3456 \| 1.3021 \| \| 0.5596 \| 1153.0 \| 3459 \| 1.3238 \| \| 0.5596 \| 1154.0 \| 3462 \| 1.3351 \| \| 0.5596 \| 1155.0 \| 3465 \| 1.3334 \| \| 0.5596 \| 1156.0 \| 3468 \| 1.3274 \| \| 0.5596 \| 1157.0 \| 3471 \| 1.3276 \| \| 0.5596 \| 1158.0 \| 3474 \| 1.3119 \| \| 0.5596 \| 1159.0 \| 3477 \| 1.2913 \| \| 0.5596 \| 1160.0 \| 3480 \| 1.2919 \| \| 0.5596 \| 1161.0 \| 3483 \| 1.2927 \| \| 0.5596 \| 1162.0 \| 3486 \| 1.3079 \| \| 0.5596 \| 1163.0 \| 3489 \| 1.3195 \| \| 0.5596 \| 1164.0 \| 3492 \| 1.3286 \| \| 0.5596 \| 1165.0 \| 3495 \| 1.3375 \| \| 0.5596 \| 1166.0 \| 3498 \| 1.3493 \| \| 0.5594 \| 1167.0 \| 3501 \| 1.3599 \| \| 0.5594 \| 1168.0 \| 3504 \| 1.3644 \| \| 0.5594 \| 1169.0 \| 3507 \| 1.3595 \| \| 0.5594 \| 1170.0 \| 3510 \| 1.3476 \| \| 0.5594 \| 1171.0 \| 3513 \| 1.3464 \| \| 0.5594 \| 1172.0 \| 3516 \| 1.3592 \| \| 0.5594 \| 1173.0 \| 3519 \| 1.3673 \| \| 0.5594 \| 1174.0 \| 3522 \| 1.3682 \| \| 0.5594 \| 1175.0 \| 3525 \| 1.3569 \| \| 0.5594 \| 1176.0 \| 3528 \| 1.3434 \| \| 0.5594 \| 1177.0 \| 3531 \| 1.3439 \| \| 0.5594 \| 1178.0 \| 3534 \| 1.3386 \| \| 0.5594 \| 1179.0 \| 3537 \| 1.3180 \| \| 0.5594 \| 1180.0 \| 3540 \| 1.2994 \| \| 0.5594 \| 1181.0 \| 3543 \| 1.2888 \| \| 0.5594 \| 1182.0 \| 3546 \| 1.2911 \| \| 0.5594 \| 1183.0 \| 3549 \| 1.2966 \| \| 0.5594 \| 1184.0 \| 3552 \| 1.2888 \| \| 0.5594 \| 1185.0 \| 3555 \| 1.2784 \| \| 0.5594 \| 1186.0 \| 3558 \| 1.2811 \| \| 0.5594 \| 1187.0 \| 3561 \| 1.2813 \| \| 0.5594 \| 1188.0 \| 3564 \| 1.2797 \| \| 0.5594 \| 1189.0 \| 3567 \| 1.2683 \| \| 0.5594 \| 1190.0 \| 3570 \| 1.2736 \| \| 0.5594 \| 1191.0 \| 3573 \| 1.2614 \| \| 0.5594 \| 1192.0 \| 3576 \| 1.2485 \| \| 0.5594 \| 1193.0 \| 3579 \| 1.2446 \| \| 0.5594 \| 1194.0 \| 3582 \| 1.2077 \| \| 0.5594 \| 1195.0 \| 3585 \| 1.1880 \| \| 0.5594 \| 1196.0 \| 3588 \| 1.1797 \| \| 0.5594 \| 1197.0 \| 3591 \| 1.1750 \| \| 0.5594 \| 1198.0 \| 3594 \| 1.1964 \| \| 0.5594 \| 1199.0 \| 3597 \| 1.2570 \| \| 0.5594 \| 1200.0 \| 3600 \| 1.3173 \| \| 0.5594 \| 1201.0 \| 3603 \| 1.3393 \| \| 0.5594 \| 1202.0 \| 3606 \| 1.3465 \| \| 0.5594 \| 1203.0 \| 3609 \| 1.3254 \| \| 0.5594 \| 1204.0 \| 3612 \| 1.3003 \| \| 0.5594 \| 1205.0 \| 3615 \| 1.2560 \| \| 0.5594 \| 1206.0 \| 3618 \| 1.2008 \| \| 0.5594 \| 1207.0 \| 3621 \| 1.1804 \| \| 0.5594 \| 1208.0 \| 3624 \| 1.1725 \| \| 0.5594 \| 1209.0 \| 3627 \| 1.1634 \| \| 0.5594 \| 1210.0 \| 3630 \| 1.1744 \| \| 0.5594 \| 1211.0 \| 3633 \| 1.1912 \| \| 0.5594 \| 1212.0 \| 3636 \| 1.2141 \| \| 0.5594 \| 1213.0 \| 3639 \| 1.2444 \| \| 0.5594 \| 1214.0 \| 3642 \| 1.2703 \| \| 0.5594 \| 1215.0 \| 3645 \| 1.2812 \| \| 0.5594 \| 1216.0 \| 3648 \| 1.2849 \| \| 0.5594 \| 1217.0 \| 3651 \| 1.2871 \| \| 0.5594 \| 1218.0 \| 3654 \| 1.2800 \| \| 0.5594 \| 1219.0 \| 3657 \| 1.2755 \| \| 0.5594 \| 1220.0 \| 3660 \| 1.2668 \| \| 0.5594 \| 1221.0 \| 3663 \| 1.2512 \| \| 0.5594 \| 1222.0 \| 3666 \| 1.2390 \| \| 0.5594 \| 1223.0 \| 3669 \| 1.2268 \| \| 0.5594 \| 1224.0 \| 3672 \| 1.2071 \| \| 0.5594 \| 1225.0 \| 3675 \| 1.1804 \| \| 0.5594 \| 1226.0 \| 3678 \| 1.1572 \| \| 0.5594 \| 1227.0 \| 3681 \| 1.1618 \| \| 0.5594 \| 1228.0 \| 3684 \| 1.1741 \| \| 0.5594 \| 1229.0 \| 3687 \| 1.1867 \| \| 0.5594 \| 1230.0 \| 3690 \| 1.1978 \| \| 0.5594 \| 1231.0 \| 3693 \| 1.2180 \| \| 0.5594 \| 1232.0 \| 3696 \| 1.2379 \| \| 0.5594 \| 1233.0 \| 3699 \| 1.2486 \| \| 0.5594 \| 1234.0 \| 3702 \| 1.2526 \| \| 0.5594 \| 1235.0 \| 3705 \| 1.2632 \| \| 0.5594 \| 1236.0 \| 3708 \| 1.2866 \| \| 0.5594 \| 1237.0 \| 3711 \| 1.2903 \| \| 0.5594 \| 1238.0 \| 3714 \| 1.2655 \| \| 0.5594 \| 1239.0 \| 3717 \| 1.2452 \| \| 0.5594 \| 1240.0 \| 3720 \| 1.2348 \| \| 0.5594 \| 1241.0 \| 3723 \| 1.1997 \| \| 0.5594 \| 1242.0 \| 3726 \| 1.1615 \| \| 0.5594 \| 1243.0 \| 3729 \| 1.1294 \| \| 0.5594 \| 1244.0 \| 3732 \| 1.1171 \| \| 0.5594 \| 1245.0 \| 3735 \| 1.1613 \| \| 0.5594 \| 1246.0 \| 3738 \| 1.2428 \| \| 0.5594 \| 1247.0 \| 3741 \| 1.2627 \| \| 0.5594 \| 1248.0 \| 3744 \| 1.2525 \| \| 0.5594 \| 1249.0 \| 3747 \| 1.2029 \| \| 0.5594 \| 1250.0 \| 3750 \| 1.1155 \| \| 0.5594 \| 1251.0 \| 3753 \| 1.0784 \| \| 0.5594 \| 1252.0 \| 3756 \| 1.0683 \| \| 0.5594 \| 1253.0 \| 3759 \| 1.0901 \| \| 0.5594 \| 1254.0 \| 3762 \| 1.1788 \| \| 0.5594 \| 1255.0 \| 3765 \| 1.2079 \| \| 0.5594 \| 1256.0 \| 3768 \| 1.2129 \| \| 0.5594 \| 1257.0 \| 3771 \| 1.2088 \| \| 0.5594 \| 1258.0 \| 3774 \| 1.1948 \| \| 0.5594 \| 1259.0 \| 3777 \| 1.1811 \| \| 0.5594 \| 1260.0 \| 3780 \| 1.1757 \| \| 0.5594 \| 1261.0 \| 3783 \| 1.1764 \| \| 0.5594 \| 1262.0 \| 3786 \| 1.1673 \| \| 0.5594 \| 1263.0 \| 3789 \| 1.1421 \| \| 0.5594 \| 1264.0 \| 3792 \| 1.1351 \| \| 0.5594 \| 1265.0 \| 3795 \| 1.1570 \| \| 0.5594 \| 1266.0 \| 3798 \| 1.1854 \| \| 0.5594 \| 1267.0 \| 3801 \| 1.1974 \| \| 0.5594 \| 1268.0 \| 3804 \| 1.2039 \| \| 0.5594 \| 1269.0 \| 3807 \| 1.1966 \| \| 0.5594 \| 1270.0 \| 3810 \| 1.2079 \| \| 0.5594 \| 1271.0 \| 3813 \| 1.2104 \| \| 0.5594 \| 1272.0 \| 3816 \| 1.2171 \| \| 0.5594 \| 1273.0 \| 3819 \| 1.2335 \| \| 0.5594 \| 1274.0 \| 3822 \| 1.2483 \| \| 0.5594 \| 1275.0 \| 3825 \| 1.2607 \| \| 0.5594 \| 1276.0 \| 3828 \| 1.2586 \| \| 0.5594 \| 1277.0 \| 3831 \| 1.2527 \| \| 0.5594 \| 1278.0 \| 3834 \| 1.2457 \| \| 0.5594 \| 1279.0 \| 3837 \| 1.2451 \| \| 0.5594 \| 1280.0 \| 3840 \| 1.2669 \| \| 0.5594 \| 1281.0 \| 3843 \| 1.2651 \| \| 0.5594 \| 1282.0 \| 3846 \| 1.2585 \| \| 0.5594 \| 1283.0 \| 3849 \| 1.2459 \| \| 0.5594 \| 1284.0 \| 3852 \| 1.2272 \| \| 0.5594 \| 1285.0 \| 3855 \| 1.2195 \| \| 0.5594 \| 1286.0 \| 3858 \| 1.2154 \| \| 0.5594 \| 1287.0 \| 3861 \| 1.2234 \| \| 0.5594 \| 1288.0 \| 3864 \| 1.2386 \| \| 0.5594 \| 1289.0 \| 3867 \| 1.2574 \| \| 0.5594 \| 1290.0 \| 3870 \| 1.2844 \| \| 0.5594 \| 1291.0 \| 3873 \| 1.3160 \| \| 0.5594 \| 1292.0 \| 3876 \| 1.3283 \| \| 0.5594 \| 1293.0 \| 3879 \| 1.3256 \| \| 0.5594 \| 1294.0 \| 3882 \| 1.3101 \| \| 0.5594 \| 1295.0 \| 3885 \| 1.2981 \| \| 0.5594 \| 1296.0 \| 3888 \| 1.2863 \| \| 0.5594 \| 1297.0 \| 3891 \| 1.2822 \| \| 0.5594 \| 1298.0 \| 3894 \| 1.2751 \| \| 0.5594 \| 1299.0 \| 3897 \| 1.2609 \| \| 0.5594 \| 1300.0 \| 3900 \| 1.2539 \| \| 0.5594 \| 1301.0 \| 3903 \| 1.2455 \| \| 0.5594 \| 1302.0 \| 3906 \| 1.2458 \| \| 0.5594 \| 1303.0 \| 3909 \| 1.2390 \| \| 0.5594 \| 1304.0 \| 3912 \| 1.2530 \| \| 0.5594 \| 1305.0 \| 3915 \| 1.2605 \| \| 0.5594 \| 1306.0 \| 3918 \| 1.2669 \| \| 0.5594 \| 1307.0 \| 3921 \| 1.2699 \| \| 0.5594 \| 1308.0 \| 3924 \| 1.2581 \| \| 0.5594 \| 1309.0 \| 3927 \| 1.2481 \| \| 0.5594 \| 1310.0 \| 3930 \| 1.2469 \| \| 0.5594 \| 1311.0 \| 3933 \| 1.2540 \| \| 0.5594 \| 1312.0 \| 3936 \| 1.2708 \| \| 0.5594 \| 1313.0 \| 3939 \| 1.2828 \| \| 0.5594 \| 1314.0 \| 3942 \| 1.2897 \| \| 0.5594 \| 1315.0 \| 3945 \| 1.2939 \| \| 0.5594 \| 1316.0 \| 3948 \| 1.2995 \| \| 0.5594 \| 1317.0 \| 3951 \| 1.3066 \| \| 0.5594 \| 1318.0 \| 3954 \| 1.3168 \| \| 0.5594 \| 1319.0 \| 3957 \| 1.3175 \| \| 0.5594 \| 1320.0 \| 3960 \| 1.3122 \| \| 0.5594 \| 1321.0 \| 3963 \| 1.3059 \| \| 0.5594 \| 1322.0 \| 3966 \| 1.2981 \| \| 0.5594 \| 1323.0 \| 3969 \| 1.2889 \| \| 0.5594 \| 1324.0 \| 3972 \| 1.2831 \| \| 0.5594 \| 1325.0 \| 3975 \| 1.2885 \| \| 0.5594 \| 1326.0 \| 3978 \| 1.2866 \| \| 0.5594 \| 1327.0 \| 3981 \| 1.2813 \| \| 0.5594 \| 1328.0 \| 3984 \| 1.2779 \| \| 0.5594 \| 1329.0 \| 3987 \| 1.2776 \| \| 0.5594 \| 1330.0 \| 3990 \| 1.2799 \| \| 0.5594 \| 1331.0 \| 3993 \| 1.2826 \| \| 0.5594 \| 1332.0 \| 3996 \| 1.2839 \| \| 0.5594 \| 1333.0 \| 3999 \| 1.2864 \| \| 0.5596 \| 1334.0 \| 4002 \| 1.2831 \| \| 0.5596 \| 1335.0 \| 4005 \| 1.2768 \| \| 0.5596 \| 1336.0 \| 4008 \| 1.2694 \| \| 0.5596 \| 1337.0 \| 4011 \| 1.2594 \| \| 0.5596 \| 1338.0 \| 4014 \| 1.2453 \| \| 0.5596 \| 1339.0 \| 4017 \| 1.2447 \| \| 0.5596 \| 1340.0 \| 4020 \| 1.2359 \| \| 0.5596 \| 1341.0 \| 4023 \| 1.2253 \| \| 0.5596 \| 1342.0 \| 4026 \| 1.2114 \| \| 0.5596 \| 1343.0 \| 4029 \| 1.2037 \| \| 0.5596 \| 1344.0 \| 4032 \| 1.1957 \| \| 0.5596 \| 1345.0 \| 4035 \| 1.2045 \| \| 0.5596 \| 1346.0 \| 4038 \| 1.2123 \| \| 0.5596 \| 1347.0 \| 4041 \| 1.2362 \| \| 0.5596 \| 1348.0 \| 4044 \| 1.2613 \| \| 0.5596 \| 1349.0 \| 4047 \| 1.2745 \| \| 0.5596 \| 1350.0 \| 4050 \| 1.2848 \| \| 0.5596 \| 1351.0 \| 4053 \| 1.2939 \| \| 0.5596 \| 1352.0 \| 4056 \| 1.2986 \| \| 0.5596 \| 1353.0 \| 4059 \| 1.2994 \| \| 0.5596 \| 1354.0 \| 4062 \| 1.3032 \| \| 0.5596 \| 1355.0 \| 4065 \| 1.3034 \| \| 0.5596 \| 1356.0 \| 4068 \| 1.3160 \| \| 0.5596 \| 1357.0 \| 4071 \| 1.3207 \| \| 0.5596 \| 1358.0 \| 4074 \| 1.3250 \| \| 0.5596 \| 1359.0 \| 4077 \| 1.3295 \| \| 0.5596 \| 1360.0 \| 4080 \| 1.3291 \| \| 0.5596 \| 1361.0 \| 4083 \| 1.3191 \| \| 0.5596 \| 1362.0 \| 4086 \| 1.3077 \| \| 0.5596 \| 1363.0 \| 4089 \| 1.3023 \| \| 0.5596 \| 1364.0 \| 4092 \| 1.2966 \| \| 0.5596 \| 1365.0 \| 4095 \| 1.2871 \| \| 0.5596 \| 1366.0 \| 4098 \| 1.2758 \| \| 0.5596 \| 1367.0 \| 4101 \| 1.2703 \| \| 0.5596 \| 1368.0 \| 4104 \| 1.2790 \| \| 0.5596 \| 1369.0 \| 4107 \| 1.2936 \| \| 0.5596 \| 1370.0 \| 4110 \| 1.3103 \| \| 0.5596 \| 1371.0 \| 4113 \| 1.3330 \| \| 0.5596 \| 1372.0 \| 4116 \| 1.3600 \| \| 0.5596 \| 1373.0 \| 4119 \| 1.3767 \| \| 0.5596 \| 1374.0 \| 4122 \| 1.3858 \| \| 0.5596 \| 1375.0 \| 4125 \| 1.3881 \| \| 0.5596 \| 1376.0 \| 4128 \| 1.4005 \| \| 0.5596 \| 1377.0 \| 4131 \| 1.4086 \| \| 0.5596 \| 1378.0 \| 4134 \| 1.4082 \| \| 0.5596 \| 1379.0 \| 4137 \| 1.4018 \| \| 0.5596 \| 1380.0 \| 4140 \| 1.3900 \| \| 0.5596 \| 1381.0 \| 4143 \| 1.3746 \| \| 0.5596 \| 1382.0 \| 4146 \| 1.3608 \| \| 0.5596 \| 1383.0 \| 4149 \| 1.3483 \| \| 0.5596 \| 1384.0 \| 4152 \| 1.3343 \| \| 0.5596 \| 1385.0 \| 4155 \| 1.3260 \| \| 0.5596 \| 1386.0 \| 4158 \| 1.3144 \| \| 0.5596 \| 1387.0 \| 4161 \| 1.3131 \| \| 0.5596 \| 1388.0 \| 4164 \| 1.3051 \| \| 0.5596 \| 1389.0 \| 4167 \| 1.2853 \| \| 0.5596 \| 1390.0 \| 4170 \| 1.2701 \| \| 0.5596 \| 1391.0 \| 4173 \| 1.2635 \| \| 0.5596 \| 1392.0 \| 4176 \| 1.2494 \| \| 0.5596 \| 1393.0 \| 4179 \| 1.2337 \| \| 0.5596 \| 1394.0 \| 4182 \| 1.2267 \| \| 0.5596 \| 1395.0 \| 4185 \| 1.2422 \| \| 0.5596 \| 1396.0 \| 4188 \| 1.2575 \| \| 0.5596 \| 1397.0 \| 4191 \| 1.2733 \| \| 0.5596 \| 1398.0 \| 4194 \| 1.2838 \| \| 0.5596 \| 1399.0 \| 4197 \| 1.2898 \| \| 0.5596 \| 1400.0 \| 4200 \| 1.2937 \| \| 0.5596 \| 1401.0 \| 4203 \| 1.2934 \| \| 0.5596 \| 1402.0 \| 4206 \| 1.2967 \| \| 0.5596 \| 1403.0 \| 4209 \| 1.2893 \| \| 0.5596 \| 1404.0 \| 4212 \| 1.2796 \| \| 0.5596 \| 1405.0 \| 4215 \| 1.2877 \| \| 0.5596 \| 1406.0 \| 4218 \| 1.3098 \| \| 0.5596 \| 1407.0 \| 4221 \| 1.3252 \| \| 0.5596 \| 1408.0 \| 4224 \| 1.3205 \| \| 0.5596 \| 1409.0 \| 4227 \| 1.3168 \| \| 0.5596 \| 1410.0 \| 4230 \| 1.3169 \| \| 0.5596 \| 1411.0 \| 4233 \| 1.3142 \| \| 0.5596 \| 1412.0 \| 4236 \| 1.2923 \| \| 0.5596 \| 1413.0 \| 4239 \| 1.2575 \| \| 0.5596 \| 1414.0 \| 4242 \| 1.2282 \| \| 0.5596 \| 1415.0 \| 4245 \| 1.2126 \| \| 0.5596 \| 1416.0 \| 4248 \| 1.2228 \| \| 0.5596 \| 1417.0 \| 4251 \| 1.2357 \| \| 0.5596 \| 1418.0 \| 4254 \| 1.2567 \| \| 0.5596 \| 1419.0 \| 4257 \| 1.2732 \| \| 0.5596 \| 1420.0 \| 4260 \| 1.2618 \| \| 0.5596 \| 1421.0 \| 4263 \| 1.2471 \| \| 0.5596 \| 1422.0 \| 4266 \| 1.2476 \| \| 0.5596 \| 1423.0 \| 4269 \| 1.2638 \| \| 0.5596 \| 1424.0 \| 4272 \| 1.3039 \| \| 0.5596 \| 1425.0 \| 4275 \| 1.3291 \| \| 0.5596 \| 1426.0 \| 4278 \| 1.3451 \| \| 0.5596 \| 1427.0 \| 4281 \| 1.3500 \| \| 0.5596 \| 1428.0 \| 4284 \| 1.3546 \| \| 0.5596 \| 1429.0 \| 4287 \| 1.3582 \| \| 0.5596 \| 1430.0 \| 4290 \| 1.3553 \| \| 0.5596 \| 1431.0 \| 4293 \| 1.3562 \| \| 0.5596 \| 1432.0 \| 4296 \| 1.3554 \| \| 0.5596 \| 1433.0 \| 4299 \| 1.3519 \| \| 0.5596 \| 1434.0 \| 4302 \| 1.3437 \| \| 0.5596 \| 1435.0 \| 4305 \| 1.3434 \| \| 0.5596 \| 1436.0 \| 4308 \| 1.3346 \| \| 0.5596 \| 1437.0 \| 4311 \| 1.3225 \| \| 0.5596 \| 1438.0 \| 4314 \| 1.3157 \| \| 0.5596 \| 1439.0 \| 4317 \| 1.3004 \| \| 0.5596 \| 1440.0 \| 4320 \| 1.2806 \| \| 0.5596 \| 1441.0 \| 4323 \| 1.2519 \| \| 0.5596 \| 1442.0 \| 4326 \| 1.2243 \| \| 0.5596 \| 1443.0 \| 4329 \| 1.2038 \| \| 0.5596 \| 1444.0 \| 4332 \| 1.1953 \| \| 0.5596 \| 1445.0 \| 4335 \| 1.1985 \| \| 0.5596 \| 1446.0 \| 4338 \| 1.2112 \| \| 0.5596 \| 1447.0 \| 4341 \| 1.2292 \| \| 0.5596 \| 1448.0 \| 4344 \| 1.2461 \| \| 0.5596 \| 1449.0 \| 4347 \| 1.2468 \| \| 0.5596 \| 1450.0 \| 4350 \| 1.2530 \| \| 0.5596 \| 1451.0 \| 4353 \| 1.2572 \| \| 0.5596 \| 1452.0 \| 4356 \| 1.2665 \| \| 0.5596 \| 1453.0 \| 4359 \| 1.2700 \| \| 0.5596 \| 1454.0 \| 4362 \| 1.2696 \| \| 0.5596 \| 1455.0 \| 4365 \| 1.2611 \| \| 0.5596 \| 1456.0 \| 4368 \| 1.2537 \| \| 0.5596 \| 1457.0 \| 4371 \| 1.2517 \| \| 0.5596 \| 1458.0 \| 4374 \| 1.2511 \| \| 0.5596 \| 1459.0 \| 4377 \| 1.2543 \| \| 0.5596 \| 1460.0 \| 4380 \| 1.2578 \| \| 0.5596 \| 1461.0 \| 4383 \| 1.2540 \| \| 0.5596 \| 1462.0 \| 4386 \| 1.2508 \| \| 0.5596 \| 1463.0 \| 4389 \| 1.2523 \| \| 0.5596 \| 1464.0 \| 4392 \| 1.2553 \| \| 0.5596 \| 1465.0 \| 4395 \| 1.2546 \| \| 0.5596 \| 1466.0 \| 4398 \| 1.2581 \| \| 0.5596 \| 1467.0 \| 4401 \| 1.2649 \| \| 0.5596 \| 1468.0 \| 4404 \| 1.2735 \| \| 0.5596 \| 1469.0 \| 4407 \| 1.2883 \| \| 0.5596 \| 1470.0 \| 4410 \| 1.3074 \| \| 0.5596 \| 1471.0 \| 4413 \| 1.3192 \| \| 0.5596 \| 1472.0 \| 4416 \| 1.3282 \| \| 0.5596 \| 1473.0 \| 4419 \| 1.3325 \| \| 0.5596 \| 1474.0 \| 4422 \| 1.3314 \| \| 0.5596 \| 1475.0 \| 4425 \| 1.3250 \| \| 0.5596 \| 1476.0 \| 4428 \| 1.3163 \| \| 0.5596 \| 1477.0 \| 4431 \| 1.3089 \| \| 0.5596 \| 1478.0 \| 4434 \| 1.3000 \| \| 0.5596 \| 1479.0 \| 4437 \| 1.3028 \| \| 0.5596 \| 1480.0 \| 4440 \| 1.3035 \| \| 0.5596 \| 1481.0 \| 4443 \| 1.3072 \| \| 0.5596 \| 1482.0 \| 4446 \| 1.3023 \| \| 0.5596 \| 1483.0 \| 4449 \| 1.3073 \| \| 0.5596 \| 1484.0 \| 4452 \| 1.3085 \| \| 0.5596 \| 1485.0 \| 4455 \| 1.3051 \| \| 0.5596 \| 1486.0 \| 4458 \| 1.3017 \| \| 0.5596 \| 1487.0 \| 4461 \| 1.2962 \| \| 0.5596 \| 1488.0 \| 4464 \| 1.2828 \| \| 0.5596 \| 1489.0 \| 4467 \| 1.2675 \| \| 0.5596 \| 1490.0 \| 4470 \| 1.2643 \| \| 0.5596 \| 1491.0 \| 4473 \| 1.2747 \| \| 0.5596 \| 1492.0 \| 4476 \| 1.2961 \| \| 0.5596 \| 1493.0 \| 4479 \| 1.3016 \| \| 0.5596 \| 1494.0 \| 4482 \| 1.2982 \| \| 0.5596 \| 1495.0 \| 4485 \| 1.2902 \| \| 0.5596 \| 1496.0 \| 4488 \| 1.2810 \| \| 0.5596 \| 1497.0 \| 4491 \| 1.2799 \| \| 0.5596 \| 1498.0 \| 4494 \| 1.2838 \| \| 0.5596 \| 1499.0 \| 4497 \| 1.2849 \| \| 0.5585 \| 1500.0 \| 4500 \| 1.2817 \| \| 0.5585 \| 1501.0 \| 4503 \| 1.2623 \| \| 0.5585 \| 1502.0 \| 4506 \| 1.2476 \| \| 0.5585 \| 1503.0 \| 4509 \| 1.2396 \| \| 0.5585 \| 1504.0 \| 4512 \| 1.2270 \| \| 0.5585 \| 1505.0 \| 4515 \| 1.2198 \| \| 0.5585 \| 1506.0 \| 4518 \| 1.2175 \| \| 0.5585 \| 1507.0 \| 4521 \| 1.2237 \| \| 0.5585 \| 1508.0 \| 4524 \| 1.2332 \| \| 0.5585 \| 1509.0 \| 4527 \| 1.2437 \| \| 0.5585 \| 1510.0 \| 4530 \| 1.2509 \| \| 0.5585 \| 1511.0 \| 4533 \| 1.2516 \| \| 0.5585 \| 1512.0 \| 4536 \| 1.2541 \| \| 0.5585 \| 1513.0 \| 4539 \| 1.2481 \| \| 0.5585 \| 1514.0 \| 4542 \| 1.2460 \| \| 0.5585 \| 1515.0 \| 4545 \| 1.2456 \| \| 0.5585 \| 1516.0 \| 4548 \| 1.2450 \| \| 0.5585 \| 1517.0 \| 4551 \| 1.2441 \| \| 0.5585 \| 1518.0 \| 4554 \| 1.2437 \| \| 0.5585 \| 1519.0 \| 4557 \| 1.2446 \| \| 0.5585 \| 1520.0 \| 4560 \| 1.2490 \| \| 0.5585 \| 1521.0 \| 4563 \| 1.2540 \| \| 0.5585 \| 1522.0 \| 4566 \| 1.2620 \| \| 0.5585 \| 1523.0 \| 4569 \| 1.2615 \| \| 0.5585 \| 1524.0 \| 4572 \| 1.2570 \| \| 0.5585 \| 1525.0 \| 4575 \| 1.2569 \| \| 0.5585 \| 1526.0 \| 4578 \| 1.2570 \| \| 0.5585 \| 1527.0 \| 4581 \| 1.2681 \| \| 0.5585 \| 1528.0 \| 4584 \| 1.2824 \| \| 0.5585 \| 1529.0 \| 4587 \| 1.2947 \| \| 0.5585 \| 1530.0 \| 4590 \| 1.2917 \| \| 0.5585 \| 1531.0 \| 4593 \| 1.2866 \| \| 0.5585 \| 1532.0 \| 4596 \| 1.2758 \| \| 0.5585 \| 1533.0 \| 4599 \| 1.2622 \| \| 0.5585 \| 1534.0 \| 4602 \| 1.2540 \| \| 0.5585 \| 1535.0 \| 4605 \| 1.2411 \| \| 0.5585 \| 1536.0 \| 4608 \| 1.2433 \| \| 0.5585 \| 1537.0 \| 4611 \| 1.2553 \| \| 0.5585 \| 1538.0 \| 4614 \| 1.2590 \| \| 0.5585 \| 1539.0 \| 4617 \| 1.2535 \| \| 0.5585 \| 1540.0 \| 4620 \| 1.2439 \| \| 0.5585 \| 1541.0 \| 4623 \| 1.2461 \| \| 0.5585 \| 1542.0 \| 4626 \| 1.2506 \| \| 0.5585 \| 1543.0 \| 4629 \| 1.2483 \| \| 0.5585 \| 1544.0 \| 4632 \| 1.2488 \| \| 0.5585 \| 1545.0 \| 4635 \| 1.2463 \| \| 0.5585 \| 1546.0 \| 4638 \| 1.2497 \| \| 0.5585 \| 1547.0 \| 4641 \| 1.2608 \| \| 0.5585 \| 1548.0 \| 4644 \| 1.2711 \| \| 0.5585 \| 1549.0 \| 4647 \| 1.2785 \| \| 0.5585 \| 1550.0 \| 4650 \| 1.2751 \| \| 0.5585 \| 1551.0 \| 4653 \| 1.2641 \| \| 0.5585 \| 1552.0 \| 4656 \| 1.2510 \| \| 0.5585 \| 1553.0 \| 4659 \| 1.2358 \| \| 0.5585 \| 1554.0 \| 4662 \| 1.2287 \| \| 0.5585 \| 1555.0 \| 4665 \| 1.2247 \| \| 0.5585 \| 1556.0 \| 4668 \| 1.2228 \| \| 0.5585 \| 1557.0 \| 4671 \| 1.2226 \| \| 0.5585 \| 1558.0 \| 4674 \| 1.2310 \| \| 0.5585 \| 1559.0 \| 4677 \| 1.2332 \| \| 0.5585 \| 1560.0 \| 4680 \| 1.2375 \| \| 0.5585 \| 1561.0 \| 4683 \| 1.2369 \| \| 0.5585 \| 1562.0 \| 4686 \| 1.2275 \| \| 0.5585 \| 1563.0 \| 4689 \| 1.2133 \| \| 0.5585 \| 1564.0 \| 4692 \| 1.1939 \| \| 0.5585 \| 1565.0 \| 4695 \| 1.1805 \| \| 0.5585 \| 1566.0 \| 4698 \| 1.1668 \| \| 0.5585 \| 1567.0 \| 4701 \| 1.1570 \| \| 0.5585 \| 1568.0 \| 4704 \| 1.1510 \| \| 0.5585 \| 1569.0 \| 4707 \| 1.1499 \| \| 0.5585 \| 1570.0 \| 4710 \| 1.1548 \| \| 0.5585 \| 1571.0 \| 4713 \| 1.1644 \| \| 0.5585 \| 1572.0 \| 4716 \| 1.1659 \| \| 0.5585 \| 1573.0 \| 4719 \| 1.1751 \| \| 0.5585 \| 1574.0 \| 4722 \| 1.1975 \| \| 0.5585 \| 1575.0 \| 4725 \| 1.2115 \| \| 0.5585 \| 1576.0 \| 4728 \| 1.2144 \| \| 0.5585 \| 1577.0 \| 4731 \| 1.2082 \| \| 0.5585 \| 1578.0 \| 4734 \| 1.1975 \| \| 0.5585 \| 1579.0 \| 4737 \| 1.1939 \| \| 0.5585 \| 1580.0 \| 4740 \| 1.1906 \| \| 0.5585 \| 1581.0 \| 4743 \| 1.1783 \| \| 0.5585 \| 1582.0 \| 4746 \| 1.1757 \| \| 0.5585 \| 1583.0 \| 4749 \| 1.1792 \| \| 0.5585 \| 1584.0 \| 4752 \| 1.1950 \| \| 0.5585 \| 1585.0 \| 4755 \| 1.2039 \| \| 0.5585 \| 1586.0 \| 4758 \| 1.2107 \| \| 0.5585 \| 1587.0 \| 4761 \| 1.2178 \| \| 0.5585 \| 1588.0 \| 4764 \| 1.2261 \| \| 0.5585 \| 1589.0 \| 4767 \| 1.2340 \| \| 0.5585 \| 1590.0 \| 4770 \| 1.2420 \| \| 0.5585 \| 1591.0 \| 4773 \| 1.2525 \| \| 0.5585 \| 1592.0 \| 4776 \| 1.2740 \| \| 0.5585 \| 1593.0 \| 4779 \| 1.2903 \| \| 0.5585 \| 1594.0 \| 4782 \| 1.2987 \| \| 0.5585 \| 1595.0 \| 4785 \| 1.2991 \| \| 0.5585 \| 1596.0 \| 4788 \| 1.2934 \| \| 0.5585 \| 1597.0 \| 4791 \| 1.2862 \| \| 0.5585 \| 1598.0 \| 4794 \| 1.2868 \| \| 0.5585 \| 1599.0 \| 4797 \| 1.2803 \| \| 0.5585 \| 1600.0 \| 4800 \| 1.2826 \| \| 0.5585 \| 1601.0 \| 4803 \| 1.2763 \| \| 0.5585 \| 1602.0 \| 4806 \| 1.2718 \| \| 0.5585 \| 1603.0 \| 4809 \| 1.2646 \| \| 0.5585 \| 1604.0 \| 4812 \| 1.2668 \| \| 0.5585 \| 1605.0 \| 4815 \| 1.2755 \| \| 0.5585 \| 1606.0 \| 4818 \| 1.2812 \| \| 0.5585 \| 1607.0 \| 4821 \| 1.2905 \| \| 0.5585 \| 1608.0 \| 4824 \| 1.2896 \| \| 0.5585 \| 1609.0 \| 4827 \| 1.2850 \| \| 0.5585 \| 1610.0 \| 4830 \| 1.2822 \| \| 0.5585 \| 1611.0 \| 4833 \| 1.2768 \| \| 0.5585 \| 1612.0 \| 4836 \| 1.2710 \| \| 0.5585 \| 1613.0 \| 4839 \| 1.2660 \| \| 0.5585 \| 1614.0 \| 4842 \| 1.2627 \| \| 0.5585 \| 1615.0 \| 4845 \| 1.2584 \| \| 0.5585 \| 1616.0 \| 4848 \| 1.2485 \| \| 0.5585 \| 1617.0 \| 4851 \| 1.2344 \| \| 0.5585 \| 1618.0 \| 4854 \| 1.2201 \| \| 0.5585 \| 1619.0 \| 4857 \| 1.2069 \| \| 0.5585 \| 1620.0 \| 4860 \| 1.1927 \| \| 0.5585 \| 1621.0 \| 4863 \| 1.1971 \| \| 0.5585 \| 1622.0 \| 4866 \| 1.2042 \| \| 0.5585 \| 1623.0 \| 4869 \| 1.2124 \| \| 0.5585 \| 1624.0 \| 4872 \| 1.2249 \| \| 0.5585 \| 1625.0 \| 4875 \| 1.2413 \| \| 0.5585 \| 1626.0 \| 4878 \| 1.2477 \| \| 0.5585 \| 1627.0 \| 4881 \| 1.2600 \| \| 0.5585 \| 1628.0 \| 4884 \| 1.2676 \| \| 0.5585 \| 1629.0 \| 4887 \| 1.2724 \| \| 0.5585 \| 1630.0 \| 4890 \| 1.2755 \| \| 0.5585 \| 1631.0 \| 4893 \| 1.2782 \| \| 0.5585 \| 1632.0 \| 4896 \| 1.2968 \| \| 0.5585 \| 1633.0 \| 4899 \| 1.3072 \| \| 0.5585 \| 1634.0 \| 4902 \| 1.3119 \| \| 0.5585 \| 1635.0 \| 4905 \| 1.3116 \| \| 0.5585 \| 1636.0 \| 4908 \| 1.3104 \| \| 0.5585 \| 1637.0 \| 4911 \| 1.3071 \| \| 0.5585 \| 1638.0 \| 4914 \| 1.3022 \| \| 0.5585 \| 1639.0 \| 4917 \| 1.2993 \| \| 0.5585 \| 1640.0 \| 4920 \| 1.2960 \| \| 0.5585 \| 1641.0 \| 4923 \| 1.2829 \| \| 0.5585 \| 1642.0 \| 4926 \| 1.2700 \| \| 0.5585 \| 1643.0 \| 4929 \| 1.2669 \| \| 0.5585 \| 1644.0 \| 4932 \| 1.2658 \| \| 0.5585 \| 1645.0 \| 4935 \| 1.2583 \| \| 0.5585 \| 1646.0 \| 4938 \| 1.2580 \| \| 0.5585 \| 1647.0 \| 4941 \| 1.2485 \| \| 0.5585 \| 1648.0 \| 4944 \| 1.2374 \| \| 0.5585 \| 1649.0 \| 4947 \| 1.2234 \| \| 0.5585 \| 1650.0 \| 4950 \| 1.2172 \| \| 0.5585 \| 1651.0 \| 4953 \| 1.2044 \| \| 0.5585 \| 1652.0 \| 4956 \| 1.1955 \| \| 0.5585 \| 1653.0 \| 4959 \| 1.1854 \| \| 0.5585 \| 1654.0 \| 4962 \| 1.1917 \| \| 0.5585 \| 1655.0 \| 4965 \| 1.1924 \| \| 0.5585 \| 1656.0 \| 4968 \| 1.1886 \| \| 0.5585 \| 1657.0 \| 4971 \| 1.1910 \| \| 0.5585 \| 1658.0 \| 4974 \| 1.1913 \| \| 0.5585 \| 1659.0 \| 4977 \| 1.1960 \| \| 0.5585 \| 1660.0 \| 4980 \| 1.2030 \| \| 0.5585 \| 1661.0 \| 4983 \| 1.2132 \| \| 0.5585 \| 1662.0 \| 4986 \| 1.2263 \| \| 0.5585 \| 1663.0 \| 4989 \| 1.2411 \| \| 0.5585 \| 1664.0 \| 4992 \| 1.2572 \| \| 0.5585 \| 1665.0 \| 4995 \| 1.2714 \| \| 0.5585 \| 1666.0 \| 4998 \| 1.2824 \| \| 0.5584 \| 1667.0 \| 5001 \| 1.2862 \| \| 0.5584 \| 1668.0 \| 5004 \| 1.2866 \| \| 0.5584 \| 1669.0 \| 5007 \| 1.2883 \| \| 0.5584 \| 1670.0 \| 5010 \| 1.2868 \| \| 0.5584 \| 1671.0 \| 5013 \| 1.2821 \| \| 0.5584 \| 1672.0 \| 5016 \| 1.2769 \| \| 0.5584 \| 1673.0 \| 5019 \| 1.2708 \| \| 0.5584 \| 1674.0 \| 5022 \| 1.2631 \| \| 0.5584 \| 1675.0 \| 5025 \| 1.2573 \| \| 0.5584 \| 1676.0 \| 5028 \| 1.2570 \| \| 0.5584 \| 1677.0 \| 5031 \| 1.2558 \| \| 0.5584 \| 1678.0 \| 5034 \| 1.2561 \| \| 0.5584 \| 1679.0 \| 5037 \| 1.2551 \| \| 0.5584 \| 1680.0 \| 5040 \| 1.2521 \| \| 0.5584 \| 1681.0 \| 5043 \| 1.2414 \| \| 0.5584 \| 1682.0 \| 5046 \| 1.2274 \| \| 0.5584 \| 1683.0 \| 5049 \| 1.2122 \| \| 0.5584 \| 1684.0 \| 5052 \| 1.1951 \| \| 0.5584 \| 1685.0 \| 5055 \| 1.1893 \| \| 0.5584 \| 1686.0 \| 5058 \| 1.1823 \| \| 0.5584 \| 1687.0 \| 5061 \| 1.1763 \| \| 0.5584 \| 1688.0 \| 5064 \| 1.1725 \| \| 0.5584 \| 1689.0 \| 5067 \| 1.1744 \| \| 0.5584 \| 1690.0 \| 5070 \| 1.1875 \| \| 0.5584 \| 1691.0 \| 5073 \| 1.1946 \| \| 0.5584 \| 1692.0 \| 5076 \| 1.2012 \| \| 0.5584 \| 1693.0 \| 5079 \| 1.2053 \| \| 0.5584 \| 1694.0 \| 5082 \| 1.2083 \| \| 0.5584 \| 1695.0 \| 5085 \| 1.2196 \| \| 0.5584 \| 1696.0 \| 5088 \| 1.2435 \| \| 0.5584 \| 1697.0 \| 5091 \| 1.2554 \| \| 0.5584 \| 1698.0 \| 5094 \| 1.2650 \| \| 0.5584 \| 1699.0 \| 5097 \| 1.2680 \| \| 0.5584 \| 1700.0 \| 5100 \| 1.2642 \| \| 0.5584 \| 1701.0 \| 5103 \| 1.2682 \| \| 0.5584 \| 1702.0 \| 5106 \| 1.2741 \| \| 0.5584 \| 1703.0 \| 5109 \| 1.2736 \| \| 0.5584 \| 1704.0 \| 5112 \| 1.2641 \| \| 0.5584 \| 1705.0 \| 5115 \| 1.2590 \| \| 0.5584 \| 1706.0 \| 5118 \| 1.2602 \| \| 0.5584 \| 1707.0 \| 5121 \| 1.2610 \| \| 0.5584 \| 1708.0 \| 5124 \| 1.2628 \| \| 0.5584 \| 1709.0 \| 5127 \| 1.2661 \| \| 0.5584 \| 1710.0 \| 5130 \| 1.2716 \| \| 0.5584 \| 1711.0 \| 5133 \| 1.2769 \| \| 0.5584 \| 1712.0 \| 5136 \| 1.2820 \| \| 0.5584 \| 1713.0 \| 5139 \| 1.2837 \| \| 0.5584 \| 1714.0 \| 5142 \| 1.2823 \| \| 0.5584 \| 1715.0 \| 5145 \| 1.2832 \| \| 0.5584 \| 1716.0 \| 5148 \| 1.2814 \| \| 0.5584 \| 1717.0 \| 5151 \| 1.2819 \| \| 0.5584 \| 1718.0 \| 5154 \| 1.2820 \| \| 0.5584 \| 1719.0 \| 5157 \| 1.2816 \| \| 0.5584 \| 1720.0 \| 5160 \| 1.2814 \| \| 0.5584 \| 1721.0 \| 5163 \| 1.2813 \| \| 0.5584 \| 1722.0 \| 5166 \| 1.2787 \| \| 0.5584 \| 1723.0 \| 5169 \| 1.2741 \| \| 0.5584 \| 1724.0 \| 5172 \| 1.2706 \| \| 0.5584 \| 1725.0 \| 5175 \| 1.2711 \| \| 0.5584 \| 1726.0 \| 5178 \| 1.2760 \| \| 0.5584 \| 1727.0 \| 5181 \| 1.2812 \| \| 0.5584 \| 1728.0 \| 5184 \| 1.2847 \| \| 0.5584 \| 1729.0 \| 5187 \| 1.2863 \| \| 0.5584 \| 1730.0 \| 5190 \| 1.2881 \| \| 0.5584 \| 1731.0 \| 5193 \| 1.2861 \| \| 0.5584 \| 1732.0 \| 5196 \| 1.2846 \| \| 0.5584 \| 1733.0 \| 5199 \| 1.2825 \| \| 0.5584 \| 1734.0 \| 5202 \| 1.2793 \| \| 0.5584 \| 1735.0 \| 5205 \| 1.2799 \| \| 0.5584 \| 1736.0 \| 5208 \| 1.2794 \| \| 0.5584 \| 1737.0 \| 5211 \| 1.2769 \| \| 0.5584 \| 1738.0 \| 5214 \| 1.2734 \| \| 0.5584 \| 1739.0 \| 5217 \| 1.2713 \| \| 0.5584 \| 1740.0 \| 5220 \| 1.2720 \| \| 0.5584 \| 1741.0 \| 5223 \| 1.2751 \| \| 0.5584 \| 1742.0 \| 5226 \| 1.2776 \| \| 0.5584 \| 1743.0 \| 5229 \| 1.2792 \| \| 0.5584 \| 1744.0 \| 5232 \| 1.2830 \| \| 0.5584 \| 1745.0 \| 5235 \| 1.2845 \| \| 0.5584 \| 1746.0 \| 5238 \| 1.2858 \| \| 0.5584 \| 1747.0 \| 5241 \| 1.2844 \| \| 0.5584 \| 1748.0 \| 5244 \| 1.2823 \| \| 0.5584 \| 1749.0 \| 5247 \| 1.2819 \| \| 0.5584 \| 1750.0 \| 5250 \| 1.2809 \| \| 0.5584 \| 1751.0 \| 5253 \| 1.2805 \| \| 0.5584 \| 1752.0 \| 5256 \| 1.2779 \| \| 0.5584 \| 1753.0 \| 5259 \| 1.2749 \| \| 0.5584 \| 1754.0 \| 5262 \| 1.2768 \| \| 0.5584 \| 1755.0 \| 5265 \| 1.2799 \| \| 0.5584 \| 1756.0 \| 5268 \| 1.2808 \| \| 0.5584 \| 1757.0 \| 5271 \| 1.2788 \| \| 0.5584 \| 1758.0 \| 5274 \| 1.2726 \| \| 0.5584 \| 1759.0 \| 5277 \| 1.2663 \| \| 0.5584 \| 1760.0 \| 5280 \| 1.2611 \| \| 0.5584 \| 1761.0 \| 5283 \| 1.2576 \| \| 0.5584 \| 1762.0 \| 5286 \| 1.2551 \| \| 0.5584 \| 1763.0 \| 5289 \| 1.2647 \| \| 0.5584 \| 1764.0 \| 5292 \| 1.2732 \| \| 0.5584 \| 1765.0 \| 5295 \| 1.2749 \| \| 0.5584 \| 1766.0 \| 5298 \| 1.2798 \| \| 0.5584 \| 1767.0 \| 5301 \| 1.2798 \| \| 0.5584 \| 1768.0 \| 5304 \| 1.2799 \| \| 0.5584 \| 1769.0 \| 5307 \| 1.2805 \| \| 0.5584 \| 1770.0 \| 5310 \| 1.2787 \| \| 0.5584 \| 1771.0 \| 5313 \| 1.2751 \| \| 0.5584 \| 1772.0 \| 5316 \| 1.2724 \| \| 0.5584 \| 1773.0 \| 5319 \| 1.2702 \| \| 0.5584 \| 1774.0 \| 5322 \| 1.2681 \| \| 0.5584 \| 1775.0 \| 5325 \| 1.2680 \| \| 0.5584 \| 1776.0 \| 5328 \| 1.2762 \| \| 0.5584 \| 1777.0 \| 5331 \| 1.2824 \| \| 0.5584 \| 1778.0 \| 5334 \| 1.2878 \| \| 0.5584 \| 1779.0 \| 5337 \| 1.2896 \| \| 0.5584 \| 1780.0 \| 5340 \| 1.2924 \| \| 0.5584 \| 1781.0 \| 5343 \| 1.2972 \| \| 0.5584 \| 1782.0 \| 5346 \| 1.2993 \| \| 0.5584 \| 1783.0 \| 5349 \| 1.2992 \| \| 0.5584 \| 1784.0 \| 5352 \| 1.2982 \| \| 0.5584 \| 1785.0 \| 5355 \| 1.2968 \| \| 0.5584 \| 1786.0 \| 5358 \| 1.2951 \| \| 0.5584 \| 1787.0 \| 5361 \| 1.2933 \| \| 0.5584 \| 1788.0 \| 5364 \| 1.2933 \| \| 0.5584 \| 1789.0 \| 5367 \| 1.2916 \| \| 0.5584 \| 1790.0 \| 5370 \| 1.2882 \| \| 0.5584 \| 1791.0 \| 5373 \| 1.2879 \| \| 0.5584 \| 1792.0 \| 5376 \| 1.2876 \| \| 0.5584 \| 1793.0 \| 5379 \| 1.2848 \| \| 0.5584 \| 1794.0 \| 5382 \| 1.2832 \| \| 0.5584 \| 1795.0 \| 5385 \| 1.2809 \| \| 0.5584 \| 1796.0 \| 5388 \| 1.2803 \| \| 0.5584 \| 1797.0 \| 5391 \| 1.2786 \| \| 0.5584 \| 1798.0 \| 5394 \| 1.2740 \| \| 0.5584 \| 1799.0 \| 5397 \| 1.2691 \| \| 0.5584 \| 1800.0 \| 5400 \| 1.2653 \| \| 0.5584 \| 1801.0 \| 5403 \| 1.2605 \| \| 0.5584 \| 1802.0 \| 5406 \| 1.2591 \| \| 0.5584 \| 1803.0 \| 5409 \| 1.2564 \| \| 0.5584 \| 1804.0 \| 5412 \| 1.2520 \| \| 0.5584 \| 1805.0 \| 5415 \| 1.2478 \| \| 0.5584 \| 1806.0 \| 5418 \| 1.2489 \| \| 0.5584 \| 1807.0 \| 5421 \| 1.2499 \| \| 0.5584 \| 1808.0 \| 5424 \| 1.2530 \| \| 0.5584 \| 1809.0 \| 5427 \| 1.2525 \| \| 0.5584 \| 1810.0 \| 5430 \| 1.2523 \| \| 0.5584 \| 1811.0 \| 5433 \| 1.2526 \| \| 0.5584 \| 1812.0 \| 5436 \| 1.2536 \| \| 0.5584 \| 1813.0 \| 5439 \| 1.2507 \| \| 0.5584 \| 1814.0 \| 5442 \| 1.2481 \| \| 0.5584 \| 1815.0 \| 5445 \| 1.2451 \| \| 0.5584 \| 1816.0 \| 5448 \| 1.2370 \| \| 0.5584 \| 1817.0 \| 5451 \| 1.2326 \| \| 0.5584 \| 1818.0 \| 5454 \| 1.2316 \| \| 0.5584 \| 1819.0 \| 5457 \| 1.2329 \| \| 0.5584 \| 1820.0 \| 5460 \| 1.2352 \| \| 0.5584 \| 1821.0 \| 5463 \| 1.2331 \| \| 0.5584 \| 1822.0 \| 5466 \| 1.2283 \| \| 0.5584 \| 1823.0 \| 5469 \| 1.2228 \| \| 0.5584 \| 1824.0 \| 5472 \| 1.2207 \| \| 0.5584 \| 1825.0 \| 5475 \| 1.2197 \| \| 0.5584 \| 1826.0 \| 5478 \| 1.2164 \| \| 0.5584 \| 1827.0 \| 5481 \| 1.2152 \| \| 0.5584 \| 1828.0 \| 5484 \| 1.2172 \| \| 0.5584 \| 1829.0 \| 5487 \| 1.2181 \| \| 0.5584 \| 1830.0 \| 5490 \| 1.2158 \| \| 0.5584 \| 1831.0 \| 5493 \| 1.2166 \| \| 0.5584 \| 1832.0 \| 5496 \| 1.2138 \| \| 0.5584 \| 1833.0 \| 5499 \| 1.2109 \| \| 0.5585 \| 1834.0 \| 5502 \| 1.2170 \| \| 0.5585 \| 1835.0 \| 5505 \| 1.2216 \| \| 0.5585 \| 1836.0 \| 5508 \| 1.2244 \| \| 0.5585 \| 1837.0 \| 5511 \| 1.2267 \| \| 0.5585 \| 1838.0 \| 5514 \| 1.2321 \| \| 0.5585 \| 1839.0 \| 5517 \| 1.2359 \| \| 0.5585 \| 1840.0 \| 5520 \| 1.2415 \| \| 0.5585 \| 1841.0 \| 5523 \| 1.2507 \| \| 0.5585 \| 1842.0 \| 5526 \| 1.2623 \| \| 0.5585 \| 1843.0 \| 5529 \| 1.2675 \| \| 0.5585 \| 1844.0 \| 5532 \| 1.2701 \| \| 0.5585 \| 1845.0 \| 5535 \| 1.2701 \| \| 0.5585 \| 1846.0 \| 5538 \| 1.2698 \| \| 0.5585 \| 1847.0 \| 5541 \| 1.2720 \| \| 0.5585 \| 1848.0 \| 5544 \| 1.2740 \| \| 0.5585 \| 1849.0 \| 5547 \| 1.2751 \| \| 0.5585 \| 1850.0 \| 5550 \| 1.2771 \| \| 0.5585 \| 1851.0 \| 5553 \| 1.2801 \| \| 0.5585 \| 1852.0 \| 5556 \| 1.2817 \| \| 0.5585 \| 1853.0 \| 5559 \| 1.2834 \| \| 0.5585 \| 1854.0 \| 5562 \| 1.2851 \| \| 0.5585 \| 1855.0 \| 5565 \| 1.2870 \| \| 0.5585 \| 1856.0 \| 5568 \| 1.2885 \| \| 0.5585 \| 1857.0 \| 5571 \| 1.2872 \| \| 0.5585 \| 1858.0 \| 5574 \| 1.2855 \| \| 0.5585 \| 1859.0 \| 5577 \| 1.2835 \| \| 0.5585 \| 1860.0 \| 5580 \| 1.2837 \| \| 0.5585 \| 1861.0 \| 5583 \| 1.2837 \| \| 0.5585 \| 1862.0 \| 5586 \| 1.2828 \| \| 0.5585 \| 1863.0 \| 5589 \| 1.2814 \| \| 0.5585 \| 1864.0 \| 5592 \| 1.2794 \| \| 0.5585 \| 1865.0 \| 5595 \| 1.2781 \| \| 0.5585 \| 1866.0 \| 5598 \| 1.2806 \| \| 0.5585 \| 1867.0 \| 5601 \| 1.2827 \| \| 0.5585 \| 1868.0 \| 5604 \| 1.2827 \| \| 0.5585 \| 1869.0 \| 5607 \| 1.2828 \| \| 0.5585 \| 1870.0 \| 5610 \| 1.2827 \| \| 0.5585 \| 1871.0 \| 5613 \| 1.2810 \| \| 0.5585 \| 1872.0 \| 5616 \| 1.2799 \| \| 0.5585 \| 1873.0 \| 5619 \| 1.2784 \| \| 0.5585 \| 1874.0 \| 5622 \| 1.2760 \| \| 0.5585 \| 1875.0 \| 5625 \| 1.2729 \| \| 0.5585 \| 1876.0 \| 5628 \| 1.2710 \| \| 0.5585 \| 1877.0 \| 5631 \| 1.2718 \| \| 0.5585 \| 1878.0 \| 5634 \| 1.2747 \| \| 0.5585 \| 1879.0 \| 5637 \| 1.2779 \| \| 0.5585 \| 1880.0 \| 5640 \| 1.2808 \| \| 0.5585 \| 1881.0 \| 5643 \| 1.2827 \| \| 0.5585 \| 1882.0 \| 5646 \| 1.2821 \| \| 0.5585 \| 1883.0 \| 5649 \| 1.2822 \| \| 0.5585 \| 1884.0 \| 5652 \| 1.2834 \| \| 0.5585 \| 1885.0 \| 5655 \| 1.2828 \| \| 0.5585 \| 1886.0 \| 5658 \| 1.2808 \| \| 0.5585 \| 1887.0 \| 5661 \| 1.2784 \| \| 0.5585 \| 1888.0 \| 5664 \| 1.2760 \| \| 0.5585 \| 1889.0 \| 5667 \| 1.2731 \| \| 0.5585 \| 1890.0 \| 5670 \| 1.2704 \| \| 0.5585 \| 1891.0 \| 5673 \| 1.2704 \| \| 0.5585 \| 1892.0 \| 5676 \| 1.2701 \| \| 0.5585 \| 1893.0 \| 5679 \| 1.2696 \| \| 0.5585 \| 1894.0 \| 5682 \| 1.2657 \| \| 0.5585 \| 1895.0 \| 5685 \| 1.2590 \| \| 0.5585 \| 1896.0 \| 5688 \| 1.2525 \| \| 0.5585 \| 1897.0 \| 5691 \| 1.2475 \| \| 0.5585 \| 1898.0 \| 5694 \| 1.2441 \| \| 0.5585 \| 1899.0 \| 5697 \| 1.2416 \| \| 0.5585 \| 1900.0 \| 5700 \| 1.2422 \| \| 0.5585 \| 1901.0 \| 5703 \| 1.2433 \| \| 0.5585 \| 1902.0 \| 5706 \| 1.2443 \| \| 0.5585 \| 1903.0 \| 5709 \| 1.2453 \| \| 0.5585 \| 1904.0 \| 5712 \| 1.2513 \| \| 0.5585 \| 1905.0 \| 5715 \| 1.2538 \| \| 0.5585 \| 1906.0 \| 5718 \| 1.2554 \| \| 0.5585 \| 1907.0 \| 5721 \| 1.2567 \| \| 0.5585 \| 1908.0 \| 5724 \| 1.2573 \| \| 0.5585 \| 1909.0 \| 5727 \| 1.2580 \| \| 0.5585 \| 1910.0 \| 5730 \| 1.2579 \| \| 0.5585 \| 1911.0 \| 5733 \| 1.2576 \| \| 0.5585 \| 1912.0 \| 5736 \| 1.2567 \| \| 0.5585 \| 1913.0 \| 5739 \| 1.2552 \| \| 0.5585 \| 1914.0 \| 5742 \| 1.2542 \| \| 0.5585 \| 1915.0 \| 5745 \| 1.2539 \| \| 0.5585 \| 1916.0 \| 5748 \| 1.2530 \| \| 0.5585 \| 1917.0 \| 5751 \| 1.2534 \| \| 0.5585 \| 1918.0 \| 5754 \| 1.2542 \| \| 0.5585 \| 1919.0 \| 5757 \| 1.2537 \| \| 0.5585 \| 1920.0 \| 5760 \| 1.2527 \| \| 0.5585 \| 1921.0 \| 5763 \| 1.2517 \| \| 0.5585 \| 1922.0 \| 5766 \| 1.2510 \| \| 0.5585 \| 1923.0 \| 5769 \| 1.2496 \| \| 0.5585 \| 1924.0 \| 5772 \| 1.2497 \| \| 0.5585 \| 1925.0 \| 5775 \| 1.2491 \| \| 0.5585 \| 1926.0 \| 5778 \| 1.2483 \| \| 0.5585 \| 1927.0 \| 5781 \| 1.2462 \| \| 0.5585 \| 1928.0 \| 5784 \| 1.2437 \| \| 0.5585 \| 1929.0 \| 5787 \| 1.2406 \| \| 0.5585 \| 1930.0 \| 5790 \| 1.2390 \| \| 0.5585 \| 1931.0 \| 5793 \| 1.2390 \| \| 0.5585 \| 1932.0 \| 5796 \| 1.2390 \| \| 0.5585 \| 1933.0 \| 5799 \| 1.2409 \| \| 0.5585 \| 1934.0 \| 5802 \| 1.2442 \| \| 0.5585 \| 1935.0 \| 5805 \| 1.2473 \| \| 0.5585 \| 1936.0 \| 5808 \| 1.2490 \| \| 0.5585 \| 1937.0 \| 5811 \| 1.2516 \| \| 0.5585 \| 1938.0 \| 5814 \| 1.2542 \| \| 0.5585 \| 1939.0 \| 5817 \| 1.2565 \| \| 0.5585 \| 1940.0 \| 5820 \| 1.2594 \| \| 0.5585 \| 1941.0 \| 5823 \| 1.2610 \| \| 0.5585 \| 1942.0 \| 5826 \| 1.2623 \| \| 0.5585 \| 1943.0 \| 5829 \| 1.2636 \| \| 0.5585 \| 1944.0 \| 5832 \| 1.2657 \| \| 0.5585 \| 1945.0 \| 5835 \| 1.2667 \| \| 0.5585 \| 1946.0 \| 5838 \| 1.2676 \| \| 0.5585 \| 1947.0 \| 5841 \| 1.2685 \| \| 0.5585 \| 1948.0 \| 5844 \| 1.2696 \| \| 0.5585 \| 1949.0 \| 5847 \| 1.2707 \| \| 0.5585 \| 1950.0 \| 5850 \| 1.2707 \| \| 0.5585 \| 1951.0 \| 5853 \| 1.2710 \| \| 0.5585 \| 1952.0 \| 5856 \| 1.2707 \| \| 0.5585 \| 1953.0 \| 5859 \| 1.2694 \| \| 0.5585 \| 1954.0 \| 5862 \| 1.2673 \| \| 0.5585 \| 1955.0 \| 5865 \| 1.2650 \| \| 0.5585 \| 1956.0 \| 5868 \| 1.2625 \| \| 0.5585 \| 1957.0 \| 5871 \| 1.2614 \| \| 0.5585 \| 1958.0 \| 5874 \| 1.2605 \| \| 0.5585 \| 1959.0 \| 5877 \| 1.2599 \| \| 0.5585 \| 1960.0 \| 5880 \| 1.2599 \| \| 0.5585 \| 1961.0 \| 5883 \| 1.2598 \| \| 0.5585 \| 1962.0 \| 5886 \| 1.2585 \| \| 0.5585 \| 1963.0 \| 5889 \| 1.2572 \| \| 0.5585 \| 1964.0 \| 5892 \| 1.2555 \| \| 0.5585 \| 1965.0 \| 5895 \| 1.2527 \| \| 0.5585 \| 1966.0 \| 5898 \| 1.2513 \| \| 0.5585 \| 1967.0 \| 5901 \| 1.2504 \| \| 0.5585 \| 1968.0 \| 5904 \| 1.2508 \| \| 0.5585 \| 1969.0 \| 5907 \| 1.2511 \| \| 0.5585 \| 1970.0 \| 5910 \| 1.2517 \| \| 0.5585 \| 1971.0 \| 5913 \| 1.2528 \| \| 0.5585 \| 1972.0 \| 5916 \| 1.2537 \| \| 0.5585 \| 1973.0 \| 5919 \| 1.2543 \| \| 0.5585 \| 1974.0 \| 5922 \| 1.2549 \| \| 0.5585 \| 1975.0 \| 5925 \| 1.2554 \| \| 0.5585 \| 1976.0 \| 5928 \| 1.2554 \| \| 0.5585 \| 1977.0 \| 5931 \| 1.2555 \| \| 0.5585 \| 1978.0 \| 5934 \| 1.2554 \| \| 0.5585 \| 1979.0 \| 5937 \| 1.2553 \| \| 0.5585 \| 1980.0 \| 5940 \| 1.2554 \| \| 0.5585 \| 1981.0 \| 5943 \| 1.2556 \| \| 0.5585 \| 1982.0 \| 5946 \| 1.2563 \| \| 0.5585 \| 1983.0 \| 5949 \| 1.2567 \| \| 0.5585 \| 1984.0 \| 5952 \| 1.2567 \| \| 0.5585 \| 1985.0 \| 5955 \| 1.2567 \| \| 0.5585 \| 1986.0 \| 5958 \| 1.2566 \| \| 0.5585 \| 1987.0 \| 5961 \| 1.2566 \| \| 0.5585 \| 1988.0 \| 5964 \| 1.2564 \| \| 0.5585 \| 1989.0 \| 5967 \| 1.2563 \| \| 0.5585 \| 1990.0 \| 5970 \| 1.2564 \| \| 0.5585 \| 1991.0 \| 5973 \| 1.2564 \| \| 0.5585 \| 1992.0 \| 5976 \| 1.2564 \| \| 0.5585 \| 1993.0 \| 5979 \| 1.2565 \| \| 0.5585 \| 1994.0 \| 5982 \| 1.2565 \| \| 0.5585 \| 1995.0 \| 5985 \| 1.2564 \| \| 0.5585 \| 1996.0 \| 5988 \| 1.2563 \| \| 0.5585 \| 1997.0 \| 5991 \| 1.2563 \| \| 0.5585 \| 1998.0 \| 5994 \| 1.2562 \| \| 0.5585 \| 1999.0 \| 5997 \| 1.2562 \| \| 0.558 \| 2000.0 \| 6000 \| 1.2562 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.7 - Tokenizers 0.15.0
QuizzerPrivate/lora-trained-xl	QuizzerPrivate	2024-03-08T00:22:36Z	1	2	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-07T19:46:13Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog widget: - text: A photo of sks dog in a bucket output: url: image_0.png - text: A photo of sks dog in a bucket output: url: image_1.png - text: A photo of sks dog in a bucket output: url: image_2.png - text: A photo of sks dog in a bucket output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - QuizzerPrivate/lora-trained-xl <Gallery /> ## Model description These are QuizzerPrivate/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](QuizzerPrivate/lora-trained-xl/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ColeD0/Claud.ai-2	ColeD0	2024-03-08T00:19:56Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-07T19:53:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ycfNTU/bloomz-560m_PROMPT_TUNING_textgrading_CASUAL_LM_v1	ycfNTU	2024-03-08T00:19:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-08T00:19:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
shleeeee/mistral-ko-tech-science-v1	shleeeee	2024-03-08T00:18:13Z	2,267	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-10T05:02:38Z	--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-tech-science-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)
shleeeee/mistral-ko-OpenOrca-Platypus-v2	shleeeee	2024-03-08T00:17:46Z	124	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-18T06:57:43Z	--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-OpenOrca-Platypus-v2 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)
shleeeee/mistral-ko-openorca-platypus-1epoch	shleeeee	2024-03-08T00:17:16Z	0	0	peft	[ "peft", "safetensors", "mistral", "text-generation", "ko", "license:other", "region:us" ]	text-generation	2023-12-21T08:21:55Z	--- library_name: peft license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-openorca-platypus-1epoch It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park) ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
shleeeee/mistral-7b-ko-v1	shleeeee	2024-03-08T00:16:36Z	0	0	peft	[ "peft", "safetensors", "mistral", "text-generation", "ko", "license:other", "region:us" ]	text-generation	2023-12-27T04:27:10Z	--- library_name: peft license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-7b-ko-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park) ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
OwOOwO/eacc_contTrain_m2_25	OwOOwO	2024-03-08T00:16:21Z	89	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T00:13:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
shleeeee/mistral-ko-exo-mrc-v1	shleeeee	2024-03-08T00:15:03Z	109	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-11T08:10:49Z	--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-exo-mrc-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)
shleeeee/mistral-ko-7b-tech	shleeeee	2024-03-08T00:14:25Z	104	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-29T15:35:30Z	--- language: - ko pipeline_tag: text-generation tags: - finetune license: other --- # Model Card for mistral-ko-7b-tech It is a fine-tuned model using Korean in the mistral-7b model. ## Model Details * Model Developers : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park) * Repository : To be added * Model Architecture : The mistral-ko-7b-wiki-neft is is a fine-tuned version of the Mistral-7B-v0.1. * Lora target modules : q_proj, k_proj, v_proj, o_proj,gate_proj * train_batch : 4 * Max_step : 500 ## Dataset Korean Custom Dataset(2000) ## Prompt template: Mistral ``` <s>[INST]{['instruction']}[/INST]{['output']}</s> ``` ## Usage ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-7b-tech") model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-7b-tech") # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shleeeee/mistral-ko-7b-tech") ``` ## Evaluation ![image/png](https://cdn-uploads.huggingface.co/production/uploads/654495fa893aec5da96e9134/6z75dYa8TdTy4Y7EIl0CK.png)
shleeeee/mistral-ko-exo-wiki-quiz-v1	shleeeee	2024-03-08T00:12:49Z	96	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-06T03:23:55Z	--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-exo-wiki-quiz-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)
shleeeee/mistral-ko-OpenOrca-2000	shleeeee	2024-03-08T00:11:32Z	95	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "ko", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-04T13:17:54Z	--- language: - ko pipeline_tag: text-generation tags: - finetune --- # Model Card for mistral-ko-OpenOrca-2000 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * Model Developers : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park) * Repository : To be added * Model Architecture : The shleeeee/mistral-ko-OpenOrca-2000 is is a fine-tuned version of the Mistral-7B-v0.1. * Lora target modules : q_proj, k_proj, v_proj, o_proj,gate_proj * train_batch : 4 * epochs : 2 ## Dataset 2000 ko-OpenOrca datasets ## Prompt template: Mistral ``` <s>[INST]{['instruction']}[/INST]{['output']}</s> ``` ## Usage ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-OpenOrca-2000") model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-OpenOrca-2000") # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shleeeee/mistral-ko-OpenOrca-2000") ``` ## Evaluation To be added
shleeeee/mistral-ko-7b-wiki-neft	shleeeee	2024-03-08T00:11:04Z	2,286	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "ko", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-29T04:46:44Z	--- language: - ko pipeline_tag: text-generation tags: - finetune --- # Model Card for mistral-ko-7b-wiki-neft It is a fine-tuned model using Korean and NEFT in the mistral-7b model. ## Model Details * Model Developers : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park) * Repository : To be added * Model Architecture : The mistral-ko-7b-wiki-neft is is a fine-tuned version of the Mistral-7B-v0.1. * Lora target modules : q_proj, k_proj, v_proj, o_proj,gate_proj * train_batch : 4 * neftune_noise_alpha : 5 * Max_step : 1000 ## Dataset Korean Custom Dataset ## Prompt template: Mistral ``` <s>[INST]{['instruction']}[/INST]{['output']}</s> ``` ## Usage ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-7b-wiki") model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-7b-wiki") # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shleeeee/mistral-7b-wiki") ``` ## Evaluation ![image/png](https://cdn-uploads.huggingface.co/production/uploads/654495fa893aec5da96e9134/p1aJ4YMdP_E9YzhTcuaFx.png)
Holarissun/phi2-airl_sft-tldr-seqsampler	Holarissun	2024-03-08T00:06:49Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-08T00:06:42Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2-airl_sft-tldr-seqsampler results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi2-airl_sft-tldr-seqsampler This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
OwOOwO/eacc_contTrain_m2_55_orig	OwOOwO	2024-03-08T00:01:33Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T23:59:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
imsarfaroz/fine-tuned-albert-tweets	imsarfaroz	2024-03-07T23:58:52Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "albert", "text-classification", "generated_from_trainer", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-07T23:47:33Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy base_model: albert-base-v2 model-index: - name: fine-tuned-albert-tweets results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fine-tuned-albert-tweets This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6212 - Accuracy: 0.6785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 179 \| 0.6264 \| 0.6377 \| \| No log \| 2.0 \| 358 \| 0.6212 \| 0.6785 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_64_0.05_8_0.0002	ferrazzipietro	2024-03-07T23:56:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T23:55:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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s14pe/poca-SoccerTwos	s14pe	2024-03-07T23:54:13Z	21	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-07T23:53:39Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: s14pe/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MarkBW/no-bra-club	MarkBW	2024-03-07T23:52:25Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]	text-to-image	2024-03-07T23:52:22Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0P\0o\0s\0t\0p\0r\0o\0c\0e\0s\0s\0 \0u\0p\0s\0c\0a\0l\0e\0 \0b\0y\0:\0 \04\0,\0 \0P\0o\0s\0t\0p\0r\0o\0c\0e\0s\0s\0 \0u\0p\0s\0c\0a\0l\0e\0r\0:\0 \0R\0-\0E\0S\0R\0G\0A\0N\0 \04\0x\0+" output: url: images/wrefds.jpeg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: dmnoy, crop top --- # no-bra-club <Gallery /> ## Trigger words You should use `dmnoy` to trigger the image generation. You should use `crop top` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/MarkBW/no-bra-club/tree/main) them in the Files & versions tab.
adityahrudayam/T5_qa_model	adityahrudayam	2024-03-07T23:52:21Z	32	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2024-03-07T23:42:04Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer model-index: - name: T5_qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # T5_qa_model This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| nan \| \| No log \| 2.0 \| 2 \| nan \| \| No log \| 3.0 \| 3 \| nan \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
panos-span/ppo-SoccerTwos2	panos-span	2024-03-07T23:50:16Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-07T23:49:58Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: panos-span/ppo-SoccerTwos2 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
andysalerno/openchat-nectar-0.1	andysalerno	2024-03-07T23:45:02Z	13	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:berkeley-nest/Nectar", "base_model:openchat/openchat-3.5-0106", "base_model:finetune:openchat/openchat-3.5-0106", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-11T08:02:43Z	--- license: apache-2.0 datasets: - berkeley-nest/Nectar base_model: openchat/openchat-3.5-0106 model-index: - name: openchat-nectar-0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.99 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 54.22 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 69.67 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard --- This is openchat/openchat-3.5-0106, tuned with DPO on a tiny subset Nectar. Only 200 steps, so nowhere close to a full epoch. Careful attention was paid to make sure the chat template was followed properly. Summary of versions: [openchat-nectar-0.1](https://huggingface.co/andysalerno/openchat-nectar-0.1) - 200 steps, no filtering on Nectar dataset, 5e-5 learning rate [openchat-nectar-0.2](https://huggingface.co/andysalerno/openchat-nectar-0.2) - empty repo, failed training. ignore it [openchat-nectar-0.3](https://huggingface.co/andysalerno/openchat-nectar-0.3) - 500 steps, no filtering on Nectar dataset, 5e-5 learning rate (same as 1 but with more steps) [openchat-nectar-0.4](https://huggingface.co/andysalerno/openchat-nectar-0.4) - 500 steps, filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-5 learning rate [openchat-nectar-0.5](https://huggingface.co/andysalerno/openchat-nectar-0.5) - 5000 steps (over a full epoch), filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-6 learning rate. Same as 0.4 but with 10x the steps, and 1/10th the learning rate [openchat-nectar-0.6](https://huggingface.co/andysalerno/openchat-nectar-0.6) - 500 steps, filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-5 learning rate. Same as 0.5 but with 1/10th the steps, and 10x the learning rate # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|69.94\| \|AI2 Reasoning Challenge (25-Shot)\|66.21\| \|HellaSwag (10-Shot) \|82.99\| \|MMLU (5-Shot) \|65.17\| \|TruthfulQA (0-shot) \|54.22\| \|Winogrande (5-shot) \|81.37\| \|GSM8k (5-shot) \|69.67\|
dranger003/LWM-Text-Chat-128K-iMat.GGUF	dranger003	2024-03-07T23:33:58Z	143	8	gguf	[ "gguf", "text-generation", "license:llama2", "endpoints_compatible", "region:us" ]	text-generation	2024-02-14T14:14:59Z	--- license: llama2 pipeline_tag: text-generation library_name: gguf --- GGUF importance matrix (imatrix) quants for https://huggingface.co/LargeWorldModel/LWM-Text-Chat-128K The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw. * The imatrix Q4-K quant fits with 32K context on 24GB and gives me ~100 t/s inference on a 3090. * With IQ3_XXS it seems to fit ~37K context on 24GB (and it is even faster than Q4-K). * With either quant on a 3090 it seems to decode context at well over 2000 t/s. * Using Q8 K-cache (instead of F16) you can fit up to 43-44K context but inference speed goes down a little bit. * Also for some reason I need to use 1.0 penalty to avoid the response being cut-off. \| Layers \| Context \| [Template](https://github.com/LargeWorldModel/LWM/blob/9aaaa1e864bfcf31b66028e782395a22f4817535/scripts/eval_needle.py#L48) \| \| --- \| --- \| --- \| \| <pre>32</pre> \| <pre>131072</pre> \| <pre>You are a helpful assistant.<br>USER:<br>{context}<br>{question}<br>Don't give information outside the document or repeat your findings. Keep your response short and direct.<br>ASSISTANT:<br>{response}</pre> \|
iampedroalz/gemma-2b-4bit-alpaca-spanish	iampedroalz	2024-03-07T23:33:25Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-07T23:31:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ChaoticNeutrals/Eris_Floramix_DPO_7B	ChaoticNeutrals	2024-03-07T23:30:49Z	231	6	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED", "dataset:ResplendentAI/Synthetic_Soul_1k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T23:09:39Z	--- library_name: transformers license: other datasets: - athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED - ResplendentAI/Synthetic_Soul_1k language: - en --- # Eris Floramix DPO This is a mix between Eris Remix DPO and Flora DPO, a finetune of the original Eris Remix on the Synthetic_Soul_1k dataset. Applied this DPO dataset: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED
shamekhjr/ppo-Huggy	shamekhjr	2024-03-07T23:28:49Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-07T23:28:21Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: shamekhjr/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Alpaca69B/phi2-2b-absa	Alpaca69B	2024-03-07T23:21:02Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T02:56:41Z	--- library_name: transformers tags: [] --- --- # phi2-2b-absa: Fine-Tuned Aspect-Based Sentiment Analysis Model ## Model Description The phi2-2b-absa model is a fine-tuned aspect-based sentiment analysis (ABSA) model based on the Microsoft Phi-2 model. It has been trained on the semeval2016-full-absa-reviews-english-translated-resampled dataset. The model predicts sentiments towards different aspects mentioned in a given sentence. ## Fine-Tuning Details The fine tuning can be revisited on [Google Colab](https://colab.research.google.com/drive/1n3ykETLpHQPXwPhUcOe-z9cG3ThrDkSi?usp=sharing). ### Dataset - Name: semeval2016-full-absa-reviews-english-translated-resampled - Description: Annotated dataset for ABSA containing sentences, aspects, sentiments, and additional contextual text. It is split into train and test sets. ### Model Architecture - Base Model: Microsoft Phi-2 - Fine-Tuned Model: phi2-2b-absa ### Fine-Tuning Parameters - LoRA Attention Dimension (lora_r): 64 - LoRA Scaling Parameter (lora_alpha): 16 - LoRA Dropout Probability (lora_dropout): 0.1 ### BitsAndBytes Quantization - Activate 4-bit Precision: True - Compute Dtype for 4-bit Models: float16 - Quantization Type: nf4 ### Training Parameters - Number of Training Epochs: 1 - Batch Size per GPU for Training: 4 - Batch Size per GPU for Evaluation: 4 - Gradient Accumulation Steps: 1 - Learning Rate: 2e-4 - Weight Decay: 0.001 - Optimizer: PagedAdamW (32-bit) - Learning Rate Scheduler: Cosine ### SFT Parameters - Maximum Sequence Length: None - Packing: False ## How to Use ``` from transformers import AutoTokenizer, pipeline import torch model = "Alpaca69B/llama-2-7b-absa-semeval-2016" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.float16, device="auto", ) input_sentence = "the first thing that attracts attention is the warm reception and the smiling receptionists." sequences = pipeline( f'### Human: {input_sentence} ### Assistant: aspect:', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) sequences[0]['generated_text'] ``` Testing can be seen on [Google Colab](https://colab.research.google.com/drive/1eKdZYYWiivyeCQDsocGBstVODMLZyT-_?usp=sharing) ## Acknowledgments - The fine-tuning process and model development were performed by Ben Kampmann. ---
dolainu/Nyanners_loraXL_Vtuber	dolainu	2024-03-07T23:15:36Z	4	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "license:apache-2.0", "region:us" ]	text-to-image	2024-03-07T22:36:54Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09104-3065517054.png - text: >- <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, shirt lift, bed, legs up, pussy, hugging own legs parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09101-2699702684.png - text: >- <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, shirt lift, bed, masturbating, pussy parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09094-3843125815.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09082-3207580297.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, medium hair, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/00028-4212975261.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, medium hair, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09078-3444304162.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, kneeling, shirt lift parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09085-1394382796.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:1>, nyanners2st, long hair, kneeling, closed mouth, shirt lift parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09018-3981033982.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, closed mouth, shirt lift, smug, petite, lying, bed parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09035-1317649319.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, kneeling, closed mouth, shirt lift, smug, petite parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09026-560715627.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, kneeling, closed mouth, shirt lift, smug, petite parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09024-4125556276.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09077-834539960.png base_model: stablediffusionapi/pony-diffusion-v6-xl instance_prompt: null license: apache-2.0 --- # Nyanners <Gallery /> ## Model description Works best with Ponydiffusion V6 XL TESTED AT 0.87 STRENGTH Prompts: short hair ver.: "nyanners1st, purple eyes"---optional: "medium hair" long hair ver.: "nyanners2st, long hair, purple eyes" ## Download model Weights for this model are available in Safetensors format. [Download](/dolainu/Nyanners_lora_Vtuber/tree/main) them in the Files & versions tab.
dolainu/Natsuiro_Matsuri_loraXL_Vtuber	dolainu	2024-03-07T23:09:30Z	3	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "license:apache-2.0", "region:us" ]	text-to-image	2024-03-07T23:09:23Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, sitting, bikini parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09658-1693014100.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, sitting, crossed legs parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09663-282707023.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, shirt lift, nipples, kneeling, small breasts parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, full body shot output: url: images/09673-1053123366.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, shirt lift, nipples, kneeling, small breasts, condom wrapper in mouth parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, full body shot output: url: images/09675-1053123366.png - text: >- score_9, score_8_up, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, lying, bed, spread legs, masturbating, pussy, fingering, hand on breast, small breasts, nipples parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09700-4195931212.png - text: >- score_9, score_8_up, <lora:NatsuiroMatsuriXL_V0.2R:0.8>, namatsuri, 1girl, (petite), green eyes, lying, bed, spread legs, [[[spread pussy]]] parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09705-4195931212.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2R:0.8>, namatsuri, 1girl, petite, green eyes, leaning towards viewer parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09710-2211139587.png base_model: stablediffusionapi/pony-diffusion-v6-xl instance_prompt: null license: apache-2.0 --- # Natsuiro Matsuri <Gallery /> ## Model description Works best with Ponydiffusion V6 XL TESTED AT 0.8 STRENGTH. Trigger Words: "namatsuri, 1girl, (petite), green eyes" ## Download model Weights for this model are available in Safetensors format. [Download](/dolainu/Natsuiro_Matsuri_loraXL_Vtuber/tree/main) them in the Files & versions tab.
ArtMindia/artmindia3k	ArtMindia	2024-03-07T23:07:06Z	0	0	adapter-transformers	[ "adapter-transformers", "pytorch", "mistral", "question-answering", "en", "license:apache-2.0", "region:us" ]	question-answering	2023-10-28T00:43:48Z	--- license: apache-2.0 language: - en library_name: adapter-transformers metrics: - accuracy pipeline_tag: question-answering --- --- license: apache-2.0 language: - en This is just a test card with a few thousand rows of data. I wish I had more to add but that is all. How much do you need. Here it is. fsdf asdf sdf dsafs fsd fad sfs f sadf dafs dfs fa sf asf sf s afs fsdf f s f sf sdf sf sf s This model is not too short
Sebas012/mi-super-modelo	Sebas012	2024-03-07T23:06:44Z	4	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-07T23:00:10Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: bert-base-cased metrics: - accuracy model-index: - name: mi-super-modelo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mi-super-modelo This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7111 - Accuracy: 0.15 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.7596 \| 0.5 \| 5 \| 1.7679 \| 0.15 \| \| 1.8268 \| 1.0 \| 10 \| 1.7111 \| 0.15 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cpu - Datasets 2.18.0 - Tokenizers 0.15.2
AIdenU/Gemma-7b-ko-Y24_v2.0	AIdenU	2024-03-07T23:04:05Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T00:12:43Z	--- license: apache-2.0 language: - ko pipeline_tag: text-generation tags: - gemma --- ### BaseModel - [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Model Generation ``` from transforemrs import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AIdenU/Gemma-7b-ko-Y24_v2.0", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("AIdenU/Gemma-7b-ko-Y24_v2.0", use_fast=True) systemPrompt = "당신은 유능한 AI입니다." prompt = "지렁이도 밟으면 꿈틀하나요?" outputs = model.generate( **tokenizer( f"### instruction: {system}\n{prompt} \n### output: ", return_tensors='pt' ).to('cuda'), max_new_tokens=256, temperature=0.2, top_p=1, do_sample=True ) print(tokenizer.decode(outputs[0])) ```
AIdenU/LLAMA-2-13b-koen-Y24_v1.0	AIdenU	2024-03-07T23:01:59Z	211	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama2", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T01:25:26Z	--- license: apache-2.0 language: - ko pipeline_tag: text-generation tags: - llama2 --- ### BaseModel - [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ### Model Generation ``` from transforemrs import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AIdenU/LLAMA-2-13b-koen-Y24_v1.0", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("AIdenU/LLAMA-2-13b-koen-Y24_v1.0", use_fast=True) systemPrompt = "당신은 유능한 AI입니다." prompt = "지렁이도 밟으면 꿈틀하나요?" outputs = model.generate( **tokenizer( f"[INST] <<SYS>>\n{systemPrompt}\n<</SYS>>\n\n{prompt} [/INST] ", return_tensors='pt' ).to('cuda'), max_new_tokens=256, temperature=0.2, top_p=1, do_sample=True ) print(tokenizer.decode(outputs[0])) ```
AlexandreManai/dqn-SpaceInvadersNoFrameskip-v4	AlexandreManai	2024-03-07T22:59:30Z	6	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-07T22:58:58Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 512.00 +/- 139.13 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AlexandreManai -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AlexandreManai -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AlexandreManai ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
dolainu/SmugAlana_loraXL_Vtuber	dolainu	2024-03-07T22:58:46Z	3	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "license:apache-2.0", "region:us" ]	text-to-image	2024-03-07T22:40:52Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- score_9, <lora:SmugAlanaV0.1:0.87>, smalana, 1girl, smug, kneeling, bed, condom wrapper in mouth parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09290-1373470824.png - text: score_9, <lora:SmugAlanaV0.1:0.8>, smalana, 1girl, nude, bed parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09328-362448161.png - text: >- score_9, <lora:SmugAlanaV0.1:0.8>, smalana, 1girl, sitting, clothes down, nipple parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09316-1813936606.png - text: score_9, <lora:SmugAlanaV0.1:0.87>, smalana, 1girl, smug, sitting parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09309-4054020249.png - text: >- <lora:SmugAlanaV0.1:0.8>, smalana, 1girl, <lora:Smooth Anime 2 Style SDXL_LoRA_Pony Diffusion V6 XL:1> parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09340-1383936240.png - text: score_9, <lora:SmugAlanaV0.1:0.87>, smalana, 1girl, smug, sitting parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09343-915960868.png base_model: stablediffusionapi/pony-diffusion-v6-xl instance_prompt: null license: apache-2.0 --- # SmugAlana <Gallery /> ## Model description Works best with Ponydiffusion V6 XL TESTED BETWEEN '0.8 - 0.9' STRENGTH Trigger words: smalana, 1girl ## Download model Weights for this model are available in Safetensors format. [Download](/dolainu/SmugAlana_lora_Vtuber/tree/main) them in the Files & versions tab.
colerobertson/wav2vec2-base-ogma-phoneme	colerobertson	2024-03-07T22:41:39Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-07T22:22:42Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer model-index: - name: wav2vec2-base-ogma-phoneme results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-ogma-phoneme This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Cer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:------------------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 67.3394 \| 1.0 \| 5 \| 62.1245 \| 6.7376 \| \| 73.2076 \| 2.0 \| 10 \| nan \| 6.7376 \| \| -257055208286820768.0000 \| 3.0 \| 15 \| nan \| 6.7376 \| \| 64.2241 \| 4.0 \| 20 \| nan \| 6.7376 \| \| 65.3601 \| 5.0 \| 25 \| 62.1245 \| 6.7376 \| \| 64.2295 \| 6.0 \| 30 \| 62.1157 \| 6.7376 \| \| 45.425 \| 7.0 \| 35 \| 62.1251 \| 6.7376 \| \| 50.6118 \| 8.0 \| 40 \| nan \| 6.7178 \| \| 64.6394 \| 9.0 \| 45 \| 62.0582 \| 6.6188 \| \| 48.7615 \| 10.0 \| 50 \| nan \| 6.6188 \| \| 54.5817 \| 11.0 \| 55 \| nan \| 6.4950 \| \| 48.1198 \| 12.0 \| 60 \| nan \| 6.4950 \| \| 56.9202 \| 13.0 \| 65 \| nan \| 6.3465 \| \| 57.3656 \| 14.0 \| 70 \| nan \| 6.4406 \| \| 68.163 \| 15.0 \| 75 \| 61.7497 \| 6.2129 \| \| 56.806 \| 16.0 \| 80 \| nan \| 6.2129 \| \| 69.1218 \| 17.0 \| 85 \| 61.6119 \| 5.7574 \| \| 55.5282 \| 18.0 \| 90 \| 61.5413 \| 5.4158 \| \| -6752.4055 \| 19.0 \| 95 \| 61.2303 \| 4.9257 \| \| 64.744 \| 20.0 \| 100 \| 60.9641 \| 4.4455 \| \| 66.7382 \| 21.0 \| 105 \| 60.3274 \| 3.5198 \| \| -21060.9078 \| 22.0 \| 110 \| nan \| 3.5198 \| \| 51.2619 \| 23.0 \| 115 \| 59.9896 \| 3.1089 \| \| 51.398 \| 24.0 \| 120 \| nan \| 2.7772 \| \| 63.6242 \| 25.0 \| 125 \| 59.3321 \| 2.5149 \| \| 59.6308 \| 26.0 \| 130 \| 58.7697 \| 2.1931 \| \| 62.0615 \| 27.0 \| 135 \| nan \| 1.8366 \| \| -46.2037 \| 28.0 \| 140 \| 57.9474 \| 1.7475 \| \| 60.5632 \| 29.0 \| 145 \| 57.5041 \| 1.5941 \| \| 55.4431 \| 30.0 \| 150 \| 56.7507 \| 1.4307 \| \| 40.8661 \| 31.0 \| 155 \| 56.6063 \| 1.4059 \| \| 63.784 \| 32.0 \| 160 \| 56.1097 \| 1.2327 \| \| 42.2708 \| 33.0 \| 165 \| nan \| 1.2327 \| \| 53.7813 \| 34.0 \| 170 \| nan \| 1.2426 \| \| 57.459 \| 35.0 \| 175 \| 55.8894 \| 1.2228 \| \| 58.9998 \| 36.0 \| 180 \| nan \| 1.0 \| \| 0.0 \| 37.0 \| 185 \| nan \| 1.0 \| \| 0.0 \| 38.0 \| 190 \| nan \| 1.0 \| \| 0.0 \| 39.0 \| 195 \| nan \| 1.0 \| \| 0.0 \| 40.0 \| 200 \| nan \| 1.0 \| \| 0.0 \| 41.0 \| 205 \| nan \| 1.0 \| \| 0.0 \| 42.0 \| 210 \| nan \| 1.0 \| \| 0.0 \| 43.0 \| 215 \| nan \| 1.0 \| \| 0.0 \| 44.0 \| 220 \| nan \| 1.0 \| \| 0.0 \| 45.0 \| 225 \| nan \| 1.0 \| \| 0.0 \| 46.0 \| 230 \| nan \| 1.0 \| \| 0.0 \| 47.0 \| 235 \| nan \| 1.0 \| \| 0.0 \| 48.0 \| 240 \| nan \| 1.0 \| \| 0.0 \| 49.0 \| 245 \| nan \| 1.0 \| \| 0.0 \| 50.0 \| 250 \| nan \| 1.0 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_32_0.01_4_0.0002	ferrazzipietro	2024-03-07T22:39:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T22:39:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ShubhamJain18/ppo-Huggy	ShubhamJain18	2024-03-07T22:35:47Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-07T22:34:03Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ShubhamJain18/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF	MaziyarPanahi	2024-03-07T22:33:24Z	54	2	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "tensorboard", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "en", "dataset:HuggingFaceH4/deita-10k-v0-sft", "base_model:google/gemma-7b", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "base_model:quantized:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1" ]	text-generation	2024-03-07T22:03:29Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - tensorboard - safetensors - gemma - text-generation - alignment-handbook - trl - sft - generated_from_trainer - conversational - en - dataset:HuggingFaceH4/deita-10k-v0-sft - base_model:google/gemma-7b - license:other - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: zephyr-7b-gemma-sft-v0.1-GGUF base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 inference: false model_creator: HuggingFaceH4 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) - Model creator: [HuggingFaceH4](https://huggingface.co/HuggingFaceH4) - Original model: [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) ## Description [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) contains GGUF format model files for [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) and below it, a specific filename to download, such as: zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
tsavage68/mistralit2_1000_STEPS_1e7_SFT_SFT	tsavage68	2024-03-07T22:31:26Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T22:25:45Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_1e7_SFT_SFT results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralit2_1000_STEPS_1e7_SFT_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.393 \| 0.1 \| 50 \| 1.3650 \| \| 0.9828 \| 0.2 \| 100 \| 0.9080 \| \| 0.3975 \| 0.29 \| 150 \| 0.3765 \| \| 0.3465 \| 0.39 \| 200 \| 0.3516 \| \| 0.3422 \| 0.49 \| 250 \| 0.3418 \| \| 0.3436 \| 0.59 \| 300 \| 0.3365 \| \| 0.3244 \| 0.68 \| 350 \| 0.3329 \| \| 0.3332 \| 0.78 \| 400 \| 0.3298 \| \| 0.3221 \| 0.88 \| 450 \| 0.3275 \| \| 0.3293 \| 0.98 \| 500 \| 0.3260 \| \| 0.3143 \| 1.07 \| 550 \| 0.3251 \| \| 0.3279 \| 1.17 \| 600 \| 0.3246 \| \| 0.3336 \| 1.27 \| 650 \| 0.3243 \| \| 0.3045 \| 1.37 \| 700 \| 0.3241 \| \| 0.3199 \| 1.46 \| 750 \| 0.3240 \| \| 0.3227 \| 1.56 \| 800 \| 0.3240 \| \| 0.3217 \| 1.66 \| 850 \| 0.3239 \| \| 0.3256 \| 1.76 \| 900 \| 0.3239 \| \| 0.3383 \| 1.86 \| 950 \| 0.3239 \| \| 0.3305 \| 1.95 \| 1000 \| 0.3239 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
masonjar/mixtral_test	masonjar	2024-03-07T22:31:22Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-07T21:45:29Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model-index: - name: mixtral_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mixtral_test This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0 - Datasets 2.15.0 - Tokenizers 0.15.2
Maqqq/OpenHermes-2.5-Mistral-7B-15	Maqqq	2024-03-07T22:26:33Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T21:55:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_32_0.05_16_0.0002	ferrazzipietro	2024-03-07T22:20:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T17:23:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Arczisan/christy-doa	Arczisan	2024-03-07T22:17:25Z	2	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]	text-to-image	2024-03-07T22:17:21Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/chisty.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null --- # Dead or Alive - Christy <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Arczisan/christy-doa/tree/main) them in the Files & versions tab.
s14pe/ppo-Pyramid	s14pe	2024-03-07T22:17:18Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2024-03-07T19:18:02Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: s14pe/ppo-Pyramid 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dataequity/DE-LM-7B	dataequity	2024-03-07T22:07:29Z	6	0	transformers	[ "transformers", "safetensors", "deci", "text-generation", "conversational", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-03-07T21:54:59Z	--- license: apache-2.0 language: - en --- # DE-LM-7B DE-LM-7B is a 7.04 billion parameter decoder-only text generation model, released under the Apache 2.0 license. This is an instruction tuned model built on top of Deci/DeciLM-7B fine-tuned for data filtering and API generation. ### Model Description - Language(s) (NLP): English - License: Apache 2.0 ## Model Architecture \| Parameters \| Layers \| Heads \| Sequence Length \| GQA num_key_value_heads* \| \|:----------\|:----------\|:----------\|:----------\|:----------\| \| 7.04 billion \| 32 \| 32 \| 8192 \| Variable \| ## Uses The model is intended for commercial and research use in English and can be fine-tuned for various tasks and languages. ## How to Get Started with the Model Use the code below to get started with the model. ```bibtex import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "dataequity/DE-LM-7B" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", trust_remote_code=True).to(device) inputs = tokenizer.encode("List the top 10 financial APIs", return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95) print(tokenizer.decode(outputs[0])) # The model can also be used via the text-generation pipeline interface from transformers import pipeline generator = pipeline("text-generation", "dataequity/DE-LM-7B", torch_dtype="auto", trust_remote_code=True, device=device) outputs = generator("List the top 10 financial APIs", max_new_tokens=100, do_sample=True, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## Ethical Considerations and Limitations DE-LM-7B is a new technology that comes with inherent risks associated with its use. The testing conducted so far has been primarily in English and does not encompass all possible scenarios. Like those of all large language models, DE-LM-7B's outputs are unpredictable, and the model may generate responses that are inaccurate, biased, or otherwise objectionable. Consequently, developers planning to use DE-LM-7B should undertake thorough safety testing and tuning designed explicitly for their intended applications of the model before deployment. ## Citation ```bibtex @misc{DeciFoundationModels, title = {DeciLM-7B}, author = {DeciAI Research Team}, year = {2023} url={https://huggingface.co/Deci/DeciLM-7B}, } ```
HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit	HuggingFaceM4	2024-03-07T22:05:47Z	8,619	43	transformers	[ "transformers", "safetensors", "siglip", "zero-shot-image-classification", "custom_code", "arxiv:2307.06304", "license:apache-2.0", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2024-01-30T19:31:08Z	--- license: apache-2.0 --- Same as https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2 with two changes: - increase max resolution to 980 x 980 (instead of 384 x 384) by interpolating the position embeddings - implement the strategy in [NaViT](https://arxiv.org/abs/2307.06304) to allow a/ variable resoltion images, b/ aspect ratio preserved images These changes only apply to the vision tower. No changes to the text tower. Implementation is fully backward compatible to `https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2` -> just don't specify the `patch_attention_mask` Usage: ```python import torch from modeling_siglip import SiglipVisionModel DEVICE = torch.device("cuda:0") PATCH_SIZE = 14 pixel_values = torch.randn(2, 3, 28, 42, dtype=torch.bfloat16, device=DEVICE) pixel_attention_mask = [ [ [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, ], [ [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, ], ] pixel_attention_mask = torch.tensor(pixel_attention_mask, dtype=torch.bool, device=DEVICE) patches_subgrid = pixel_attention_mask.unfold( dimension=1, size=PATCH_SIZE, step=PATCH_SIZE ).unfold(dimension=2, size=PATCH_SIZE, step=PATCH_SIZE) patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() model = SiglipVisionModel.from_pretrained("HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit", _flash_attn_2_enabled=True) model.train() model.vision_model.to(DEVICE, dtype=torch.bfloat16) output = model.vision_model(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) ```
Maqqq/OpenHermes-2.5-Mistral-7B-14	Maqqq	2024-03-07T21:58:58Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T19:57:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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twhoool02/Llama-2-7b-hf-AWQ	twhoool02	2024-03-07T21:58:42Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "AWQ", "llama-2", "en", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:quantized:meta-llama/Llama-2-7b-hf", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-03T21:37:40Z	--- language: en license: other tags: - facebook - meta - AWQ - llama-2 - llama base_model: meta-llama/Llama-2-7b-hf model_name: Llama-2-7b-hf-AWQ library: - Transformers - AWQ arxiv: https://arxiv.org/abs/2306.00978 model_type: llama pipeline_tag: text-generation qunatized_by: twhoool02 --- # Model Card for Llama-2-7b-hf-AWQ <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This model is a quantized version of the meta-llama/Llama-2-7b-hf model. The model was quantized using AWQ. - Developed by: Ted Whooley - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: llama - Language(s) (NLP): en - License: other - Finetuned from model [optional]: meta-llama/Llama-2-7b-hf ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Nusrat1234/Mistral-7B-User-Profile	Nusrat1234	2024-03-07T21:44:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T21:43:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.0002	ferrazzipietro	2024-03-07T21:43:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T16:45:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dominic1021/ohwxsarah	dominic1021	2024-03-07T21:40:04Z	2	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-03-07T19:23:40Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: ohwxsarah woman tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
pypy/VGMShield	pypy	2024-03-07T21:32:06Z	0	3	null	[ "Fake Video Detection", "Fake Video Source Tracing", "video-classification", "dataset:OpenGVLab/InternVid", "dataset:TempoFunk/webvid-10M", "arxiv:2402.13126", "license:apache-2.0", "region:us" ]	video-classification	2024-02-21T20:23:29Z	--- license: apache-2.0 datasets: - OpenGVLab/InternVid - TempoFunk/webvid-10M pipeline_tag: video-classification tags: - Fake Video Detection - Fake Video Source Tracing --- <div style="text-align: center;"> <img src="./symbol.png" alt="symbol" style="height: 100px;"/> </div> # VGMShield: Mitigating Misuse of Video Generative Models This repository pre-trained checkpoints to evaluate our detection and source tracing models. Our paper can be found at [here](https://arxiv.org/abs/2402.13126). Detection Model: [I3D](./detect/i3d/invid_i3d_i2v_i2v_best_model.pth) (0 True 1 False) [MAE](./detect/mae/invid_mae_i2v_i2v_best_model.pth) (0 True 1 False) [XCLIP](./detect/xclip/invid_xclip_i2v_i2v_best_model.pth) (0 True 1 False) [MAE-sora](./detect/mae/detection_ft_sora.pt) (0 True 1 False) Source Tracing Model > 0 Hotshot-xl 1 i2vgen-xl(i2v) 2 i2vgen-xl(t2v) 3 LaVie 4 SEINE 5 Show-1 6 Stable Video Diffusion 7 VideoCrafter(i2v) 8 VideoCrafter(t2v) [I3D](./source_tracing/i3d/invid_i3d_st_best_model.pth)-based source tracing model [MAE](./source_tracing/mae/invid_mae_st_best_model.pth)-based source tracing model [XCLIP](./source_tracing/xclip/invid_xclip_st_best_model.pth)-based source tracing model [MAE](./source_tracing/mae/source_tracing_ft_sora.pt)-based(sora) source tracing model sora is label 9.
cmu-lti/sotopia-pi-mistral-7b-BC	cmu-lti	2024-03-07T21:30:09Z	4	0	peft	[ "peft", "region:us" ]	null	2024-03-07T21:24:20Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_16_64_0.01_16_0.0002	ferrazzipietro	2024-03-07T21:24:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-07T16:26:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Shawt/Shawt	Shawt	2024-03-07T21:23:17Z	1	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/sdxl-turbo", "base_model:finetune:stabilityai/sdxl-turbo", "region:us" ]	text-to-image	2023-07-11T04:45:40Z	--- base_model: stabilityai/sdxl-turbo instance_prompt: <shawt> tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
MaziyarPanahi/phi-2-super-GGUF	MaziyarPanahi	2024-03-07T21:20:57Z	101	4	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "phi", "text-generation", "convAI", "conversational", "custom_code", "en", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space", "base_model:abacaj/phi-2-super", "base_model:quantized:abacaj/phi-2-super" ]	text-generation	2024-03-07T21:09:42Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - phi - text-generation - convAI - conversational - custom_code - en - license:mit - model-index - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - has_space - text-generation model_name: phi-2-super-GGUF base_model: abacaj/phi-2-super inference: false model_creator: abacaj pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) - Model creator: [abacaj](https://huggingface.co/abacaj) - Original model: [abacaj/phi-2-super](https://huggingface.co/abacaj/phi-2-super) ## Description [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) contains GGUF format model files for [abacaj/phi-2-super](https://huggingface.co/abacaj/phi-2-super). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) and below it, a specific filename to download, such as: phi-2-super-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/phi-2-super-GGUF phi-2-super-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/phi-2-super-GGUF phi-2-super-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m phi-2-super-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./phi-2-super-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./phi-2-super-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
mehdirafiei/SQLCODER16L	mehdirafiei	2024-03-07T21:18:40Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T21:10:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
panos-span/a2c-PandaReachDense-v3	panos-span	2024-03-07T21:18:19Z	4	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-07T21:14:32Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.20 +/- 0.11 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

om-ashish-soni/output

om-ashish-soni

2024-03-08T04:27:13Z

10

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:om-ashish-soni/shiv-mahapuran-ai", "base_model:finetune:om-ashish-soni/shiv-mahapuran-ai", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T15:34:24Z

--- base_model: om-ashish-soni/shiv-mahapuran-ai tags: - generated_from_trainer model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [om-ashish-soni/shiv-mahapuran-ai](https://huggingface.co/om-ashish-soni/shiv-mahapuran-ai) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

om-ashish-soni/shripad-charitramrutam-lm-v2

om-ashish-soni

2024-03-08T04:27:07Z

5

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T04:26:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

okeanos/uptimeai-8273

okeanos

2024-03-08T04:26:46Z

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Phind/Phind-CodeLlama-34B-v2", "base_model:merge:Phind/Phind-CodeLlama-34B-v2", "base_model:codellama/CodeLlama-34b-Instruct-hf", "base_model:merge:codellama/CodeLlama-34b-Instruct-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T18:56:55Z

--- base_model: - codellama/CodeLlama-34b-Instruct-hf - Phind/Phind-CodeLlama-34B-v2 library_name: transformers tags: - mergekit - merge --- # merge-legacy This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) as a base. ### Models Merged The following models were included in the merge: * [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: codellama/CodeLlama-34b-Instruct-hf parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: Phind/Phind-CodeLlama-34B-v2 parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient merge_method: dare_ties base_model: codellama/CodeLlama-34b-Instruct-hf parameters: normalize: true int8_mask: true dtype: float16 ```

ahmedgongi10/mistral_instruct_devops11

ahmedgongi10

2024-03-08T04:23:40Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T04:23:26Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

saqidr/pegasus-samsum

saqidr

2024-03-08T04:18:13Z

71

0

transformers

[ "transformers", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-cnn_dailymail", "base_model:finetune:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-08T03:54:21Z

--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4849 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6267 | 0.54 | 500 | 1.4849 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

frankenmerger/delta-4b-notso-base

frankenmerger

2024-03-08T04:04:06Z

66

0

transformers

[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-06T18:41:10Z

--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - conversational --- ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "gmonsoon/Delta-4B-notso-base" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

Lemunite/vietinbank-vistral-7b-chat_merged

Lemunite

2024-03-08T03:56:29Z

3

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T03:52:34Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Sumail/Silver_Waves04_2b

Sumail

2024-03-08T03:56:12Z

3

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:deepnetguy/gemma-55", "base_model:merge:deepnetguy/gemma-55", "base_model:tomaszki/gemma-31", "base_model:merge:tomaszki/gemma-31", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T03:47:28Z

--- base_model: - deepnetguy/gemma-55 - 0x0dad0/nous_nb20_plus - tomaszki/gemma-31 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [tomaszki/gemma-31](https://huggingface.co/tomaszki/gemma-31) as a base. ### Models Merged The following models were included in the merge: * [deepnetguy/gemma-55](https://huggingface.co/deepnetguy/gemma-55) * [0x0dad0/nous_nb20_plus](https://huggingface.co/0x0dad0/nous_nb20_plus) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: tomaszki/gemma-31 # no parameters necessary for base model - model: deepnetguy/gemma-55 parameters: density: 0.5 weight: 0.4 - model: 0x0dad0/nous_nb20_plus parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: tomaszki/gemma-31 parameters: normalize: true dtype: bfloat16 ```

Neela/layoutlm-funsd

Neela

2024-03-08T03:53:17Z

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "layoutlm", "token-classification", "generated_from_trainer", "dataset:funsd", "base_model:microsoft/layoutlm-base-uncased", "base_model:finetune:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-07T17:07:05Z

--- license: mit base_model: microsoft/layoutlm-base-uncased tags: - generated_from_trainer datasets: - funsd model-index: - name: layoutlm-funsd results: [] ---  # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 1.1243 - Answer: {'precision': 0.40076335877862596, 'recall': 0.519159456118665, 'f1': 0.4523424878836834, 'number': 809} - Header: {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} - Question: {'precision': 0.5280065897858319, 'recall': 0.6018779342723005, 'f1': 0.5625274243089073, 'number': 1065} - Overall Precision: 0.4616 - Overall Recall: 0.5459 - Overall F1: 0.5002 - Overall Accuracy: 0.6215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.7728 | 1.0 | 10 | 1.5441 | {'precision': 0.04580152671755725, 'recall': 0.059332509270704575, 'f1': 0.05169628432956382, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.20335429769392033, 'recall': 0.18215962441314554, 'f1': 0.19217434373452202, 'number': 1065} | 0.1209 | 0.1214 | 0.1212 | 0.3719 | | 1.4551 | 2.0 | 20 | 1.3517 | {'precision': 0.20478234212139793, 'recall': 0.41285537700865266, 'f1': 0.27377049180327867, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26090225563909775, 'recall': 0.32582159624413143, 'f1': 0.28977035490605424, 'number': 1065} | 0.2297 | 0.3417 | 0.2747 | 0.4263 | | 1.295 | 3.0 | 30 | 1.2465 | {'precision': 0.26224426534407935, 'recall': 0.522867737948084, 'f1': 0.34929810074318746, 'number': 809} | {'precision': 0.058823529411764705, 'recall': 0.01680672268907563, 'f1': 0.026143790849673203, 'number': 119} | {'precision': 0.3458528951486698, 'recall': 0.41502347417840374, 'f1': 0.37729406743491256, 'number': 1065} | 0.2964 | 0.4350 | 0.3526 | 0.4803 | | 1.1635 | 4.0 | 40 | 1.1449 | {'precision': 0.28778467908902694, 'recall': 0.515451174289246, 'f1': 0.3693534100974314, 'number': 809} | {'precision': 0.2638888888888889, 'recall': 0.15966386554621848, 'f1': 0.19895287958115182, 'number': 119} | {'precision': 0.412396694214876, 'recall': 0.46854460093896716, 'f1': 0.4386813186813187, 'number': 1065} | 0.3424 | 0.4691 | 0.3959 | 0.5521 | | 1.0456 | 5.0 | 50 | 1.0703 | {'precision': 0.3060240963855422, 'recall': 0.47095179233621753, 'f1': 0.37098344693281404, 'number': 809} | {'precision': 0.3472222222222222, 'recall': 0.21008403361344538, 'f1': 0.2617801047120419, 'number': 119} | {'precision': 0.40298507462686567, 'recall': 0.5830985915492958, 'f1': 0.476592478894858, 'number': 1065} | 0.3593 | 0.5153 | 0.4234 | 0.5797 | | 0.9601 | 6.0 | 60 | 1.2304 | {'precision': 0.30907920154539603, 'recall': 0.5933250927070457, 'f1': 0.40643522438611346, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.16806722689075632, 'f1': 0.223463687150838, 'number': 119} | {'precision': 0.4642857142857143, 'recall': 0.4394366197183099, 'f1': 0.4515195369030391, 'number': 1065} | 0.3693 | 0.4857 | 0.4196 | 0.5479 | | 0.9153 | 7.0 | 70 | 1.1091 | {'precision': 0.35518157661647476, 'recall': 0.4956736711990111, 'f1': 0.41382868937048506, 'number': 809} | {'precision': 0.3125, 'recall': 0.21008403361344538, 'f1': 0.25125628140703515, 'number': 119} | {'precision': 0.5262645914396887, 'recall': 0.507981220657277, 'f1': 0.5169612995699953, 'number': 1065} | 0.4323 | 0.4852 | 0.4572 | 0.6011 | | 0.8346 | 8.0 | 80 | 1.0632 | {'precision': 0.35597826086956524, 'recall': 0.4857849196538937, 'f1': 0.4108729743857816, 'number': 809} | {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} | {'precision': 0.46401799100449775, 'recall': 0.5812206572769953, 'f1': 0.516048353480617, 'number': 1065} | 0.4102 | 0.5213 | 0.4591 | 0.6103 | | 0.7789 | 9.0 | 90 | 1.0955 | {'precision': 0.3817062445030783, 'recall': 0.5364647713226205, 'f1': 0.44604316546762585, 'number': 809} | {'precision': 0.26, 'recall': 0.2184873949579832, 'f1': 0.23744292237442924, 'number': 119} | {'precision': 0.5137693631669535, 'recall': 0.5605633802816902, 'f1': 0.5361472833408173, 'number': 1065} | 0.4406 | 0.5304 | 0.4813 | 0.6082 | | 0.7751 | 10.0 | 100 | 1.1232 | {'precision': 0.38474434199497065, 'recall': 0.5673671199011124, 'f1': 0.45854145854145856, 'number': 809} | {'precision': 0.3010752688172043, 'recall': 0.23529411764705882, 'f1': 0.2641509433962264, 'number': 119} | {'precision': 0.5040358744394619, 'recall': 0.5276995305164319, 'f1': 0.5155963302752293, 'number': 1065} | 0.4369 | 0.5263 | 0.4775 | 0.6032 | | 0.6875 | 11.0 | 110 | 1.1092 | {'precision': 0.39342723004694835, 'recall': 0.5179233621755254, 'f1': 0.44717182497331914, 'number': 809} | {'precision': 0.34146341463414637, 'recall': 0.23529411764705882, 'f1': 0.27860696517412936, 'number': 119} | {'precision': 0.5076305220883535, 'recall': 0.5934272300469483, 'f1': 0.5471861471861472, 'number': 1065} | 0.4511 | 0.5414 | 0.4921 | 0.6233 | | 0.6808 | 12.0 | 120 | 1.1286 | {'precision': 0.40641158221303, 'recall': 0.4857849196538937, 'f1': 0.44256756756756754, 'number': 809} | {'precision': 0.24561403508771928, 'recall': 0.23529411764705882, 'f1': 0.24034334763948498, 'number': 119} | {'precision': 0.49772036474164133, 'recall': 0.6150234741784038, 'f1': 0.5501889962200757, 'number': 1065} | 0.4489 | 0.5399 | 0.4902 | 0.6159 | | 0.656 | 13.0 | 130 | 1.1237 | {'precision': 0.39822134387351776, 'recall': 0.49814585908529047, 'f1': 0.442613948380011, 'number': 809} | {'precision': 0.2967032967032967, 'recall': 0.226890756302521, 'f1': 0.2571428571428572, 'number': 119} | {'precision': 0.5141732283464567, 'recall': 0.6131455399061033, 'f1': 0.5593147751605996, 'number': 1065} | 0.4564 | 0.5434 | 0.4961 | 0.6179 | | 0.6359 | 14.0 | 140 | 1.1296 | {'precision': 0.3996399639963996, 'recall': 0.5488257107540173, 'f1': 0.46249999999999997, 'number': 809} | {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119} | {'precision': 0.5376712328767124, 'recall': 0.5896713615023474, 'f1': 0.5624720107478729, 'number': 1065} | 0.4655 | 0.5514 | 0.5048 | 0.6173 | | 0.6117 | 15.0 | 150 | 1.1243 | {'precision': 0.40076335877862596, 'recall': 0.519159456118665, 'f1': 0.4523424878836834, 'number': 809} | {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} | {'precision': 0.5280065897858319, 'recall': 0.6018779342723005, 'f1': 0.5625274243089073, 'number': 1065} | 0.4616 | 0.5459 | 0.5002 | 0.6215 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

OwOOwO/eacc_contTrain_m6_2

OwOOwO

2024-03-08T03:52:45Z

89

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T03:50:24Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

debasishdas/llama2-7b-chat-finetuned-legal

debasishdas

2024-03-08T03:36:52Z

0

null

[ "safetensors", "generated_from_trainer", "region:us" ]

null

2024-03-07T08:55:42Z

--- tags: - generated_from_trainer model-index: - name: llama2-7b-chat-finetuned-legal results: [] ---  # llama2-7b-chat-finetuned-legal This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Sumail/Silver_Waves03_2b

Sumail

2024-03-08T03:31:47Z

5

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "mergewss]", "mergekit", "lazymergekit", "tomaszki/gemma-31", "0x0dad0/nous_nb20_plus", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T03:19:41Z

--- license: apache-2.0 tags: - mergewss] - mergekit - lazymergekit - tomaszki/gemma-31 - 0x0dad0/nous_nb20_plus --- # Silver_Waves03_2b Silver_Waves03_2b is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [tomaszki/gemma-31](https://huggingface.co/tomaszki/gemma-31) * [0x0dad0/nous_nb20_plus](https://huggingface.co/0x0dad0/nous_nb20_plus) ## 🧩 Configuration ```yaml models: - model: deepnetguy/gemma-55 # no parameters necessary for base model - model: tomaszki/gemma-31 parameters: density: 0.5 weight: 0.3 - model: 0x0dad0/nous_nb20_plus parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: deepnetguy/gemma-55 parameters: normalize: true dtype: bfloat16 ```

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_64_0.05_4_0.0002

ferrazzipietro

2024-03-08T03:30:52Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T03:29:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Aayush232730/Unit1

Aayush232730

2024-03-08T03:30:45Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-03-07T23:25:26Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.74 +/- 21.90 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

KhangSimple/output

KhangSimple

2024-03-08T03:27:55Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-08T02:29:51Z

--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_trainer model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

dbvenkat/code-search-net-tokenizer

dbvenkat

2024-03-08T03:17:33Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T03:17:33Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Jayfeather1024/alpaca_struq

Jayfeather1024

2024-03-08T03:11:52Z

20

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2402.06363", "license:unknown", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-02-23T16:04:40Z

--- license: unknown --- Unofficial checkpoint for the StruQ defense method against prompt injection attack. The base model is https://huggingface.co/chavinlo/alpaca-native. StruQ: Defending Against Prompt Injection with Structured Queries (https://arxiv.org/abs/2402.06363)

Kudod/hoa-1b4_model_kaggle_format

Kudod

2024-03-08T03:09:38Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:vlsp-2023-vllm/hoa-1b4", "base_model:adapter:vlsp-2023-vllm/hoa-1b4", "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2024-03-08T02:55:15Z

--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: vlsp-2023-vllm/hoa-1b4 model-index: - name: hoa-1b4_model_kaggle_format results: [] ---  # hoa-1b4_model_kaggle_format This model is a fine-tuned version of [vlsp-2023-vllm/hoa-1b4](https://huggingface.co/vlsp-2023-vllm/hoa-1b4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 65 | 2.6363 | | No log | 2.0 | 130 | 1.8356 | | No log | 3.0 | 195 | 1.3984 | | No log | 4.0 | 260 | 1.1658 | | No log | 5.0 | 325 | 0.9857 | | No log | 6.0 | 390 | 0.8724 | | No log | 7.0 | 455 | 0.8085 | | 1.4171 | 8.0 | 520 | 0.7400 | | 1.4171 | 9.0 | 585 | 0.6925 | | 1.4171 | 10.0 | 650 | 0.6654 | | 1.4171 | 11.0 | 715 | 0.6383 | | 1.4171 | 12.0 | 780 | 0.6341 | | 1.4171 | 13.0 | 845 | 0.6148 | | 1.4171 | 14.0 | 910 | 0.5979 | | 1.4171 | 15.0 | 975 | 0.6061 | | 0.2596 | 16.0 | 1040 | 0.5960 | | 0.2596 | 17.0 | 1105 | 0.5810 | | 0.2596 | 18.0 | 1170 | 0.5812 | | 0.2596 | 19.0 | 1235 | 0.5761 | | 0.2596 | 20.0 | 1300 | 0.5724 | | 0.2596 | 21.0 | 1365 | 0.5600 | | 0.2596 | 22.0 | 1430 | 0.5927 | | 0.2596 | 23.0 | 1495 | 0.5627 | | 0.1245 | 24.0 | 1560 | 0.5500 | | 0.1245 | 25.0 | 1625 | 0.5706 | | 0.1245 | 26.0 | 1690 | 0.5551 | | 0.1245 | 27.0 | 1755 | 0.5548 | | 0.1245 | 28.0 | 1820 | 0.5573 | | 0.1245 | 29.0 | 1885 | 0.5642 | | 0.1245 | 30.0 | 1950 | 0.5712 | | 0.0896 | 31.0 | 2015 | 0.5524 | | 0.0896 | 32.0 | 2080 | 0.5644 | | 0.0896 | 33.0 | 2145 | 0.5511 | | 0.0896 | 34.0 | 2210 | 0.5648 | | 0.0896 | 35.0 | 2275 | 0.5722 | | 0.0896 | 36.0 | 2340 | 0.5619 | | 0.0896 | 37.0 | 2405 | 0.5632 | | 0.0896 | 38.0 | 2470 | 0.5628 | | 0.0746 | 39.0 | 2535 | 0.5593 | | 0.0746 | 40.0 | 2600 | 0.5624 | | 0.0746 | 41.0 | 2665 | 0.5744 | | 0.0746 | 42.0 | 2730 | 0.5525 | | 0.0746 | 43.0 | 2795 | 0.5858 | | 0.0746 | 44.0 | 2860 | 0.5615 | | 0.0746 | 45.0 | 2925 | 0.5614 | | 0.0746 | 46.0 | 2990 | 0.5678 | | 0.0696 | 47.0 | 3055 | 0.5735 | | 0.0696 | 48.0 | 3120 | 0.5674 | | 0.0696 | 49.0 | 3185 | 0.5637 | | 0.0696 | 50.0 | 3250 | 0.5623 | | 0.0696 | 51.0 | 3315 | 0.5668 | | 0.0696 | 52.0 | 3380 | 0.5625 | | 0.0696 | 53.0 | 3445 | 0.5630 | | 0.0636 | 54.0 | 3510 | 0.5675 | | 0.0636 | 55.0 | 3575 | 0.5646 | | 0.0636 | 56.0 | 3640 | 0.5702 | | 0.0636 | 57.0 | 3705 | 0.5729 | | 0.0636 | 58.0 | 3770 | 0.5745 | | 0.0636 | 59.0 | 3835 | 0.5737 | | 0.0636 | 60.0 | 3900 | 0.5724 | | 0.0636 | 61.0 | 3965 | 0.5658 | | 0.0579 | 62.0 | 4030 | 0.5759 | | 0.0579 | 63.0 | 4095 | 0.5777 | | 0.0579 | 64.0 | 4160 | 0.5722 | | 0.0579 | 65.0 | 4225 | 0.5721 | | 0.0579 | 66.0 | 4290 | 0.5772 | | 0.0579 | 67.0 | 4355 | 0.5747 | | 0.0579 | 68.0 | 4420 | 0.5800 | | 0.0579 | 69.0 | 4485 | 0.5814 | | 0.0557 | 70.0 | 4550 | 0.5777 | | 0.0557 | 71.0 | 4615 | 0.5765 | | 0.0557 | 72.0 | 4680 | 0.5790 | | 0.0557 | 73.0 | 4745 | 0.5845 | | 0.0557 | 74.0 | 4810 | 0.5788 | | 0.0557 | 75.0 | 4875 | 0.5836 | | 0.0557 | 76.0 | 4940 | 0.5911 | | 0.052 | 77.0 | 5005 | 0.5841 | | 0.052 | 78.0 | 5070 | 0.5822 | | 0.052 | 79.0 | 5135 | 0.5828 | | 0.052 | 80.0 | 5200 | 0.5868 | | 0.052 | 81.0 | 5265 | 0.5858 | | 0.052 | 82.0 | 5330 | 0.5899 | | 0.052 | 83.0 | 5395 | 0.5888 | | 0.052 | 84.0 | 5460 | 0.5871 | | 0.0478 | 85.0 | 5525 | 0.5867 | | 0.0478 | 86.0 | 5590 | 0.5894 | | 0.0478 | 87.0 | 5655 | 0.5899 | | 0.0478 | 88.0 | 5720 | 0.5899 | | 0.0478 | 89.0 | 5785 | 0.5915 | | 0.0478 | 90.0 | 5850 | 0.5901 | | 0.0478 | 91.0 | 5915 | 0.5919 | | 0.0478 | 92.0 | 5980 | 0.5919 | | 0.0458 | 93.0 | 6045 | 0.5916 | | 0.0458 | 94.0 | 6110 | 0.5914 | | 0.0458 | 95.0 | 6175 | 0.5929 | | 0.0458 | 96.0 | 6240 | 0.5920 | | 0.0458 | 97.0 | 6305 | 0.5922 | | 0.0458 | 98.0 | 6370 | 0.5922 | | 0.0458 | 99.0 | 6435 | 0.5924 | | 0.0425 | 100.0 | 6500 | 0.5927 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2

core-3/kuno-royale-v3-7b

core-3

2024-03-08T03:01:36Z

57

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "SanjiWatsuki/Kunoichi-DPO-v2-7B", "eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3", "base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:merge:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3", "base_model:merge:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3", "license:cc-by-nc-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-06T14:55:59Z

--- license: cc-by-nc-2.0 tags: - merge - mergekit - lazymergekit - SanjiWatsuki/Kunoichi-DPO-v2-7B - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3 base_model: - SanjiWatsuki/Kunoichi-DPO-v2-7B - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3 model-index: - name: kuno-royale-v3-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.23 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 71.13 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.81 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=core-3/kuno-royale-v3-7b name: Open LLM Leaderboard --- # kuno-royale-v3-7b Another experimental combination of eren23's ongo-monarch-jaskier merges and Kunoichi-DPO-v2-7B. Untested. kuno-royale-v3-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3) ## 🧩 Configuration ```yaml slices: - sources: - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [0, 32] - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3 layer_range: [0, 32] merge_method: slerp base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "core-3/kuno-royale-v3-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_core-3__kuno-royale-v3-7b) | Metric |Value| |---------------------------------|----:| |Avg. |74.88| |AI2 Reasoning Challenge (25-Shot)|71.76| |HellaSwag (10-Shot) |88.23| |MMLU (5-Shot) |65.06| |TruthfulQA (0-shot) |71.13| |Winogrande (5-shot) |82.32| |GSM8k (5-shot) |70.81|

madroid/qwen1.5-0.5B-4bit-new

madroid

2024-03-08T02:57:06Z

5

0

mlx

[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "license:other", "region:us" ]

text-generation

2024-03-08T02:56:16Z

--- language: - en license: other tags: - chat - mlx - mlx license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE pipeline_tag: text-generation --- # madroid/Qwen1.5-0.5B-4bit-new This model was converted to MLX format from [`mlx-community/Qwen1.5-0.5B-Chat-4bit`](). Refer to the [original model card](https://huggingface.co/mlx-community/Qwen1.5-0.5B-Chat-4bit) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("madroid/Qwen1.5-0.5B-4bit-new") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.01_8_0.0002

ferrazzipietro

2024-03-08T02:51:23Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T02:50:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

denyslinkov/sentiment-lora-dpo

denyslinkov

2024-03-08T02:47:24Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T02:40:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

OwOOwO/eacc_bm_sl3

OwOOwO

2024-03-08T02:35:23Z

4

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-03-07T01:29:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.05_16_0.0002

ferrazzipietro

2024-03-08T02:11:33Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T02:10:21Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EricValen/ppo-LunarLander-v2-CleanRL

EricValen

2024-03-08T02:11:33Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2024-03-08T02:10:51Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -10.07 +/- 84.91 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.0001 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'EricValen/ppo-LunarLander-v2-CleanRL' 'batch_size': 512 'minibatch_size': 128} ```

OwOOwO/eacc_contTrain_m2_55_ori2

OwOOwO

2024-03-08T02:09:24Z

88

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T02:06:59Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.05_8_0.0002

ferrazzipietro

2024-03-08T01:52:08Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T01:51:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

automerger/Experiment27Inex12-7B

automerger

2024-03-08T01:45:29Z

4

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "base_model:MSL7/INEX12-7b", "base_model:merge:MSL7/INEX12-7b", "base_model:yam-peleg/Experiment27-7B", "base_model:merge:yam-peleg/Experiment27-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T01:44:27Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit base_model: - yam-peleg/Experiment27-7B - MSL7/INEX12-7b --- # Experiment27Inex12-7B Experiment27Inex12-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [yam-peleg/Experiment27-7B](https://huggingface.co/yam-peleg/Experiment27-7B) * [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: yam-peleg/Experiment27-7B layer_range: [0, 32] - model: MSL7/INEX12-7b layer_range: [0, 32] merge_method: slerp base_model: yam-peleg/Experiment27-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment27Inex12-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

AyeSee/roberta-large-lora-token-classification_v1

AyeSee

2024-03-08T01:35:39Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T13:48:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

abhishekchoudhary0509/distilbert-base-uncased-lora-text-classification

abhishekchoudhary0509

2024-03-08T01:32:52Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-03-08T01:32:46Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-lora-text-classification results: [] ---  # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2642 - Accuracy: {'accuracy': 0.902} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:| | No log | 1.0 | 63 | 0.2566 | {'accuracy': 0.902} | | No log | 2.0 | 126 | 0.2642 | {'accuracy': 0.902} | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_64_32_0.05_4_0.0002

ferrazzipietro

2024-03-08T01:32:18Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T01:31:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

daze-unlv/axolotl-medmcqa-2-epoch

daze-unlv

2024-03-08T01:28:30Z

0

peft

[ "peft", "tensorboard", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-03-07T22:54:28Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: lora-out/medmcqa-2-epoch results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: daze-unlv/medmcqa_axolotl type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0 output_dir: ./lora-out/medmcqa-2-epoch eval_sample_packing: false adapter: lora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 2 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: false sdp_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details> # lora-out/medmcqa-2-epoch This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0

farid1088/RoBERTa-legal-de-cased_German_legal_SQuAD_1000

farid1088

2024-03-08T01:27:10Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2024-03-05T20:54:11Z

--- tags: - generated_from_trainer model-index: - name: RoBERTa-legal-de-cased_German_legal_SQuAD_1000 results: [] ---  # RoBERTa-legal-de-cased_German_legal_SQuAD_1000 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 160 - eval_batch_size: 40 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 1.0 | 2 | 6.2392 | | No log | 2.0 | 4 | 6.3606 | | No log | 3.0 | 6 | 6.3302 | | No log | 4.0 | 8 | 6.2556 | | No log | 5.0 | 10 | 5.9838 | | No log | 6.0 | 12 | 5.6434 | | No log | 7.0 | 14 | 5.4625 | | No log | 8.0 | 16 | 5.3216 | | No log | 9.0 | 18 | 5.1803 | | No log | 10.0 | 20 | 5.0230 | | No log | 11.0 | 22 | 4.8791 | | No log | 12.0 | 24 | 4.8112 | | No log | 13.0 | 26 | 4.6359 | | No log | 14.0 | 28 | 4.4133 | | No log | 15.0 | 30 | 4.2477 | | No log | 16.0 | 32 | 4.0479 | | No log | 17.0 | 34 | 3.8281 | | No log | 18.0 | 36 | 3.6850 | | No log | 19.0 | 38 | 3.5521 | | No log | 20.0 | 40 | 3.3836 | | No log | 21.0 | 42 | 3.2738 | | No log | 22.0 | 44 | 3.1723 | | No log | 23.0 | 46 | 3.1062 | | No log | 24.0 | 48 | 3.0506 | | No log | 25.0 | 50 | 2.9974 | | No log | 26.0 | 52 | 2.8952 | | No log | 27.0 | 54 | 2.8692 | | No log | 28.0 | 56 | 2.8122 | | No log | 29.0 | 58 | 2.7477 | | No log | 30.0 | 60 | 2.7818 | | No log | 31.0 | 62 | 2.7222 | | No log | 32.0 | 64 | 2.6513 | | No log | 33.0 | 66 | 2.5553 | | No log | 34.0 | 68 | 2.4697 | | No log | 35.0 | 70 | 2.5147 | | No log | 36.0 | 72 | 2.4701 | | No log | 37.0 | 74 | 2.3817 | | No log | 38.0 | 76 | 2.3397 | | No log | 39.0 | 78 | 2.3285 | | No log | 40.0 | 80 | 2.3427 | | No log | 41.0 | 82 | 2.1274 | | No log | 42.0 | 84 | 2.0858 | | No log | 43.0 | 86 | 2.0831 | | No log | 44.0 | 88 | 1.9282 | | No log | 45.0 | 90 | 1.9103 | | No log | 46.0 | 92 | 1.8713 | | No log | 47.0 | 94 | 1.7713 | | No log | 48.0 | 96 | 1.7105 | | No log | 49.0 | 98 | 1.6483 | | No log | 50.0 | 100 | 1.6115 | | No log | 51.0 | 102 | 1.5694 | | No log | 52.0 | 104 | 1.5768 | | No log | 53.0 | 106 | 1.4820 | | No log | 54.0 | 108 | 1.4422 | | No log | 55.0 | 110 | 1.4515 | | No log | 56.0 | 112 | 1.3668 | | No log | 57.0 | 114 | 1.4229 | | No log | 58.0 | 116 | 1.3764 | | No log | 59.0 | 118 | 1.3159 | | No log | 60.0 | 120 | 1.3684 | | No log | 61.0 | 122 | 1.4024 | | No log | 62.0 | 124 | 1.4022 | | No log | 63.0 | 126 | 1.4163 | | No log | 64.0 | 128 | 1.3030 | | No log | 65.0 | 130 | 1.3511 | | No log | 66.0 | 132 | 1.4307 | | No log | 67.0 | 134 | 1.3482 | | No log | 68.0 | 136 | 1.2050 | | No log | 69.0 | 138 | 1.2197 | | No log | 70.0 | 140 | 1.2955 | | No log | 71.0 | 142 | 1.2603 | | No log | 72.0 | 144 | 1.2188 | | No log | 73.0 | 146 | 1.2209 | | No log | 74.0 | 148 | 1.3233 | | No log | 75.0 | 150 | 1.3907 | | No log | 76.0 | 152 | 1.2892 | | No log | 77.0 | 154 | 1.2385 | | No log | 78.0 | 156 | 1.2649 | | No log | 79.0 | 158 | 1.2912 | | No log | 80.0 | 160 | 1.2787 | | No log | 81.0 | 162 | 1.2894 | | No log | 82.0 | 164 | 1.2219 | | No log | 83.0 | 166 | 1.2526 | | No log | 84.0 | 168 | 1.3134 | | No log | 85.0 | 170 | 1.2738 | | No log | 86.0 | 172 | 1.1862 | | No log | 87.0 | 174 | 1.1754 | | No log | 88.0 | 176 | 1.1856 | | No log | 89.0 | 178 | 1.1411 | | No log | 90.0 | 180 | 1.1468 | | No log | 91.0 | 182 | 1.2219 | | No log | 92.0 | 184 | 1.2348 | | No log | 93.0 | 186 | 1.2539 | | No log | 94.0 | 188 | 1.3142 | | No log | 95.0 | 190 | 1.3426 | | No log | 96.0 | 192 | 1.2950 | | No log | 97.0 | 194 | 1.1578 | | No log | 98.0 | 196 | 1.1482 | | No log | 99.0 | 198 | 1.2214 | | No log | 100.0 | 200 | 1.2621 | | No log | 101.0 | 202 | 1.2568 | | No log | 102.0 | 204 | 1.2313 | | No log | 103.0 | 206 | 1.1385 | | No log | 104.0 | 208 | 1.1336 | | No log | 105.0 | 210 | 1.1972 | | No log | 106.0 | 212 | 1.2738 | | No log | 107.0 | 214 | 1.2061 | | No log | 108.0 | 216 | 1.1259 | | No log | 109.0 | 218 | 1.1295 | | No log | 110.0 | 220 | 1.1641 | | No log | 111.0 | 222 | 1.1672 | | No log | 112.0 | 224 | 1.2211 | | No log | 113.0 | 226 | 1.2340 | | No log | 114.0 | 228 | 1.2608 | | No log | 115.0 | 230 | 1.2590 | | No log | 116.0 | 232 | 1.2412 | | No log | 117.0 | 234 | 1.2275 | | No log | 118.0 | 236 | 1.2798 | | No log | 119.0 | 238 | 1.3240 | | No log | 120.0 | 240 | 1.2910 | | No log | 121.0 | 242 | 1.2228 | | No log | 122.0 | 244 | 1.1676 | | No log | 123.0 | 246 | 1.2019 | | No log | 124.0 | 248 | 1.2762 | | No log | 125.0 | 250 | 1.3170 | | No log | 126.0 | 252 | 1.2557 | | No log | 127.0 | 254 | 1.2017 | | No log | 128.0 | 256 | 1.2145 | | No log | 129.0 | 258 | 1.3000 | | No log | 130.0 | 260 | 1.3371 | | No log | 131.0 | 262 | 1.3282 | | No log | 132.0 | 264 | 1.2549 | | No log | 133.0 | 266 | 1.2636 | | No log | 134.0 | 268 | 1.3543 | | No log | 135.0 | 270 | 1.3776 | | No log | 136.0 | 272 | 1.3820 | | No log | 137.0 | 274 | 1.3624 | | No log | 138.0 | 276 | 1.3286 | | No log | 139.0 | 278 | 1.3389 | | No log | 140.0 | 280 | 1.3843 | | No log | 141.0 | 282 | 1.4119 | | No log | 142.0 | 284 | 1.3404 | | No log | 143.0 | 286 | 1.2233 | | No log | 144.0 | 288 | 1.1634 | | No log | 145.0 | 290 | 1.1743 | | No log | 146.0 | 292 | 1.2216 | | No log | 147.0 | 294 | 1.2615 | | No log | 148.0 | 296 | 1.2698 | | No log | 149.0 | 298 | 1.2574 | | No log | 150.0 | 300 | 1.2013 | | No log | 151.0 | 302 | 1.1782 | | No log | 152.0 | 304 | 1.1868 | | No log | 153.0 | 306 | 1.2209 | | No log | 154.0 | 308 | 1.2650 | | No log | 155.0 | 310 | 1.2678 | | No log | 156.0 | 312 | 1.2483 | | No log | 157.0 | 314 | 1.2249 | | No log | 158.0 | 316 | 1.2192 | | No log | 159.0 | 318 | 1.2685 | | No log | 160.0 | 320 | 1.3042 | | No log | 161.0 | 322 | 1.3329 | | No log | 162.0 | 324 | 1.3820 | | No log | 163.0 | 326 | 1.3776 | | No log | 164.0 | 328 | 1.3062 | | No log | 165.0 | 330 | 1.2287 | | No log | 166.0 | 332 | 1.1804 | | No log | 167.0 | 334 | 1.1878 | | No log | 168.0 | 336 | 1.2288 | | No log | 169.0 | 338 | 1.2620 | | No log | 170.0 | 340 | 1.2738 | | No log | 171.0 | 342 | 1.2856 | | No log | 172.0 | 344 | 1.3189 | | No log | 173.0 | 346 | 1.2971 | | No log | 174.0 | 348 | 1.2227 | | No log | 175.0 | 350 | 1.2113 | | No log | 176.0 | 352 | 1.2372 | | No log | 177.0 | 354 | 1.2345 | | No log | 178.0 | 356 | 1.2357 | | No log | 179.0 | 358 | 1.2578 | | No log | 180.0 | 360 | 1.2575 | | No log | 181.0 | 362 | 1.2438 | | No log | 182.0 | 364 | 1.2362 | | No log | 183.0 | 366 | 1.2906 | | No log | 184.0 | 368 | 1.3564 | | No log | 185.0 | 370 | 1.3361 | | No log | 186.0 | 372 | 1.3235 | | No log | 187.0 | 374 | 1.3131 | | No log | 188.0 | 376 | 1.3451 | | No log | 189.0 | 378 | 1.3708 | | No log | 190.0 | 380 | 1.3735 | | No log | 191.0 | 382 | 1.3659 | | No log | 192.0 | 384 | 1.3499 | | No log | 193.0 | 386 | 1.3248 | | No log | 194.0 | 388 | 1.2972 | | No log | 195.0 | 390 | 1.3089 | | No log | 196.0 | 392 | 1.3088 | | No log | 197.0 | 394 | 1.3057 | | No log | 198.0 | 396 | 1.2836 | | No log | 199.0 | 398 | 1.2748 | | No log | 200.0 | 400 | 1.2783 | | No log | 201.0 | 402 | 1.3234 | | No log | 202.0 | 404 | 1.3851 | | No log | 203.0 | 406 | 1.4287 | | No log | 204.0 | 408 | 1.3798 | | No log | 205.0 | 410 | 1.2660 | | No log | 206.0 | 412 | 1.2068 | | No log | 207.0 | 414 | 1.2213 | | No log | 208.0 | 416 | 1.2811 | | No log | 209.0 | 418 | 1.3142 | | No log | 210.0 | 420 | 1.3317 | | No log | 211.0 | 422 | 1.3334 | | No log | 212.0 | 424 | 1.3037 | | No log | 213.0 | 426 | 1.2620 | | No log | 214.0 | 428 | 1.2192 | | No log | 215.0 | 430 | 1.2268 | | No log | 216.0 | 432 | 1.2740 | | No log | 217.0 | 434 | 1.3298 | | No log | 218.0 | 436 | 1.3930 | | No log | 219.0 | 438 | 1.4287 | | No log | 220.0 | 440 | 1.4227 | | No log | 221.0 | 442 | 1.3803 | | No log | 222.0 | 444 | 1.3389 | | No log | 223.0 | 446 | 1.3402 | | No log | 224.0 | 448 | 1.3458 | | No log | 225.0 | 450 | 1.3779 | | No log | 226.0 | 452 | 1.4241 | | No log | 227.0 | 454 | 1.4453 | | No log | 228.0 | 456 | 1.4269 | | No log | 229.0 | 458 | 1.3875 | | No log | 230.0 | 460 | 1.3527 | | No log | 231.0 | 462 | 1.3338 | | No log | 232.0 | 464 | 1.3420 | | No log | 233.0 | 466 | 1.3536 | | No log | 234.0 | 468 | 1.3931 | | No log | 235.0 | 470 | 1.4257 | | No log | 236.0 | 472 | 1.4281 | | No log | 237.0 | 474 | 1.4027 | | No log | 238.0 | 476 | 1.3635 | | No log | 239.0 | 478 | 1.3048 | | No log | 240.0 | 480 | 1.2874 | | No log | 241.0 | 482 | 1.3135 | | No log | 242.0 | 484 | 1.3534 | | No log | 243.0 | 486 | 1.3877 | | No log | 244.0 | 488 | 1.4125 | | No log | 245.0 | 490 | 1.4280 | | No log | 246.0 | 492 | 1.4330 | | No log | 247.0 | 494 | 1.4254 | | No log | 248.0 | 496 | 1.4343 | | No log | 249.0 | 498 | 1.3983 | | 0.4984 | 250.0 | 500 | 1.3501 | | 0.4984 | 251.0 | 502 | 1.3319 | | 0.4984 | 252.0 | 504 | 1.3261 | | 0.4984 | 253.0 | 506 | 1.3543 | | 0.4984 | 254.0 | 508 | 1.3817 | | 0.4984 | 255.0 | 510 | 1.4107 | | 0.4984 | 256.0 | 512 | 1.4216 | | 0.4984 | 257.0 | 514 | 1.3670 | | 0.4984 | 258.0 | 516 | 1.3489 | | 0.4984 | 259.0 | 518 | 1.3245 | | 0.4984 | 260.0 | 520 | 1.3046 | | 0.4984 | 261.0 | 522 | 1.3024 | | 0.4984 | 262.0 | 524 | 1.2989 | | 0.4984 | 263.0 | 526 | 1.3072 | | 0.4984 | 264.0 | 528 | 1.3100 | | 0.4984 | 265.0 | 530 | 1.3296 | | 0.4984 | 266.0 | 532 | 1.3444 | | 0.4984 | 267.0 | 534 | 1.3580 | | 0.4984 | 268.0 | 536 | 1.3623 | | 0.4984 | 269.0 | 538 | 1.3863 | | 0.4984 | 270.0 | 540 | 1.4010 | | 0.4984 | 271.0 | 542 | 1.4060 | | 0.4984 | 272.0 | 544 | 1.4048 | | 0.4984 | 273.0 | 546 | 1.4001 | | 0.4984 | 274.0 | 548 | 1.3804 | | 0.4984 | 275.0 | 550 | 1.3607 | | 0.4984 | 276.0 | 552 | 1.3414 | | 0.4984 | 277.0 | 554 | 1.3338 | | 0.4984 | 278.0 | 556 | 1.3401 | | 0.4984 | 279.0 | 558 | 1.3512 | | 0.4984 | 280.0 | 560 | 1.3606 | | 0.4984 | 281.0 | 562 | 1.3636 | | 0.4984 | 282.0 | 564 | 1.3589 | | 0.4984 | 283.0 | 566 | 1.3478 | | 0.4984 | 284.0 | 568 | 1.3387 | | 0.4984 | 285.0 | 570 | 1.3533 | | 0.4984 | 286.0 | 572 | 1.3818 | | 0.4984 | 287.0 | 574 | 1.4216 | | 0.4984 | 288.0 | 576 | 1.4690 | | 0.4984 | 289.0 | 578 | 1.4980 | | 0.4984 | 290.0 | 580 | 1.5126 | | 0.4984 | 291.0 | 582 | 1.5328 | | 0.4984 | 292.0 | 584 | 1.5507 | | 0.4984 | 293.0 | 586 | 1.5507 | | 0.4984 | 294.0 | 588 | 1.5699 | | 0.4984 | 295.0 | 590 | 1.5493 | | 0.4984 | 296.0 | 592 | 1.5112 | | 0.4984 | 297.0 | 594 | 1.4635 | | 0.4984 | 298.0 | 596 | 1.4157 | | 0.4984 | 299.0 | 598 | 1.3829 | | 0.4984 | 300.0 | 600 | 1.3594 | | 0.4984 | 301.0 | 602 | 1.3757 | | 0.4984 | 302.0 | 604 | 1.4016 | | 0.4984 | 303.0 | 606 | 1.4373 | | 0.4984 | 304.0 | 608 | 1.4400 | | 0.4984 | 305.0 | 610 | 1.4478 | | 0.4984 | 306.0 | 612 | 1.4511 | | 0.4984 | 307.0 | 614 | 1.4484 | | 0.4984 | 308.0 | 616 | 1.4229 | | 0.4984 | 309.0 | 618 | 1.3912 | | 0.4984 | 310.0 | 620 | 1.3733 | | 0.4984 | 311.0 | 622 | 1.3450 | | 0.4984 | 312.0 | 624 | 1.3264 | | 0.4984 | 313.0 | 626 | 1.3251 | | 0.4984 | 314.0 | 628 | 1.3312 | | 0.4984 | 315.0 | 630 | 1.3335 | | 0.4984 | 316.0 | 632 | 1.3298 | | 0.4984 | 317.0 | 634 | 1.3226 | | 0.4984 | 318.0 | 636 | 1.3150 | | 0.4984 | 319.0 | 638 | 1.3055 | | 0.4984 | 320.0 | 640 | 1.2983 | | 0.4984 | 321.0 | 642 | 1.2899 | | 0.4984 | 322.0 | 644 | 1.2646 | | 0.4984 | 323.0 | 646 | 1.2413 | | 0.4984 | 324.0 | 648 | 1.2316 | | 0.4984 | 325.0 | 650 | 1.2295 | | 0.4984 | 326.0 | 652 | 1.2300 | | 0.4984 | 327.0 | 654 | 1.2594 | | 0.4984 | 328.0 | 656 | 1.2869 | | 0.4984 | 329.0 | 658 | 1.2923 | | 0.4984 | 330.0 | 660 | 1.3231 | | 0.4984 | 331.0 | 662 | 1.3421 | | 0.4984 | 332.0 | 664 | 1.3503 | | 0.4984 | 333.0 | 666 | 1.3452 | | 0.4984 | 334.0 | 668 | 1.3347 | | 0.4984 | 335.0 | 670 | 1.3203 | | 0.4984 | 336.0 | 672 | 1.3098 | | 0.4984 | 337.0 | 674 | 1.3021 | | 0.4984 | 338.0 | 676 | 1.3016 | | 0.4984 | 339.0 | 678 | 1.3007 | | 0.4984 | 340.0 | 680 | 1.3001 | | 0.4984 | 341.0 | 682 | 1.3070 | | 0.4984 | 342.0 | 684 | 1.3475 | | 0.4984 | 343.0 | 686 | 1.3788 | | 0.4984 | 344.0 | 688 | 1.3991 | | 0.4984 | 345.0 | 690 | 1.4028 | | 0.4984 | 346.0 | 692 | 1.4028 | | 0.4984 | 347.0 | 694 | 1.3971 | | 0.4984 | 348.0 | 696 | 1.3793 | | 0.4984 | 349.0 | 698 | 1.3543 | | 0.4984 | 350.0 | 700 | 1.3296 | | 0.4984 | 351.0 | 702 | 1.3322 | | 0.4984 | 352.0 | 704 | 1.3556 | | 0.4984 | 353.0 | 706 | 1.3936 | | 0.4984 | 354.0 | 708 | 1.4202 | | 0.4984 | 355.0 | 710 | 1.4235 | | 0.4984 | 356.0 | 712 | 1.3934 | | 0.4984 | 357.0 | 714 | 1.3511 | | 0.4984 | 358.0 | 716 | 1.2957 | | 0.4984 | 359.0 | 718 | 1.2690 | | 0.4984 | 360.0 | 720 | 1.2670 | | 0.4984 | 361.0 | 722 | 1.2906 | | 0.4984 | 362.0 | 724 | 1.3083 | | 0.4984 | 363.0 | 726 | 1.3239 | | 0.4984 | 364.0 | 728 | 1.3353 | | 0.4984 | 365.0 | 730 | 1.3442 | | 0.4984 | 366.0 | 732 | 1.3308 | | 0.4984 | 367.0 | 734 | 1.3172 | | 0.4984 | 368.0 | 736 | 1.3009 | | 0.4984 | 369.0 | 738 | 1.2826 | | 0.4984 | 370.0 | 740 | 1.2781 | | 0.4984 | 371.0 | 742 | 1.2796 | | 0.4984 | 372.0 | 744 | 1.2815 | | 0.4984 | 373.0 | 746 | 1.3100 | | 0.4984 | 374.0 | 748 | 1.3447 | | 0.4984 | 375.0 | 750 | 1.3591 | | 0.4984 | 376.0 | 752 | 1.3892 | | 0.4984 | 377.0 | 754 | 1.4185 | | 0.4984 | 378.0 | 756 | 1.4329 | | 0.4984 | 379.0 | 758 | 1.4273 | | 0.4984 | 380.0 | 760 | 1.4074 | | 0.4984 | 381.0 | 762 | 1.3999 | | 0.4984 | 382.0 | 764 | 1.3906 | | 0.4984 | 383.0 | 766 | 1.3857 | | 0.4984 | 384.0 | 768 | 1.3740 | | 0.4984 | 385.0 | 770 | 1.3637 | | 0.4984 | 386.0 | 772 | 1.3600 | | 0.4984 | 387.0 | 774 | 1.3614 | | 0.4984 | 388.0 | 776 | 1.3720 | | 0.4984 | 389.0 | 778 | 1.3822 | | 0.4984 | 390.0 | 780 | 1.3862 | | 0.4984 | 391.0 | 782 | 1.3850 | | 0.4984 | 392.0 | 784 | 1.3857 | | 0.4984 | 393.0 | 786 | 1.3859 | | 0.4984 | 394.0 | 788 | 1.3968 | | 0.4984 | 395.0 | 790 | 1.4054 | | 0.4984 | 396.0 | 792 | 1.4105 | | 0.4984 | 397.0 | 794 | 1.4135 | | 0.4984 | 398.0 | 796 | 1.4122 | | 0.4984 | 399.0 | 798 | 1.3965 | | 0.4984 | 400.0 | 800 | 1.3806 | | 0.4984 | 401.0 | 802 | 1.3833 | | 0.4984 | 402.0 | 804 | 1.3848 | | 0.4984 | 403.0 | 806 | 1.3755 | | 0.4984 | 404.0 | 808 | 1.3663 | | 0.4984 | 405.0 | 810 | 1.3541 | | 0.4984 | 406.0 | 812 | 1.3481 | | 0.4984 | 407.0 | 814 | 1.3484 | | 0.4984 | 408.0 | 816 | 1.3506 | | 0.4984 | 409.0 | 818 | 1.3486 | | 0.4984 | 410.0 | 820 | 1.3474 | | 0.4984 | 411.0 | 822 | 1.3512 | | 0.4984 | 412.0 | 824 | 1.3562 | | 0.4984 | 413.0 | 826 | 1.3683 | | 0.4984 | 414.0 | 828 | 1.3778 | | 0.4984 | 415.0 | 830 | 1.3839 | | 0.4984 | 416.0 | 832 | 1.3879 | | 0.4984 | 417.0 | 834 | 1.3888 | | 0.4984 | 418.0 | 836 | 1.3952 | | 0.4984 | 419.0 | 838 | 1.4006 | | 0.4984 | 420.0 | 840 | 1.3990 | | 0.4984 | 421.0 | 842 | 1.3698 | | 0.4984 | 422.0 | 844 | 1.3452 | | 0.4984 | 423.0 | 846 | 1.3087 | | 0.4984 | 424.0 | 848 | 1.2798 | | 0.4984 | 425.0 | 850 | 1.2656 | | 0.4984 | 426.0 | 852 | 1.2812 | | 0.4984 | 427.0 | 854 | 1.2965 | | 0.4984 | 428.0 | 856 | 1.3184 | | 0.4984 | 429.0 | 858 | 1.3456 | | 0.4984 | 430.0 | 860 | 1.3730 | | 0.4984 | 431.0 | 862 | 1.3882 | | 0.4984 | 432.0 | 864 | 1.3960 | | 0.4984 | 433.0 | 866 | 1.3961 | | 0.4984 | 434.0 | 868 | 1.3904 | | 0.4984 | 435.0 | 870 | 1.3826 | | 0.4984 | 436.0 | 872 | 1.3876 | | 0.4984 | 437.0 | 874 | 1.3942 | | 0.4984 | 438.0 | 876 | 1.3903 | | 0.4984 | 439.0 | 878 | 1.4131 | | 0.4984 | 440.0 | 880 | 1.4386 | | 0.4984 | 441.0 | 882 | 1.4533 | | 0.4984 | 442.0 | 884 | 1.4633 | | 0.4984 | 443.0 | 886 | 1.4364 | | 0.4984 | 444.0 | 888 | 1.3961 | | 0.4984 | 445.0 | 890 | 1.3603 | | 0.4984 | 446.0 | 892 | 1.3205 | | 0.4984 | 447.0 | 894 | 1.2876 | | 0.4984 | 448.0 | 896 | 1.2629 | | 0.4984 | 449.0 | 898 | 1.2929 | | 0.4984 | 450.0 | 900 | 1.3158 | | 0.4984 | 451.0 | 902 | 1.3561 | | 0.4984 | 452.0 | 904 | 1.4016 | | 0.4984 | 453.0 | 906 | 1.4331 | | 0.4984 | 454.0 | 908 | 1.4514 | | 0.4984 | 455.0 | 910 | 1.4568 | | 0.4984 | 456.0 | 912 | 1.4481 | | 0.4984 | 457.0 | 914 | 1.4331 | | 0.4984 | 458.0 | 916 | 1.4101 | | 0.4984 | 459.0 | 918 | 1.4124 | | 0.4984 | 460.0 | 920 | 1.4035 | | 0.4984 | 461.0 | 922 | 1.3846 | | 0.4984 | 462.0 | 924 | 1.3591 | | 0.4984 | 463.0 | 926 | 1.3337 | | 0.4984 | 464.0 | 928 | 1.3211 | | 0.4984 | 465.0 | 930 | 1.3289 | | 0.4984 | 466.0 | 932 | 1.3686 | | 0.4984 | 467.0 | 934 | 1.4247 | | 0.4984 | 468.0 | 936 | 1.4679 | | 0.4984 | 469.0 | 938 | 1.4892 | | 0.4984 | 470.0 | 940 | 1.5036 | | 0.4984 | 471.0 | 942 | 1.5144 | | 0.4984 | 472.0 | 944 | 1.5118 | | 0.4984 | 473.0 | 946 | 1.4974 | | 0.4984 | 474.0 | 948 | 1.4768 | | 0.4984 | 475.0 | 950 | 1.4562 | | 0.4984 | 476.0 | 952 | 1.4385 | | 0.4984 | 477.0 | 954 | 1.4229 | | 0.4984 | 478.0 | 956 | 1.4084 | | 0.4984 | 479.0 | 958 | 1.4004 | | 0.4984 | 480.0 | 960 | 1.4004 | | 0.4984 | 481.0 | 962 | 1.3982 | | 0.4984 | 482.0 | 964 | 1.3999 | | 0.4984 | 483.0 | 966 | 1.4041 | | 0.4984 | 484.0 | 968 | 1.4065 | | 0.4984 | 485.0 | 970 | 1.4074 | | 0.4984 | 486.0 | 972 | 1.3975 | | 0.4984 | 487.0 | 974 | 1.4100 | | 0.4984 | 488.0 | 976 | 1.4375 | | 0.4984 | 489.0 | 978 | 1.4597 | | 0.4984 | 490.0 | 980 | 1.4732 | | 0.4984 | 491.0 | 982 | 1.4704 | | 0.4984 | 492.0 | 984 | 1.4610 | | 0.4984 | 493.0 | 986 | 1.4437 | | 0.4984 | 494.0 | 988 | 1.4284 | | 0.4984 | 495.0 | 990 | 1.4139 | | 0.4984 | 496.0 | 992 | 1.4026 | | 0.4984 | 497.0 | 994 | 1.3938 | | 0.4984 | 498.0 | 996 | 1.4228 | | 0.4984 | 499.0 | 998 | 1.4441 | | 0.0013 | 500.0 | 1000 | 1.4600 | | 0.0013 | 501.0 | 1002 | 1.4651 | | 0.0013 | 502.0 | 1004 | 1.4571 | | 0.0013 | 503.0 | 1006 | 1.4481 | | 0.0013 | 504.0 | 1008 | 1.4398 | | 0.0013 | 505.0 | 1010 | 1.4303 | | 0.0013 | 506.0 | 1012 | 1.4208 | | 0.0013 | 507.0 | 1014 | 1.4074 | | 0.0013 | 508.0 | 1016 | 1.3926 | | 0.0013 | 509.0 | 1018 | 1.3814 | | 0.0013 | 510.0 | 1020 | 1.3729 | | 0.0013 | 511.0 | 1022 | 1.3687 | | 0.0013 | 512.0 | 1024 | 1.3629 | | 0.0013 | 513.0 | 1026 | 1.3900 | | 0.0013 | 514.0 | 1028 | 1.4067 | | 0.0013 | 515.0 | 1030 | 1.3830 | | 0.0013 | 516.0 | 1032 | 1.3642 | | 0.0013 | 517.0 | 1034 | 1.3945 | | 0.0013 | 518.0 | 1036 | 1.4173 | | 0.0013 | 519.0 | 1038 | 1.4311 | | 0.0013 | 520.0 | 1040 | 1.4405 | | 0.0013 | 521.0 | 1042 | 1.4485 | | 0.0013 | 522.0 | 1044 | 1.4568 | | 0.0013 | 523.0 | 1046 | 1.4552 | | 0.0013 | 524.0 | 1048 | 1.4257 | | 0.0013 | 525.0 | 1050 | 1.3988 | | 0.0013 | 526.0 | 1052 | 1.3722 | | 0.0013 | 527.0 | 1054 | 1.3477 | | 0.0013 | 528.0 | 1056 | 1.3285 | | 0.0013 | 529.0 | 1058 | 1.3126 | | 0.0013 | 530.0 | 1060 | 1.2998 | | 0.0013 | 531.0 | 1062 | 1.2948 | | 0.0013 | 532.0 | 1064 | 1.2972 | | 0.0013 | 533.0 | 1066 | 1.2976 | | 0.0013 | 534.0 | 1068 | 1.2979 | | 0.0013 | 535.0 | 1070 | 1.3181 | | 0.0013 | 536.0 | 1072 | 1.3510 | | 0.0013 | 537.0 | 1074 | 1.3788 | | 0.0013 | 538.0 | 1076 | 1.3992 | | 0.0013 | 539.0 | 1078 | 1.4265 | | 0.0013 | 540.0 | 1080 | 1.4463 | | 0.0013 | 541.0 | 1082 | 1.4578 | | 0.0013 | 542.0 | 1084 | 1.4586 | | 0.0013 | 543.0 | 1086 | 1.4551 | | 0.0013 | 544.0 | 1088 | 1.4510 | | 0.0013 | 545.0 | 1090 | 1.4462 | | 0.0013 | 546.0 | 1092 | 1.4394 | | 0.0013 | 547.0 | 1094 | 1.4334 | | 0.0013 | 548.0 | 1096 | 1.4384 | | 0.0013 | 549.0 | 1098 | 1.4397 | | 0.0013 | 550.0 | 1100 | 1.4445 | | 0.0013 | 551.0 | 1102 | 1.4514 | | 0.0013 | 552.0 | 1104 | 1.4554 | | 0.0013 | 553.0 | 1106 | 1.4576 | | 0.0013 | 554.0 | 1108 | 1.4583 | | 0.0013 | 555.0 | 1110 | 1.4601 | | 0.0013 | 556.0 | 1112 | 1.4597 | | 0.0013 | 557.0 | 1114 | 1.4596 | | 0.0013 | 558.0 | 1116 | 1.4577 | | 0.0013 | 559.0 | 1118 | 1.4520 | | 0.0013 | 560.0 | 1120 | 1.4491 | | 0.0013 | 561.0 | 1122 | 1.4455 | | 0.0013 | 562.0 | 1124 | 1.4424 | | 0.0013 | 563.0 | 1126 | 1.4388 | | 0.0013 | 564.0 | 1128 | 1.4303 | | 0.0013 | 565.0 | 1130 | 1.4266 | | 0.0013 | 566.0 | 1132 | 1.4235 | | 0.0013 | 567.0 | 1134 | 1.4207 | | 0.0013 | 568.0 | 1136 | 1.4185 | | 0.0013 | 569.0 | 1138 | 1.4172 | | 0.0013 | 570.0 | 1140 | 1.4145 | | 0.0013 | 571.0 | 1142 | 1.4177 | | 0.0013 | 572.0 | 1144 | 1.4230 | | 0.0013 | 573.0 | 1146 | 1.4247 | | 0.0013 | 574.0 | 1148 | 1.4152 | | 0.0013 | 575.0 | 1150 | 1.4082 | | 0.0013 | 576.0 | 1152 | 1.4027 | | 0.0013 | 577.0 | 1154 | 1.4000 | | 0.0013 | 578.0 | 1156 | 1.3985 | | 0.0013 | 579.0 | 1158 | 1.4005 | | 0.0013 | 580.0 | 1160 | 1.4054 | | 0.0013 | 581.0 | 1162 | 1.4075 | | 0.0013 | 582.0 | 1164 | 1.4120 | | 0.0013 | 583.0 | 1166 | 1.4161 | | 0.0013 | 584.0 | 1168 | 1.4199 | | 0.0013 | 585.0 | 1170 | 1.4222 | | 0.0013 | 586.0 | 1172 | 1.4239 | | 0.0013 | 587.0 | 1174 | 1.4254 | | 0.0013 | 588.0 | 1176 | 1.4162 | | 0.0013 | 589.0 | 1178 | 1.4203 | | 0.0013 | 590.0 | 1180 | 1.4341 | | 0.0013 | 591.0 | 1182 | 1.4659 | | 0.0013 | 592.0 | 1184 | 1.4891 | | 0.0013 | 593.0 | 1186 | 1.5046 | | 0.0013 | 594.0 | 1188 | 1.5110 | | 0.0013 | 595.0 | 1190 | 1.5053 | | 0.0013 | 596.0 | 1192 | 1.5001 | | 0.0013 | 597.0 | 1194 | 1.4795 | | 0.0013 | 598.0 | 1196 | 1.4530 | | 0.0013 | 599.0 | 1198 | 1.4300 | | 0.0013 | 600.0 | 1200 | 1.4101 | | 0.0013 | 601.0 | 1202 | 1.3887 | | 0.0013 | 602.0 | 1204 | 1.3722 | | 0.0013 | 603.0 | 1206 | 1.3588 | | 0.0013 | 604.0 | 1208 | 1.3521 | | 0.0013 | 605.0 | 1210 | 1.3470 | | 0.0013 | 606.0 | 1212 | 1.3519 | | 0.0013 | 607.0 | 1214 | 1.3647 | | 0.0013 | 608.0 | 1216 | 1.3756 | | 0.0013 | 609.0 | 1218 | 1.3838 | | 0.0013 | 610.0 | 1220 | 1.3876 | | 0.0013 | 611.0 | 1222 | 1.3876 | | 0.0013 | 612.0 | 1224 | 1.3871 | | 0.0013 | 613.0 | 1226 | 1.3861 | | 0.0013 | 614.0 | 1228 | 1.3932 | | 0.0013 | 615.0 | 1230 | 1.4157 | | 0.0013 | 616.0 | 1232 | 1.4386 | | 0.0013 | 617.0 | 1234 | 1.4567 | | 0.0013 | 618.0 | 1236 | 1.4693 | | 0.0013 | 619.0 | 1238 | 1.4772 | | 0.0013 | 620.0 | 1240 | 1.4793 | | 0.0013 | 621.0 | 1242 | 1.4671 | | 0.0013 | 622.0 | 1244 | 1.4450 | | 0.0013 | 623.0 | 1246 | 1.4167 | | 0.0013 | 624.0 | 1248 | 1.3841 | | 0.0013 | 625.0 | 1250 | 1.3548 | | 0.0013 | 626.0 | 1252 | 1.3333 | | 0.0013 | 627.0 | 1254 | 1.3233 | | 0.0013 | 628.0 | 1256 | 1.3179 | | 0.0013 | 629.0 | 1258 | 1.3158 | | 0.0013 | 630.0 | 1260 | 1.3153 | | 0.0013 | 631.0 | 1262 | 1.3201 | | 0.0013 | 632.0 | 1264 | 1.3260 | | 0.0013 | 633.0 | 1266 | 1.3341 | | 0.0013 | 634.0 | 1268 | 1.3430 | | 0.0013 | 635.0 | 1270 | 1.3519 | | 0.0013 | 636.0 | 1272 | 1.3612 | | 0.0013 | 637.0 | 1274 | 1.3718 | | 0.0013 | 638.0 | 1276 | 1.3815 | | 0.0013 | 639.0 | 1278 | 1.3941 | | 0.0013 | 640.0 | 1280 | 1.4047 | | 0.0013 | 641.0 | 1282 | 1.4108 | | 0.0013 | 642.0 | 1284 | 1.4149 | | 0.0013 | 643.0 | 1286 | 1.4114 | | 0.0013 | 644.0 | 1288 | 1.4072 | | 0.0013 | 645.0 | 1290 | 1.4023 | | 0.0013 | 646.0 | 1292 | 1.3963 | | 0.0013 | 647.0 | 1294 | 1.3909 | | 0.0013 | 648.0 | 1296 | 1.3862 | | 0.0013 | 649.0 | 1298 | 1.3821 | | 0.0013 | 650.0 | 1300 | 1.3786 | | 0.0013 | 651.0 | 1302 | 1.3785 | | 0.0013 | 652.0 | 1304 | 1.3798 | | 0.0013 | 653.0 | 1306 | 1.3825 | | 0.0013 | 654.0 | 1308 | 1.3856 | | 0.0013 | 655.0 | 1310 | 1.3837 | | 0.0013 | 656.0 | 1312 | 1.3796 | | 0.0013 | 657.0 | 1314 | 1.3739 | | 0.0013 | 658.0 | 1316 | 1.3675 | | 0.0013 | 659.0 | 1318 | 1.3617 | | 0.0013 | 660.0 | 1320 | 1.3569 | | 0.0013 | 661.0 | 1322 | 1.3516 | | 0.0013 | 662.0 | 1324 | 1.3562 | | 0.0013 | 663.0 | 1326 | 1.3711 | | 0.0013 | 664.0 | 1328 | 1.3824 | | 0.0013 | 665.0 | 1330 | 1.3873 | | 0.0013 | 666.0 | 1332 | 1.3901 | | 0.0013 | 667.0 | 1334 | 1.3912 | | 0.0013 | 668.0 | 1336 | 1.3906 | | 0.0013 | 669.0 | 1338 | 1.3892 | | 0.0013 | 670.0 | 1340 | 1.3863 | | 0.0013 | 671.0 | 1342 | 1.3834 | | 0.0013 | 672.0 | 1344 | 1.3811 | | 0.0013 | 673.0 | 1346 | 1.3789 | | 0.0013 | 674.0 | 1348 | 1.3783 | | 0.0013 | 675.0 | 1350 | 1.3775 | | 0.0013 | 676.0 | 1352 | 1.3765 | | 0.0013 | 677.0 | 1354 | 1.3750 | | 0.0013 | 678.0 | 1356 | 1.3732 | | 0.0013 | 679.0 | 1358 | 1.3714 | | 0.0013 | 680.0 | 1360 | 1.3701 | | 0.0013 | 681.0 | 1362 | 1.3690 | | 0.0013 | 682.0 | 1364 | 1.3669 | | 0.0013 | 683.0 | 1366 | 1.3650 | | 0.0013 | 684.0 | 1368 | 1.3652 | | 0.0013 | 685.0 | 1370 | 1.3661 | | 0.0013 | 686.0 | 1372 | 1.3711 | | 0.0013 | 687.0 | 1374 | 1.3762 | | 0.0013 | 688.0 | 1376 | 1.3815 | | 0.0013 | 689.0 | 1378 | 1.3849 | | 0.0013 | 690.0 | 1380 | 1.3866 | | 0.0013 | 691.0 | 1382 | 1.3856 | | 0.0013 | 692.0 | 1384 | 1.3827 | | 0.0013 | 693.0 | 1386 | 1.3785 | | 0.0013 | 694.0 | 1388 | 1.3752 | | 0.0013 | 695.0 | 1390 | 1.3722 | | 0.0013 | 696.0 | 1392 | 1.3719 | | 0.0013 | 697.0 | 1394 | 1.3713 | | 0.0013 | 698.0 | 1396 | 1.3706 | | 0.0013 | 699.0 | 1398 | 1.3682 | | 0.0013 | 700.0 | 1400 | 1.3655 | | 0.0013 | 701.0 | 1402 | 1.3735 | | 0.0013 | 702.0 | 1404 | 1.3824 | | 0.0013 | 703.0 | 1406 | 1.3917 | | 0.0013 | 704.0 | 1408 | 1.3977 | | 0.0013 | 705.0 | 1410 | 1.4018 | | 0.0013 | 706.0 | 1412 | 1.4048 | | 0.0013 | 707.0 | 1414 | 1.4069 | | 0.0013 | 708.0 | 1416 | 1.4071 | | 0.0013 | 709.0 | 1418 | 1.4056 | | 0.0013 | 710.0 | 1420 | 1.4038 | | 0.0013 | 711.0 | 1422 | 1.4027 | | 0.0013 | 712.0 | 1424 | 1.3999 | | 0.0013 | 713.0 | 1426 | 1.3940 | | 0.0013 | 714.0 | 1428 | 1.3880 | | 0.0013 | 715.0 | 1430 | 1.3814 | | 0.0013 | 716.0 | 1432 | 1.3756 | | 0.0013 | 717.0 | 1434 | 1.3708 | | 0.0013 | 718.0 | 1436 | 1.3658 | | 0.0013 | 719.0 | 1438 | 1.3619 | | 0.0013 | 720.0 | 1440 | 1.3605 | | 0.0013 | 721.0 | 1442 | 1.3587 | | 0.0013 | 722.0 | 1444 | 1.3685 | | 0.0013 | 723.0 | 1446 | 1.3823 | | 0.0013 | 724.0 | 1448 | 1.3939 | | 0.0013 | 725.0 | 1450 | 1.4022 | | 0.0013 | 726.0 | 1452 | 1.4089 | | 0.0013 | 727.0 | 1454 | 1.4147 | | 0.0013 | 728.0 | 1456 | 1.4190 | | 0.0013 | 729.0 | 1458 | 1.4273 | | 0.0013 | 730.0 | 1460 | 1.4373 | | 0.0013 | 731.0 | 1462 | 1.4448 | | 0.0013 | 732.0 | 1464 | 1.4494 | | 0.0013 | 733.0 | 1466 | 1.4507 | | 0.0013 | 734.0 | 1468 | 1.4513 | | 0.0013 | 735.0 | 1470 | 1.4585 | | 0.0013 | 736.0 | 1472 | 1.4685 | | 0.0013 | 737.0 | 1474 | 1.4767 | | 0.0013 | 738.0 | 1476 | 1.4740 | | 0.0013 | 739.0 | 1478 | 1.4713 | | 0.0013 | 740.0 | 1480 | 1.4689 | | 0.0013 | 741.0 | 1482 | 1.4668 | | 0.0013 | 742.0 | 1484 | 1.4648 | | 0.0013 | 743.0 | 1486 | 1.4631 | | 0.0013 | 744.0 | 1488 | 1.4613 | | 0.0013 | 745.0 | 1490 | 1.4588 | | 0.0013 | 746.0 | 1492 | 1.4550 | | 0.0013 | 747.0 | 1494 | 1.4507 | | 0.0013 | 748.0 | 1496 | 1.4456 | | 0.0013 | 749.0 | 1498 | 1.4401 | | 0.0003 | 750.0 | 1500 | 1.4360 | | 0.0003 | 751.0 | 1502 | 1.4327 | | 0.0003 | 752.0 | 1504 | 1.4302 | | 0.0003 | 753.0 | 1506 | 1.4290 | | 0.0003 | 754.0 | 1508 | 1.4285 | | 0.0003 | 755.0 | 1510 | 1.4290 | | 0.0003 | 756.0 | 1512 | 1.4267 | | 0.0003 | 757.0 | 1514 | 1.4248 | | 0.0003 | 758.0 | 1516 | 1.4228 | | 0.0003 | 759.0 | 1518 | 1.4206 | | 0.0003 | 760.0 | 1520 | 1.4184 | | 0.0003 | 761.0 | 1522 | 1.4166 | | 0.0003 | 762.0 | 1524 | 1.4148 | | 0.0003 | 763.0 | 1526 | 1.4132 | | 0.0003 | 764.0 | 1528 | 1.4126 | | 0.0003 | 765.0 | 1530 | 1.4171 | | 0.0003 | 766.0 | 1532 | 1.4209 | | 0.0003 | 767.0 | 1534 | 1.4356 | | 0.0003 | 768.0 | 1536 | 1.4466 | | 0.0003 | 769.0 | 1538 | 1.4545 | | 0.0003 | 770.0 | 1540 | 1.4605 | | 0.0003 | 771.0 | 1542 | 1.4648 | | 0.0003 | 772.0 | 1544 | 1.4678 | | 0.0003 | 773.0 | 1546 | 1.4697 | | 0.0003 | 774.0 | 1548 | 1.4707 | | 0.0003 | 775.0 | 1550 | 1.4709 | | 0.0003 | 776.0 | 1552 | 1.4680 | | 0.0003 | 777.0 | 1554 | 1.4634 | | 0.0003 | 778.0 | 1556 | 1.4592 | | 0.0003 | 779.0 | 1558 | 1.4550 | | 0.0003 | 780.0 | 1560 | 1.4512 | | 0.0003 | 781.0 | 1562 | 1.4479 | | 0.0003 | 782.0 | 1564 | 1.4652 | | 0.0003 | 783.0 | 1566 | 1.4978 | | 0.0003 | 784.0 | 1568 | 1.5235 | | 0.0003 | 785.0 | 1570 | 1.5399 | | 0.0003 | 786.0 | 1572 | 1.5518 | | 0.0003 | 787.0 | 1574 | 1.5597 | | 0.0003 | 788.0 | 1576 | 1.5629 | | 0.0003 | 789.0 | 1578 | 1.5628 | | 0.0003 | 790.0 | 1580 | 1.5599 | | 0.0003 | 791.0 | 1582 | 1.5538 | | 0.0003 | 792.0 | 1584 | 1.5479 | | 0.0003 | 793.0 | 1586 | 1.5405 | | 0.0003 | 794.0 | 1588 | 1.5318 | | 0.0003 | 795.0 | 1590 | 1.5236 | | 0.0003 | 796.0 | 1592 | 1.5222 | | 0.0003 | 797.0 | 1594 | 1.5259 | | 0.0003 | 798.0 | 1596 | 1.5279 | | 0.0003 | 799.0 | 1598 | 1.5291 | | 0.0003 | 800.0 | 1600 | 1.5242 | | 0.0003 | 801.0 | 1602 | 1.5197 | | 0.0003 | 802.0 | 1604 | 1.5153 | | 0.0003 | 803.0 | 1606 | 1.5091 | | 0.0003 | 804.0 | 1608 | 1.5018 | | 0.0003 | 805.0 | 1610 | 1.4950 | | 0.0003 | 806.0 | 1612 | 1.4887 | | 0.0003 | 807.0 | 1614 | 1.4833 | | 0.0003 | 808.0 | 1616 | 1.4786 | | 0.0003 | 809.0 | 1618 | 1.4726 | | 0.0003 | 810.0 | 1620 | 1.4676 | | 0.0003 | 811.0 | 1622 | 1.4762 | | 0.0003 | 812.0 | 1624 | 1.4831 | | 0.0003 | 813.0 | 1626 | 1.4911 | | 0.0003 | 814.0 | 1628 | 1.5145 | | 0.0003 | 815.0 | 1630 | 1.5310 | | 0.0003 | 816.0 | 1632 | 1.5441 | | 0.0003 | 817.0 | 1634 | 1.5537 | | 0.0003 | 818.0 | 1636 | 1.5606 | | 0.0003 | 819.0 | 1638 | 1.5644 | | 0.0003 | 820.0 | 1640 | 1.5652 | | 0.0003 | 821.0 | 1642 | 1.5639 | | 0.0003 | 822.0 | 1644 | 1.5595 | | 0.0003 | 823.0 | 1646 | 1.5473 | | 0.0003 | 824.0 | 1648 | 1.5360 | | 0.0003 | 825.0 | 1650 | 1.5237 | | 0.0003 | 826.0 | 1652 | 1.5143 | | 0.0003 | 827.0 | 1654 | 1.5092 | | 0.0003 | 828.0 | 1656 | 1.4986 | | 0.0003 | 829.0 | 1658 | 1.4837 | | 0.0003 | 830.0 | 1660 | 1.4722 | | 0.0003 | 831.0 | 1662 | 1.4626 | | 0.0003 | 832.0 | 1664 | 1.4545 | | 0.0003 | 833.0 | 1666 | 1.4480 | | 0.0003 | 834.0 | 1668 | 1.4345 | | 0.0003 | 835.0 | 1670 | 1.4235 | | 0.0003 | 836.0 | 1672 | 1.4138 | | 0.0003 | 837.0 | 1674 | 1.4071 | | 0.0003 | 838.0 | 1676 | 1.4051 | | 0.0003 | 839.0 | 1678 | 1.4036 | | 0.0003 | 840.0 | 1680 | 1.4020 | | 0.0003 | 841.0 | 1682 | 1.3985 | | 0.0003 | 842.0 | 1684 | 1.3947 | | 0.0003 | 843.0 | 1686 | 1.3917 | | 0.0003 | 844.0 | 1688 | 1.3896 | | 0.0003 | 845.0 | 1690 | 1.3882 | | 0.0003 | 846.0 | 1692 | 1.3870 | | 0.0003 | 847.0 | 1694 | 1.4005 | | 0.0003 | 848.0 | 1696 | 1.4152 | | 0.0003 | 849.0 | 1698 | 1.4301 | | 0.0003 | 850.0 | 1700 | 1.4422 | | 0.0003 | 851.0 | 1702 | 1.4517 | | 0.0003 | 852.0 | 1704 | 1.4587 | | 0.0003 | 853.0 | 1706 | 1.4637 | | 0.0003 | 854.0 | 1708 | 1.4669 | | 0.0003 | 855.0 | 1710 | 1.4685 | | 0.0003 | 856.0 | 1712 | 1.4689 | | 0.0003 | 857.0 | 1714 | 1.4679 | | 0.0003 | 858.0 | 1716 | 1.4595 | | 0.0003 | 859.0 | 1718 | 1.4518 | | 0.0003 | 860.0 | 1720 | 1.4440 | | 0.0003 | 861.0 | 1722 | 1.4372 | | 0.0003 | 862.0 | 1724 | 1.4310 | | 0.0003 | 863.0 | 1726 | 1.4251 | | 0.0003 | 864.0 | 1728 | 1.4212 | | 0.0003 | 865.0 | 1730 | 1.4181 | | 0.0003 | 866.0 | 1732 | 1.4154 | | 0.0003 | 867.0 | 1734 | 1.4129 | | 0.0003 | 868.0 | 1736 | 1.4109 | | 0.0003 | 869.0 | 1738 | 1.4092 | | 0.0003 | 870.0 | 1740 | 1.4077 | | 0.0003 | 871.0 | 1742 | 1.4063 | | 0.0003 | 872.0 | 1744 | 1.4045 | | 0.0003 | 873.0 | 1746 | 1.4027 | | 0.0003 | 874.0 | 1748 | 1.4011 | | 0.0003 | 875.0 | 1750 | 1.3993 | | 0.0003 | 876.0 | 1752 | 1.4034 | | 0.0003 | 877.0 | 1754 | 1.4118 | | 0.0003 | 878.0 | 1756 | 1.4173 | | 0.0003 | 879.0 | 1758 | 1.4212 | | 0.0003 | 880.0 | 1760 | 1.4245 | | 0.0003 | 881.0 | 1762 | 1.4271 | | 0.0003 | 882.0 | 1764 | 1.4292 | | 0.0003 | 883.0 | 1766 | 1.4308 | | 0.0003 | 884.0 | 1768 | 1.4316 | | 0.0003 | 885.0 | 1770 | 1.4318 | | 0.0003 | 886.0 | 1772 | 1.4317 | | 0.0003 | 887.0 | 1774 | 1.4315 | | 0.0003 | 888.0 | 1776 | 1.4311 | | 0.0003 | 889.0 | 1778 | 1.4301 | | 0.0003 | 890.0 | 1780 | 1.4281 | | 0.0003 | 891.0 | 1782 | 1.4265 | | 0.0003 | 892.0 | 1784 | 1.4248 | | 0.0003 | 893.0 | 1786 | 1.4226 | | 0.0003 | 894.0 | 1788 | 1.4189 | | 0.0003 | 895.0 | 1790 | 1.4158 | | 0.0003 | 896.0 | 1792 | 1.4134 | | 0.0003 | 897.0 | 1794 | 1.4114 | | 0.0003 | 898.0 | 1796 | 1.4095 | | 0.0003 | 899.0 | 1798 | 1.4070 | | 0.0003 | 900.0 | 1800 | 1.4048 | | 0.0003 | 901.0 | 1802 | 1.4032 | | 0.0003 | 902.0 | 1804 | 1.4020 | | 0.0003 | 903.0 | 1806 | 1.4013 | | 0.0003 | 904.0 | 1808 | 1.4006 | | 0.0003 | 905.0 | 1810 | 1.4000 | | 0.0003 | 906.0 | 1812 | 1.3997 | | 0.0003 | 907.0 | 1814 | 1.3994 | | 0.0003 | 908.0 | 1816 | 1.3990 | | 0.0003 | 909.0 | 1818 | 1.3983 | | 0.0003 | 910.0 | 1820 | 1.3979 | | 0.0003 | 911.0 | 1822 | 1.3978 | | 0.0003 | 912.0 | 1824 | 1.3986 | | 0.0003 | 913.0 | 1826 | 1.3978 | | 0.0003 | 914.0 | 1828 | 1.3970 | | 0.0003 | 915.0 | 1830 | 1.3964 | | 0.0003 | 916.0 | 1832 | 1.3958 | | 0.0003 | 917.0 | 1834 | 1.3953 | | 0.0003 | 918.0 | 1836 | 1.3945 | | 0.0003 | 919.0 | 1838 | 1.3944 | | 0.0003 | 920.0 | 1840 | 1.3942 | | 0.0003 | 921.0 | 1842 | 1.3940 | | 0.0003 | 922.0 | 1844 | 1.3935 | | 0.0003 | 923.0 | 1846 | 1.3932 | | 0.0003 | 924.0 | 1848 | 1.3927 | | 0.0003 | 925.0 | 1850 | 1.3925 | | 0.0003 | 926.0 | 1852 | 1.3925 | | 0.0003 | 927.0 | 1854 | 1.3926 | | 0.0003 | 928.0 | 1856 | 1.3928 | | 0.0003 | 929.0 | 1858 | 1.3928 | | 0.0003 | 930.0 | 1860 | 1.3903 | | 0.0003 | 931.0 | 1862 | 1.3883 | | 0.0003 | 932.0 | 1864 | 1.3866 | | 0.0003 | 933.0 | 1866 | 1.3853 | | 0.0003 | 934.0 | 1868 | 1.3842 | | 0.0003 | 935.0 | 1870 | 1.3834 | | 0.0003 | 936.0 | 1872 | 1.3826 | | 0.0003 | 937.0 | 1874 | 1.3818 | | 0.0003 | 938.0 | 1876 | 1.3803 | | 0.0003 | 939.0 | 1878 | 1.3791 | | 0.0003 | 940.0 | 1880 | 1.3782 | | 0.0003 | 941.0 | 1882 | 1.3776 | | 0.0003 | 942.0 | 1884 | 1.3770 | | 0.0003 | 943.0 | 1886 | 1.3764 | | 0.0003 | 944.0 | 1888 | 1.3758 | | 0.0003 | 945.0 | 1890 | 1.3760 | | 0.0003 | 946.0 | 1892 | 1.3763 | | 0.0003 | 947.0 | 1894 | 1.3766 | | 0.0003 | 948.0 | 1896 | 1.3770 | | 0.0003 | 949.0 | 1898 | 1.3773 | | 0.0003 | 950.0 | 1900 | 1.3776 | | 0.0003 | 951.0 | 1902 | 1.3778 | | 0.0003 | 952.0 | 1904 | 1.3780 | | 0.0003 | 953.0 | 1906 | 1.3796 | | 0.0003 | 954.0 | 1908 | 1.3821 | | 0.0003 | 955.0 | 1910 | 1.3841 | | 0.0003 | 956.0 | 1912 | 1.3858 | | 0.0003 | 957.0 | 1914 | 1.3859 | | 0.0003 | 958.0 | 1916 | 1.3858 | | 0.0003 | 959.0 | 1918 | 1.3859 | | 0.0003 | 960.0 | 1920 | 1.3857 | | 0.0003 | 961.0 | 1922 | 1.3853 | | 0.0003 | 962.0 | 1924 | 1.3850 | | 0.0003 | 963.0 | 1926 | 1.3848 | | 0.0003 | 964.0 | 1928 | 1.3847 | | 0.0003 | 965.0 | 1930 | 1.3845 | | 0.0003 | 966.0 | 1932 | 1.3843 | | 0.0003 | 967.0 | 1934 | 1.3841 | | 0.0003 | 968.0 | 1936 | 1.3839 | | 0.0003 | 969.0 | 1938 | 1.3837 | | 0.0003 | 970.0 | 1940 | 1.3836 | | 0.0003 | 971.0 | 1942 | 1.3836 | | 0.0003 | 972.0 | 1944 | 1.3836 | | 0.0003 | 973.0 | 1946 | 1.3835 | | 0.0003 | 974.0 | 1948 | 1.3838 | | 0.0003 | 975.0 | 1950 | 1.3843 | | 0.0003 | 976.0 | 1952 | 1.3847 | | 0.0003 | 977.0 | 1954 | 1.3850 | | 0.0003 | 978.0 | 1956 | 1.3852 | | 0.0003 | 979.0 | 1958 | 1.3853 | | 0.0003 | 980.0 | 1960 | 1.3854 | | 0.0003 | 981.0 | 1962 | 1.3855 | | 0.0003 | 982.0 | 1964 | 1.3855 | | 0.0003 | 983.0 | 1966 | 1.3854 | | 0.0003 | 984.0 | 1968 | 1.3854 | | 0.0003 | 985.0 | 1970 | 1.3855 | | 0.0003 | 986.0 | 1972 | 1.3857 | | 0.0003 | 987.0 | 1974 | 1.3858 | | 0.0003 | 988.0 | 1976 | 1.3859 | | 0.0003 | 989.0 | 1978 | 1.3860 | | 0.0003 | 990.0 | 1980 | 1.3860 | | 0.0003 | 991.0 | 1982 | 1.3861 | | 0.0003 | 992.0 | 1984 | 1.3860 | | 0.0003 | 993.0 | 1986 | 1.3860 | | 0.0003 | 994.0 | 1988 | 1.3860 | | 0.0003 | 995.0 | 1990 | 1.3860 | | 0.0003 | 996.0 | 1992 | 1.3860 | | 0.0003 | 997.0 | 1994 | 1.3859 | | 0.0003 | 998.0 | 1996 | 1.3859 | | 0.0003 | 999.0 | 1998 | 1.3859 | | 0.0002 | 1000.0 | 2000 | 1.3859 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.7 - Tokenizers 0.15.0

daze-unlv/axolotl-medmcqa-4-epoch

daze-unlv

2024-03-08T01:23:40Z

4

0

peft

[ "peft", "tensorboard", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-03-07T23:04:09Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: lora-out/medmcqa-4-epoch results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: daze-unlv/medmcqa_axolotl type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0 output_dir: ./lora-out/medmcqa-4-epoch eval_sample_packing: false adapter: lora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 4 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: false sdp_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details> # lora-out/medmcqa-4-epoch This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0

Maqqq/OpenHermes-2.5-Mistral-7B-16

Maqqq

2024-03-08T01:17:04Z

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T00:56:38Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_64_0.01_8_0.0002

ferrazzipietro

2024-03-08T00:53:29Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T00:52:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

keanurefresh/73981

keanurefresh

2024-03-08T00:44:46Z

0

null

[ "region:us" ]

null

2024-03-08T00:00:03Z

Include in your prompt <lora:facialized:1>, cum, facial.You might want to include in your negative prompt cum on breasts, cum on body. The model works best with low steps and CFG, I get good results with 10 steps and a CFG of 3 or 4.

not-lain/BaseModelWithConfigAndNamedParameter

not-lain

2024-03-08T00:41:29Z

5

0

transformers

[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]

null

2024-03-08T00:29:16Z

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using ****: - Repo: [More Information Needed] - Docs: [More Information Needed]

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_64_0.01_4_0.0002

ferrazzipietro

2024-03-08T00:34:20Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T00:33:44Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

not-lain/BaseModelWithJustConfig

not-lain

2024-03-08T00:25:52Z

5

0

transformers

[ "transformers", "pytorch", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]

null

2024-03-08T00:24:23Z

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using ****: - Repo: [More Information Needed] - Docs: [More Information Needed]

farid1088/GQA_BERT_legal_SQuAD_complete_augmented_2000

farid1088

2024-03-08T00:22:50Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2024-03-07T21:47:56Z

--- tags: - generated_from_trainer model-index: - name: GQA_BERT_legal_SQuAD_complete_augmented_2000 results: [] ---  # GQA_BERT_legal_SQuAD_complete_augmented_2000 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 160 - eval_batch_size: 40 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 1.0 | 3 | 5.1193 | | No log | 2.0 | 6 | 4.5794 | | No log | 3.0 | 9 | 3.9562 | | No log | 4.0 | 12 | 3.6226 | | No log | 5.0 | 15 | 3.1767 | | No log | 6.0 | 18 | 2.8026 | | No log | 7.0 | 21 | 2.5106 | | No log | 8.0 | 24 | 2.2343 | | No log | 9.0 | 27 | 2.0290 | | No log | 10.0 | 30 | 1.8059 | | No log | 11.0 | 33 | 1.6448 | | No log | 12.0 | 36 | 1.4814 | | No log | 13.0 | 39 | 1.3270 | | No log | 14.0 | 42 | 1.2522 | | No log | 15.0 | 45 | 1.1957 | | No log | 16.0 | 48 | 1.1489 | | No log | 17.0 | 51 | 1.1251 | | No log | 18.0 | 54 | 1.1000 | | No log | 19.0 | 57 | 1.0762 | | No log | 20.0 | 60 | 1.0465 | | No log | 21.0 | 63 | 1.0398 | | No log | 22.0 | 66 | 1.0363 | | No log | 23.0 | 69 | 1.0388 | | No log | 24.0 | 72 | 1.0330 | | No log | 25.0 | 75 | 1.0242 | | No log | 26.0 | 78 | 1.0188 | | No log | 27.0 | 81 | 1.0227 | | No log | 28.0 | 84 | 1.0281 | | No log | 29.0 | 87 | 1.0362 | | No log | 30.0 | 90 | 1.0278 | | No log | 31.0 | 93 | 1.0463 | | No log | 32.0 | 96 | 1.0733 | | No log | 33.0 | 99 | 1.0895 | | No log | 34.0 | 102 | 1.0818 | | No log | 35.0 | 105 | 1.0836 | | No log | 36.0 | 108 | 1.0664 | | No log | 37.0 | 111 | 1.0578 | | No log | 38.0 | 114 | 1.0792 | | No log | 39.0 | 117 | 1.0465 | | No log | 40.0 | 120 | 1.0288 | | No log | 41.0 | 123 | 1.0609 | | No log | 42.0 | 126 | 1.0676 | | No log | 43.0 | 129 | 1.0343 | | No log | 44.0 | 132 | 1.0653 | | No log | 45.0 | 135 | 1.1017 | | No log | 46.0 | 138 | 1.0780 | | No log | 47.0 | 141 | 1.0841 | | No log | 48.0 | 144 | 1.0921 | | No log | 49.0 | 147 | 1.0919 | | No log | 50.0 | 150 | 1.1088 | | No log | 51.0 | 153 | 1.0983 | | No log | 52.0 | 156 | 1.0897 | | No log | 53.0 | 159 | 1.0991 | | No log | 54.0 | 162 | 1.1124 | | No log | 55.0 | 165 | 1.0800 | | No log | 56.0 | 168 | 1.1173 | | No log | 57.0 | 171 | 1.1244 | | No log | 58.0 | 174 | 1.1127 | | No log | 59.0 | 177 | 1.1290 | | No log | 60.0 | 180 | 1.1127 | | No log | 61.0 | 183 | 1.1141 | | No log | 62.0 | 186 | 1.1494 | | No log | 63.0 | 189 | 1.1185 | | No log | 64.0 | 192 | 1.1394 | | No log | 65.0 | 195 | 1.1624 | | No log | 66.0 | 198 | 1.1620 | | No log | 67.0 | 201 | 1.1518 | | No log | 68.0 | 204 | 1.1353 | | No log | 69.0 | 207 | 1.2165 | | No log | 70.0 | 210 | 1.1765 | | No log | 71.0 | 213 | 1.1964 | | No log | 72.0 | 216 | 1.2078 | | No log | 73.0 | 219 | 1.1245 | | No log | 74.0 | 222 | 1.1631 | | No log | 75.0 | 225 | 1.1314 | | No log | 76.0 | 228 | 1.0521 | | No log | 77.0 | 231 | 1.1047 | | No log | 78.0 | 234 | 1.1412 | | No log | 79.0 | 237 | 1.1133 | | No log | 80.0 | 240 | 1.1257 | | No log | 81.0 | 243 | 1.1375 | | No log | 82.0 | 246 | 1.0486 | | No log | 83.0 | 249 | 1.1223 | | No log | 84.0 | 252 | 1.1664 | | No log | 85.0 | 255 | 1.0748 | | No log | 86.0 | 258 | 1.1151 | | No log | 87.0 | 261 | 1.1358 | | No log | 88.0 | 264 | 1.0981 | | No log | 89.0 | 267 | 1.2120 | | No log | 90.0 | 270 | 1.1805 | | No log | 91.0 | 273 | 1.1296 | | No log | 92.0 | 276 | 1.3029 | | No log | 93.0 | 279 | 1.2570 | | No log | 94.0 | 282 | 1.1256 | | No log | 95.0 | 285 | 1.1910 | | No log | 96.0 | 288 | 1.2814 | | No log | 97.0 | 291 | 1.1195 | | No log | 98.0 | 294 | 1.0572 | | No log | 99.0 | 297 | 1.1948 | | No log | 100.0 | 300 | 1.1649 | | No log | 101.0 | 303 | 1.0716 | | No log | 102.0 | 306 | 1.1648 | | No log | 103.0 | 309 | 1.1558 | | No log | 104.0 | 312 | 1.1381 | | No log | 105.0 | 315 | 1.2201 | | No log | 106.0 | 318 | 1.2335 | | No log | 107.0 | 321 | 1.0798 | | No log | 108.0 | 324 | 1.1202 | | No log | 109.0 | 327 | 1.2209 | | No log | 110.0 | 330 | 1.2331 | | No log | 111.0 | 333 | 1.1878 | | No log | 112.0 | 336 | 1.2108 | | No log | 113.0 | 339 | 1.2244 | | No log | 114.0 | 342 | 1.1712 | | No log | 115.0 | 345 | 1.1699 | | No log | 116.0 | 348 | 1.2039 | | No log | 117.0 | 351 | 1.0968 | | No log | 118.0 | 354 | 1.1880 | | No log | 119.0 | 357 | 1.1514 | | No log | 120.0 | 360 | 1.0878 | | No log | 121.0 | 363 | 1.1416 | | No log | 122.0 | 366 | 1.1696 | | No log | 123.0 | 369 | 1.1387 | | No log | 124.0 | 372 | 1.1488 | | No log | 125.0 | 375 | 1.1840 | | No log | 126.0 | 378 | 1.1501 | | No log | 127.0 | 381 | 1.1900 | | No log | 128.0 | 384 | 1.1478 | | No log | 129.0 | 387 | 1.2309 | | No log | 130.0 | 390 | 1.3350 | | No log | 131.0 | 393 | 1.2147 | | No log | 132.0 | 396 | 1.1993 | | No log | 133.0 | 399 | 1.2747 | | No log | 134.0 | 402 | 1.2372 | | No log | 135.0 | 405 | 1.2479 | | No log | 136.0 | 408 | 1.2942 | | No log | 137.0 | 411 | 1.2322 | | No log | 138.0 | 414 | 1.2148 | | No log | 139.0 | 417 | 1.2922 | | No log | 140.0 | 420 | 1.3430 | | No log | 141.0 | 423 | 1.3824 | | No log | 142.0 | 426 | 1.2082 | | No log | 143.0 | 429 | 1.1967 | | No log | 144.0 | 432 | 1.2483 | | No log | 145.0 | 435 | 1.1599 | | No log | 146.0 | 438 | 1.0864 | | No log | 147.0 | 441 | 1.1238 | | No log | 148.0 | 444 | 1.2074 | | No log | 149.0 | 447 | 1.1902 | | No log | 150.0 | 450 | 1.1397 | | No log | 151.0 | 453 | 1.1546 | | No log | 152.0 | 456 | 1.2126 | | No log | 153.0 | 459 | 1.2443 | | No log | 154.0 | 462 | 1.2378 | | No log | 155.0 | 465 | 1.2335 | | No log | 156.0 | 468 | 1.1798 | | No log | 157.0 | 471 | 1.1297 | | No log | 158.0 | 474 | 1.1737 | | No log | 159.0 | 477 | 1.0970 | | No log | 160.0 | 480 | 1.1708 | | No log | 161.0 | 483 | 1.1551 | | No log | 162.0 | 486 | 1.1848 | | No log | 163.0 | 489 | 1.1971 | | No log | 164.0 | 492 | 1.1720 | | No log | 165.0 | 495 | 1.1960 | | No log | 166.0 | 498 | 1.2754 | | 1.0047 | 167.0 | 501 | 1.2083 | | 1.0047 | 168.0 | 504 | 1.0888 | | 1.0047 | 169.0 | 507 | 1.2684 | | 1.0047 | 170.0 | 510 | 1.3395 | | 1.0047 | 171.0 | 513 | 1.2508 | | 1.0047 | 172.0 | 516 | 1.1460 | | 1.0047 | 173.0 | 519 | 1.2464 | | 1.0047 | 174.0 | 522 | 1.2131 | | 1.0047 | 175.0 | 525 | 1.1181 | | 1.0047 | 176.0 | 528 | 1.2012 | | 1.0047 | 177.0 | 531 | 1.2957 | | 1.0047 | 178.0 | 534 | 1.1890 | | 1.0047 | 179.0 | 537 | 1.1628 | | 1.0047 | 180.0 | 540 | 1.1929 | | 1.0047 | 181.0 | 543 | 1.2900 | | 1.0047 | 182.0 | 546 | 1.3240 | | 1.0047 | 183.0 | 549 | 1.2145 | | 1.0047 | 184.0 | 552 | 1.2942 | | 1.0047 | 185.0 | 555 | 1.3425 | | 1.0047 | 186.0 | 558 | 1.1772 | | 1.0047 | 187.0 | 561 | 1.2255 | | 1.0047 | 188.0 | 564 | 1.4528 | | 1.0047 | 189.0 | 567 | 1.3898 | | 1.0047 | 190.0 | 570 | 1.1862 | | 1.0047 | 191.0 | 573 | 1.1700 | | 1.0047 | 192.0 | 576 | 1.2801 | | 1.0047 | 193.0 | 579 | 1.2571 | | 1.0047 | 194.0 | 582 | 1.1962 | | 1.0047 | 195.0 | 585 | 1.2228 | | 1.0047 | 196.0 | 588 | 1.2153 | | 1.0047 | 197.0 | 591 | 1.1498 | | 1.0047 | 198.0 | 594 | 1.1130 | | 1.0047 | 199.0 | 597 | 1.1537 | | 1.0047 | 200.0 | 600 | 1.2239 | | 1.0047 | 201.0 | 603 | 1.1742 | | 1.0047 | 202.0 | 606 | 1.1292 | | 1.0047 | 203.0 | 609 | 1.1688 | | 1.0047 | 204.0 | 612 | 1.1844 | | 1.0047 | 205.0 | 615 | 1.1928 | | 1.0047 | 206.0 | 618 | 1.2253 | | 1.0047 | 207.0 | 621 | 1.2585 | | 1.0047 | 208.0 | 624 | 1.3174 | | 1.0047 | 209.0 | 627 | 1.3660 | | 1.0047 | 210.0 | 630 | 1.2523 | | 1.0047 | 211.0 | 633 | 1.2249 | | 1.0047 | 212.0 | 636 | 1.4178 | | 1.0047 | 213.0 | 639 | 1.3895 | | 1.0047 | 214.0 | 642 | 1.2523 | | 1.0047 | 215.0 | 645 | 1.1921 | | 1.0047 | 216.0 | 648 | 1.2245 | | 1.0047 | 217.0 | 651 | 1.3426 | | 1.0047 | 218.0 | 654 | 1.3673 | | 1.0047 | 219.0 | 657 | 1.1933 | | 1.0047 | 220.0 | 660 | 1.1469 | | 1.0047 | 221.0 | 663 | 1.2684 | | 1.0047 | 222.0 | 666 | 1.4222 | | 1.0047 | 223.0 | 669 | 1.4067 | | 1.0047 | 224.0 | 672 | 1.3425 | | 1.0047 | 225.0 | 675 | 1.3358 | | 1.0047 | 226.0 | 678 | 1.4246 | | 1.0047 | 227.0 | 681 | 1.3301 | | 1.0047 | 228.0 | 684 | 1.1915 | | 1.0047 | 229.0 | 687 | 1.2654 | | 1.0047 | 230.0 | 690 | 1.4043 | | 1.0047 | 231.0 | 693 | 1.3357 | | 1.0047 | 232.0 | 696 | 1.2512 | | 1.0047 | 233.0 | 699 | 1.2383 | | 1.0047 | 234.0 | 702 | 1.1516 | | 1.0047 | 235.0 | 705 | 1.1382 | | 1.0047 | 236.0 | 708 | 1.2749 | | 1.0047 | 237.0 | 711 | 1.3747 | | 1.0047 | 238.0 | 714 | 1.1791 | | 1.0047 | 239.0 | 717 | 1.1527 | | 1.0047 | 240.0 | 720 | 1.2194 | | 1.0047 | 241.0 | 723 | 1.2754 | | 1.0047 | 242.0 | 726 | 1.3448 | | 1.0047 | 243.0 | 729 | 1.3382 | | 1.0047 | 244.0 | 732 | 1.2932 | | 1.0047 | 245.0 | 735 | 1.3135 | | 1.0047 | 246.0 | 738 | 1.3671 | | 1.0047 | 247.0 | 741 | 1.3735 | | 1.0047 | 248.0 | 744 | 1.4142 | | 1.0047 | 249.0 | 747 | 1.4000 | | 1.0047 | 250.0 | 750 | 1.2954 | | 1.0047 | 251.0 | 753 | 1.2629 | | 1.0047 | 252.0 | 756 | 1.2982 | | 1.0047 | 253.0 | 759 | 1.2750 | | 1.0047 | 254.0 | 762 | 1.2273 | | 1.0047 | 255.0 | 765 | 1.2209 | | 1.0047 | 256.0 | 768 | 1.2359 | | 1.0047 | 257.0 | 771 | 1.2626 | | 1.0047 | 258.0 | 774 | 1.1799 | | 1.0047 | 259.0 | 777 | 1.1506 | | 1.0047 | 260.0 | 780 | 1.1846 | | 1.0047 | 261.0 | 783 | 1.2278 | | 1.0047 | 262.0 | 786 | 1.2040 | | 1.0047 | 263.0 | 789 | 1.1920 | | 1.0047 | 264.0 | 792 | 1.1921 | | 1.0047 | 265.0 | 795 | 1.2421 | | 1.0047 | 266.0 | 798 | 1.2557 | | 1.0047 | 267.0 | 801 | 1.2245 | | 1.0047 | 268.0 | 804 | 1.2240 | | 1.0047 | 269.0 | 807 | 1.3193 | | 1.0047 | 270.0 | 810 | 1.3523 | | 1.0047 | 271.0 | 813 | 1.3143 | | 1.0047 | 272.0 | 816 | 1.2657 | | 1.0047 | 273.0 | 819 | 1.3099 | | 1.0047 | 274.0 | 822 | 1.2485 | | 1.0047 | 275.0 | 825 | 1.1617 | | 1.0047 | 276.0 | 828 | 1.2186 | | 1.0047 | 277.0 | 831 | 1.2683 | | 1.0047 | 278.0 | 834 | 1.2432 | | 1.0047 | 279.0 | 837 | 1.3252 | | 1.0047 | 280.0 | 840 | 1.4173 | | 1.0047 | 281.0 | 843 | 1.3807 | | 1.0047 | 282.0 | 846 | 1.3895 | | 1.0047 | 283.0 | 849 | 1.3531 | | 1.0047 | 284.0 | 852 | 1.2847 | | 1.0047 | 285.0 | 855 | 1.2734 | | 1.0047 | 286.0 | 858 | 1.2917 | | 1.0047 | 287.0 | 861 | 1.3048 | | 1.0047 | 288.0 | 864 | 1.3169 | | 1.0047 | 289.0 | 867 | 1.3620 | | 1.0047 | 290.0 | 870 | 1.4486 | | 1.0047 | 291.0 | 873 | 1.3860 | | 1.0047 | 292.0 | 876 | 1.3026 | | 1.0047 | 293.0 | 879 | 1.2993 | | 1.0047 | 294.0 | 882 | 1.2825 | | 1.0047 | 295.0 | 885 | 1.2764 | | 1.0047 | 296.0 | 888 | 1.3134 | | 1.0047 | 297.0 | 891 | 1.3452 | | 1.0047 | 298.0 | 894 | 1.3714 | | 1.0047 | 299.0 | 897 | 1.3125 | | 1.0047 | 300.0 | 900 | 1.2099 | | 1.0047 | 301.0 | 903 | 1.2298 | | 1.0047 | 302.0 | 906 | 1.3122 | | 1.0047 | 303.0 | 909 | 1.3047 | | 1.0047 | 304.0 | 912 | 1.2591 | | 1.0047 | 305.0 | 915 | 1.2820 | | 1.0047 | 306.0 | 918 | 1.2770 | | 1.0047 | 307.0 | 921 | 1.2783 | | 1.0047 | 308.0 | 924 | 1.3475 | | 1.0047 | 309.0 | 927 | 1.3819 | | 1.0047 | 310.0 | 930 | 1.2759 | | 1.0047 | 311.0 | 933 | 1.1658 | | 1.0047 | 312.0 | 936 | 1.1919 | | 1.0047 | 313.0 | 939 | 1.3712 | | 1.0047 | 314.0 | 942 | 1.4586 | | 1.0047 | 315.0 | 945 | 1.4405 | | 1.0047 | 316.0 | 948 | 1.2275 | | 1.0047 | 317.0 | 951 | 1.2043 | | 1.0047 | 318.0 | 954 | 1.3147 | | 1.0047 | 319.0 | 957 | 1.4305 | | 1.0047 | 320.0 | 960 | 1.3858 | | 1.0047 | 321.0 | 963 | 1.2997 | | 1.0047 | 322.0 | 966 | 1.2348 | | 1.0047 | 323.0 | 969 | 1.2264 | | 1.0047 | 324.0 | 972 | 1.2819 | | 1.0047 | 325.0 | 975 | 1.3146 | | 1.0047 | 326.0 | 978 | 1.3341 | | 1.0047 | 327.0 | 981 | 1.3511 | | 1.0047 | 328.0 | 984 | 1.3223 | | 1.0047 | 329.0 | 987 | 1.3236 | | 1.0047 | 330.0 | 990 | 1.3429 | | 1.0047 | 331.0 | 993 | 1.2715 | | 1.0047 | 332.0 | 996 | 1.2452 | | 1.0047 | 333.0 | 999 | 1.2350 | | 0.5933 | 334.0 | 1002 | 1.1789 | | 0.5933 | 335.0 | 1005 | 1.2327 | | 0.5933 | 336.0 | 1008 | 1.2986 | | 0.5933 | 337.0 | 1011 | 1.2372 | | 0.5933 | 338.0 | 1014 | 1.1142 | | 0.5933 | 339.0 | 1017 | 1.1219 | | 0.5933 | 340.0 | 1020 | 1.2149 | | 0.5933 | 341.0 | 1023 | 1.3215 | | 0.5933 | 342.0 | 1026 | 1.3930 | | 0.5933 | 343.0 | 1029 | 1.3952 | | 0.5933 | 344.0 | 1032 | 1.3798 | | 0.5933 | 345.0 | 1035 | 1.3870 | | 0.5933 | 346.0 | 1038 | 1.3835 | | 0.5933 | 347.0 | 1041 | 1.2778 | | 0.5933 | 348.0 | 1044 | 1.2079 | | 0.5933 | 349.0 | 1047 | 1.2545 | | 0.5933 | 350.0 | 1050 | 1.3546 | | 0.5933 | 351.0 | 1053 | 1.3485 | | 0.5933 | 352.0 | 1056 | 1.2388 | | 0.5933 | 353.0 | 1059 | 1.1877 | | 0.5933 | 354.0 | 1062 | 1.1707 | | 0.5933 | 355.0 | 1065 | 1.3036 | | 0.5933 | 356.0 | 1068 | 1.4033 | | 0.5933 | 357.0 | 1071 | 1.3046 | | 0.5933 | 358.0 | 1074 | 1.1871 | | 0.5933 | 359.0 | 1077 | 1.2303 | | 0.5933 | 360.0 | 1080 | 1.4086 | | 0.5933 | 361.0 | 1083 | 1.3546 | | 0.5933 | 362.0 | 1086 | 1.1697 | | 0.5933 | 363.0 | 1089 | 1.1320 | | 0.5933 | 364.0 | 1092 | 1.1799 | | 0.5933 | 365.0 | 1095 | 1.2172 | | 0.5933 | 366.0 | 1098 | 1.3199 | | 0.5933 | 367.0 | 1101 | 1.3302 | | 0.5933 | 368.0 | 1104 | 1.3020 | | 0.5933 | 369.0 | 1107 | 1.2652 | | 0.5933 | 370.0 | 1110 | 1.3420 | | 0.5933 | 371.0 | 1113 | 1.3486 | | 0.5933 | 372.0 | 1116 | 1.2853 | | 0.5933 | 373.0 | 1119 | 1.2203 | | 0.5933 | 374.0 | 1122 | 1.1671 | | 0.5933 | 375.0 | 1125 | 1.3050 | | 0.5933 | 376.0 | 1128 | 1.4090 | | 0.5933 | 377.0 | 1131 | 1.3682 | | 0.5933 | 378.0 | 1134 | 1.2919 | | 0.5933 | 379.0 | 1137 | 1.2611 | | 0.5933 | 380.0 | 1140 | 1.2714 | | 0.5933 | 381.0 | 1143 | 1.3204 | | 0.5933 | 382.0 | 1146 | 1.3206 | | 0.5933 | 383.0 | 1149 | 1.2592 | | 0.5933 | 384.0 | 1152 | 1.1575 | | 0.5933 | 385.0 | 1155 | 1.1801 | | 0.5933 | 386.0 | 1158 | 1.2966 | | 0.5933 | 387.0 | 1161 | 1.3092 | | 0.5933 | 388.0 | 1164 | 1.3284 | | 0.5933 | 389.0 | 1167 | 1.3397 | | 0.5933 | 390.0 | 1170 | 1.3137 | | 0.5933 | 391.0 | 1173 | 1.2775 | | 0.5933 | 392.0 | 1176 | 1.1970 | | 0.5933 | 393.0 | 1179 | 1.1671 | | 0.5933 | 394.0 | 1182 | 1.3037 | | 0.5933 | 395.0 | 1185 | 1.3400 | | 0.5933 | 396.0 | 1188 | 1.2243 | | 0.5933 | 397.0 | 1191 | 1.2322 | | 0.5933 | 398.0 | 1194 | 1.3279 | | 0.5933 | 399.0 | 1197 | 1.3577 | | 0.5933 | 400.0 | 1200 | 1.3690 | | 0.5933 | 401.0 | 1203 | 1.3068 | | 0.5933 | 402.0 | 1206 | 1.2011 | | 0.5933 | 403.0 | 1209 | 1.2389 | | 0.5933 | 404.0 | 1212 | 1.3540 | | 0.5933 | 405.0 | 1215 | 1.3858 | | 0.5933 | 406.0 | 1218 | 1.3326 | | 0.5933 | 407.0 | 1221 | 1.2234 | | 0.5933 | 408.0 | 1224 | 1.1657 | | 0.5933 | 409.0 | 1227 | 1.1664 | | 0.5933 | 410.0 | 1230 | 1.2766 | | 0.5933 | 411.0 | 1233 | 1.3610 | | 0.5933 | 412.0 | 1236 | 1.3622 | | 0.5933 | 413.0 | 1239 | 1.3024 | | 0.5933 | 414.0 | 1242 | 1.2516 | | 0.5933 | 415.0 | 1245 | 1.2160 | | 0.5933 | 416.0 | 1248 | 1.1839 | | 0.5933 | 417.0 | 1251 | 1.1225 | | 0.5933 | 418.0 | 1254 | 1.1113 | | 0.5933 | 419.0 | 1257 | 1.1720 | | 0.5933 | 420.0 | 1260 | 1.3755 | | 0.5933 | 421.0 | 1263 | 1.3626 | | 0.5933 | 422.0 | 1266 | 1.2200 | | 0.5933 | 423.0 | 1269 | 1.2175 | | 0.5933 | 424.0 | 1272 | 1.3046 | | 0.5933 | 425.0 | 1275 | 1.3120 | | 0.5933 | 426.0 | 1278 | 1.3499 | | 0.5933 | 427.0 | 1281 | 1.3850 | | 0.5933 | 428.0 | 1284 | 1.3673 | | 0.5933 | 429.0 | 1287 | 1.3124 | | 0.5933 | 430.0 | 1290 | 1.2314 | | 0.5933 | 431.0 | 1293 | 1.1724 | | 0.5933 | 432.0 | 1296 | 1.2057 | | 0.5933 | 433.0 | 1299 | 1.3040 | | 0.5933 | 434.0 | 1302 | 1.3551 | | 0.5933 | 435.0 | 1305 | 1.3777 | | 0.5933 | 436.0 | 1308 | 1.3375 | | 0.5933 | 437.0 | 1311 | 1.2963 | | 0.5933 | 438.0 | 1314 | 1.3388 | | 0.5933 | 439.0 | 1317 | 1.3685 | | 0.5933 | 440.0 | 1320 | 1.3634 | | 0.5933 | 441.0 | 1323 | 1.3484 | | 0.5933 | 442.0 | 1326 | 1.3536 | | 0.5933 | 443.0 | 1329 | 1.3584 | | 0.5933 | 444.0 | 1332 | 1.3452 | | 0.5933 | 445.0 | 1335 | 1.3379 | | 0.5933 | 446.0 | 1338 | 1.3434 | | 0.5933 | 447.0 | 1341 | 1.3378 | | 0.5933 | 448.0 | 1344 | 1.3451 | | 0.5933 | 449.0 | 1347 | 1.3583 | | 0.5933 | 450.0 | 1350 | 1.3498 | | 0.5933 | 451.0 | 1353 | 1.3202 | | 0.5933 | 452.0 | 1356 | 1.3219 | | 0.5933 | 453.0 | 1359 | 1.3534 | | 0.5933 | 454.0 | 1362 | 1.3738 | | 0.5933 | 455.0 | 1365 | 1.3947 | | 0.5933 | 456.0 | 1368 | 1.3863 | | 0.5933 | 457.0 | 1371 | 1.3747 | | 0.5933 | 458.0 | 1374 | 1.3685 | | 0.5933 | 459.0 | 1377 | 1.3519 | | 0.5933 | 460.0 | 1380 | 1.3706 | | 0.5933 | 461.0 | 1383 | 1.3956 | | 0.5933 | 462.0 | 1386 | 1.3628 | | 0.5933 | 463.0 | 1389 | 1.3669 | | 0.5933 | 464.0 | 1392 | 1.3338 | | 0.5933 | 465.0 | 1395 | 1.3316 | | 0.5933 | 466.0 | 1398 | 1.3641 | | 0.5933 | 467.0 | 1401 | 1.3980 | | 0.5933 | 468.0 | 1404 | 1.4046 | | 0.5933 | 469.0 | 1407 | 1.3757 | | 0.5933 | 470.0 | 1410 | 1.3437 | | 0.5933 | 471.0 | 1413 | 1.3552 | | 0.5933 | 472.0 | 1416 | 1.3930 | | 0.5933 | 473.0 | 1419 | 1.3926 | | 0.5933 | 474.0 | 1422 | 1.3316 | | 0.5933 | 475.0 | 1425 | 1.2435 | | 0.5933 | 476.0 | 1428 | 1.2005 | | 0.5933 | 477.0 | 1431 | 1.2154 | | 0.5933 | 478.0 | 1434 | 1.2495 | | 0.5933 | 479.0 | 1437 | 1.2615 | | 0.5933 | 480.0 | 1440 | 1.2665 | | 0.5933 | 481.0 | 1443 | 1.2593 | | 0.5933 | 482.0 | 1446 | 1.2442 | | 0.5933 | 483.0 | 1449 | 1.2603 | | 0.5933 | 484.0 | 1452 | 1.2821 | | 0.5933 | 485.0 | 1455 | 1.2940 | | 0.5933 | 486.0 | 1458 | 1.2904 | | 0.5933 | 487.0 | 1461 | 1.2815 | | 0.5933 | 488.0 | 1464 | 1.2719 | | 0.5933 | 489.0 | 1467 | 1.2950 | | 0.5933 | 490.0 | 1470 | 1.3589 | | 0.5933 | 491.0 | 1473 | 1.4231 | | 0.5933 | 492.0 | 1476 | 1.4325 | | 0.5933 | 493.0 | 1479 | 1.3372 | | 0.5933 | 494.0 | 1482 | 1.2722 | | 0.5933 | 495.0 | 1485 | 1.3250 | | 0.5933 | 496.0 | 1488 | 1.4279 | | 0.5933 | 497.0 | 1491 | 1.4185 | | 0.5933 | 498.0 | 1494 | 1.3254 | | 0.5933 | 499.0 | 1497 | 1.2996 | | 0.5698 | 500.0 | 1500 | 1.2436 | | 0.5698 | 501.0 | 1503 | 1.2112 | | 0.5698 | 502.0 | 1506 | 1.2390 | | 0.5698 | 503.0 | 1509 | 1.2883 | | 0.5698 | 504.0 | 1512 | 1.3407 | | 0.5698 | 505.0 | 1515 | 1.3793 | | 0.5698 | 506.0 | 1518 | 1.4309 | | 0.5698 | 507.0 | 1521 | 1.4088 | | 0.5698 | 508.0 | 1524 | 1.3966 | | 0.5698 | 509.0 | 1527 | 1.4082 | | 0.5698 | 510.0 | 1530 | 1.3814 | | 0.5698 | 511.0 | 1533 | 1.3396 | | 0.5698 | 512.0 | 1536 | 1.3387 | | 0.5698 | 513.0 | 1539 | 1.3057 | | 0.5698 | 514.0 | 1542 | 1.2687 | | 0.5698 | 515.0 | 1545 | 1.2707 | | 0.5698 | 516.0 | 1548 | 1.4157 | | 0.5698 | 517.0 | 1551 | 1.4618 | | 0.5698 | 518.0 | 1554 | 1.4597 | | 0.5698 | 519.0 | 1557 | 1.4605 | | 0.5698 | 520.0 | 1560 | 1.4481 | | 0.5698 | 521.0 | 1563 | 1.4423 | | 0.5698 | 522.0 | 1566 | 1.4312 | | 0.5698 | 523.0 | 1569 | 1.4020 | | 0.5698 | 524.0 | 1572 | 1.3645 | | 0.5698 | 525.0 | 1575 | 1.3438 | | 0.5698 | 526.0 | 1578 | 1.3205 | | 0.5698 | 527.0 | 1581 | 1.3053 | | 0.5698 | 528.0 | 1584 | 1.2944 | | 0.5698 | 529.0 | 1587 | 1.3649 | | 0.5698 | 530.0 | 1590 | 1.4252 | | 0.5698 | 531.0 | 1593 | 1.4653 | | 0.5698 | 532.0 | 1596 | 1.4664 | | 0.5698 | 533.0 | 1599 | 1.4386 | | 0.5698 | 534.0 | 1602 | 1.3703 | | 0.5698 | 535.0 | 1605 | 1.3156 | | 0.5698 | 536.0 | 1608 | 1.3263 | | 0.5698 | 537.0 | 1611 | 1.3055 | | 0.5698 | 538.0 | 1614 | 1.3066 | | 0.5698 | 539.0 | 1617 | 1.3549 | | 0.5698 | 540.0 | 1620 | 1.4445 | | 0.5698 | 541.0 | 1623 | 1.4701 | | 0.5698 | 542.0 | 1626 | 1.4265 | | 0.5698 | 543.0 | 1629 | 1.3599 | | 0.5698 | 544.0 | 1632 | 1.3451 | | 0.5698 | 545.0 | 1635 | 1.3428 | | 0.5698 | 546.0 | 1638 | 1.3231 | | 0.5698 | 547.0 | 1641 | 1.3266 | | 0.5698 | 548.0 | 1644 | 1.3216 | | 0.5698 | 549.0 | 1647 | 1.2599 | | 0.5698 | 550.0 | 1650 | 1.2338 | | 0.5698 | 551.0 | 1653 | 1.2140 | | 0.5698 | 552.0 | 1656 | 1.2297 | | 0.5698 | 553.0 | 1659 | 1.2842 | | 0.5698 | 554.0 | 1662 | 1.3357 | | 0.5698 | 555.0 | 1665 | 1.3797 | | 0.5698 | 556.0 | 1668 | 1.3690 | | 0.5698 | 557.0 | 1671 | 1.3163 | | 0.5698 | 558.0 | 1674 | 1.2510 | | 0.5698 | 559.0 | 1677 | 1.2714 | | 0.5698 | 560.0 | 1680 | 1.3403 | | 0.5698 | 561.0 | 1683 | 1.4387 | | 0.5698 | 562.0 | 1686 | 1.4697 | | 0.5698 | 563.0 | 1689 | 1.4641 | | 0.5698 | 564.0 | 1692 | 1.4123 | | 0.5698 | 565.0 | 1695 | 1.3808 | | 0.5698 | 566.0 | 1698 | 1.3325 | | 0.5698 | 567.0 | 1701 | 1.3470 | | 0.5698 | 568.0 | 1704 | 1.3301 | | 0.5698 | 569.0 | 1707 | 1.3255 | | 0.5698 | 570.0 | 1710 | 1.3614 | | 0.5698 | 571.0 | 1713 | 1.4034 | | 0.5698 | 572.0 | 1716 | 1.4201 | | 0.5698 | 573.0 | 1719 | 1.4221 | | 0.5698 | 574.0 | 1722 | 1.4100 | | 0.5698 | 575.0 | 1725 | 1.3791 | | 0.5698 | 576.0 | 1728 | 1.3478 | | 0.5698 | 577.0 | 1731 | 1.3398 | | 0.5698 | 578.0 | 1734 | 1.3408 | | 0.5698 | 579.0 | 1737 | 1.3577 | | 0.5698 | 580.0 | 1740 | 1.3780 | | 0.5698 | 581.0 | 1743 | 1.3871 | | 0.5698 | 582.0 | 1746 | 1.3754 | | 0.5698 | 583.0 | 1749 | 1.3487 | | 0.5698 | 584.0 | 1752 | 1.3299 | | 0.5698 | 585.0 | 1755 | 1.3215 | | 0.5698 | 586.0 | 1758 | 1.3004 | | 0.5698 | 587.0 | 1761 | 1.2819 | | 0.5698 | 588.0 | 1764 | 1.2804 | | 0.5698 | 589.0 | 1767 | 1.2724 | | 0.5698 | 590.0 | 1770 | 1.2975 | | 0.5698 | 591.0 | 1773 | 1.3615 | | 0.5698 | 592.0 | 1776 | 1.4006 | | 0.5698 | 593.0 | 1779 | 1.4037 | | 0.5698 | 594.0 | 1782 | 1.3882 | | 0.5698 | 595.0 | 1785 | 1.3919 | | 0.5698 | 596.0 | 1788 | 1.3759 | | 0.5698 | 597.0 | 1791 | 1.3215 | | 0.5698 | 598.0 | 1794 | 1.3130 | | 0.5698 | 599.0 | 1797 | 1.3547 | | 0.5698 | 600.0 | 1800 | 1.3832 | | 0.5698 | 601.0 | 1803 | 1.3755 | | 0.5698 | 602.0 | 1806 | 1.3555 | | 0.5698 | 603.0 | 1809 | 1.3085 | | 0.5698 | 604.0 | 1812 | 1.3235 | | 0.5698 | 605.0 | 1815 | 1.3616 | | 0.5698 | 606.0 | 1818 | 1.4128 | | 0.5698 | 607.0 | 1821 | 1.4333 | | 0.5698 | 608.0 | 1824 | 1.4124 | | 0.5698 | 609.0 | 1827 | 1.3622 | | 0.5698 | 610.0 | 1830 | 1.2583 | | 0.5698 | 611.0 | 1833 | 1.2334 | | 0.5698 | 612.0 | 1836 | 1.2316 | | 0.5698 | 613.0 | 1839 | 1.2430 | | 0.5698 | 614.0 | 1842 | 1.2659 | | 0.5698 | 615.0 | 1845 | 1.2801 | | 0.5698 | 616.0 | 1848 | 1.3092 | | 0.5698 | 617.0 | 1851 | 1.3340 | | 0.5698 | 618.0 | 1854 | 1.3543 | | 0.5698 | 619.0 | 1857 | 1.3771 | | 0.5698 | 620.0 | 1860 | 1.3764 | | 0.5698 | 621.0 | 1863 | 1.3577 | | 0.5698 | 622.0 | 1866 | 1.3255 | | 0.5698 | 623.0 | 1869 | 1.2972 | | 0.5698 | 624.0 | 1872 | 1.2877 | | 0.5698 | 625.0 | 1875 | 1.3092 | | 0.5698 | 626.0 | 1878 | 1.3348 | | 0.5698 | 627.0 | 1881 | 1.3486 | | 0.5698 | 628.0 | 1884 | 1.3543 | | 0.5698 | 629.0 | 1887 | 1.3504 | | 0.5698 | 630.0 | 1890 | 1.3544 | | 0.5698 | 631.0 | 1893 | 1.3419 | | 0.5698 | 632.0 | 1896 | 1.3093 | | 0.5698 | 633.0 | 1899 | 1.2775 | | 0.5698 | 634.0 | 1902 | 1.2783 | | 0.5698 | 635.0 | 1905 | 1.2753 | | 0.5698 | 636.0 | 1908 | 1.2506 | | 0.5698 | 637.0 | 1911 | 1.2332 | | 0.5698 | 638.0 | 1914 | 1.2763 | | 0.5698 | 639.0 | 1917 | 1.3084 | | 0.5698 | 640.0 | 1920 | 1.3237 | | 0.5698 | 641.0 | 1923 | 1.3340 | | 0.5698 | 642.0 | 1926 | 1.3339 | | 0.5698 | 643.0 | 1929 | 1.3103 | | 0.5698 | 644.0 | 1932 | 1.2959 | | 0.5698 | 645.0 | 1935 | 1.2915 | | 0.5698 | 646.0 | 1938 | 1.3321 | | 0.5698 | 647.0 | 1941 | 1.3656 | | 0.5698 | 648.0 | 1944 | 1.3728 | | 0.5698 | 649.0 | 1947 | 1.3629 | | 0.5698 | 650.0 | 1950 | 1.3502 | | 0.5698 | 651.0 | 1953 | 1.3297 | | 0.5698 | 652.0 | 1956 | 1.3057 | | 0.5698 | 653.0 | 1959 | 1.3008 | | 0.5698 | 654.0 | 1962 | 1.2932 | | 0.5698 | 655.0 | 1965 | 1.2945 | | 0.5698 | 656.0 | 1968 | 1.2929 | | 0.5698 | 657.0 | 1971 | 1.3073 | | 0.5698 | 658.0 | 1974 | 1.3311 | | 0.5698 | 659.0 | 1977 | 1.3472 | | 0.5698 | 660.0 | 1980 | 1.3409 | | 0.5698 | 661.0 | 1983 | 1.3315 | | 0.5698 | 662.0 | 1986 | 1.3154 | | 0.5698 | 663.0 | 1989 | 1.3030 | | 0.5698 | 664.0 | 1992 | 1.3006 | | 0.5698 | 665.0 | 1995 | 1.2968 | | 0.5698 | 666.0 | 1998 | 1.3045 | | 0.5609 | 667.0 | 2001 | 1.3166 | | 0.5609 | 668.0 | 2004 | 1.3430 | | 0.5609 | 669.0 | 2007 | 1.3718 | | 0.5609 | 670.0 | 2010 | 1.3945 | | 0.5609 | 671.0 | 2013 | 1.3919 | | 0.5609 | 672.0 | 2016 | 1.3895 | | 0.5609 | 673.0 | 2019 | 1.3659 | | 0.5609 | 674.0 | 2022 | 1.3276 | | 0.5609 | 675.0 | 2025 | 1.3060 | | 0.5609 | 676.0 | 2028 | 1.2941 | | 0.5609 | 677.0 | 2031 | 1.2893 | | 0.5609 | 678.0 | 2034 | 1.2937 | | 0.5609 | 679.0 | 2037 | 1.3019 | | 0.5609 | 680.0 | 2040 | 1.3119 | | 0.5609 | 681.0 | 2043 | 1.3222 | | 0.5609 | 682.0 | 2046 | 1.3238 | | 0.5609 | 683.0 | 2049 | 1.3280 | | 0.5609 | 684.0 | 2052 | 1.3324 | | 0.5609 | 685.0 | 2055 | 1.3401 | | 0.5609 | 686.0 | 2058 | 1.3452 | | 0.5609 | 687.0 | 2061 | 1.3752 | | 0.5609 | 688.0 | 2064 | 1.3987 | | 0.5609 | 689.0 | 2067 | 1.4118 | | 0.5609 | 690.0 | 2070 | 1.4179 | | 0.5609 | 691.0 | 2073 | 1.4122 | | 0.5609 | 692.0 | 2076 | 1.3909 | | 0.5609 | 693.0 | 2079 | 1.3439 | | 0.5609 | 694.0 | 2082 | 1.3072 | | 0.5609 | 695.0 | 2085 | 1.2981 | | 0.5609 | 696.0 | 2088 | 1.3195 | | 0.5609 | 697.0 | 2091 | 1.3502 | | 0.5609 | 698.0 | 2094 | 1.3783 | | 0.5609 | 699.0 | 2097 | 1.3925 | | 0.5609 | 700.0 | 2100 | 1.4000 | | 0.5609 | 701.0 | 2103 | 1.3797 | | 0.5609 | 702.0 | 2106 | 1.3620 | | 0.5609 | 703.0 | 2109 | 1.3533 | | 0.5609 | 704.0 | 2112 | 1.3492 | | 0.5609 | 705.0 | 2115 | 1.3400 | | 0.5609 | 706.0 | 2118 | 1.3346 | | 0.5609 | 707.0 | 2121 | 1.3254 | | 0.5609 | 708.0 | 2124 | 1.3290 | | 0.5609 | 709.0 | 2127 | 1.3406 | | 0.5609 | 710.0 | 2130 | 1.3619 | | 0.5609 | 711.0 | 2133 | 1.3898 | | 0.5609 | 712.0 | 2136 | 1.3945 | | 0.5609 | 713.0 | 2139 | 1.3817 | | 0.5609 | 714.0 | 2142 | 1.3686 | | 0.5609 | 715.0 | 2145 | 1.3627 | | 0.5609 | 716.0 | 2148 | 1.3617 | | 0.5609 | 717.0 | 2151 | 1.3548 | | 0.5609 | 718.0 | 2154 | 1.3464 | | 0.5609 | 719.0 | 2157 | 1.3368 | | 0.5609 | 720.0 | 2160 | 1.3138 | | 0.5609 | 721.0 | 2163 | 1.3073 | | 0.5609 | 722.0 | 2166 | 1.3203 | | 0.5609 | 723.0 | 2169 | 1.3342 | | 0.5609 | 724.0 | 2172 | 1.3562 | | 0.5609 | 725.0 | 2175 | 1.3725 | | 0.5609 | 726.0 | 2178 | 1.3748 | | 0.5609 | 727.0 | 2181 | 1.3711 | | 0.5609 | 728.0 | 2184 | 1.3717 | | 0.5609 | 729.0 | 2187 | 1.3627 | | 0.5609 | 730.0 | 2190 | 1.3515 | | 0.5609 | 731.0 | 2193 | 1.3373 | | 0.5609 | 732.0 | 2196 | 1.3160 | | 0.5609 | 733.0 | 2199 | 1.3125 | | 0.5609 | 734.0 | 2202 | 1.3301 | | 0.5609 | 735.0 | 2205 | 1.3197 | | 0.5609 | 736.0 | 2208 | 1.3125 | | 0.5609 | 737.0 | 2211 | 1.3072 | | 0.5609 | 738.0 | 2214 | 1.2798 | | 0.5609 | 739.0 | 2217 | 1.2672 | | 0.5609 | 740.0 | 2220 | 1.2533 | | 0.5609 | 741.0 | 2223 | 1.2383 | | 0.5609 | 742.0 | 2226 | 1.2450 | | 0.5609 | 743.0 | 2229 | 1.2557 | | 0.5609 | 744.0 | 2232 | 1.2751 | | 0.5609 | 745.0 | 2235 | 1.3235 | | 0.5609 | 746.0 | 2238 | 1.3708 | | 0.5609 | 747.0 | 2241 | 1.3867 | | 0.5609 | 748.0 | 2244 | 1.3686 | | 0.5609 | 749.0 | 2247 | 1.3309 | | 0.5609 | 750.0 | 2250 | 1.2811 | | 0.5609 | 751.0 | 2253 | 1.2294 | | 0.5609 | 752.0 | 2256 | 1.1340 | | 0.5609 | 753.0 | 2259 | 1.1346 | | 0.5609 | 754.0 | 2262 | 1.2078 | | 0.5609 | 755.0 | 2265 | 1.2462 | | 0.5609 | 756.0 | 2268 | 1.2557 | | 0.5609 | 757.0 | 2271 | 1.2358 | | 0.5609 | 758.0 | 2274 | 1.2225 | | 0.5609 | 759.0 | 2277 | 1.2298 | | 0.5609 | 760.0 | 2280 | 1.2561 | | 0.5609 | 761.0 | 2283 | 1.2861 | | 0.5609 | 762.0 | 2286 | 1.3017 | | 0.5609 | 763.0 | 2289 | 1.3228 | | 0.5609 | 764.0 | 2292 | 1.3235 | | 0.5609 | 765.0 | 2295 | 1.3232 | | 0.5609 | 766.0 | 2298 | 1.3236 | | 0.5609 | 767.0 | 2301 | 1.3289 | | 0.5609 | 768.0 | 2304 | 1.3324 | | 0.5609 | 769.0 | 2307 | 1.3325 | | 0.5609 | 770.0 | 2310 | 1.3282 | | 0.5609 | 771.0 | 2313 | 1.3176 | | 0.5609 | 772.0 | 2316 | 1.2927 | | 0.5609 | 773.0 | 2319 | 1.2773 | | 0.5609 | 774.0 | 2322 | 1.2617 | | 0.5609 | 775.0 | 2325 | 1.2578 | | 0.5609 | 776.0 | 2328 | 1.2454 | | 0.5609 | 777.0 | 2331 | 1.2212 | | 0.5609 | 778.0 | 2334 | 1.2459 | | 0.5609 | 779.0 | 2337 | 1.3040 | | 0.5609 | 780.0 | 2340 | 1.3453 | | 0.5609 | 781.0 | 2343 | 1.3773 | | 0.5609 | 782.0 | 2346 | 1.3942 | | 0.5609 | 783.0 | 2349 | 1.3854 | | 0.5609 | 784.0 | 2352 | 1.3637 | | 0.5609 | 785.0 | 2355 | 1.3213 | | 0.5609 | 786.0 | 2358 | 1.2795 | | 0.5609 | 787.0 | 2361 | 1.2844 | | 0.5609 | 788.0 | 2364 | 1.3058 | | 0.5609 | 789.0 | 2367 | 1.3198 | | 0.5609 | 790.0 | 2370 | 1.3251 | | 0.5609 | 791.0 | 2373 | 1.3193 | | 0.5609 | 792.0 | 2376 | 1.3021 | | 0.5609 | 793.0 | 2379 | 1.3105 | | 0.5609 | 794.0 | 2382 | 1.3310 | | 0.5609 | 795.0 | 2385 | 1.3574 | | 0.5609 | 796.0 | 2388 | 1.3642 | | 0.5609 | 797.0 | 2391 | 1.3580 | | 0.5609 | 798.0 | 2394 | 1.3255 | | 0.5609 | 799.0 | 2397 | 1.2785 | | 0.5609 | 800.0 | 2400 | 1.2199 | | 0.5609 | 801.0 | 2403 | 1.1221 | | 0.5609 | 802.0 | 2406 | 1.1233 | | 0.5609 | 803.0 | 2409 | 1.1873 | | 0.5609 | 804.0 | 2412 | 1.3435 | | 0.5609 | 805.0 | 2415 | 1.3522 | | 0.5609 | 806.0 | 2418 | 1.3800 | | 0.5609 | 807.0 | 2421 | 1.3976 | | 0.5609 | 808.0 | 2424 | 1.3899 | | 0.5609 | 809.0 | 2427 | 1.3480 | | 0.5609 | 810.0 | 2430 | 1.1934 | | 0.5609 | 811.0 | 2433 | 1.1259 | | 0.5609 | 812.0 | 2436 | 1.1836 | | 0.5609 | 813.0 | 2439 | 1.2207 | | 0.5609 | 814.0 | 2442 | 1.3393 | | 0.5609 | 815.0 | 2445 | 1.4465 | | 0.5609 | 816.0 | 2448 | 1.4166 | | 0.5609 | 817.0 | 2451 | 1.3814 | | 0.5609 | 818.0 | 2454 | 1.3636 | | 0.5609 | 819.0 | 2457 | 1.3334 | | 0.5609 | 820.0 | 2460 | 1.2854 | | 0.5609 | 821.0 | 2463 | 1.2674 | | 0.5609 | 822.0 | 2466 | 1.2533 | | 0.5609 | 823.0 | 2469 | 1.2967 | | 0.5609 | 824.0 | 2472 | 1.3504 | | 0.5609 | 825.0 | 2475 | 1.3052 | | 0.5609 | 826.0 | 2478 | 1.2894 | | 0.5609 | 827.0 | 2481 | 1.3342 | | 0.5609 | 828.0 | 2484 | 1.4139 | | 0.5609 | 829.0 | 2487 | 1.4048 | | 0.5609 | 830.0 | 2490 | 1.3678 | | 0.5609 | 831.0 | 2493 | 1.3604 | | 0.5609 | 832.0 | 2496 | 1.3533 | | 0.5609 | 833.0 | 2499 | 1.3609 | | 0.5608 | 834.0 | 2502 | 1.3909 | | 0.5608 | 835.0 | 2505 | 1.4105 | | 0.5608 | 836.0 | 2508 | 1.4294 | | 0.5608 | 837.0 | 2511 | 1.4313 | | 0.5608 | 838.0 | 2514 | 1.4112 | | 0.5608 | 839.0 | 2517 | 1.3844 | | 0.5608 | 840.0 | 2520 | 1.3769 | | 0.5608 | 841.0 | 2523 | 1.3679 | | 0.5608 | 842.0 | 2526 | 1.3449 | | 0.5608 | 843.0 | 2529 | 1.3389 | | 0.5608 | 844.0 | 2532 | 1.3366 | | 0.5608 | 845.0 | 2535 | 1.3453 | | 0.5608 | 846.0 | 2538 | 1.3726 | | 0.5608 | 847.0 | 2541 | 1.3670 | | 0.5608 | 848.0 | 2544 | 1.3503 | | 0.5608 | 849.0 | 2547 | 1.3262 | | 0.5608 | 850.0 | 2550 | 1.3017 | | 0.5608 | 851.0 | 2553 | 1.2902 | | 0.5608 | 852.0 | 2556 | 1.2662 | | 0.5608 | 853.0 | 2559 | 1.2408 | | 0.5608 | 854.0 | 2562 | 1.2208 | | 0.5608 | 855.0 | 2565 | 1.2003 | | 0.5608 | 856.0 | 2568 | 1.2038 | | 0.5608 | 857.0 | 2571 | 1.2344 | | 0.5608 | 858.0 | 2574 | 1.2968 | | 0.5608 | 859.0 | 2577 | 1.3401 | | 0.5608 | 860.0 | 2580 | 1.3674 | | 0.5608 | 861.0 | 2583 | 1.3837 | | 0.5608 | 862.0 | 2586 | 1.3753 | | 0.5608 | 863.0 | 2589 | 1.3121 | | 0.5608 | 864.0 | 2592 | 1.2480 | | 0.5608 | 865.0 | 2595 | 1.2293 | | 0.5608 | 866.0 | 2598 | 1.2000 | | 0.5608 | 867.0 | 2601 | 1.2027 | | 0.5608 | 868.0 | 2604 | 1.2281 | | 0.5608 | 869.0 | 2607 | 1.2710 | | 0.5608 | 870.0 | 2610 | 1.3535 | | 0.5608 | 871.0 | 2613 | 1.3937 | | 0.5608 | 872.0 | 2616 | 1.4003 | | 0.5608 | 873.0 | 2619 | 1.3758 | | 0.5608 | 874.0 | 2622 | 1.3253 | | 0.5608 | 875.0 | 2625 | 1.2449 | | 0.5608 | 876.0 | 2628 | 1.1745 | | 0.5608 | 877.0 | 2631 | 1.1366 | | 0.5608 | 878.0 | 2634 | 1.1655 | | 0.5608 | 879.0 | 2637 | 1.2965 | | 0.5608 | 880.0 | 2640 | 1.3166 | | 0.5608 | 881.0 | 2643 | 1.3225 | | 0.5608 | 882.0 | 2646 | 1.3141 | | 0.5608 | 883.0 | 2649 | 1.2992 | | 0.5608 | 884.0 | 2652 | 1.2834 | | 0.5608 | 885.0 | 2655 | 1.2698 | | 0.5608 | 886.0 | 2658 | 1.2829 | | 0.5608 | 887.0 | 2661 | 1.3100 | | 0.5608 | 888.0 | 2664 | 1.3314 | | 0.5608 | 889.0 | 2667 | 1.3393 | | 0.5608 | 890.0 | 2670 | 1.3354 | | 0.5608 | 891.0 | 2673 | 1.3278 | | 0.5608 | 892.0 | 2676 | 1.3333 | | 0.5608 | 893.0 | 2679 | 1.3443 | | 0.5608 | 894.0 | 2682 | 1.3343 | | 0.5608 | 895.0 | 2685 | 1.3148 | | 0.5608 | 896.0 | 2688 | 1.2858 | | 0.5608 | 897.0 | 2691 | 1.2698 | | 0.5608 | 898.0 | 2694 | 1.2777 | | 0.5608 | 899.0 | 2697 | 1.2901 | | 0.5608 | 900.0 | 2700 | 1.3008 | | 0.5608 | 901.0 | 2703 | 1.3260 | | 0.5608 | 902.0 | 2706 | 1.3440 | | 0.5608 | 903.0 | 2709 | 1.3438 | | 0.5608 | 904.0 | 2712 | 1.3380 | | 0.5608 | 905.0 | 2715 | 1.3237 | | 0.5608 | 906.0 | 2718 | 1.3145 | | 0.5608 | 907.0 | 2721 | 1.3022 | | 0.5608 | 908.0 | 2724 | 1.2902 | | 0.5608 | 909.0 | 2727 | 1.2793 | | 0.5608 | 910.0 | 2730 | 1.2909 | | 0.5608 | 911.0 | 2733 | 1.3084 | | 0.5608 | 912.0 | 2736 | 1.3185 | | 0.5608 | 913.0 | 2739 | 1.3250 | | 0.5608 | 914.0 | 2742 | 1.3412 | | 0.5608 | 915.0 | 2745 | 1.3491 | | 0.5608 | 916.0 | 2748 | 1.3561 | | 0.5608 | 917.0 | 2751 | 1.3675 | | 0.5608 | 918.0 | 2754 | 1.3759 | | 0.5608 | 919.0 | 2757 | 1.3829 | | 0.5608 | 920.0 | 2760 | 1.3805 | | 0.5608 | 921.0 | 2763 | 1.3669 | | 0.5608 | 922.0 | 2766 | 1.3605 | | 0.5608 | 923.0 | 2769 | 1.3455 | | 0.5608 | 924.0 | 2772 | 1.3373 | | 0.5608 | 925.0 | 2775 | 1.3440 | | 0.5608 | 926.0 | 2778 | 1.3408 | | 0.5608 | 927.0 | 2781 | 1.3424 | | 0.5608 | 928.0 | 2784 | 1.3414 | | 0.5608 | 929.0 | 2787 | 1.3383 | | 0.5608 | 930.0 | 2790 | 1.3371 | | 0.5608 | 931.0 | 2793 | 1.3406 | | 0.5608 | 932.0 | 2796 | 1.3432 | | 0.5608 | 933.0 | 2799 | 1.3564 | | 0.5608 | 934.0 | 2802 | 1.3773 | | 0.5608 | 935.0 | 2805 | 1.3931 | | 0.5608 | 936.0 | 2808 | 1.4030 | | 0.5608 | 937.0 | 2811 | 1.3998 | | 0.5608 | 938.0 | 2814 | 1.3955 | | 0.5608 | 939.0 | 2817 | 1.3937 | | 0.5608 | 940.0 | 2820 | 1.3801 | | 0.5608 | 941.0 | 2823 | 1.3729 | | 0.5608 | 942.0 | 2826 | 1.3679 | | 0.5608 | 943.0 | 2829 | 1.3550 | | 0.5608 | 944.0 | 2832 | 1.3437 | | 0.5608 | 945.0 | 2835 | 1.3347 | | 0.5608 | 946.0 | 2838 | 1.3220 | | 0.5608 | 947.0 | 2841 | 1.2968 | | 0.5608 | 948.0 | 2844 | 1.2799 | | 0.5608 | 949.0 | 2847 | 1.2549 | | 0.5608 | 950.0 | 2850 | 1.2459 | | 0.5608 | 951.0 | 2853 | 1.2461 | | 0.5608 | 952.0 | 2856 | 1.2299 | | 0.5608 | 953.0 | 2859 | 1.2177 | | 0.5608 | 954.0 | 2862 | 1.2640 | | 0.5608 | 955.0 | 2865 | 1.2997 | | 0.5608 | 956.0 | 2868 | 1.2971 | | 0.5608 | 957.0 | 2871 | 1.2788 | | 0.5608 | 958.0 | 2874 | 1.2858 | | 0.5608 | 959.0 | 2877 | 1.2694 | | 0.5608 | 960.0 | 2880 | 1.2542 | | 0.5608 | 961.0 | 2883 | 1.2733 | | 0.5608 | 962.0 | 2886 | 1.3086 | | 0.5608 | 963.0 | 2889 | 1.3123 | | 0.5608 | 964.0 | 2892 | 1.3039 | | 0.5608 | 965.0 | 2895 | 1.2834 | | 0.5608 | 966.0 | 2898 | 1.2809 | | 0.5608 | 967.0 | 2901 | 1.2696 | | 0.5608 | 968.0 | 2904 | 1.2567 | | 0.5608 | 969.0 | 2907 | 1.2497 | | 0.5608 | 970.0 | 2910 | 1.2639 | | 0.5608 | 971.0 | 2913 | 1.2809 | | 0.5608 | 972.0 | 2916 | 1.2881 | | 0.5608 | 973.0 | 2919 | 1.3082 | | 0.5608 | 974.0 | 2922 | 1.3283 | | 0.5608 | 975.0 | 2925 | 1.3331 | | 0.5608 | 976.0 | 2928 | 1.3384 | | 0.5608 | 977.0 | 2931 | 1.3405 | | 0.5608 | 978.0 | 2934 | 1.3515 | | 0.5608 | 979.0 | 2937 | 1.3734 | | 0.5608 | 980.0 | 2940 | 1.3875 | | 0.5608 | 981.0 | 2943 | 1.3766 | | 0.5608 | 982.0 | 2946 | 1.3530 | | 0.5608 | 983.0 | 2949 | 1.3309 | | 0.5608 | 984.0 | 2952 | 1.3178 | | 0.5608 | 985.0 | 2955 | 1.2963 | | 0.5608 | 986.0 | 2958 | 1.2672 | | 0.5608 | 987.0 | 2961 | 1.2697 | | 0.5608 | 988.0 | 2964 | 1.2620 | | 0.5608 | 989.0 | 2967 | 1.2438 | | 0.5608 | 990.0 | 2970 | 1.2488 | | 0.5608 | 991.0 | 2973 | 1.2630 | | 0.5608 | 992.0 | 2976 | 1.2496 | | 0.5608 | 993.0 | 2979 | 1.2646 | | 0.5608 | 994.0 | 2982 | 1.3051 | | 0.5608 | 995.0 | 2985 | 1.3445 | | 0.5608 | 996.0 | 2988 | 1.3551 | | 0.5608 | 997.0 | 2991 | 1.3600 | | 0.5608 | 998.0 | 2994 | 1.3566 | | 0.5608 | 999.0 | 2997 | 1.3485 | | 0.5596 | 1000.0 | 3000 | 1.3403 | | 0.5596 | 1001.0 | 3003 | 1.3328 | | 0.5596 | 1002.0 | 3006 | 1.3367 | | 0.5596 | 1003.0 | 3009 | 1.3306 | | 0.5596 | 1004.0 | 3012 | 1.3026 | | 0.5596 | 1005.0 | 3015 | 1.2606 | | 0.5596 | 1006.0 | 3018 | 1.2459 | | 0.5596 | 1007.0 | 3021 | 1.2332 | | 0.5596 | 1008.0 | 3024 | 1.2062 | | 0.5596 | 1009.0 | 3027 | 1.1985 | | 0.5596 | 1010.0 | 3030 | 1.1937 | | 0.5596 | 1011.0 | 3033 | 1.1920 | | 0.5596 | 1012.0 | 3036 | 1.1953 | | 0.5596 | 1013.0 | 3039 | 1.1919 | | 0.5596 | 1014.0 | 3042 | 1.1809 | | 0.5596 | 1015.0 | 3045 | 1.1649 | | 0.5596 | 1016.0 | 3048 | 1.1612 | | 0.5596 | 1017.0 | 3051 | 1.1667 | | 0.5596 | 1018.0 | 3054 | 1.1732 | | 0.5596 | 1019.0 | 3057 | 1.1847 | | 0.5596 | 1020.0 | 3060 | 1.1990 | | 0.5596 | 1021.0 | 3063 | 1.2160 | | 0.5596 | 1022.0 | 3066 | 1.2672 | | 0.5596 | 1023.0 | 3069 | 1.3042 | | 0.5596 | 1024.0 | 3072 | 1.3417 | | 0.5596 | 1025.0 | 3075 | 1.3652 | | 0.5596 | 1026.0 | 3078 | 1.3665 | | 0.5596 | 1027.0 | 3081 | 1.3571 | | 0.5596 | 1028.0 | 3084 | 1.3403 | | 0.5596 | 1029.0 | 3087 | 1.3310 | | 0.5596 | 1030.0 | 3090 | 1.3274 | | 0.5596 | 1031.0 | 3093 | 1.3228 | | 0.5596 | 1032.0 | 3096 | 1.2960 | | 0.5596 | 1033.0 | 3099 | 1.2831 | | 0.5596 | 1034.0 | 3102 | 1.2817 | | 0.5596 | 1035.0 | 3105 | 1.2808 | | 0.5596 | 1036.0 | 3108 | 1.2747 | | 0.5596 | 1037.0 | 3111 | 1.2732 | | 0.5596 | 1038.0 | 3114 | 1.2738 | | 0.5596 | 1039.0 | 3117 | 1.2797 | | 0.5596 | 1040.0 | 3120 | 1.2912 | | 0.5596 | 1041.0 | 3123 | 1.3257 | | 0.5596 | 1042.0 | 3126 | 1.3495 | | 0.5596 | 1043.0 | 3129 | 1.3620 | | 0.5596 | 1044.0 | 3132 | 1.3673 | | 0.5596 | 1045.0 | 3135 | 1.3723 | | 0.5596 | 1046.0 | 3138 | 1.3709 | | 0.5596 | 1047.0 | 3141 | 1.3701 | | 0.5596 | 1048.0 | 3144 | 1.3690 | | 0.5596 | 1049.0 | 3147 | 1.3811 | | 0.5596 | 1050.0 | 3150 | 1.3936 | | 0.5596 | 1051.0 | 3153 | 1.3898 | | 0.5596 | 1052.0 | 3156 | 1.3976 | | 0.5596 | 1053.0 | 3159 | 1.3920 | | 0.5596 | 1054.0 | 3162 | 1.3665 | | 0.5596 | 1055.0 | 3165 | 1.3330 | | 0.5596 | 1056.0 | 3168 | 1.3195 | | 0.5596 | 1057.0 | 3171 | 1.3350 | | 0.5596 | 1058.0 | 3174 | 1.3444 | | 0.5596 | 1059.0 | 3177 | 1.3567 | | 0.5596 | 1060.0 | 3180 | 1.3821 | | 0.5596 | 1061.0 | 3183 | 1.3965 | | 0.5596 | 1062.0 | 3186 | 1.4039 | | 0.5596 | 1063.0 | 3189 | 1.4126 | | 0.5596 | 1064.0 | 3192 | 1.4127 | | 0.5596 | 1065.0 | 3195 | 1.4188 | | 0.5596 | 1066.0 | 3198 | 1.4220 | | 0.5596 | 1067.0 | 3201 | 1.4240 | | 0.5596 | 1068.0 | 3204 | 1.4197 | | 0.5596 | 1069.0 | 3207 | 1.4138 | | 0.5596 | 1070.0 | 3210 | 1.4155 | | 0.5596 | 1071.0 | 3213 | 1.4155 | | 0.5596 | 1072.0 | 3216 | 1.4227 | | 0.5596 | 1073.0 | 3219 | 1.4209 | | 0.5596 | 1074.0 | 3222 | 1.4186 | | 0.5596 | 1075.0 | 3225 | 1.4118 | | 0.5596 | 1076.0 | 3228 | 1.3992 | | 0.5596 | 1077.0 | 3231 | 1.3924 | | 0.5596 | 1078.0 | 3234 | 1.3884 | | 0.5596 | 1079.0 | 3237 | 1.3913 | | 0.5596 | 1080.0 | 3240 | 1.3882 | | 0.5596 | 1081.0 | 3243 | 1.3765 | | 0.5596 | 1082.0 | 3246 | 1.3725 | | 0.5596 | 1083.0 | 3249 | 1.3893 | | 0.5596 | 1084.0 | 3252 | 1.3933 | | 0.5596 | 1085.0 | 3255 | 1.4005 | | 0.5596 | 1086.0 | 3258 | 1.4017 | | 0.5596 | 1087.0 | 3261 | 1.4086 | | 0.5596 | 1088.0 | 3264 | 1.4195 | | 0.5596 | 1089.0 | 3267 | 1.4274 | | 0.5596 | 1090.0 | 3270 | 1.4258 | | 0.5596 | 1091.0 | 3273 | 1.4179 | | 0.5596 | 1092.0 | 3276 | 1.4090 | | 0.5596 | 1093.0 | 3279 | 1.3901 | | 0.5596 | 1094.0 | 3282 | 1.3714 | | 0.5596 | 1095.0 | 3285 | 1.3512 | | 0.5596 | 1096.0 | 3288 | 1.3355 | | 0.5596 | 1097.0 | 3291 | 1.3368 | | 0.5596 | 1098.0 | 3294 | 1.3421 | | 0.5596 | 1099.0 | 3297 | 1.3195 | | 0.5596 | 1100.0 | 3300 | 1.2919 | | 0.5596 | 1101.0 | 3303 | 1.2551 | | 0.5596 | 1102.0 | 3306 | 1.2370 | | 0.5596 | 1103.0 | 3309 | 1.2445 | | 0.5596 | 1104.0 | 3312 | 1.2213 | | 0.5596 | 1105.0 | 3315 | 1.2361 | | 0.5596 | 1106.0 | 3318 | 1.3104 | | 0.5596 | 1107.0 | 3321 | 1.3632 | | 0.5596 | 1108.0 | 3324 | 1.3822 | | 0.5596 | 1109.0 | 3327 | 1.3887 | | 0.5596 | 1110.0 | 3330 | 1.3920 | | 0.5596 | 1111.0 | 3333 | 1.3876 | | 0.5596 | 1112.0 | 3336 | 1.3874 | | 0.5596 | 1113.0 | 3339 | 1.3850 | | 0.5596 | 1114.0 | 3342 | 1.3685 | | 0.5596 | 1115.0 | 3345 | 1.3439 | | 0.5596 | 1116.0 | 3348 | 1.3327 | | 0.5596 | 1117.0 | 3351 | 1.3158 | | 0.5596 | 1118.0 | 3354 | 1.3046 | | 0.5596 | 1119.0 | 3357 | 1.2996 | | 0.5596 | 1120.0 | 3360 | 1.2958 | | 0.5596 | 1121.0 | 3363 | 1.2871 | | 0.5596 | 1122.0 | 3366 | 1.2576 | | 0.5596 | 1123.0 | 3369 | 1.2534 | | 0.5596 | 1124.0 | 3372 | 1.2344 | | 0.5596 | 1125.0 | 3375 | 1.2290 | | 0.5596 | 1126.0 | 3378 | 1.2363 | | 0.5596 | 1127.0 | 3381 | 1.2271 | | 0.5596 | 1128.0 | 3384 | 1.2219 | | 0.5596 | 1129.0 | 3387 | 1.2365 | | 0.5596 | 1130.0 | 3390 | 1.2537 | | 0.5596 | 1131.0 | 3393 | 1.2754 | | 0.5596 | 1132.0 | 3396 | 1.2962 | | 0.5596 | 1133.0 | 3399 | 1.3161 | | 0.5596 | 1134.0 | 3402 | 1.3244 | | 0.5596 | 1135.0 | 3405 | 1.3309 | | 0.5596 | 1136.0 | 3408 | 1.3317 | | 0.5596 | 1137.0 | 3411 | 1.3369 | | 0.5596 | 1138.0 | 3414 | 1.3336 | | 0.5596 | 1139.0 | 3417 | 1.3099 | | 0.5596 | 1140.0 | 3420 | 1.2747 | | 0.5596 | 1141.0 | 3423 | 1.2515 | | 0.5596 | 1142.0 | 3426 | 1.2653 | | 0.5596 | 1143.0 | 3429 | 1.2975 | | 0.5596 | 1144.0 | 3432 | 1.3184 | | 0.5596 | 1145.0 | 3435 | 1.3373 | | 0.5596 | 1146.0 | 3438 | 1.3265 | | 0.5596 | 1147.0 | 3441 | 1.3195 | | 0.5596 | 1148.0 | 3444 | 1.3177 | | 0.5596 | 1149.0 | 3447 | 1.3045 | | 0.5596 | 1150.0 | 3450 | 1.3045 | | 0.5596 | 1151.0 | 3453 | 1.3020 | | 0.5596 | 1152.0 | 3456 | 1.3021 | | 0.5596 | 1153.0 | 3459 | 1.3238 | | 0.5596 | 1154.0 | 3462 | 1.3351 | | 0.5596 | 1155.0 | 3465 | 1.3334 | | 0.5596 | 1156.0 | 3468 | 1.3274 | | 0.5596 | 1157.0 | 3471 | 1.3276 | | 0.5596 | 1158.0 | 3474 | 1.3119 | | 0.5596 | 1159.0 | 3477 | 1.2913 | | 0.5596 | 1160.0 | 3480 | 1.2919 | | 0.5596 | 1161.0 | 3483 | 1.2927 | | 0.5596 | 1162.0 | 3486 | 1.3079 | | 0.5596 | 1163.0 | 3489 | 1.3195 | | 0.5596 | 1164.0 | 3492 | 1.3286 | | 0.5596 | 1165.0 | 3495 | 1.3375 | | 0.5596 | 1166.0 | 3498 | 1.3493 | | 0.5594 | 1167.0 | 3501 | 1.3599 | | 0.5594 | 1168.0 | 3504 | 1.3644 | | 0.5594 | 1169.0 | 3507 | 1.3595 | | 0.5594 | 1170.0 | 3510 | 1.3476 | | 0.5594 | 1171.0 | 3513 | 1.3464 | | 0.5594 | 1172.0 | 3516 | 1.3592 | | 0.5594 | 1173.0 | 3519 | 1.3673 | | 0.5594 | 1174.0 | 3522 | 1.3682 | | 0.5594 | 1175.0 | 3525 | 1.3569 | | 0.5594 | 1176.0 | 3528 | 1.3434 | | 0.5594 | 1177.0 | 3531 | 1.3439 | | 0.5594 | 1178.0 | 3534 | 1.3386 | | 0.5594 | 1179.0 | 3537 | 1.3180 | | 0.5594 | 1180.0 | 3540 | 1.2994 | | 0.5594 | 1181.0 | 3543 | 1.2888 | | 0.5594 | 1182.0 | 3546 | 1.2911 | | 0.5594 | 1183.0 | 3549 | 1.2966 | | 0.5594 | 1184.0 | 3552 | 1.2888 | | 0.5594 | 1185.0 | 3555 | 1.2784 | | 0.5594 | 1186.0 | 3558 | 1.2811 | | 0.5594 | 1187.0 | 3561 | 1.2813 | | 0.5594 | 1188.0 | 3564 | 1.2797 | | 0.5594 | 1189.0 | 3567 | 1.2683 | | 0.5594 | 1190.0 | 3570 | 1.2736 | | 0.5594 | 1191.0 | 3573 | 1.2614 | | 0.5594 | 1192.0 | 3576 | 1.2485 | | 0.5594 | 1193.0 | 3579 | 1.2446 | | 0.5594 | 1194.0 | 3582 | 1.2077 | | 0.5594 | 1195.0 | 3585 | 1.1880 | | 0.5594 | 1196.0 | 3588 | 1.1797 | | 0.5594 | 1197.0 | 3591 | 1.1750 | | 0.5594 | 1198.0 | 3594 | 1.1964 | | 0.5594 | 1199.0 | 3597 | 1.2570 | | 0.5594 | 1200.0 | 3600 | 1.3173 | | 0.5594 | 1201.0 | 3603 | 1.3393 | | 0.5594 | 1202.0 | 3606 | 1.3465 | | 0.5594 | 1203.0 | 3609 | 1.3254 | | 0.5594 | 1204.0 | 3612 | 1.3003 | | 0.5594 | 1205.0 | 3615 | 1.2560 | | 0.5594 | 1206.0 | 3618 | 1.2008 | | 0.5594 | 1207.0 | 3621 | 1.1804 | | 0.5594 | 1208.0 | 3624 | 1.1725 | | 0.5594 | 1209.0 | 3627 | 1.1634 | | 0.5594 | 1210.0 | 3630 | 1.1744 | | 0.5594 | 1211.0 | 3633 | 1.1912 | | 0.5594 | 1212.0 | 3636 | 1.2141 | | 0.5594 | 1213.0 | 3639 | 1.2444 | | 0.5594 | 1214.0 | 3642 | 1.2703 | | 0.5594 | 1215.0 | 3645 | 1.2812 | | 0.5594 | 1216.0 | 3648 | 1.2849 | | 0.5594 | 1217.0 | 3651 | 1.2871 | | 0.5594 | 1218.0 | 3654 | 1.2800 | | 0.5594 | 1219.0 | 3657 | 1.2755 | | 0.5594 | 1220.0 | 3660 | 1.2668 | | 0.5594 | 1221.0 | 3663 | 1.2512 | | 0.5594 | 1222.0 | 3666 | 1.2390 | | 0.5594 | 1223.0 | 3669 | 1.2268 | | 0.5594 | 1224.0 | 3672 | 1.2071 | | 0.5594 | 1225.0 | 3675 | 1.1804 | | 0.5594 | 1226.0 | 3678 | 1.1572 | | 0.5594 | 1227.0 | 3681 | 1.1618 | | 0.5594 | 1228.0 | 3684 | 1.1741 | | 0.5594 | 1229.0 | 3687 | 1.1867 | | 0.5594 | 1230.0 | 3690 | 1.1978 | | 0.5594 | 1231.0 | 3693 | 1.2180 | | 0.5594 | 1232.0 | 3696 | 1.2379 | | 0.5594 | 1233.0 | 3699 | 1.2486 | | 0.5594 | 1234.0 | 3702 | 1.2526 | | 0.5594 | 1235.0 | 3705 | 1.2632 | | 0.5594 | 1236.0 | 3708 | 1.2866 | | 0.5594 | 1237.0 | 3711 | 1.2903 | | 0.5594 | 1238.0 | 3714 | 1.2655 | | 0.5594 | 1239.0 | 3717 | 1.2452 | | 0.5594 | 1240.0 | 3720 | 1.2348 | | 0.5594 | 1241.0 | 3723 | 1.1997 | | 0.5594 | 1242.0 | 3726 | 1.1615 | | 0.5594 | 1243.0 | 3729 | 1.1294 | | 0.5594 | 1244.0 | 3732 | 1.1171 | | 0.5594 | 1245.0 | 3735 | 1.1613 | | 0.5594 | 1246.0 | 3738 | 1.2428 | | 0.5594 | 1247.0 | 3741 | 1.2627 | | 0.5594 | 1248.0 | 3744 | 1.2525 | | 0.5594 | 1249.0 | 3747 | 1.2029 | | 0.5594 | 1250.0 | 3750 | 1.1155 | | 0.5594 | 1251.0 | 3753 | 1.0784 | | 0.5594 | 1252.0 | 3756 | 1.0683 | | 0.5594 | 1253.0 | 3759 | 1.0901 | | 0.5594 | 1254.0 | 3762 | 1.1788 | | 0.5594 | 1255.0 | 3765 | 1.2079 | | 0.5594 | 1256.0 | 3768 | 1.2129 | | 0.5594 | 1257.0 | 3771 | 1.2088 | | 0.5594 | 1258.0 | 3774 | 1.1948 | | 0.5594 | 1259.0 | 3777 | 1.1811 | | 0.5594 | 1260.0 | 3780 | 1.1757 | | 0.5594 | 1261.0 | 3783 | 1.1764 | | 0.5594 | 1262.0 | 3786 | 1.1673 | | 0.5594 | 1263.0 | 3789 | 1.1421 | | 0.5594 | 1264.0 | 3792 | 1.1351 | | 0.5594 | 1265.0 | 3795 | 1.1570 | | 0.5594 | 1266.0 | 3798 | 1.1854 | | 0.5594 | 1267.0 | 3801 | 1.1974 | | 0.5594 | 1268.0 | 3804 | 1.2039 | | 0.5594 | 1269.0 | 3807 | 1.1966 | | 0.5594 | 1270.0 | 3810 | 1.2079 | | 0.5594 | 1271.0 | 3813 | 1.2104 | | 0.5594 | 1272.0 | 3816 | 1.2171 | | 0.5594 | 1273.0 | 3819 | 1.2335 | | 0.5594 | 1274.0 | 3822 | 1.2483 | | 0.5594 | 1275.0 | 3825 | 1.2607 | | 0.5594 | 1276.0 | 3828 | 1.2586 | | 0.5594 | 1277.0 | 3831 | 1.2527 | | 0.5594 | 1278.0 | 3834 | 1.2457 | | 0.5594 | 1279.0 | 3837 | 1.2451 | | 0.5594 | 1280.0 | 3840 | 1.2669 | | 0.5594 | 1281.0 | 3843 | 1.2651 | | 0.5594 | 1282.0 | 3846 | 1.2585 | | 0.5594 | 1283.0 | 3849 | 1.2459 | | 0.5594 | 1284.0 | 3852 | 1.2272 | | 0.5594 | 1285.0 | 3855 | 1.2195 | | 0.5594 | 1286.0 | 3858 | 1.2154 | | 0.5594 | 1287.0 | 3861 | 1.2234 | | 0.5594 | 1288.0 | 3864 | 1.2386 | | 0.5594 | 1289.0 | 3867 | 1.2574 | | 0.5594 | 1290.0 | 3870 | 1.2844 | | 0.5594 | 1291.0 | 3873 | 1.3160 | | 0.5594 | 1292.0 | 3876 | 1.3283 | | 0.5594 | 1293.0 | 3879 | 1.3256 | | 0.5594 | 1294.0 | 3882 | 1.3101 | | 0.5594 | 1295.0 | 3885 | 1.2981 | | 0.5594 | 1296.0 | 3888 | 1.2863 | | 0.5594 | 1297.0 | 3891 | 1.2822 | | 0.5594 | 1298.0 | 3894 | 1.2751 | | 0.5594 | 1299.0 | 3897 | 1.2609 | | 0.5594 | 1300.0 | 3900 | 1.2539 | | 0.5594 | 1301.0 | 3903 | 1.2455 | | 0.5594 | 1302.0 | 3906 | 1.2458 | | 0.5594 | 1303.0 | 3909 | 1.2390 | | 0.5594 | 1304.0 | 3912 | 1.2530 | | 0.5594 | 1305.0 | 3915 | 1.2605 | | 0.5594 | 1306.0 | 3918 | 1.2669 | | 0.5594 | 1307.0 | 3921 | 1.2699 | | 0.5594 | 1308.0 | 3924 | 1.2581 | | 0.5594 | 1309.0 | 3927 | 1.2481 | | 0.5594 | 1310.0 | 3930 | 1.2469 | | 0.5594 | 1311.0 | 3933 | 1.2540 | | 0.5594 | 1312.0 | 3936 | 1.2708 | | 0.5594 | 1313.0 | 3939 | 1.2828 | | 0.5594 | 1314.0 | 3942 | 1.2897 | | 0.5594 | 1315.0 | 3945 | 1.2939 | | 0.5594 | 1316.0 | 3948 | 1.2995 | | 0.5594 | 1317.0 | 3951 | 1.3066 | | 0.5594 | 1318.0 | 3954 | 1.3168 | | 0.5594 | 1319.0 | 3957 | 1.3175 | | 0.5594 | 1320.0 | 3960 | 1.3122 | | 0.5594 | 1321.0 | 3963 | 1.3059 | | 0.5594 | 1322.0 | 3966 | 1.2981 | | 0.5594 | 1323.0 | 3969 | 1.2889 | | 0.5594 | 1324.0 | 3972 | 1.2831 | | 0.5594 | 1325.0 | 3975 | 1.2885 | | 0.5594 | 1326.0 | 3978 | 1.2866 | | 0.5594 | 1327.0 | 3981 | 1.2813 | | 0.5594 | 1328.0 | 3984 | 1.2779 | | 0.5594 | 1329.0 | 3987 | 1.2776 | | 0.5594 | 1330.0 | 3990 | 1.2799 | | 0.5594 | 1331.0 | 3993 | 1.2826 | | 0.5594 | 1332.0 | 3996 | 1.2839 | | 0.5594 | 1333.0 | 3999 | 1.2864 | | 0.5596 | 1334.0 | 4002 | 1.2831 | | 0.5596 | 1335.0 | 4005 | 1.2768 | | 0.5596 | 1336.0 | 4008 | 1.2694 | | 0.5596 | 1337.0 | 4011 | 1.2594 | | 0.5596 | 1338.0 | 4014 | 1.2453 | | 0.5596 | 1339.0 | 4017 | 1.2447 | | 0.5596 | 1340.0 | 4020 | 1.2359 | | 0.5596 | 1341.0 | 4023 | 1.2253 | | 0.5596 | 1342.0 | 4026 | 1.2114 | | 0.5596 | 1343.0 | 4029 | 1.2037 | | 0.5596 | 1344.0 | 4032 | 1.1957 | | 0.5596 | 1345.0 | 4035 | 1.2045 | | 0.5596 | 1346.0 | 4038 | 1.2123 | | 0.5596 | 1347.0 | 4041 | 1.2362 | | 0.5596 | 1348.0 | 4044 | 1.2613 | | 0.5596 | 1349.0 | 4047 | 1.2745 | | 0.5596 | 1350.0 | 4050 | 1.2848 | | 0.5596 | 1351.0 | 4053 | 1.2939 | | 0.5596 | 1352.0 | 4056 | 1.2986 | | 0.5596 | 1353.0 | 4059 | 1.2994 | | 0.5596 | 1354.0 | 4062 | 1.3032 | | 0.5596 | 1355.0 | 4065 | 1.3034 | | 0.5596 | 1356.0 | 4068 | 1.3160 | | 0.5596 | 1357.0 | 4071 | 1.3207 | | 0.5596 | 1358.0 | 4074 | 1.3250 | | 0.5596 | 1359.0 | 4077 | 1.3295 | | 0.5596 | 1360.0 | 4080 | 1.3291 | | 0.5596 | 1361.0 | 4083 | 1.3191 | | 0.5596 | 1362.0 | 4086 | 1.3077 | | 0.5596 | 1363.0 | 4089 | 1.3023 | | 0.5596 | 1364.0 | 4092 | 1.2966 | | 0.5596 | 1365.0 | 4095 | 1.2871 | | 0.5596 | 1366.0 | 4098 | 1.2758 | | 0.5596 | 1367.0 | 4101 | 1.2703 | | 0.5596 | 1368.0 | 4104 | 1.2790 | | 0.5596 | 1369.0 | 4107 | 1.2936 | | 0.5596 | 1370.0 | 4110 | 1.3103 | | 0.5596 | 1371.0 | 4113 | 1.3330 | | 0.5596 | 1372.0 | 4116 | 1.3600 | | 0.5596 | 1373.0 | 4119 | 1.3767 | | 0.5596 | 1374.0 | 4122 | 1.3858 | | 0.5596 | 1375.0 | 4125 | 1.3881 | | 0.5596 | 1376.0 | 4128 | 1.4005 | | 0.5596 | 1377.0 | 4131 | 1.4086 | | 0.5596 | 1378.0 | 4134 | 1.4082 | | 0.5596 | 1379.0 | 4137 | 1.4018 | | 0.5596 | 1380.0 | 4140 | 1.3900 | | 0.5596 | 1381.0 | 4143 | 1.3746 | | 0.5596 | 1382.0 | 4146 | 1.3608 | | 0.5596 | 1383.0 | 4149 | 1.3483 | | 0.5596 | 1384.0 | 4152 | 1.3343 | | 0.5596 | 1385.0 | 4155 | 1.3260 | | 0.5596 | 1386.0 | 4158 | 1.3144 | | 0.5596 | 1387.0 | 4161 | 1.3131 | | 0.5596 | 1388.0 | 4164 | 1.3051 | | 0.5596 | 1389.0 | 4167 | 1.2853 | | 0.5596 | 1390.0 | 4170 | 1.2701 | | 0.5596 | 1391.0 | 4173 | 1.2635 | | 0.5596 | 1392.0 | 4176 | 1.2494 | | 0.5596 | 1393.0 | 4179 | 1.2337 | | 0.5596 | 1394.0 | 4182 | 1.2267 | | 0.5596 | 1395.0 | 4185 | 1.2422 | | 0.5596 | 1396.0 | 4188 | 1.2575 | | 0.5596 | 1397.0 | 4191 | 1.2733 | | 0.5596 | 1398.0 | 4194 | 1.2838 | | 0.5596 | 1399.0 | 4197 | 1.2898 | | 0.5596 | 1400.0 | 4200 | 1.2937 | | 0.5596 | 1401.0 | 4203 | 1.2934 | | 0.5596 | 1402.0 | 4206 | 1.2967 | | 0.5596 | 1403.0 | 4209 | 1.2893 | | 0.5596 | 1404.0 | 4212 | 1.2796 | | 0.5596 | 1405.0 | 4215 | 1.2877 | | 0.5596 | 1406.0 | 4218 | 1.3098 | | 0.5596 | 1407.0 | 4221 | 1.3252 | | 0.5596 | 1408.0 | 4224 | 1.3205 | | 0.5596 | 1409.0 | 4227 | 1.3168 | | 0.5596 | 1410.0 | 4230 | 1.3169 | | 0.5596 | 1411.0 | 4233 | 1.3142 | | 0.5596 | 1412.0 | 4236 | 1.2923 | | 0.5596 | 1413.0 | 4239 | 1.2575 | | 0.5596 | 1414.0 | 4242 | 1.2282 | | 0.5596 | 1415.0 | 4245 | 1.2126 | | 0.5596 | 1416.0 | 4248 | 1.2228 | | 0.5596 | 1417.0 | 4251 | 1.2357 | | 0.5596 | 1418.0 | 4254 | 1.2567 | | 0.5596 | 1419.0 | 4257 | 1.2732 | | 0.5596 | 1420.0 | 4260 | 1.2618 | | 0.5596 | 1421.0 | 4263 | 1.2471 | | 0.5596 | 1422.0 | 4266 | 1.2476 | | 0.5596 | 1423.0 | 4269 | 1.2638 | | 0.5596 | 1424.0 | 4272 | 1.3039 | | 0.5596 | 1425.0 | 4275 | 1.3291 | | 0.5596 | 1426.0 | 4278 | 1.3451 | | 0.5596 | 1427.0 | 4281 | 1.3500 | | 0.5596 | 1428.0 | 4284 | 1.3546 | | 0.5596 | 1429.0 | 4287 | 1.3582 | | 0.5596 | 1430.0 | 4290 | 1.3553 | | 0.5596 | 1431.0 | 4293 | 1.3562 | | 0.5596 | 1432.0 | 4296 | 1.3554 | | 0.5596 | 1433.0 | 4299 | 1.3519 | | 0.5596 | 1434.0 | 4302 | 1.3437 | | 0.5596 | 1435.0 | 4305 | 1.3434 | | 0.5596 | 1436.0 | 4308 | 1.3346 | | 0.5596 | 1437.0 | 4311 | 1.3225 | | 0.5596 | 1438.0 | 4314 | 1.3157 | | 0.5596 | 1439.0 | 4317 | 1.3004 | | 0.5596 | 1440.0 | 4320 | 1.2806 | | 0.5596 | 1441.0 | 4323 | 1.2519 | | 0.5596 | 1442.0 | 4326 | 1.2243 | | 0.5596 | 1443.0 | 4329 | 1.2038 | | 0.5596 | 1444.0 | 4332 | 1.1953 | | 0.5596 | 1445.0 | 4335 | 1.1985 | | 0.5596 | 1446.0 | 4338 | 1.2112 | | 0.5596 | 1447.0 | 4341 | 1.2292 | | 0.5596 | 1448.0 | 4344 | 1.2461 | | 0.5596 | 1449.0 | 4347 | 1.2468 | | 0.5596 | 1450.0 | 4350 | 1.2530 | | 0.5596 | 1451.0 | 4353 | 1.2572 | | 0.5596 | 1452.0 | 4356 | 1.2665 | | 0.5596 | 1453.0 | 4359 | 1.2700 | | 0.5596 | 1454.0 | 4362 | 1.2696 | | 0.5596 | 1455.0 | 4365 | 1.2611 | | 0.5596 | 1456.0 | 4368 | 1.2537 | | 0.5596 | 1457.0 | 4371 | 1.2517 | | 0.5596 | 1458.0 | 4374 | 1.2511 | | 0.5596 | 1459.0 | 4377 | 1.2543 | | 0.5596 | 1460.0 | 4380 | 1.2578 | | 0.5596 | 1461.0 | 4383 | 1.2540 | | 0.5596 | 1462.0 | 4386 | 1.2508 | | 0.5596 | 1463.0 | 4389 | 1.2523 | | 0.5596 | 1464.0 | 4392 | 1.2553 | | 0.5596 | 1465.0 | 4395 | 1.2546 | | 0.5596 | 1466.0 | 4398 | 1.2581 | | 0.5596 | 1467.0 | 4401 | 1.2649 | | 0.5596 | 1468.0 | 4404 | 1.2735 | | 0.5596 | 1469.0 | 4407 | 1.2883 | | 0.5596 | 1470.0 | 4410 | 1.3074 | | 0.5596 | 1471.0 | 4413 | 1.3192 | | 0.5596 | 1472.0 | 4416 | 1.3282 | | 0.5596 | 1473.0 | 4419 | 1.3325 | | 0.5596 | 1474.0 | 4422 | 1.3314 | | 0.5596 | 1475.0 | 4425 | 1.3250 | | 0.5596 | 1476.0 | 4428 | 1.3163 | | 0.5596 | 1477.0 | 4431 | 1.3089 | | 0.5596 | 1478.0 | 4434 | 1.3000 | | 0.5596 | 1479.0 | 4437 | 1.3028 | | 0.5596 | 1480.0 | 4440 | 1.3035 | | 0.5596 | 1481.0 | 4443 | 1.3072 | | 0.5596 | 1482.0 | 4446 | 1.3023 | | 0.5596 | 1483.0 | 4449 | 1.3073 | | 0.5596 | 1484.0 | 4452 | 1.3085 | | 0.5596 | 1485.0 | 4455 | 1.3051 | | 0.5596 | 1486.0 | 4458 | 1.3017 | | 0.5596 | 1487.0 | 4461 | 1.2962 | | 0.5596 | 1488.0 | 4464 | 1.2828 | | 0.5596 | 1489.0 | 4467 | 1.2675 | | 0.5596 | 1490.0 | 4470 | 1.2643 | | 0.5596 | 1491.0 | 4473 | 1.2747 | | 0.5596 | 1492.0 | 4476 | 1.2961 | | 0.5596 | 1493.0 | 4479 | 1.3016 | | 0.5596 | 1494.0 | 4482 | 1.2982 | | 0.5596 | 1495.0 | 4485 | 1.2902 | | 0.5596 | 1496.0 | 4488 | 1.2810 | | 0.5596 | 1497.0 | 4491 | 1.2799 | | 0.5596 | 1498.0 | 4494 | 1.2838 | | 0.5596 | 1499.0 | 4497 | 1.2849 | | 0.5585 | 1500.0 | 4500 | 1.2817 | | 0.5585 | 1501.0 | 4503 | 1.2623 | | 0.5585 | 1502.0 | 4506 | 1.2476 | | 0.5585 | 1503.0 | 4509 | 1.2396 | | 0.5585 | 1504.0 | 4512 | 1.2270 | | 0.5585 | 1505.0 | 4515 | 1.2198 | | 0.5585 | 1506.0 | 4518 | 1.2175 | | 0.5585 | 1507.0 | 4521 | 1.2237 | | 0.5585 | 1508.0 | 4524 | 1.2332 | | 0.5585 | 1509.0 | 4527 | 1.2437 | | 0.5585 | 1510.0 | 4530 | 1.2509 | | 0.5585 | 1511.0 | 4533 | 1.2516 | | 0.5585 | 1512.0 | 4536 | 1.2541 | | 0.5585 | 1513.0 | 4539 | 1.2481 | | 0.5585 | 1514.0 | 4542 | 1.2460 | | 0.5585 | 1515.0 | 4545 | 1.2456 | | 0.5585 | 1516.0 | 4548 | 1.2450 | | 0.5585 | 1517.0 | 4551 | 1.2441 | | 0.5585 | 1518.0 | 4554 | 1.2437 | | 0.5585 | 1519.0 | 4557 | 1.2446 | | 0.5585 | 1520.0 | 4560 | 1.2490 | | 0.5585 | 1521.0 | 4563 | 1.2540 | | 0.5585 | 1522.0 | 4566 | 1.2620 | | 0.5585 | 1523.0 | 4569 | 1.2615 | | 0.5585 | 1524.0 | 4572 | 1.2570 | | 0.5585 | 1525.0 | 4575 | 1.2569 | | 0.5585 | 1526.0 | 4578 | 1.2570 | | 0.5585 | 1527.0 | 4581 | 1.2681 | | 0.5585 | 1528.0 | 4584 | 1.2824 | | 0.5585 | 1529.0 | 4587 | 1.2947 | | 0.5585 | 1530.0 | 4590 | 1.2917 | | 0.5585 | 1531.0 | 4593 | 1.2866 | | 0.5585 | 1532.0 | 4596 | 1.2758 | | 0.5585 | 1533.0 | 4599 | 1.2622 | | 0.5585 | 1534.0 | 4602 | 1.2540 | | 0.5585 | 1535.0 | 4605 | 1.2411 | | 0.5585 | 1536.0 | 4608 | 1.2433 | | 0.5585 | 1537.0 | 4611 | 1.2553 | | 0.5585 | 1538.0 | 4614 | 1.2590 | | 0.5585 | 1539.0 | 4617 | 1.2535 | | 0.5585 | 1540.0 | 4620 | 1.2439 | | 0.5585 | 1541.0 | 4623 | 1.2461 | | 0.5585 | 1542.0 | 4626 | 1.2506 | | 0.5585 | 1543.0 | 4629 | 1.2483 | | 0.5585 | 1544.0 | 4632 | 1.2488 | | 0.5585 | 1545.0 | 4635 | 1.2463 | | 0.5585 | 1546.0 | 4638 | 1.2497 | | 0.5585 | 1547.0 | 4641 | 1.2608 | | 0.5585 | 1548.0 | 4644 | 1.2711 | | 0.5585 | 1549.0 | 4647 | 1.2785 | | 0.5585 | 1550.0 | 4650 | 1.2751 | | 0.5585 | 1551.0 | 4653 | 1.2641 | | 0.5585 | 1552.0 | 4656 | 1.2510 | | 0.5585 | 1553.0 | 4659 | 1.2358 | | 0.5585 | 1554.0 | 4662 | 1.2287 | | 0.5585 | 1555.0 | 4665 | 1.2247 | | 0.5585 | 1556.0 | 4668 | 1.2228 | | 0.5585 | 1557.0 | 4671 | 1.2226 | | 0.5585 | 1558.0 | 4674 | 1.2310 | | 0.5585 | 1559.0 | 4677 | 1.2332 | | 0.5585 | 1560.0 | 4680 | 1.2375 | | 0.5585 | 1561.0 | 4683 | 1.2369 | | 0.5585 | 1562.0 | 4686 | 1.2275 | | 0.5585 | 1563.0 | 4689 | 1.2133 | | 0.5585 | 1564.0 | 4692 | 1.1939 | | 0.5585 | 1565.0 | 4695 | 1.1805 | | 0.5585 | 1566.0 | 4698 | 1.1668 | | 0.5585 | 1567.0 | 4701 | 1.1570 | | 0.5585 | 1568.0 | 4704 | 1.1510 | | 0.5585 | 1569.0 | 4707 | 1.1499 | | 0.5585 | 1570.0 | 4710 | 1.1548 | | 0.5585 | 1571.0 | 4713 | 1.1644 | | 0.5585 | 1572.0 | 4716 | 1.1659 | | 0.5585 | 1573.0 | 4719 | 1.1751 | | 0.5585 | 1574.0 | 4722 | 1.1975 | | 0.5585 | 1575.0 | 4725 | 1.2115 | | 0.5585 | 1576.0 | 4728 | 1.2144 | | 0.5585 | 1577.0 | 4731 | 1.2082 | | 0.5585 | 1578.0 | 4734 | 1.1975 | | 0.5585 | 1579.0 | 4737 | 1.1939 | | 0.5585 | 1580.0 | 4740 | 1.1906 | | 0.5585 | 1581.0 | 4743 | 1.1783 | | 0.5585 | 1582.0 | 4746 | 1.1757 | | 0.5585 | 1583.0 | 4749 | 1.1792 | | 0.5585 | 1584.0 | 4752 | 1.1950 | | 0.5585 | 1585.0 | 4755 | 1.2039 | | 0.5585 | 1586.0 | 4758 | 1.2107 | | 0.5585 | 1587.0 | 4761 | 1.2178 | | 0.5585 | 1588.0 | 4764 | 1.2261 | | 0.5585 | 1589.0 | 4767 | 1.2340 | | 0.5585 | 1590.0 | 4770 | 1.2420 | | 0.5585 | 1591.0 | 4773 | 1.2525 | | 0.5585 | 1592.0 | 4776 | 1.2740 | | 0.5585 | 1593.0 | 4779 | 1.2903 | | 0.5585 | 1594.0 | 4782 | 1.2987 | | 0.5585 | 1595.0 | 4785 | 1.2991 | | 0.5585 | 1596.0 | 4788 | 1.2934 | | 0.5585 | 1597.0 | 4791 | 1.2862 | | 0.5585 | 1598.0 | 4794 | 1.2868 | | 0.5585 | 1599.0 | 4797 | 1.2803 | | 0.5585 | 1600.0 | 4800 | 1.2826 | | 0.5585 | 1601.0 | 4803 | 1.2763 | | 0.5585 | 1602.0 | 4806 | 1.2718 | | 0.5585 | 1603.0 | 4809 | 1.2646 | | 0.5585 | 1604.0 | 4812 | 1.2668 | | 0.5585 | 1605.0 | 4815 | 1.2755 | | 0.5585 | 1606.0 | 4818 | 1.2812 | | 0.5585 | 1607.0 | 4821 | 1.2905 | | 0.5585 | 1608.0 | 4824 | 1.2896 | | 0.5585 | 1609.0 | 4827 | 1.2850 | | 0.5585 | 1610.0 | 4830 | 1.2822 | | 0.5585 | 1611.0 | 4833 | 1.2768 | | 0.5585 | 1612.0 | 4836 | 1.2710 | | 0.5585 | 1613.0 | 4839 | 1.2660 | | 0.5585 | 1614.0 | 4842 | 1.2627 | | 0.5585 | 1615.0 | 4845 | 1.2584 | | 0.5585 | 1616.0 | 4848 | 1.2485 | | 0.5585 | 1617.0 | 4851 | 1.2344 | | 0.5585 | 1618.0 | 4854 | 1.2201 | | 0.5585 | 1619.0 | 4857 | 1.2069 | | 0.5585 | 1620.0 | 4860 | 1.1927 | | 0.5585 | 1621.0 | 4863 | 1.1971 | | 0.5585 | 1622.0 | 4866 | 1.2042 | | 0.5585 | 1623.0 | 4869 | 1.2124 | | 0.5585 | 1624.0 | 4872 | 1.2249 | | 0.5585 | 1625.0 | 4875 | 1.2413 | | 0.5585 | 1626.0 | 4878 | 1.2477 | | 0.5585 | 1627.0 | 4881 | 1.2600 | | 0.5585 | 1628.0 | 4884 | 1.2676 | | 0.5585 | 1629.0 | 4887 | 1.2724 | | 0.5585 | 1630.0 | 4890 | 1.2755 | | 0.5585 | 1631.0 | 4893 | 1.2782 | | 0.5585 | 1632.0 | 4896 | 1.2968 | | 0.5585 | 1633.0 | 4899 | 1.3072 | | 0.5585 | 1634.0 | 4902 | 1.3119 | | 0.5585 | 1635.0 | 4905 | 1.3116 | | 0.5585 | 1636.0 | 4908 | 1.3104 | | 0.5585 | 1637.0 | 4911 | 1.3071 | | 0.5585 | 1638.0 | 4914 | 1.3022 | | 0.5585 | 1639.0 | 4917 | 1.2993 | | 0.5585 | 1640.0 | 4920 | 1.2960 | | 0.5585 | 1641.0 | 4923 | 1.2829 | | 0.5585 | 1642.0 | 4926 | 1.2700 | | 0.5585 | 1643.0 | 4929 | 1.2669 | | 0.5585 | 1644.0 | 4932 | 1.2658 | | 0.5585 | 1645.0 | 4935 | 1.2583 | | 0.5585 | 1646.0 | 4938 | 1.2580 | | 0.5585 | 1647.0 | 4941 | 1.2485 | | 0.5585 | 1648.0 | 4944 | 1.2374 | | 0.5585 | 1649.0 | 4947 | 1.2234 | | 0.5585 | 1650.0 | 4950 | 1.2172 | | 0.5585 | 1651.0 | 4953 | 1.2044 | | 0.5585 | 1652.0 | 4956 | 1.1955 | | 0.5585 | 1653.0 | 4959 | 1.1854 | | 0.5585 | 1654.0 | 4962 | 1.1917 | | 0.5585 | 1655.0 | 4965 | 1.1924 | | 0.5585 | 1656.0 | 4968 | 1.1886 | | 0.5585 | 1657.0 | 4971 | 1.1910 | | 0.5585 | 1658.0 | 4974 | 1.1913 | | 0.5585 | 1659.0 | 4977 | 1.1960 | | 0.5585 | 1660.0 | 4980 | 1.2030 | | 0.5585 | 1661.0 | 4983 | 1.2132 | | 0.5585 | 1662.0 | 4986 | 1.2263 | | 0.5585 | 1663.0 | 4989 | 1.2411 | | 0.5585 | 1664.0 | 4992 | 1.2572 | | 0.5585 | 1665.0 | 4995 | 1.2714 | | 0.5585 | 1666.0 | 4998 | 1.2824 | | 0.5584 | 1667.0 | 5001 | 1.2862 | | 0.5584 | 1668.0 | 5004 | 1.2866 | | 0.5584 | 1669.0 | 5007 | 1.2883 | | 0.5584 | 1670.0 | 5010 | 1.2868 | | 0.5584 | 1671.0 | 5013 | 1.2821 | | 0.5584 | 1672.0 | 5016 | 1.2769 | | 0.5584 | 1673.0 | 5019 | 1.2708 | | 0.5584 | 1674.0 | 5022 | 1.2631 | | 0.5584 | 1675.0 | 5025 | 1.2573 | | 0.5584 | 1676.0 | 5028 | 1.2570 | | 0.5584 | 1677.0 | 5031 | 1.2558 | | 0.5584 | 1678.0 | 5034 | 1.2561 | | 0.5584 | 1679.0 | 5037 | 1.2551 | | 0.5584 | 1680.0 | 5040 | 1.2521 | | 0.5584 | 1681.0 | 5043 | 1.2414 | | 0.5584 | 1682.0 | 5046 | 1.2274 | | 0.5584 | 1683.0 | 5049 | 1.2122 | | 0.5584 | 1684.0 | 5052 | 1.1951 | | 0.5584 | 1685.0 | 5055 | 1.1893 | | 0.5584 | 1686.0 | 5058 | 1.1823 | | 0.5584 | 1687.0 | 5061 | 1.1763 | | 0.5584 | 1688.0 | 5064 | 1.1725 | | 0.5584 | 1689.0 | 5067 | 1.1744 | | 0.5584 | 1690.0 | 5070 | 1.1875 | | 0.5584 | 1691.0 | 5073 | 1.1946 | | 0.5584 | 1692.0 | 5076 | 1.2012 | | 0.5584 | 1693.0 | 5079 | 1.2053 | | 0.5584 | 1694.0 | 5082 | 1.2083 | | 0.5584 | 1695.0 | 5085 | 1.2196 | | 0.5584 | 1696.0 | 5088 | 1.2435 | | 0.5584 | 1697.0 | 5091 | 1.2554 | | 0.5584 | 1698.0 | 5094 | 1.2650 | | 0.5584 | 1699.0 | 5097 | 1.2680 | | 0.5584 | 1700.0 | 5100 | 1.2642 | | 0.5584 | 1701.0 | 5103 | 1.2682 | | 0.5584 | 1702.0 | 5106 | 1.2741 | | 0.5584 | 1703.0 | 5109 | 1.2736 | | 0.5584 | 1704.0 | 5112 | 1.2641 | | 0.5584 | 1705.0 | 5115 | 1.2590 | | 0.5584 | 1706.0 | 5118 | 1.2602 | | 0.5584 | 1707.0 | 5121 | 1.2610 | | 0.5584 | 1708.0 | 5124 | 1.2628 | | 0.5584 | 1709.0 | 5127 | 1.2661 | | 0.5584 | 1710.0 | 5130 | 1.2716 | | 0.5584 | 1711.0 | 5133 | 1.2769 | | 0.5584 | 1712.0 | 5136 | 1.2820 | | 0.5584 | 1713.0 | 5139 | 1.2837 | | 0.5584 | 1714.0 | 5142 | 1.2823 | | 0.5584 | 1715.0 | 5145 | 1.2832 | | 0.5584 | 1716.0 | 5148 | 1.2814 | | 0.5584 | 1717.0 | 5151 | 1.2819 | | 0.5584 | 1718.0 | 5154 | 1.2820 | | 0.5584 | 1719.0 | 5157 | 1.2816 | | 0.5584 | 1720.0 | 5160 | 1.2814 | | 0.5584 | 1721.0 | 5163 | 1.2813 | | 0.5584 | 1722.0 | 5166 | 1.2787 | | 0.5584 | 1723.0 | 5169 | 1.2741 | | 0.5584 | 1724.0 | 5172 | 1.2706 | | 0.5584 | 1725.0 | 5175 | 1.2711 | | 0.5584 | 1726.0 | 5178 | 1.2760 | | 0.5584 | 1727.0 | 5181 | 1.2812 | | 0.5584 | 1728.0 | 5184 | 1.2847 | | 0.5584 | 1729.0 | 5187 | 1.2863 | | 0.5584 | 1730.0 | 5190 | 1.2881 | | 0.5584 | 1731.0 | 5193 | 1.2861 | | 0.5584 | 1732.0 | 5196 | 1.2846 | | 0.5584 | 1733.0 | 5199 | 1.2825 | | 0.5584 | 1734.0 | 5202 | 1.2793 | | 0.5584 | 1735.0 | 5205 | 1.2799 | | 0.5584 | 1736.0 | 5208 | 1.2794 | | 0.5584 | 1737.0 | 5211 | 1.2769 | | 0.5584 | 1738.0 | 5214 | 1.2734 | | 0.5584 | 1739.0 | 5217 | 1.2713 | | 0.5584 | 1740.0 | 5220 | 1.2720 | | 0.5584 | 1741.0 | 5223 | 1.2751 | | 0.5584 | 1742.0 | 5226 | 1.2776 | | 0.5584 | 1743.0 | 5229 | 1.2792 | | 0.5584 | 1744.0 | 5232 | 1.2830 | | 0.5584 | 1745.0 | 5235 | 1.2845 | | 0.5584 | 1746.0 | 5238 | 1.2858 | | 0.5584 | 1747.0 | 5241 | 1.2844 | | 0.5584 | 1748.0 | 5244 | 1.2823 | | 0.5584 | 1749.0 | 5247 | 1.2819 | | 0.5584 | 1750.0 | 5250 | 1.2809 | | 0.5584 | 1751.0 | 5253 | 1.2805 | | 0.5584 | 1752.0 | 5256 | 1.2779 | | 0.5584 | 1753.0 | 5259 | 1.2749 | | 0.5584 | 1754.0 | 5262 | 1.2768 | | 0.5584 | 1755.0 | 5265 | 1.2799 | | 0.5584 | 1756.0 | 5268 | 1.2808 | | 0.5584 | 1757.0 | 5271 | 1.2788 | | 0.5584 | 1758.0 | 5274 | 1.2726 | | 0.5584 | 1759.0 | 5277 | 1.2663 | | 0.5584 | 1760.0 | 5280 | 1.2611 | | 0.5584 | 1761.0 | 5283 | 1.2576 | | 0.5584 | 1762.0 | 5286 | 1.2551 | | 0.5584 | 1763.0 | 5289 | 1.2647 | | 0.5584 | 1764.0 | 5292 | 1.2732 | | 0.5584 | 1765.0 | 5295 | 1.2749 | | 0.5584 | 1766.0 | 5298 | 1.2798 | | 0.5584 | 1767.0 | 5301 | 1.2798 | | 0.5584 | 1768.0 | 5304 | 1.2799 | | 0.5584 | 1769.0 | 5307 | 1.2805 | | 0.5584 | 1770.0 | 5310 | 1.2787 | | 0.5584 | 1771.0 | 5313 | 1.2751 | | 0.5584 | 1772.0 | 5316 | 1.2724 | | 0.5584 | 1773.0 | 5319 | 1.2702 | | 0.5584 | 1774.0 | 5322 | 1.2681 | | 0.5584 | 1775.0 | 5325 | 1.2680 | | 0.5584 | 1776.0 | 5328 | 1.2762 | | 0.5584 | 1777.0 | 5331 | 1.2824 | | 0.5584 | 1778.0 | 5334 | 1.2878 | | 0.5584 | 1779.0 | 5337 | 1.2896 | | 0.5584 | 1780.0 | 5340 | 1.2924 | | 0.5584 | 1781.0 | 5343 | 1.2972 | | 0.5584 | 1782.0 | 5346 | 1.2993 | | 0.5584 | 1783.0 | 5349 | 1.2992 | | 0.5584 | 1784.0 | 5352 | 1.2982 | | 0.5584 | 1785.0 | 5355 | 1.2968 | | 0.5584 | 1786.0 | 5358 | 1.2951 | | 0.5584 | 1787.0 | 5361 | 1.2933 | | 0.5584 | 1788.0 | 5364 | 1.2933 | | 0.5584 | 1789.0 | 5367 | 1.2916 | | 0.5584 | 1790.0 | 5370 | 1.2882 | | 0.5584 | 1791.0 | 5373 | 1.2879 | | 0.5584 | 1792.0 | 5376 | 1.2876 | | 0.5584 | 1793.0 | 5379 | 1.2848 | | 0.5584 | 1794.0 | 5382 | 1.2832 | | 0.5584 | 1795.0 | 5385 | 1.2809 | | 0.5584 | 1796.0 | 5388 | 1.2803 | | 0.5584 | 1797.0 | 5391 | 1.2786 | | 0.5584 | 1798.0 | 5394 | 1.2740 | | 0.5584 | 1799.0 | 5397 | 1.2691 | | 0.5584 | 1800.0 | 5400 | 1.2653 | | 0.5584 | 1801.0 | 5403 | 1.2605 | | 0.5584 | 1802.0 | 5406 | 1.2591 | | 0.5584 | 1803.0 | 5409 | 1.2564 | | 0.5584 | 1804.0 | 5412 | 1.2520 | | 0.5584 | 1805.0 | 5415 | 1.2478 | | 0.5584 | 1806.0 | 5418 | 1.2489 | | 0.5584 | 1807.0 | 5421 | 1.2499 | | 0.5584 | 1808.0 | 5424 | 1.2530 | | 0.5584 | 1809.0 | 5427 | 1.2525 | | 0.5584 | 1810.0 | 5430 | 1.2523 | | 0.5584 | 1811.0 | 5433 | 1.2526 | | 0.5584 | 1812.0 | 5436 | 1.2536 | | 0.5584 | 1813.0 | 5439 | 1.2507 | | 0.5584 | 1814.0 | 5442 | 1.2481 | | 0.5584 | 1815.0 | 5445 | 1.2451 | | 0.5584 | 1816.0 | 5448 | 1.2370 | | 0.5584 | 1817.0 | 5451 | 1.2326 | | 0.5584 | 1818.0 | 5454 | 1.2316 | | 0.5584 | 1819.0 | 5457 | 1.2329 | | 0.5584 | 1820.0 | 5460 | 1.2352 | | 0.5584 | 1821.0 | 5463 | 1.2331 | | 0.5584 | 1822.0 | 5466 | 1.2283 | | 0.5584 | 1823.0 | 5469 | 1.2228 | | 0.5584 | 1824.0 | 5472 | 1.2207 | | 0.5584 | 1825.0 | 5475 | 1.2197 | | 0.5584 | 1826.0 | 5478 | 1.2164 | | 0.5584 | 1827.0 | 5481 | 1.2152 | | 0.5584 | 1828.0 | 5484 | 1.2172 | | 0.5584 | 1829.0 | 5487 | 1.2181 | | 0.5584 | 1830.0 | 5490 | 1.2158 | | 0.5584 | 1831.0 | 5493 | 1.2166 | | 0.5584 | 1832.0 | 5496 | 1.2138 | | 0.5584 | 1833.0 | 5499 | 1.2109 | | 0.5585 | 1834.0 | 5502 | 1.2170 | | 0.5585 | 1835.0 | 5505 | 1.2216 | | 0.5585 | 1836.0 | 5508 | 1.2244 | | 0.5585 | 1837.0 | 5511 | 1.2267 | | 0.5585 | 1838.0 | 5514 | 1.2321 | | 0.5585 | 1839.0 | 5517 | 1.2359 | | 0.5585 | 1840.0 | 5520 | 1.2415 | | 0.5585 | 1841.0 | 5523 | 1.2507 | | 0.5585 | 1842.0 | 5526 | 1.2623 | | 0.5585 | 1843.0 | 5529 | 1.2675 | | 0.5585 | 1844.0 | 5532 | 1.2701 | | 0.5585 | 1845.0 | 5535 | 1.2701 | | 0.5585 | 1846.0 | 5538 | 1.2698 | | 0.5585 | 1847.0 | 5541 | 1.2720 | | 0.5585 | 1848.0 | 5544 | 1.2740 | | 0.5585 | 1849.0 | 5547 | 1.2751 | | 0.5585 | 1850.0 | 5550 | 1.2771 | | 0.5585 | 1851.0 | 5553 | 1.2801 | | 0.5585 | 1852.0 | 5556 | 1.2817 | | 0.5585 | 1853.0 | 5559 | 1.2834 | | 0.5585 | 1854.0 | 5562 | 1.2851 | | 0.5585 | 1855.0 | 5565 | 1.2870 | | 0.5585 | 1856.0 | 5568 | 1.2885 | | 0.5585 | 1857.0 | 5571 | 1.2872 | | 0.5585 | 1858.0 | 5574 | 1.2855 | | 0.5585 | 1859.0 | 5577 | 1.2835 | | 0.5585 | 1860.0 | 5580 | 1.2837 | | 0.5585 | 1861.0 | 5583 | 1.2837 | | 0.5585 | 1862.0 | 5586 | 1.2828 | | 0.5585 | 1863.0 | 5589 | 1.2814 | | 0.5585 | 1864.0 | 5592 | 1.2794 | | 0.5585 | 1865.0 | 5595 | 1.2781 | | 0.5585 | 1866.0 | 5598 | 1.2806 | | 0.5585 | 1867.0 | 5601 | 1.2827 | | 0.5585 | 1868.0 | 5604 | 1.2827 | | 0.5585 | 1869.0 | 5607 | 1.2828 | | 0.5585 | 1870.0 | 5610 | 1.2827 | | 0.5585 | 1871.0 | 5613 | 1.2810 | | 0.5585 | 1872.0 | 5616 | 1.2799 | | 0.5585 | 1873.0 | 5619 | 1.2784 | | 0.5585 | 1874.0 | 5622 | 1.2760 | | 0.5585 | 1875.0 | 5625 | 1.2729 | | 0.5585 | 1876.0 | 5628 | 1.2710 | | 0.5585 | 1877.0 | 5631 | 1.2718 | | 0.5585 | 1878.0 | 5634 | 1.2747 | | 0.5585 | 1879.0 | 5637 | 1.2779 | | 0.5585 | 1880.0 | 5640 | 1.2808 | | 0.5585 | 1881.0 | 5643 | 1.2827 | | 0.5585 | 1882.0 | 5646 | 1.2821 | | 0.5585 | 1883.0 | 5649 | 1.2822 | | 0.5585 | 1884.0 | 5652 | 1.2834 | | 0.5585 | 1885.0 | 5655 | 1.2828 | | 0.5585 | 1886.0 | 5658 | 1.2808 | | 0.5585 | 1887.0 | 5661 | 1.2784 | | 0.5585 | 1888.0 | 5664 | 1.2760 | | 0.5585 | 1889.0 | 5667 | 1.2731 | | 0.5585 | 1890.0 | 5670 | 1.2704 | | 0.5585 | 1891.0 | 5673 | 1.2704 | | 0.5585 | 1892.0 | 5676 | 1.2701 | | 0.5585 | 1893.0 | 5679 | 1.2696 | | 0.5585 | 1894.0 | 5682 | 1.2657 | | 0.5585 | 1895.0 | 5685 | 1.2590 | | 0.5585 | 1896.0 | 5688 | 1.2525 | | 0.5585 | 1897.0 | 5691 | 1.2475 | | 0.5585 | 1898.0 | 5694 | 1.2441 | | 0.5585 | 1899.0 | 5697 | 1.2416 | | 0.5585 | 1900.0 | 5700 | 1.2422 | | 0.5585 | 1901.0 | 5703 | 1.2433 | | 0.5585 | 1902.0 | 5706 | 1.2443 | | 0.5585 | 1903.0 | 5709 | 1.2453 | | 0.5585 | 1904.0 | 5712 | 1.2513 | | 0.5585 | 1905.0 | 5715 | 1.2538 | | 0.5585 | 1906.0 | 5718 | 1.2554 | | 0.5585 | 1907.0 | 5721 | 1.2567 | | 0.5585 | 1908.0 | 5724 | 1.2573 | | 0.5585 | 1909.0 | 5727 | 1.2580 | | 0.5585 | 1910.0 | 5730 | 1.2579 | | 0.5585 | 1911.0 | 5733 | 1.2576 | | 0.5585 | 1912.0 | 5736 | 1.2567 | | 0.5585 | 1913.0 | 5739 | 1.2552 | | 0.5585 | 1914.0 | 5742 | 1.2542 | | 0.5585 | 1915.0 | 5745 | 1.2539 | | 0.5585 | 1916.0 | 5748 | 1.2530 | | 0.5585 | 1917.0 | 5751 | 1.2534 | | 0.5585 | 1918.0 | 5754 | 1.2542 | | 0.5585 | 1919.0 | 5757 | 1.2537 | | 0.5585 | 1920.0 | 5760 | 1.2527 | | 0.5585 | 1921.0 | 5763 | 1.2517 | | 0.5585 | 1922.0 | 5766 | 1.2510 | | 0.5585 | 1923.0 | 5769 | 1.2496 | | 0.5585 | 1924.0 | 5772 | 1.2497 | | 0.5585 | 1925.0 | 5775 | 1.2491 | | 0.5585 | 1926.0 | 5778 | 1.2483 | | 0.5585 | 1927.0 | 5781 | 1.2462 | | 0.5585 | 1928.0 | 5784 | 1.2437 | | 0.5585 | 1929.0 | 5787 | 1.2406 | | 0.5585 | 1930.0 | 5790 | 1.2390 | | 0.5585 | 1931.0 | 5793 | 1.2390 | | 0.5585 | 1932.0 | 5796 | 1.2390 | | 0.5585 | 1933.0 | 5799 | 1.2409 | | 0.5585 | 1934.0 | 5802 | 1.2442 | | 0.5585 | 1935.0 | 5805 | 1.2473 | | 0.5585 | 1936.0 | 5808 | 1.2490 | | 0.5585 | 1937.0 | 5811 | 1.2516 | | 0.5585 | 1938.0 | 5814 | 1.2542 | | 0.5585 | 1939.0 | 5817 | 1.2565 | | 0.5585 | 1940.0 | 5820 | 1.2594 | | 0.5585 | 1941.0 | 5823 | 1.2610 | | 0.5585 | 1942.0 | 5826 | 1.2623 | | 0.5585 | 1943.0 | 5829 | 1.2636 | | 0.5585 | 1944.0 | 5832 | 1.2657 | | 0.5585 | 1945.0 | 5835 | 1.2667 | | 0.5585 | 1946.0 | 5838 | 1.2676 | | 0.5585 | 1947.0 | 5841 | 1.2685 | | 0.5585 | 1948.0 | 5844 | 1.2696 | | 0.5585 | 1949.0 | 5847 | 1.2707 | | 0.5585 | 1950.0 | 5850 | 1.2707 | | 0.5585 | 1951.0 | 5853 | 1.2710 | | 0.5585 | 1952.0 | 5856 | 1.2707 | | 0.5585 | 1953.0 | 5859 | 1.2694 | | 0.5585 | 1954.0 | 5862 | 1.2673 | | 0.5585 | 1955.0 | 5865 | 1.2650 | | 0.5585 | 1956.0 | 5868 | 1.2625 | | 0.5585 | 1957.0 | 5871 | 1.2614 | | 0.5585 | 1958.0 | 5874 | 1.2605 | | 0.5585 | 1959.0 | 5877 | 1.2599 | | 0.5585 | 1960.0 | 5880 | 1.2599 | | 0.5585 | 1961.0 | 5883 | 1.2598 | | 0.5585 | 1962.0 | 5886 | 1.2585 | | 0.5585 | 1963.0 | 5889 | 1.2572 | | 0.5585 | 1964.0 | 5892 | 1.2555 | | 0.5585 | 1965.0 | 5895 | 1.2527 | | 0.5585 | 1966.0 | 5898 | 1.2513 | | 0.5585 | 1967.0 | 5901 | 1.2504 | | 0.5585 | 1968.0 | 5904 | 1.2508 | | 0.5585 | 1969.0 | 5907 | 1.2511 | | 0.5585 | 1970.0 | 5910 | 1.2517 | | 0.5585 | 1971.0 | 5913 | 1.2528 | | 0.5585 | 1972.0 | 5916 | 1.2537 | | 0.5585 | 1973.0 | 5919 | 1.2543 | | 0.5585 | 1974.0 | 5922 | 1.2549 | | 0.5585 | 1975.0 | 5925 | 1.2554 | | 0.5585 | 1976.0 | 5928 | 1.2554 | | 0.5585 | 1977.0 | 5931 | 1.2555 | | 0.5585 | 1978.0 | 5934 | 1.2554 | | 0.5585 | 1979.0 | 5937 | 1.2553 | | 0.5585 | 1980.0 | 5940 | 1.2554 | | 0.5585 | 1981.0 | 5943 | 1.2556 | | 0.5585 | 1982.0 | 5946 | 1.2563 | | 0.5585 | 1983.0 | 5949 | 1.2567 | | 0.5585 | 1984.0 | 5952 | 1.2567 | | 0.5585 | 1985.0 | 5955 | 1.2567 | | 0.5585 | 1986.0 | 5958 | 1.2566 | | 0.5585 | 1987.0 | 5961 | 1.2566 | | 0.5585 | 1988.0 | 5964 | 1.2564 | | 0.5585 | 1989.0 | 5967 | 1.2563 | | 0.5585 | 1990.0 | 5970 | 1.2564 | | 0.5585 | 1991.0 | 5973 | 1.2564 | | 0.5585 | 1992.0 | 5976 | 1.2564 | | 0.5585 | 1993.0 | 5979 | 1.2565 | | 0.5585 | 1994.0 | 5982 | 1.2565 | | 0.5585 | 1995.0 | 5985 | 1.2564 | | 0.5585 | 1996.0 | 5988 | 1.2563 | | 0.5585 | 1997.0 | 5991 | 1.2563 | | 0.5585 | 1998.0 | 5994 | 1.2562 | | 0.5585 | 1999.0 | 5997 | 1.2562 | | 0.558 | 2000.0 | 6000 | 1.2562 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.7 - Tokenizers 0.15.0

QuizzerPrivate/lora-trained-xl

QuizzerPrivate

2024-03-08T00:22:36Z

1

2

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-07T19:46:13Z

--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog widget: - text: A photo of sks dog in a bucket output: url: image_0.png - text: A photo of sks dog in a bucket output: url: image_1.png - text: A photo of sks dog in a bucket output: url: image_2.png - text: A photo of sks dog in a bucket output: url: image_3.png ---  # SDXL LoRA DreamBooth - QuizzerPrivate/lora-trained-xl <Gallery /> ## Model description These are QuizzerPrivate/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](QuizzerPrivate/lora-trained-xl/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

ColeD0/Claud.ai-2

ColeD0

2024-03-08T00:19:56Z

5

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-03-07T19:53:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ycfNTU/bloomz-560m_PROMPT_TUNING_textgrading_CASUAL_LM_v1

ycfNTU

2024-03-08T00:19:49Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-08T00:19:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shleeeee/mistral-ko-tech-science-v1

shleeeee

2024-03-08T00:18:13Z

2,267

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-10T05:02:38Z

--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-tech-science-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)

shleeeee/mistral-ko-OpenOrca-Platypus-v2

shleeeee

2024-03-08T00:17:46Z

124

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-18T06:57:43Z

--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-OpenOrca-Platypus-v2 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)

shleeeee/mistral-ko-openorca-platypus-1epoch

shleeeee

2024-03-08T00:17:16Z

0

peft

[ "peft", "safetensors", "mistral", "text-generation", "ko", "license:other", "region:us" ]

text-generation

2023-12-21T08:21:55Z

--- library_name: peft license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-openorca-platypus-1epoch It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park) ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

shleeeee/mistral-7b-ko-v1

shleeeee

2024-03-08T00:16:36Z

0

peft

[ "peft", "safetensors", "mistral", "text-generation", "ko", "license:other", "region:us" ]

text-generation

2023-12-27T04:27:10Z

--- library_name: peft license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-7b-ko-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park) ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

OwOOwO/eacc_contTrain_m2_25

OwOOwO

2024-03-08T00:16:21Z

89

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T00:13:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shleeeee/mistral-ko-exo-mrc-v1

shleeeee

2024-03-08T00:15:03Z

109

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-11T08:10:49Z

--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-exo-mrc-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)

shleeeee/mistral-ko-7b-tech

shleeeee

2024-03-08T00:14:25Z

104

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-29T15:35:30Z

--- language: - ko pipeline_tag: text-generation tags: - finetune license: other --- # Model Card for mistral-ko-7b-tech It is a fine-tuned model using Korean in the mistral-7b model. ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park) * **Repository** : To be added * **Model Architecture** : The mistral-ko-7b-wiki-neft is is a fine-tuned version of the Mistral-7B-v0.1. * **Lora target modules** : q_proj, k_proj, v_proj, o_proj,gate_proj * **train_batch** : 4 * **Max_step** : 500 ## Dataset Korean Custom Dataset(2000) ## Prompt template: Mistral ``` <s>[INST]{['instruction']}[/INST]{['output']}</s> ``` ## Usage ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-7b-tech") model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-7b-tech") # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shleeeee/mistral-ko-7b-tech") ``` ## Evaluation ![image/png](https://cdn-uploads.huggingface.co/production/uploads/654495fa893aec5da96e9134/6z75dYa8TdTy4Y7EIl0CK.png)

shleeeee/mistral-ko-exo-wiki-quiz-v1

shleeeee

2024-03-08T00:12:49Z

96

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "ko", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-06T03:23:55Z

--- license: other language: - ko pipeline_tag: text-generation --- # Model Card for mistral-ko-exo-wiki-quiz-v1 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee) , oopsung(Sungwoo Park)

shleeeee/mistral-ko-OpenOrca-2000

shleeeee

2024-03-08T00:11:32Z

95

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "ko", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-04T13:17:54Z

--- language: - ko pipeline_tag: text-generation tags: - finetune --- # Model Card for mistral-ko-OpenOrca-2000 It is a fine-tuned model using Korean in the mistral-7b model ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park) * **Repository** : To be added * **Model Architecture** : The shleeeee/mistral-ko-OpenOrca-2000 is is a fine-tuned version of the Mistral-7B-v0.1. * **Lora target modules** : q_proj, k_proj, v_proj, o_proj,gate_proj * **train_batch** : 4 * **epochs** : 2 ## Dataset 2000 ko-OpenOrca datasets ## Prompt template: Mistral ``` <s>[INST]{['instruction']}[/INST]{['output']}</s> ``` ## Usage ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-OpenOrca-2000") model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-OpenOrca-2000") # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shleeeee/mistral-ko-OpenOrca-2000") ``` ## Evaluation To be added

shleeeee/mistral-ko-7b-wiki-neft

shleeeee

2024-03-08T00:11:04Z

2,286

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "ko", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-29T04:46:44Z

--- language: - ko pipeline_tag: text-generation tags: - finetune --- # Model Card for mistral-ko-7b-wiki-neft It is a fine-tuned model using Korean and NEFT in the mistral-7b model. ## Model Details * **Model Developers** : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park) * **Repository** : To be added * **Model Architecture** : The mistral-ko-7b-wiki-neft is is a fine-tuned version of the Mistral-7B-v0.1. * **Lora target modules** : q_proj, k_proj, v_proj, o_proj,gate_proj * **train_batch** : 4 * **neftune_noise_alpha** : 5 * **Max_step** : 1000 ## Dataset Korean Custom Dataset ## Prompt template: Mistral ``` <s>[INST]{['instruction']}[/INST]{['output']}</s> ``` ## Usage ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-7b-wiki") model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-7b-wiki") # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shleeeee/mistral-7b-wiki") ``` ## Evaluation ![image/png](https://cdn-uploads.huggingface.co/production/uploads/654495fa893aec5da96e9134/p1aJ4YMdP_E9YzhTcuaFx.png)

Holarissun/phi2-airl_sft-tldr-seqsampler

Holarissun

2024-03-08T00:06:49Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-03-08T00:06:42Z

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2-airl_sft-tldr-seqsampler results: [] ---  # phi2-airl_sft-tldr-seqsampler This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

OwOOwO/eacc_contTrain_m2_55_orig

OwOOwO

2024-03-08T00:01:33Z

5

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T23:59:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

imsarfaroz/fine-tuned-albert-tweets

imsarfaroz

2024-03-07T23:58:52Z

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "albert", "text-classification", "generated_from_trainer", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-07T23:47:33Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy base_model: albert-base-v2 model-index: - name: fine-tuned-albert-tweets results: [] ---  # fine-tuned-albert-tweets This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6212 - Accuracy: 0.6785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 179 | 0.6264 | 0.6377 | | No log | 2.0 | 358 | 0.6212 | 0.6785 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_64_0.05_8_0.0002

ferrazzipietro

2024-03-07T23:56:11Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T23:55:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

s14pe/poca-SoccerTwos

s14pe

2024-03-07T23:54:13Z

21

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2024-03-07T23:53:39Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: s14pe/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

MarkBW/no-bra-club

MarkBW

2024-03-07T23:52:25Z

4

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]

text-to-image

2024-03-07T23:52:22Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0P\0o\0s\0t\0p\0r\0o\0c\0e\0s\0s\0 \0u\0p\0s\0c\0a\0l\0e\0 \0b\0y\0:\0 \04\0,\0 \0P\0o\0s\0t\0p\0r\0o\0c\0e\0s\0s\0 \0u\0p\0s\0c\0a\0l\0e\0r\0:\0 \0R\0-\0E\0S\0R\0G\0A\0N\0 \04\0x\0+" output: url: images/wrefds.jpeg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: dmnoy, crop top --- # no-bra-club <Gallery /> ## Trigger words You should use `dmnoy` to trigger the image generation. You should use `crop top` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/MarkBW/no-bra-club/tree/main) them in the Files & versions tab.

adityahrudayam/T5_qa_model

adityahrudayam

2024-03-07T23:52:21Z

32

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2024-03-07T23:42:04Z

--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer model-index: - name: T5_qa_model results: [] ---  # T5_qa_model This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | nan | | No log | 2.0 | 2 | nan | | No log | 3.0 | 3 | nan | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

panos-span/ppo-SoccerTwos2

panos-span

2024-03-07T23:50:16Z

4

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2024-03-07T23:49:58Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: panos-span/ppo-SoccerTwos2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

andysalerno/openchat-nectar-0.1

andysalerno

2024-03-07T23:45:02Z

13

2

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:berkeley-nest/Nectar", "base_model:openchat/openchat-3.5-0106", "base_model:finetune:openchat/openchat-3.5-0106", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-11T08:02:43Z

--- license: apache-2.0 datasets: - berkeley-nest/Nectar base_model: openchat/openchat-3.5-0106 model-index: - name: openchat-nectar-0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.99 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 54.22 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 69.67 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=andysalerno/openchat-nectar-0.1 name: Open LLM Leaderboard --- This is openchat/openchat-3.5-0106, tuned with DPO on a tiny subset Nectar. Only 200 steps, so nowhere close to a full epoch. Careful attention was paid to make sure the chat template was followed properly. Summary of versions: **[openchat-nectar-0.1](https://huggingface.co/andysalerno/openchat-nectar-0.1)** - 200 steps, no filtering on Nectar dataset, 5e-5 learning rate **[openchat-nectar-0.2](https://huggingface.co/andysalerno/openchat-nectar-0.2)** - empty repo, failed training. ignore it **[openchat-nectar-0.3](https://huggingface.co/andysalerno/openchat-nectar-0.3)** - 500 steps, no filtering on Nectar dataset, 5e-5 learning rate (same as 1 but with more steps) **[openchat-nectar-0.4](https://huggingface.co/andysalerno/openchat-nectar-0.4)** - 500 steps, filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-5 learning rate **[openchat-nectar-0.5](https://huggingface.co/andysalerno/openchat-nectar-0.5)** - 5000 steps (over a full epoch), filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-6 learning rate. Same as 0.4 but with 10x the steps, and 1/10th the learning rate **[openchat-nectar-0.6](https://huggingface.co/andysalerno/openchat-nectar-0.6)** - 500 steps, filtered dataset to only include multi-chat-turn examples, used 4th ranking response as the "rejected" instead of 3rd, filtered out "good_natured=False", 5e-5 learning rate. Same as 0.5 but with 1/10th the steps, and 10x the learning rate # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.1) | Metric |Value| |---------------------------------|----:| |Avg. |69.94| |AI2 Reasoning Challenge (25-Shot)|66.21| |HellaSwag (10-Shot) |82.99| |MMLU (5-Shot) |65.17| |TruthfulQA (0-shot) |54.22| |Winogrande (5-shot) |81.37| |GSM8k (5-shot) |69.67|

dranger003/LWM-Text-Chat-128K-iMat.GGUF

dranger003

2024-03-07T23:33:58Z

143

8

gguf

[ "gguf", "text-generation", "license:llama2", "endpoints_compatible", "region:us" ]

text-generation

2024-02-14T14:14:59Z

--- license: llama2 pipeline_tag: text-generation library_name: gguf --- GGUF importance matrix (imatrix) quants for https://huggingface.co/LargeWorldModel/LWM-Text-Chat-128K The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw. * The imatrix Q4-K quant fits with 32K context on 24GB and gives me ~100 t/s inference on a 3090. * With IQ3_XXS it seems to fit ~37K context on 24GB (and it is even faster than Q4-K). * With either quant on a 3090 it seems to decode context at well over 2000 t/s. * Using Q8 K-cache (instead of F16) you can fit up to 43-44K context but inference speed goes down a little bit. * Also for some reason I need to use 1.0 penalty to avoid the response being cut-off. | Layers | Context | [Template](https://github.com/LargeWorldModel/LWM/blob/9aaaa1e864bfcf31b66028e782395a22f4817535/scripts/eval_needle.py#L48) | | --- | --- | --- | | <pre>32</pre> | <pre>131072</pre> | <pre>You are a helpful assistant. USER: {context} {question} Don't give information outside the document or repeat your findings. Keep your response short and direct. ASSISTANT: {response}</pre> |

iampedroalz/gemma-2b-4bit-alpaca-spanish

iampedroalz

2024-03-07T23:33:25Z

4

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-03-07T23:31:26Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ChaoticNeutrals/Eris_Floramix_DPO_7B

ChaoticNeutrals

2024-03-07T23:30:49Z

231

6

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED", "dataset:ResplendentAI/Synthetic_Soul_1k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T23:09:39Z

--- library_name: transformers license: other datasets: - athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED - ResplendentAI/Synthetic_Soul_1k language: - en --- # Eris Floramix DPO This is a mix between Eris Remix DPO and Flora DPO, a finetune of the original Eris Remix on the Synthetic_Soul_1k dataset. Applied this DPO dataset: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED

shamekhjr/ppo-Huggy

shamekhjr

2024-03-07T23:28:49Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-03-07T23:28:21Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: shamekhjr/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Alpaca69B/phi2-2b-absa

Alpaca69B

2024-03-07T23:21:02Z

4

0

transformers

[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T02:56:41Z

--- library_name: transformers tags: [] --- --- # phi2-2b-absa: Fine-Tuned Aspect-Based Sentiment Analysis Model ## Model Description The **phi2-2b-absa** model is a fine-tuned aspect-based sentiment analysis (ABSA) model based on the Microsoft Phi-2 model. It has been trained on the **semeval2016-full-absa-reviews-english-translated-resampled** dataset. The model predicts sentiments towards different aspects mentioned in a given sentence. ## Fine-Tuning Details The fine tuning can be revisited on [Google Colab](https://colab.research.google.com/drive/1n3ykETLpHQPXwPhUcOe-z9cG3ThrDkSi?usp=sharing). ### Dataset - **Name:** semeval2016-full-absa-reviews-english-translated-resampled - **Description:** Annotated dataset for ABSA containing sentences, aspects, sentiments, and additional contextual text. It is split into train and test sets. ### Model Architecture - **Base Model:** Microsoft Phi-2 - **Fine-Tuned Model:** phi2-2b-absa ### Fine-Tuning Parameters - **LoRA Attention Dimension (lora_r):** 64 - **LoRA Scaling Parameter (lora_alpha):** 16 - **LoRA Dropout Probability (lora_dropout):** 0.1 ### BitsAndBytes Quantization - **Activate 4-bit Precision:** True - **Compute Dtype for 4-bit Models:** float16 - **Quantization Type:** nf4 ### Training Parameters - **Number of Training Epochs:** 1 - **Batch Size per GPU for Training:** 4 - **Batch Size per GPU for Evaluation:** 4 - **Gradient Accumulation Steps:** 1 - **Learning Rate:** 2e-4 - **Weight Decay:** 0.001 - **Optimizer:** PagedAdamW (32-bit) - **Learning Rate Scheduler:** Cosine ### SFT Parameters - **Maximum Sequence Length:** None - **Packing:** False ## How to Use ``` from transformers import AutoTokenizer, pipeline import torch model = "Alpaca69B/llama-2-7b-absa-semeval-2016" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.float16, device="auto", ) input_sentence = "the first thing that attracts attention is the warm reception and the smiling receptionists." sequences = pipeline( f'### Human: {input_sentence} ### Assistant: aspect:', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) sequences[0]['generated_text'] ``` Testing can be seen on [Google Colab](https://colab.research.google.com/drive/1eKdZYYWiivyeCQDsocGBstVODMLZyT-_?usp=sharing) ## Acknowledgments - The fine-tuning process and model development were performed by Ben Kampmann. ---

dolainu/Nyanners_loraXL_Vtuber

dolainu

2024-03-07T23:15:36Z

4

1

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "license:apache-2.0", "region:us" ]

text-to-image

2024-03-07T22:36:54Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09104-3065517054.png - text: >- <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, shirt lift, bed, legs up, pussy, hugging own legs parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09101-2699702684.png - text: >- <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, shirt lift, bed, masturbating, pussy parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09094-3843125815.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09082-3207580297.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, medium hair, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/00028-4212975261.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, medium hair, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09078-3444304162.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners1st, purple eyes, petite, closed mouth, smug, kneeling, shirt lift parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09085-1394382796.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:1>, nyanners2st, long hair, kneeling, closed mouth, shirt lift parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09018-3981033982.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, closed mouth, shirt lift, smug, petite, lying, bed parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09035-1317649319.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, kneeling, closed mouth, shirt lift, smug, petite parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09026-560715627.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, kneeling, closed mouth, shirt lift, smug, petite parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, realistic, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, motion lines output: url: images/09024-4125556276.png - text: >- score_9, score_8_up, score_7_up, <lora:NyanXL_V1_50se:0.87>, nyanners2st, long hair, purple eyes, petite, closed mouth, smug, sitting, table, drink, hand on cheek, looking at viewer, resting head parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09077-834539960.png base_model: stablediffusionapi/pony-diffusion-v6-xl instance_prompt: null license: apache-2.0 --- # Nyanners <Gallery /> ## Model description Works best with Ponydiffusion V6 XL TESTED AT 0.87 STRENGTH Prompts: short hair ver.: "nyanners1st, purple eyes"---optional: "medium hair" long hair ver.: "nyanners2st, long hair, purple eyes" ## Download model Weights for this model are available in Safetensors format. [Download](/dolainu/Nyanners_lora_Vtuber/tree/main) them in the Files & versions tab.

dolainu/Natsuiro_Matsuri_loraXL_Vtuber

dolainu

2024-03-07T23:09:30Z

3

1

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "license:apache-2.0", "region:us" ]

text-to-image

2024-03-07T23:09:23Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, sitting, bikini parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09658-1693014100.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, sitting, crossed legs parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09663-282707023.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, shirt lift, nipples, kneeling, small breasts parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, full body shot output: url: images/09673-1053123366.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, shirt lift, nipples, kneeling, small breasts, condom wrapper in mouth parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, full body shot output: url: images/09675-1053123366.png - text: >- score_9, score_8_up, <lora:NatsuiroMatsuriXL_V0.2:0.8>, namatsuri, 1girl, (petite), green eyes, lying, bed, spread legs, masturbating, pussy, fingering, hand on breast, small breasts, nipples parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09700-4195931212.png - text: >- score_9, score_8_up, <lora:NatsuiroMatsuriXL_V0.2R:0.8>, namatsuri, 1girl, (petite), green eyes, lying, bed, spread legs, [[[spread pussy]]] parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers, realistic output: url: images/09705-4195931212.png - text: >- score_9, <lora:NatsuiroMatsuriXL_V0.2R:0.8>, namatsuri, 1girl, petite, green eyes, leaning towards viewer parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09710-2211139587.png base_model: stablediffusionapi/pony-diffusion-v6-xl instance_prompt: null license: apache-2.0 --- # Natsuiro Matsuri <Gallery /> ## Model description Works best with Ponydiffusion V6 XL TESTED AT 0.8 STRENGTH. Trigger Words: "namatsuri, 1girl, (petite), green eyes" ## Download model Weights for this model are available in Safetensors format. [Download](/dolainu/Natsuiro_Matsuri_loraXL_Vtuber/tree/main) them in the Files & versions tab.

ArtMindia/artmindia3k

ArtMindia

2024-03-07T23:07:06Z

0

adapter-transformers

[ "adapter-transformers", "pytorch", "mistral", "question-answering", "en", "license:apache-2.0", "region:us" ]

question-answering

2023-10-28T00:43:48Z

--- license: apache-2.0 language: - en library_name: adapter-transformers metrics: - accuracy pipeline_tag: question-answering --- --- license: apache-2.0 language: - en This is just a test card with a few thousand rows of data. I wish I had more to add but that is all. How much do you need. Here it is. fsdf asdf sdf dsafs fsd fad sfs f sadf dafs dfs fa sf asf sf s afs fsdf f s f sf sdf sf sf s This model is not too short

Sebas012/mi-super-modelo

Sebas012

2024-03-07T23:06:44Z

4

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-07T23:00:10Z

--- license: apache-2.0 tags: - generated_from_trainer base_model: bert-base-cased metrics: - accuracy model-index: - name: mi-super-modelo results: [] ---  # mi-super-modelo This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7111 - Accuracy: 0.15 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7596 | 0.5 | 5 | 1.7679 | 0.15 | | 1.8268 | 1.0 | 10 | 1.7111 | 0.15 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cpu - Datasets 2.18.0 - Tokenizers 0.15.2

AIdenU/Gemma-7b-ko-Y24_v2.0

AIdenU

2024-03-07T23:04:05Z

6

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-05T00:12:43Z

--- license: apache-2.0 language: - ko pipeline_tag: text-generation tags: - gemma --- ### BaseModel - [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Model Generation ``` from transforemrs import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AIdenU/Gemma-7b-ko-Y24_v2.0", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("AIdenU/Gemma-7b-ko-Y24_v2.0", use_fast=True) systemPrompt = "당신은 유능한 AI입니다." prompt = "지렁이도 밟으면 꿈틀하나요?" outputs = model.generate( **tokenizer( f"### instruction: {system}\n{prompt} \n### output: ", return_tensors='pt' ).to('cuda'), max_new_tokens=256, temperature=0.2, top_p=1, do_sample=True ) print(tokenizer.decode(outputs[0])) ```

AIdenU/LLAMA-2-13b-koen-Y24_v1.0

AIdenU

2024-03-07T23:01:59Z

211

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llama2", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-02-21T01:25:26Z

--- license: apache-2.0 language: - ko pipeline_tag: text-generation tags: - llama2 --- ### BaseModel - [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ### Model Generation ``` from transforemrs import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AIdenU/LLAMA-2-13b-koen-Y24_v1.0", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("AIdenU/LLAMA-2-13b-koen-Y24_v1.0", use_fast=True) systemPrompt = "당신은 유능한 AI입니다." prompt = "지렁이도 밟으면 꿈틀하나요?" outputs = model.generate( **tokenizer( f"[INST] <<SYS>>\n{systemPrompt}\n<</SYS>>\n\n{prompt} [/INST] ", return_tensors='pt' ).to('cuda'), max_new_tokens=256, temperature=0.2, top_p=1, do_sample=True ) print(tokenizer.decode(outputs[0])) ```

AlexandreManai/dqn-SpaceInvadersNoFrameskip-v4

AlexandreManai

2024-03-07T22:59:30Z

6

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-03-07T22:58:58Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 512.00 +/- 139.13 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AlexandreManai -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AlexandreManai -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AlexandreManai ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

dolainu/SmugAlana_loraXL_Vtuber

dolainu

2024-03-07T22:58:46Z

3

1

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "license:apache-2.0", "region:us" ]

text-to-image

2024-03-07T22:40:52Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- score_9, <lora:SmugAlanaV0.1:0.87>, smalana, 1girl, smug, kneeling, bed, condom wrapper in mouth parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09290-1373470824.png - text: score_9, <lora:SmugAlanaV0.1:0.8>, smalana, 1girl, nude, bed parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09328-362448161.png - text: >- score_9, <lora:SmugAlanaV0.1:0.8>, smalana, 1girl, sitting, clothes down, nipple parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09316-1813936606.png - text: score_9, <lora:SmugAlanaV0.1:0.87>, smalana, 1girl, smug, sitting parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09309-4054020249.png - text: >- <lora:SmugAlanaV0.1:0.8>, smalana, 1girl, <lora:Smooth Anime 2 Style SDXL_LoRA_Pony Diffusion V6 XL:1> parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09340-1383936240.png - text: score_9, <lora:SmugAlanaV0.1:0.87>, smalana, 1girl, smug, sitting parameters: negative_prompt: >- censored, unfinished, sketch, messy drawing, amateur drawing, thick thighs, muscular female, bad anatomy, bad proportions, deformed, deformed anatomy, deformed fingers output: url: images/09343-915960868.png base_model: stablediffusionapi/pony-diffusion-v6-xl instance_prompt: null license: apache-2.0 --- # SmugAlana <Gallery /> ## Model description Works best with Ponydiffusion V6 XL TESTED BETWEEN '0.8 - 0.9' STRENGTH Trigger words: smalana, 1girl ## Download model Weights for this model are available in Safetensors format. [Download](/dolainu/SmugAlana_lora_Vtuber/tree/main) them in the Files & versions tab.

colerobertson/wav2vec2-base-ogma-phoneme

colerobertson

2024-03-07T22:41:39Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-03-07T22:22:42Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer model-index: - name: wav2vec2-base-ogma-phoneme results: [] ---  # wav2vec2-base-ogma-phoneme This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Cer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:------------------------:|:-----:|:----:|:---------------:|:------:| | 67.3394 | 1.0 | 5 | 62.1245 | 6.7376 | | 73.2076 | 2.0 | 10 | nan | 6.7376 | | -257055208286820768.0000 | 3.0 | 15 | nan | 6.7376 | | 64.2241 | 4.0 | 20 | nan | 6.7376 | | 65.3601 | 5.0 | 25 | 62.1245 | 6.7376 | | 64.2295 | 6.0 | 30 | 62.1157 | 6.7376 | | 45.425 | 7.0 | 35 | 62.1251 | 6.7376 | | 50.6118 | 8.0 | 40 | nan | 6.7178 | | 64.6394 | 9.0 | 45 | 62.0582 | 6.6188 | | 48.7615 | 10.0 | 50 | nan | 6.6188 | | 54.5817 | 11.0 | 55 | nan | 6.4950 | | 48.1198 | 12.0 | 60 | nan | 6.4950 | | 56.9202 | 13.0 | 65 | nan | 6.3465 | | 57.3656 | 14.0 | 70 | nan | 6.4406 | | 68.163 | 15.0 | 75 | 61.7497 | 6.2129 | | 56.806 | 16.0 | 80 | nan | 6.2129 | | 69.1218 | 17.0 | 85 | 61.6119 | 5.7574 | | 55.5282 | 18.0 | 90 | 61.5413 | 5.4158 | | -6752.4055 | 19.0 | 95 | 61.2303 | 4.9257 | | 64.744 | 20.0 | 100 | 60.9641 | 4.4455 | | 66.7382 | 21.0 | 105 | 60.3274 | 3.5198 | | -21060.9078 | 22.0 | 110 | nan | 3.5198 | | 51.2619 | 23.0 | 115 | 59.9896 | 3.1089 | | 51.398 | 24.0 | 120 | nan | 2.7772 | | 63.6242 | 25.0 | 125 | 59.3321 | 2.5149 | | 59.6308 | 26.0 | 130 | 58.7697 | 2.1931 | | 62.0615 | 27.0 | 135 | nan | 1.8366 | | -46.2037 | 28.0 | 140 | 57.9474 | 1.7475 | | 60.5632 | 29.0 | 145 | 57.5041 | 1.5941 | | 55.4431 | 30.0 | 150 | 56.7507 | 1.4307 | | 40.8661 | 31.0 | 155 | 56.6063 | 1.4059 | | 63.784 | 32.0 | 160 | 56.1097 | 1.2327 | | 42.2708 | 33.0 | 165 | nan | 1.2327 | | 53.7813 | 34.0 | 170 | nan | 1.2426 | | 57.459 | 35.0 | 175 | 55.8894 | 1.2228 | | 58.9998 | 36.0 | 180 | nan | 1.0 | | 0.0 | 37.0 | 185 | nan | 1.0 | | 0.0 | 38.0 | 190 | nan | 1.0 | | 0.0 | 39.0 | 195 | nan | 1.0 | | 0.0 | 40.0 | 200 | nan | 1.0 | | 0.0 | 41.0 | 205 | nan | 1.0 | | 0.0 | 42.0 | 210 | nan | 1.0 | | 0.0 | 43.0 | 215 | nan | 1.0 | | 0.0 | 44.0 | 220 | nan | 1.0 | | 0.0 | 45.0 | 225 | nan | 1.0 | | 0.0 | 46.0 | 230 | nan | 1.0 | | 0.0 | 47.0 | 235 | nan | 1.0 | | 0.0 | 48.0 | 240 | nan | 1.0 | | 0.0 | 49.0 | 245 | nan | 1.0 | | 0.0 | 50.0 | 250 | nan | 1.0 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_32_0.01_4_0.0002

ferrazzipietro

2024-03-07T22:39:42Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T22:39:06Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ShubhamJain18/ppo-Huggy

ShubhamJain18

2024-03-07T22:35:47Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-03-07T22:34:03Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ShubhamJain18/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF

MaziyarPanahi

2024-03-07T22:33:24Z

54

2

transformers

[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "tensorboard", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "en", "dataset:HuggingFaceH4/deita-10k-v0-sft", "base_model:google/gemma-7b", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "base_model:quantized:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1" ]

text-generation

2024-03-07T22:03:29Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - tensorboard - safetensors - gemma - text-generation - alignment-handbook - trl - sft - generated_from_trainer - conversational - en - dataset:HuggingFaceH4/deita-10k-v0-sft - base_model:google/gemma-7b - license:other - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: zephyr-7b-gemma-sft-v0.1-GGUF base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 inference: false model_creator: HuggingFaceH4 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) - Model creator: [HuggingFaceH4](https://huggingface.co/HuggingFaceH4) - Original model: [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) ## Description [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) contains GGUF format model files for [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) and below it, a specific filename to download, such as: zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/zephyr-7b-gemma-sft-v0.1-GGUF zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./zephyr-7b-gemma-sft-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

tsavage68/mistralit2_1000_STEPS_1e7_SFT_SFT

tsavage68

2024-03-07T22:31:26Z

5

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T22:25:45Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_1e7_SFT_SFT results: [] ---  # mistralit2_1000_STEPS_1e7_SFT_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.393 | 0.1 | 50 | 1.3650 | | 0.9828 | 0.2 | 100 | 0.9080 | | 0.3975 | 0.29 | 150 | 0.3765 | | 0.3465 | 0.39 | 200 | 0.3516 | | 0.3422 | 0.49 | 250 | 0.3418 | | 0.3436 | 0.59 | 300 | 0.3365 | | 0.3244 | 0.68 | 350 | 0.3329 | | 0.3332 | 0.78 | 400 | 0.3298 | | 0.3221 | 0.88 | 450 | 0.3275 | | 0.3293 | 0.98 | 500 | 0.3260 | | 0.3143 | 1.07 | 550 | 0.3251 | | 0.3279 | 1.17 | 600 | 0.3246 | | 0.3336 | 1.27 | 650 | 0.3243 | | 0.3045 | 1.37 | 700 | 0.3241 | | 0.3199 | 1.46 | 750 | 0.3240 | | 0.3227 | 1.56 | 800 | 0.3240 | | 0.3217 | 1.66 | 850 | 0.3239 | | 0.3256 | 1.76 | 900 | 0.3239 | | 0.3383 | 1.86 | 950 | 0.3239 | | 0.3305 | 1.95 | 1000 | 0.3239 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2

masonjar/mixtral_test

masonjar

2024-03-07T22:31:22Z

1

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]

null

2024-03-07T21:45:29Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model-index: - name: mixtral_test results: [] ---  # mixtral_test This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0 - Datasets 2.15.0 - Tokenizers 0.15.2

Maqqq/OpenHermes-2.5-Mistral-7B-15

Maqqq

2024-03-07T22:26:33Z

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T21:55:03Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_32_0.05_16_0.0002

ferrazzipietro

2024-03-07T22:20:09Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T17:23:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Arczisan/christy-doa

Arczisan

2024-03-07T22:17:25Z

2

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]

text-to-image

2024-03-07T22:17:21Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/chisty.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null --- # Dead or Alive - Christy <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Arczisan/christy-doa/tree/main) them in the Files & versions tab.

s14pe/ppo-Pyramid

s14pe

2024-03-07T22:17:18Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2024-03-07T19:18:02Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: s14pe/ppo-Pyramid 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

dataequity/DE-LM-7B

dataequity

2024-03-07T22:07:29Z

6

0

transformers

[ "transformers", "safetensors", "deci", "text-generation", "conversational", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "region:us" ]

text-generation

2024-03-07T21:54:59Z

--- license: apache-2.0 language: - en --- # DE-LM-7B DE-LM-7B is a 7.04 billion parameter decoder-only text generation model, released under the Apache 2.0 license. This is an instruction tuned model built on top of Deci/DeciLM-7B fine-tuned for data filtering and API generation. ### Model Description - **Language(s) (NLP):** English - **License:** Apache 2.0 ## Model Architecture | Parameters | Layers | Heads | Sequence Length | GQA num_key_value_heads* | |:----------|:----------|:----------|:----------|:----------| | 7.04 billion | 32 | 32 | 8192 | Variable | ## Uses The model is intended for commercial and research use in English and can be fine-tuned for various tasks and languages. ## How to Get Started with the Model Use the code below to get started with the model. ```bibtex import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "dataequity/DE-LM-7B" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", trust_remote_code=True).to(device) inputs = tokenizer.encode("List the top 10 financial APIs", return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95) print(tokenizer.decode(outputs[0])) # The model can also be used via the text-generation pipeline interface from transformers import pipeline generator = pipeline("text-generation", "dataequity/DE-LM-7B", torch_dtype="auto", trust_remote_code=True, device=device) outputs = generator("List the top 10 financial APIs", max_new_tokens=100, do_sample=True, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## Ethical Considerations and Limitations DE-LM-7B is a new technology that comes with inherent risks associated with its use. The testing conducted so far has been primarily in English and does not encompass all possible scenarios. Like those of all large language models, DE-LM-7B's outputs are unpredictable, and the model may generate responses that are inaccurate, biased, or otherwise objectionable. Consequently, developers planning to use DE-LM-7B should undertake thorough safety testing and tuning designed explicitly for their intended applications of the model before deployment. ## Citation ```bibtex @misc{DeciFoundationModels, title = {DeciLM-7B}, author = {DeciAI Research Team}, year = {2023} url={https://huggingface.co/Deci/DeciLM-7B}, } ```

HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit

HuggingFaceM4

2024-03-07T22:05:47Z

8,619

43

transformers

[ "transformers", "safetensors", "siglip", "zero-shot-image-classification", "custom_code", "arxiv:2307.06304", "license:apache-2.0", "endpoints_compatible", "region:us" ]

zero-shot-image-classification

2024-01-30T19:31:08Z

--- license: apache-2.0 --- Same as https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2 with two changes: - increase max resolution to 980 x 980 (instead of 384 x 384) by interpolating the position embeddings - implement the strategy in [NaViT](https://arxiv.org/abs/2307.06304) to allow a/ variable resoltion images, b/ aspect ratio preserved images These changes only apply to the vision tower. No changes to the text tower. Implementation is fully backward compatible to `https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2` -> just don't specify the `patch_attention_mask` Usage: ```python import torch from modeling_siglip import SiglipVisionModel DEVICE = torch.device("cuda:0") PATCH_SIZE = 14 pixel_values = torch.randn(2, 3, 28, 42, dtype=torch.bfloat16, device=DEVICE) pixel_attention_mask = [ [ [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, ], [ [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, ], ] pixel_attention_mask = torch.tensor(pixel_attention_mask, dtype=torch.bool, device=DEVICE) patches_subgrid = pixel_attention_mask.unfold( dimension=1, size=PATCH_SIZE, step=PATCH_SIZE ).unfold(dimension=2, size=PATCH_SIZE, step=PATCH_SIZE) patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() model = SiglipVisionModel.from_pretrained("HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit", _flash_attn_2_enabled=True) model.train() model.vision_model.to(DEVICE, dtype=torch.bfloat16) output = model.vision_model(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) ```

Maqqq/OpenHermes-2.5-Mistral-7B-14

Maqqq

2024-03-07T21:58:58Z

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T19:57:03Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

twhoool02/Llama-2-7b-hf-AWQ

twhoool02

2024-03-07T21:58:42Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "AWQ", "llama-2", "en", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:quantized:meta-llama/Llama-2-7b-hf", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

text-generation

2024-03-03T21:37:40Z

--- language: en license: other tags: - facebook - meta - AWQ - llama-2 - llama base_model: meta-llama/Llama-2-7b-hf model_name: Llama-2-7b-hf-AWQ library: - Transformers - AWQ arxiv: https://arxiv.org/abs/2306.00978 model_type: llama pipeline_tag: text-generation qunatized_by: twhoool02 --- # Model Card for Llama-2-7b-hf-AWQ  ## Model Details ### Model Description  This model is a quantized version of the meta-llama/Llama-2-7b-hf model. The model was quantized using AWQ. - **Developed by:** Ted Whooley - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** llama - **Language(s) (NLP):** en - **License:** other - **Finetuned from model [optional]:** meta-llama/Llama-2-7b-hf ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Nusrat1234/Mistral-7B-User-Profile

Nusrat1234

2024-03-07T21:44:01Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T21:43:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.0002

ferrazzipietro

2024-03-07T21:43:23Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T16:45:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dominic1021/ohwxsarah

dominic1021

2024-03-07T21:40:04Z

2

1

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2024-03-07T19:23:40Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: ohwxsarah woman tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

pypy/VGMShield

pypy

2024-03-07T21:32:06Z

0

3

null

[ "Fake Video Detection", "Fake Video Source Tracing", "video-classification", "dataset:OpenGVLab/InternVid", "dataset:TempoFunk/webvid-10M", "arxiv:2402.13126", "license:apache-2.0", "region:us" ]

video-classification

2024-02-21T20:23:29Z

--- license: apache-2.0 datasets: - OpenGVLab/InternVid - TempoFunk/webvid-10M pipeline_tag: video-classification tags: - Fake Video Detection - Fake Video Source Tracing --- <div style="text-align: center;"> <img src="./symbol.png" alt="symbol" style="height: 100px;"/> </div> # VGMShield: Mitigating Misuse of Video Generative Models This repository pre-trained checkpoints to evaluate our detection and source tracing models. Our paper can be found at [here](https://arxiv.org/abs/2402.13126). **Detection Model**: [I3D](./detect/i3d/invid_i3d_i2v_i2v_best_model.pth) (0 True 1 False) [MAE](./detect/mae/invid_mae_i2v_i2v_best_model.pth) (0 True 1 False) [XCLIP](./detect/xclip/invid_xclip_i2v_i2v_best_model.pth) (0 True 1 False) [MAE-sora](./detect/mae/detection_ft_sora.pt) (0 True 1 False) **Source Tracing Model** > 0 Hotshot-xl 1 i2vgen-xl(i2v) 2 i2vgen-xl(t2v) 3 LaVie 4 SEINE 5 Show-1 6 Stable Video Diffusion 7 VideoCrafter(i2v) 8 VideoCrafter(t2v) [I3D](./source_tracing/i3d/invid_i3d_st_best_model.pth)-based source tracing model [MAE](./source_tracing/mae/invid_mae_st_best_model.pth)-based source tracing model [XCLIP](./source_tracing/xclip/invid_xclip_st_best_model.pth)-based source tracing model [MAE](./source_tracing/mae/source_tracing_ft_sora.pt)-based(sora) source tracing model sora is label 9.

cmu-lti/sotopia-pi-mistral-7b-BC

cmu-lti

2024-03-07T21:30:09Z

4

0

peft

[ "peft", "region:us" ]

null

2024-03-07T21:24:20Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0

ferrazzipietro/Qwen1.5-14B-Chat__adapters_en.layer1_4_torch.bfloat16_16_64_0.01_16_0.0002

ferrazzipietro

2024-03-07T21:24:30Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-07T16:26:06Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Shawt/Shawt

Shawt

2024-03-07T21:23:17Z

1

0

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/sdxl-turbo", "base_model:finetune:stabilityai/sdxl-turbo", "region:us" ]

text-to-image

2023-07-11T04:45:40Z

--- base_model: stabilityai/sdxl-turbo instance_prompt: <shawt> tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

MaziyarPanahi/phi-2-super-GGUF

MaziyarPanahi

2024-03-07T21:20:57Z

101

4

transformers

[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "phi", "text-generation", "convAI", "conversational", "custom_code", "en", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space", "base_model:abacaj/phi-2-super", "base_model:quantized:abacaj/phi-2-super" ]

text-generation

2024-03-07T21:09:42Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - phi - text-generation - convAI - conversational - custom_code - en - license:mit - model-index - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - has_space - text-generation model_name: phi-2-super-GGUF base_model: abacaj/phi-2-super inference: false model_creator: abacaj pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) - Model creator: [abacaj](https://huggingface.co/abacaj) - Original model: [abacaj/phi-2-super](https://huggingface.co/abacaj/phi-2-super) ## Description [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) contains GGUF format model files for [abacaj/phi-2-super](https://huggingface.co/abacaj/phi-2-super). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) and below it, a specific filename to download, such as: phi-2-super-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/phi-2-super-GGUF phi-2-super-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/phi-2-super-GGUF](https://huggingface.co/MaziyarPanahi/phi-2-super-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/phi-2-super-GGUF phi-2-super-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m phi-2-super-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./phi-2-super-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./phi-2-super-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

mehdirafiei/SQLCODER16L

mehdirafiei

2024-03-07T21:18:40Z

4

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T21:10:19Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

panos-span/a2c-PandaReachDense-v3

panos-span

2024-03-07T21:18:19Z

4

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-03-07T21:14:32Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.20 +/- 0.11 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```