librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
baxtos/bartik03-4	baxtos	2024-07-02T08:41:35Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:39:03Z	Entry not found
DokHee/llama3-ko-bemoi-8b-VBioLLM1000	DokHee	2024-07-02T08:41:07Z	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:39:16Z	--- base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: DokHee - License: apache-2.0 - Finetuned from model : beomi/Llama-3-Open-Ko-8B-Instruct-preview This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yashss/diaratechHf_llama35506e3a-cd9b-475f-95dd-7005c87a2a94	yashss	2024-07-02T08:41:15Z	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "text-generation", "conversational", "dataset:generator", "base_model:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	text-generation	2024-07-02T08:39:48Z	--- base_model: microsoft/Phi-3-mini-128k-instruct datasets: - generator library_name: peft license: mit pipeline_tag: text-generation tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama35506e3a-cd9b-475f-95dd-7005c87a2a94 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. --> # diaratechHf_llama35506e3a-cd9b-475f-95dd-7005c87a2a94 This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the generator 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Mluleki/dyu-fr-translation	Mluleki	2024-07-02T10:44:15Z	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-07-02T08:42:04Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: Mluleki/dyu-fr-translation results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Mluleki/dyu-fr-translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0678 - Validation Loss: 2.8734 - Epoch: 9 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.6875 \| 3.2490 \| 0 \| \| 3.4758 \| 3.1470 \| 1 \| \| 3.3749 \| 3.0798 \| 2 \| \| 3.3153 \| 3.0285 \| 3 \| \| 3.2551 \| 2.9931 \| 4 \| \| 3.2077 \| 2.9603 \| 5 \| \| 3.1696 \| 2.9331 \| 6 \| \| 3.1311 \| 2.9081 \| 7 \| \| 3.0996 \| 2.8899 \| 8 \| \| 3.0678 \| 2.8734 \| 9 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.15.0 - Datasets 2.14.2 - Tokenizers 0.13.3
mayarmostafa/videomae-base-finetuned-bleeding-exp_3	mayarmostafa	2024-07-02T11:12:19Z	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-07-02T08:42:12Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-bleeding-exp_3 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. --> # videomae-base-finetuned-bleeding-exp_3 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) 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: 8 - 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_ratio: 0.1 - training_steps: 600 ### Framework versions - Transformers 4.40.2 - Pytorch 1.12.0 - Datasets 2.19.1 - Tokenizers 0.19.1
sit-justin/whisper-small-test	sit-justin	2024-07-02T09:44:39Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:custom_datset", "base_model:Kathernie/whisper-small-all", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T08:43:06Z	--- language: - zh base_model: Kathernie/whisper-small-all tags: - generated_from_trainer datasets: - custom_datset model-index: - name: Whisper Small Chinese MOE Response 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. --> # Whisper Small Chinese MOE Response This model is a fine-tuned version of [Kathernie/whisper-small-all](https://huggingface.co/Kathernie/whisper-small-all) on the MOE Response Chinese dataset. It achieves the following results on the evaluation set: - Loss: 0.2640 - Cer: 11.0180 ## 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-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 250 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.2252 \| 1.0811 \| 200 \| 0.2339 \| 12.4230 \| \| 0.1268 \| 2.1622 \| 400 \| 0.2223 \| 10.9194 \| \| 0.056 \| 3.2432 \| 600 \| 0.2242 \| 10.8701 \| \| 0.023 \| 4.3243 \| 800 \| 0.2387 \| 11.3384 \| \| 0.01 \| 5.4054 \| 1000 \| 0.2546 \| 11.2645 \| \| 0.0044 \| 6.4865 \| 1200 \| 0.2515 \| 11.2891 \| \| 0.0028 \| 7.5676 \| 1400 \| 0.2552 \| 10.9440 \| \| 0.0017 \| 8.6486 \| 1600 \| 0.2623 \| 11.2645 \| \| 0.0017 \| 9.7297 \| 1800 \| 0.2624 \| 10.9933 \| \| 0.001 \| 10.8108 \| 2000 \| 0.2640 \| 11.0180 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.0 - Datasets 2.20.0 - Tokenizers 0.19.1
dusrb37/dpo-NIPA2	dusrb37	2024-07-02T08:43:54Z	null	[ "region:us" ]	null	2024-07-02T08:43:54Z	Entry not found
SidXXD/3-only_cos-person-eps_10-person	SidXXD	2024-07-02T09:00:49Z	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-07-02T08:44:03Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_10-person These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
SidXXD/3-only_cos-person-eps_50-person	SidXXD	2024-07-02T09:01:15Z	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-07-02T08:44:17Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_50-person These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
weifar/FTAudit-mistral-7b-mix-v0.1	weifar	2024-07-02T08:46:51Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-02T08:44:33Z	--- library_name: transformers tags: - unsloth --- # 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]
DokHee/KO_AI	DokHee	2024-07-02T09:26:11Z	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:44:33Z	--- base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: DokHee - License: apache-2.0 - Finetuned from model : MLP-KTLim/llama-3-Korean-Bllossom-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
SidXXD/3-only_cos-person-eps_99-person	SidXXD	2024-07-02T09:01:14Z	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-07-02T08:44:35Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_99-person These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
baxtos/bartik04-4	baxtos	2024-07-02T08:47:12Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:44:49Z	Entry not found
RyanLee1229/Llama3_Model_V3.0	RyanLee1229	2024-07-02T08:45:08Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T08:45:08Z	--- license: apache-2.0 ---
Yntec/BetterPonyDiffusion	Yntec	2024-07-02T11:30:36Z	diffusers	[ "diffusers", "safetensors", "Base Model", "Anime", "Photorealistic", "Furry", "diffusionfanatic1173", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-07-02T08:45:52Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - Base Model - Anime - Photorealistic - Furry - diffusionfanatic1173 - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image inference: true --- # Better Pony Diffusion V6 For SD 1.5 v1 Stable Diffusion 1.5 finetuned with the SDXL Pony V6 Dataset and then with ~5000 hand-selected images by diffusionfanatic1173 to improve its aesthetics so you can use its tags like score_9, show accurate, source_anime and others I won't mention so you can check the original page at: https://civitai.com/models/544876?modelVersionId=605949 Samples and prompts: ![Free online image generator Better Pony Diffusion](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/9JyTdNKVZcFFPkfT4_gde.png) (Click for larger) Top left: source_anime, highquality, masterpiece, 1girl, Chi-Chi, :D, close up, smile, arms up, pink helmet, black hair, black eyes, blush, white bikini armor, aqua cape, pink gloves, pink boots, cleavage. cave, rock, mountain. blue collar. CHIBI Top right: score_9, show accurate, cute pony portrait, beach background Bottom left: uploaded on e621, ((by Cleon Peterson, by Sonia Delaunay, by Tomer Hanuka, by Dagasi, traditional media (artwork))), solo female ((toony judy hopps, grey body, blue eyes, white short t-shirt, dark blue short pants, small breasts)), shoulder bag, ((three-quarter portrait, three-quarter view,)) Bottom right: Highly detailed, High Quality, Masterpiece, beautiful, cute anime girl as toon link, teal headwear, Zelda
Columbia-NLP/LION-LLaMA-3-8b-sft-v1.0	Columbia-NLP	2024-07-02T08:56:32Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:46:15Z	--- 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]
Columbia-NLP/LION-Gemma-2b-sft-v1.0	Columbia-NLP	2024-07-02T08:58:39Z	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:46:52Z	--- 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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symphonia16456/Imp-v1.5-3B-196-q3f16_1	symphonia16456	2024-07-02T09:02:56Z	null	[ "region:us" ]	null	2024-07-02T08:48:09Z	Entry not found
whizzzzkid/whizzzzkid_402_2	whizzzzkid	2024-07-02T08:48:30Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:48:10Z	Entry not found
Temo27Anas/videomae-base-finetuned-ucf101-subset-200f-fixed	Temo27Anas	2024-07-02T08:50:18Z	null	[ "region:us" ]	null	2024-07-02T08:50:18Z	Entry not found
baxtos/bartik05-4	baxtos	2024-07-02T08:53:46Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:50:27Z	Entry not found
anushkamantri/llama-2-stock-sentiment-merged	anushkamantri	2024-07-02T09:20:08Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T08:50:32Z	--- license: llama2 ---
adamfendri/distilTestToDelete	adamfendri	2024-07-02T08:51:28Z	transformers	[ "transformers", "safetensors", "distilbert", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2024-07-02T08:50:56Z	--- 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]
gguichard/NuExtract_finetuned_kind_of	gguichard	2024-07-02T08:52:43Z	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "trl", "sft", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-02T08:51:17Z	--- library_name: transformers tags: - trl - sft --- # 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]
MesTruck/norwegian-gpt2	MesTruck	2024-07-02T08:51:40Z	null	[ "region:us" ]	null	2024-07-02T08:51:40Z	Entry not found
zilla0717/ChatTTS-SG2300x	zilla0717	2024-07-02T09:04:18Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T08:51:46Z	--- license: apache-2.0 ---
IreNkweke/bert-finetuned-ner-conll2003	IreNkweke	2024-07-02T09:06:08Z	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-02T08:52:42Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-conll2003 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9347898047004303 - name: Recall type: recall value: 0.9505217098619994 - name: F1 type: f1 value: 0.9425901201602136 - name: Accuracy type: accuracy value: 0.9871813739918761 --- <!-- 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. --> # bert-finetuned-ner-conll2003 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0597 - Precision: 0.9348 - Recall: 0.9505 - F1: 0.9426 - Accuracy: 0.9872 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.078 \| 1.0 \| 1756 \| 0.0798 \| 0.8891 \| 0.9233 \| 0.9059 \| 0.9789 \| \| 0.035 \| 2.0 \| 3512 \| 0.0640 \| 0.9290 \| 0.9468 \| 0.9378 \| 0.9856 \| \| 0.0222 \| 3.0 \| 5268 \| 0.0597 \| 0.9348 \| 0.9505 \| 0.9426 \| 0.9872 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
nhidinh2/xlm-roberta-base-finetuned-ner-thesis-dseb	nhidinh2	2024-07-02T08:55:43Z	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-02T08:52:45Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: xlm-roberta-base-finetuned-ner-thesis-dseb 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. --> # xlm-roberta-base-finetuned-ner-thesis-dseb This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1471 - Precision: 0.7995 - Recall: 0.9088 - F1: 0.8506 - Accuracy: 0.9605 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 1.7775 \| 1.0 \| 31 \| 0.3746 \| 0.6199 \| 0.6839 \| 0.6503 \| 0.8978 \| \| 0.1886 \| 2.0 \| 62 \| 0.0734 \| 0.9590 \| 0.9301 \| 0.9444 \| 0.9875 \| \| 0.0821 \| 3.0 \| 93 \| 0.0413 \| 0.9697 \| 0.9651 \| 0.9674 \| 0.9928 \| \| 0.0427 \| 4.0 \| 124 \| 0.0400 \| 0.9491 \| 0.9635 \| 0.9562 \| 0.9911 \| \| 0.0352 \| 5.0 \| 155 \| 0.0397 \| 0.9421 \| 0.9571 \| 0.9496 \| 0.9899 \| \| 0.0315 \| 6.0 \| 186 \| 0.0410 \| 0.9371 \| 0.9579 \| 0.9474 \| 0.9895 \| \| 0.0344 \| 7.0 \| 217 \| 0.0386 \| 0.9612 \| 0.9643 \| 0.9627 \| 0.9922 \| \| 0.0292 \| 8.0 \| 248 \| 0.0383 \| 0.9574 \| 0.9651 \| 0.9612 \| 0.9921 \| \| 0.0286 \| 9.0 \| 279 \| 0.0387 \| 0.9543 \| 0.9619 \| 0.9581 \| 0.9913 \| \| 0.0259 \| 10.0 \| 310 \| 0.0415 \| 0.9430 \| 0.9595 \| 0.9512 \| 0.9901 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
yuvimor24/whisper-small-hi	yuvimor24	2024-07-02T08:53:09Z	null	[ "region:us" ]	null	2024-07-02T08:53:09Z	Entry not found
manbeast3b/ZZZZZZZZdriver132	manbeast3b	2024-07-02T08:55:15Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:53:20Z	Entry not found
streamtune/e24cad19-5047-4f6d-b062-4e6df17b5f4d	streamtune	2024-07-02T08:57:21Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T08:55:00Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: streamtune - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
GGuGGuCat/roberta-base-finetuned-sts-f1_	GGuGGuCat	2024-07-02T09:11:16Z	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T08:55:16Z	Entry not found
Temo27Anas/videomae-base-finetuned-fixed-subset-200f	Temo27Anas	2024-07-02T08:55:49Z	null	[ "region:us" ]	null	2024-07-02T08:55:49Z	Entry not found
suji1575/llm-mistral-100	suji1575	2024-07-02T09:00:54Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-02T08:56:53Z	--- 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. 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baxtos/bartik06-4	baxtos	2024-07-02T08:59:21Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T08:57:01Z	Entry not found
klea28f/klea	klea28f	2024-07-02T08:57:53Z	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-07-02T08:57:06Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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yemen2016/dfm_1_NCST	yemen2016	2024-07-02T10:44:36Z	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T08:57:29Z	--- base_model: KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align tags: - generated_from_trainer model-index: - name: dfm_1_NCST 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. --> # dfm_1_NCST This model is a fine-tuned version of [KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align](https://huggingface.co/KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8597 - F1-score: 0.5828 ## 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-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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7084 \| 1.0 \| 528 \| 0.6993 \| 0.5614 \| \| 0.621 \| 2.0 \| 1056 \| 0.7156 \| 0.5726 \| \| 0.4739 \| 3.0 \| 1584 \| 0.8597 \| 0.5828 \| \| 0.2725 \| 4.0 \| 2112 \| 1.3816 \| 0.5533 \| \| 0.1412 \| 5.0 \| 2640 \| 2.1550 \| 0.5506 \| \| 0.0732 \| 6.0 \| 3168 \| 2.9031 \| 0.5677 \| \| 0.0351 \| 7.0 \| 3696 \| 3.3674 \| 0.5634 \| \| 0.0188 \| 8.0 \| 4224 \| 3.4715 \| 0.5702 \| \| 0.0119 \| 9.0 \| 4752 \| 3.6579 \| 0.5611 \| \| 0.0044 \| 10.0 \| 5280 \| 3.7318 \| 0.5607 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Makkoen/whisper-large-cit-synth-do015-wd0-lr1e-06-1000	Makkoen	2024-07-02T12:09:52Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T08:58:34Z	--- language: - en license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: ./whisper-large-cit-synth-do015-wd0-lr1e-06-1000 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. --> # ./whisper-large-cit-synth-do015-wd0-lr1e-06-1000 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the SF 1000 dataset. It achieves the following results on the evaluation set: - Loss: 0.3706 - Wer: 23.6647 ## 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-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - 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: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| No log \| 0.4444 \| 25 \| 0.7983 \| 35.9064 \| \| 0.967 \| 0.8889 \| 50 \| 0.6724 \| 32.3977 \| \| 0.967 \| 1.3333 \| 75 \| 0.5459 \| 30.7602 \| \| 0.6804 \| 1.7778 \| 100 \| 0.4692 \| 27.4854 \| \| 0.6804 \| 2.2222 \| 125 \| 0.4341 \| 26.3548 \| \| 0.5145 \| 2.6667 \| 150 \| 0.4143 \| 25.5361 \| \| 0.5145 \| 3.1111 \| 175 \| 0.4019 \| 25.4191 \| \| 0.4614 \| 3.5556 \| 200 \| 0.3914 \| 25.0292 \| \| 0.4614 \| 4.0 \| 225 \| 0.3879 \| 24.4444 \| \| 0.3891 \| 4.4444 \| 250 \| 0.3835 \| 24.6784 \| \| 0.3891 \| 4.8889 \| 275 \| 0.3794 \| 24.6004 \| \| 0.3765 \| 5.3333 \| 300 \| 0.3772 \| 24.0156 \| \| 0.3765 \| 5.7778 \| 325 \| 0.3745 \| 23.4308 \| \| 0.3511 \| 6.2222 \| 350 \| 0.3726 \| 23.5478 \| \| 0.3511 \| 6.6667 \| 375 \| 0.3713 \| 23.5867 \| \| 0.3307 \| 7.1111 \| 400 \| 0.3706 \| 23.4308 \| \| 0.3307 \| 7.5556 \| 425 \| 0.3699 \| 23.1189 \| \| 0.3176 \| 8.0 \| 450 \| 0.3706 \| 23.3918 \| \| 0.3176 \| 8.4444 \| 475 \| 0.3708 \| 23.6647 \| \| 0.31 \| 8.8889 \| 500 \| 0.3706 \| 23.6647 \| ### Framework versions - Transformers 4.42.3 - Pytorch 1.13.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
tz3/finetune_v6	tz3	2024-07-02T09:30:08Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T08:59:00Z	--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: finetune_v6 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. --> # finetune_v6 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3628 - Wer: 24.6544 ## 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: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| No log \| 6.6667 \| 10 \| 0.2278 \| 15.2074 \| \| No log \| 13.3333 \| 20 \| 0.3188 \| 20.2765 \| \| No log \| 20.0 \| 30 \| 0.3442 \| 29.7235 \| \| No log \| 26.6667 \| 40 \| 0.3628 \| 24.6544 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.0 - Datasets 2.20.0 - Tokenizers 0.19.1
ClementineBleuze/deberta_prefix_cont_lr_SEP	ClementineBleuze	2024-07-02T12:01:28Z	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T09:00:35Z	--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: deberta_prefix_cont_lr_SEP 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. --> # deberta_prefix_cont_lr_SEP This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1159 - F1 Weighted: 0.8781 - F1 Samples: 0.8866 - F1 Macro: 0.7534 - F1 Micro: 0.8813 - Accuracy: 0.8552 ## 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-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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Weighted \| F1 Samples \| F1 Macro \| F1 Micro \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-----------:\|:----------:\|:--------:\|:--------:\|:--------:\| \| 0.2985 \| 0.3381 \| 500 \| 0.2049 \| 0.6509 \| 0.6434 \| 0.3553 \| 0.6781 \| 0.6245 \| \| 0.1922 \| 0.6761 \| 1000 \| 0.1577 \| 0.7286 \| 0.7408 \| 0.4025 \| 0.7643 \| 0.7152 \| \| 0.1628 \| 1.0142 \| 1500 \| 0.1490 \| 0.7580 \| 0.7712 \| 0.5103 \| 0.7854 \| 0.7483 \| \| 0.1429 \| 1.3523 \| 2000 \| 0.1351 \| 0.7795 \| 0.7818 \| 0.5737 \| 0.7995 \| 0.7598 \| \| 0.1401 \| 1.6903 \| 2500 \| 0.1356 \| 0.8044 \| 0.8124 \| 0.6459 \| 0.8136 \| 0.7774 \| \| 0.1298 \| 2.0284 \| 3000 \| 0.1358 \| 0.8172 \| 0.8152 \| 0.6646 \| 0.8223 \| 0.7828 \| \| 0.115 \| 2.3665 \| 3500 \| 0.1297 \| 0.8223 \| 0.8253 \| 0.6671 \| 0.8315 \| 0.7957 \| \| 0.1089 \| 2.7045 \| 4000 \| 0.1281 \| 0.8321 \| 0.8394 \| 0.6787 \| 0.8389 \| 0.8126 \| \| 0.1064 \| 3.0426 \| 4500 \| 0.1164 \| 0.8445 \| 0.8501 \| 0.7046 \| 0.8510 \| 0.8214 \| \| 0.0892 \| 3.3807 \| 5000 \| 0.1175 \| 0.8491 \| 0.8558 \| 0.7012 \| 0.8570 \| 0.8261 \| \| 0.0859 \| 3.7187 \| 5500 \| 0.1298 \| 0.8345 \| 0.8387 \| 0.6752 \| 0.8355 \| 0.8024 \| \| 0.0877 \| 4.0568 \| 6000 \| 0.1140 \| 0.8517 \| 0.8594 \| 0.7180 \| 0.8577 \| 0.8288 \| \| 0.0733 \| 4.3949 \| 6500 \| 0.1126 \| 0.8587 \| 0.8693 \| 0.7196 \| 0.8642 \| 0.8383 \| \| 0.0716 \| 4.7329 \| 7000 \| 0.1194 \| 0.8612 \| 0.8691 \| 0.7221 \| 0.8656 \| 0.8363 \| \| 0.0718 \| 5.0710 \| 7500 \| 0.1170 \| 0.8616 \| 0.8700 \| 0.7188 \| 0.8669 \| 0.8437 \| \| 0.0644 \| 5.4091 \| 8000 \| 0.1114 \| 0.8619 \| 0.8674 \| 0.7173 \| 0.8653 \| 0.8342 \| \| 0.0576 \| 5.7471 \| 8500 \| 0.1205 \| 0.8637 \| 0.8714 \| 0.7378 \| 0.8663 \| 0.8383 \| \| 0.0536 \| 6.0852 \| 9000 \| 0.1151 \| 0.8676 \| 0.8758 \| 0.7246 \| 0.8694 \| 0.8451 \| \| 0.0499 \| 6.4233 \| 9500 \| 0.1184 \| 0.8687 \| 0.8782 \| 0.7410 \| 0.8732 \| 0.8437 \| \| 0.0489 \| 6.7613 \| 10000 \| 0.1159 \| 0.8781 \| 0.8866 \| 0.7534 \| 0.8813 \| 0.8552 \| \| 0.0468 \| 7.0994 \| 10500 \| 0.1211 \| 0.8729 \| 0.8799 \| 0.7645 \| 0.8760 \| 0.8478 \| \| 0.0407 \| 7.4375 \| 11000 \| 0.1234 \| 0.8762 \| 0.8843 \| 0.7679 \| 0.8779 \| 0.8532 \| \| 0.0415 \| 7.7755 \| 11500 \| 0.1251 \| 0.8679 \| 0.8722 \| 0.7628 \| 0.8689 \| 0.8430 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
streamtune/8bd15f1d-431f-4e00-abc3-4198fd22b2fd	streamtune	2024-07-02T09:03:59Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:01:26Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: streamtune - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
SCnetPTY/CHatBot	SCnetPTY	2024-07-02T09:02:52Z	null	[ "en", "af", "region:us" ]	null	2024-07-02T09:01:59Z	--- language: - en - af ---
baxtos/bartik07-4	baxtos	2024-07-02T09:05:00Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T09:02:37Z	Entry not found
Nitss/onxx_test_model	Nitss	2024-07-02T09:18:29Z	transformers	[ "transformers", "onnx", "bert", "feature-extraction", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	feature-extraction	2024-07-02T09:04:02Z	Entry not found
Piece-Of-Schmidt/LocNER_model_v1	Piece-Of-Schmidt	2024-07-02T09:06:48Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:06:30Z	--- 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]
Hemanth1729/SentimentAnalysis_modelv2	Hemanth1729	2024-07-02T09:07:33Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:07:32Z	--- 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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(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]
baxtos/bartik08-4	baxtos	2024-07-02T09:10:47Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T09:08:14Z	Entry not found
waylandzhang/whisper-small-hi	waylandzhang	2024-07-02T11:33:28Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T09:11:19Z	Entry not found
MichaelBr/realDataFineTune	MichaelBr	2024-07-02T12:54:33Z	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-07-02T09:11:49Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct model-index: - name: realDataFineTune 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. --> # realDataFineTune This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.0+cpu - Datasets 2.19.0 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
suji1575/llm-mistral-40	suji1575	2024-07-02T09:22:03Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-02T09:12:12Z	--- 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. 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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]
John6666/3010nc-xx-mixpony-v16-sdxl	John6666	2024-07-02T09:17:22Z	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-02T09:12:15Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - pony --- Original model is [here](https://civitai.com/models/548205/3010nc-xx-mixpony?modelVersionId=613617).
johnwee1/peft-pythoncoder-it	johnwee1	2024-07-03T00:36:41Z	null	[ "safetensors", "region:us" ]	null	2024-07-02T09:12:46Z	Entry not found
oz1115/meta_llama_peft	oz1115	2024-07-02T09:12:51Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:12:48Z	--- 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. 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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]
maxseats/SungBeom-whisper-small-ko-set15	maxseats	2024-07-02T09:13:18Z	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "speech-recognition", "ko", "dataset:maxseats/aihub-464-preprocessed-680GB-set-15", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T09:12:57Z	--- language: ko tags: - whisper - speech-recognition datasets: - maxseats/aihub-464-preprocessed-680GB-set-15 metrics: - cer --- # Model Name : maxseats/SungBeom-whisper-small-ko-set14 # Description - 파인튜닝 데이터셋 : maxseats/aihub-464-preprocessed-680GB-set-15 # 설명 - AI hub의 주요 영역별 회의 음성 데이터셋을 학습 중이에요. - 680GB 중 set_0~14 데이터(150GB)까지 파인튜닝한 모델을 불러와서, set_15 데이터(10GB)를 학습한 모델입니다. - 링크 : https://huggingface.co/datasets/maxseats/aihub-464-preprocessed-680GB-set-15
John6666/ely-pony-xl-v1-sdxl	John6666	2024-07-02T09:22:31Z	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-02T09:13:15Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - pony --- Original model is [here](https://civitai.com/models/551640/elyponyxl?modelVersionId=613842).
Reihaneh/wav2vec2_fy_nl_en_common_voice_54	Reihaneh	2024-07-02T09:13:23Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:13:22Z	--- 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]
SidXXD/3-only_cos-person-eps_99-alpha_5e-1	SidXXD	2024-07-02T09:33:18Z	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-07-02T09:13:27Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_99-alpha_5e-1 These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
SidXXD/3-only_cos-person-eps_99-alpha_5e-2	SidXXD	2024-07-02T09:33:19Z	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-07-02T09:13:39Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_99-alpha_5e-2 These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
finn03091993/naschainv76	finn03091993	2024-07-02T13:52:30Z	null	[ "region:us" ]	null	2024-07-02T09:13:55Z	Entry not found
baxtos/bartik09-4	baxtos	2024-07-02T09:16:24Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T09:14:03Z	Entry not found
streamtune/162c9955-c68a-4880-a7b5-a577592a1a52	streamtune	2024-07-02T09:17:34Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:14:29Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: streamtune - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
John6666/agenda-mix-pdxl-v15-sdxl	John6666	2024-07-02T09:20:26Z	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-02T09:14:32Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - pony --- Original model is [here](https://civitai.com/models/434919/agenda-mix-pdxl?modelVersionId=613794).
qsy71/none_quantization_medical_Gemma-1.1-7B-Chat	qsy71	2024-07-02T16:39:55Z	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T09:15:36Z	--- library_name: transformers tags: - llama-factory --- # 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]
ZZPENG/3f_Lottery_draft1	ZZPENG	2024-07-02T09:23:18Z	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T09:15:53Z	Entry not found
CHARKA/Mistral-7B-Instruct-v0.3tmaroc_edu	CHARKA	2024-07-02T09:17:03Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:16:18Z	--- 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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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]
wanib26/finetuningllama2	wanib26	2024-07-02T09:16:43Z	null	[ "region:us" ]	null	2024-07-02T09:16:43Z	Entry not found
zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF	zhentaoyu	2024-07-02T09:17:01Z	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-2", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	text-generation	2024-07-02T09:16:43Z	--- base_model: meta-llama/Llama-2-7b-chat-hf language: - en license: llama2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 - llama-cpp - gguf-my-repo extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: "### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\"Agreement\" means\ \ the terms and conditions for use, reproduction, distribution and modification\ \ of the Llama Materials set forth herein. \n\"Documentation\" means the specifications,\ \ manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/.\ \ \n\"Licensee\" or \"you\" means you, or your employer or any other person or\ \ entity (if you are entering into this Agreement on such person or entity's behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf. \n\"Llama 2\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.\n\"Llama\ \ Materials\" means, collectively, Meta's proprietary Llama 2 and documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). \n\ \nBy clicking \"I Accept\" below or by using or distributing any portion or element\ \ of the Llama Materials, you agree to be bound by this Agreement.\n1. License Rights\ \ and Redistribution. \na. Grant of Rights. You are granted a non-exclusive, worldwide,\ \ non- transferable and royalty-free limited license under Meta's intellectual property\ \ or other rights owned by Meta embodied in the Llama Materials to use, reproduce,\ \ distribute, copy, create derivative works of, and make modifications to the Llama\ \ Materials. \nb. Redistribution and Use.\ni. If you distribute or make the Llama\ \ Materials, or any derivative works thereof, available to a third party, you shall\ \ provide a copy of this Agreement to such third party. \nii. If you receive Llama\ \ Materials, or any derivative works thereof, from a Licensee as part of an integrated\ \ end user product, then Section 2 of this Agreement will not apply to you. \n\ iii. You must retain in all copies of the Llama Materials that you distribute the\ \ following attribution notice within a \"Notice\" text file distributed as a part\ \ of such copies: \"Llama 2 is licensed under the LLAMA 2 Community License, Copyright\ \ (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials\ \ must comply with applicable laws and regulations (including trade compliance\ \ laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials\ \ (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated\ \ by reference into this Agreement.\nv. You will not use the Llama Materials or\ \ any output or results of the Llama Materials to improve any other large language\ \ model (excluding Llama 2 or derivative works thereof). \n\n2. Additional Commercial\ \ Terms. If, on the Llama 2 version release date, the monthly active users of the\ \ products or services made available by or for Licensee, or Licensee's affiliates,\ \ is greater than 700 million monthly active users in the preceding calendar month,\ \ you must request a license from Meta, which Meta may grant to you in its sole\ \ discretion, and you are not authorized to exercise any of the rights under this\ \ Agreement unless or until Meta otherwise expressly grants you such rights.\n\ 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS\ \ AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\" BASIS, WITHOUT\ \ WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION,\ \ ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A\ \ PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS\ \ OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\ \ WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation\ \ of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY\ \ OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE,\ \ ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL,\ \ CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS\ \ AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n\n\ 5. 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If you institute\ \ litigation or other proceedings against Meta or any entity (including a cross-claim\ \ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs\ \ or results, or any portion of any of the foregoing, constitutes infringement\ \ of intellectual property or other rights owned or licensable by you, then any\ \ licenses granted to you under this Agreement shall terminate as of the date such\ \ litigation or claim is filed or instituted. You will indemnify and hold harmless\ \ Meta from and against any claim by any third party arising out of or related \ \ to your use or distribution of the Llama Materials.\n6. Term and Termination.\ \ The term of this Agreement will commence upon your acceptance of this Agreement\ \ or access to the Llama Materials and will continue in full force and effect until\ \ terminated in accordance with the terms and conditions herein. Meta may terminate\ \ this Agreement if you are in breach of any term or condition of this Agreement.\ \ Upon termination of this Agreement, you shall delete and cease use of the Llama\ \ Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\ \ \n7. Governing Law and Jurisdiction. This Agreement will be governed and construed\ \ under the laws of the State of California without regard to choice of law principles,\ \ and the UN Convention on Contracts for the International Sale of Goods does not\ \ apply to this Agreement. The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement. \n### Llama 2 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy\ \ (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).\n\ #### Prohibited Uses\nWe want everyone to use Llama 2 safely and responsibly. You\ \ agree you will not use, or allow others to use, Llama 2 to:\n1. Violate the law\ \ or others’ rights, including to:\n 1. Engage in, promote, generate, contribute\ \ to, encourage, plan, incite, or further illegal or unlawful activity or content,\ \ such as: \n 1. Violence or terrorism \n 2. Exploitation or harm\ \ to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4.\ \ The illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6.\ \ Any other criminal activity\n 2. Engage in, promote, incite, or facilitate\ \ the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n \ \ 4. Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices \n 5. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 6. Engage in or facilitate any\ \ action or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama 2 Materials\n 7. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system \n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Llama 2 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Llama 2 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Llama 2 or outputs are human-generated\n\ \ 6. Generating or facilitating false online engagement, including fake reviews\ \ and other means of fake online engagement \n 4. Fail to appropriately disclose\ \ to end users any known dangers of your AI system \nPlease report any violation\ \ of this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means: \n * Reporting issues with\ \ the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\n\ \ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ \ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\ \ \n * Reporting violations of the Acceptable Use Policy or unlicensed uses of\ \ Llama: [[email protected]](mailto:[email protected])" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF This model was converted to GGUF format from [`meta-llama/Llama-2-7b-chat-hf`](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -c 2048 ```
Makkoen/whisper-medium-cit-do015-wd0-lr1e-06-1000	Makkoen	2024-07-02T09:57:10Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T09:16:48Z	Entry not found
shin7965977/test	shin7965977	2024-07-02T09:17:02Z	null	[ "license:mit", "region:us" ]	null	2024-07-02T09:17:01Z	--- license: mit ---
Bramwel/segformer-b0-finetuned-segments-sidewalk-2	Bramwel	2024-07-02T12:14:57Z	transformers	[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2024-07-02T09:17:09Z	--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-sidewalk-2 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. --> # segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.6610 - Mean Iou: 0.1423 - Mean Accuracy: 0.1906 - Overall Accuracy: 0.7085 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.7735 - Accuracy Flat-sidewalk: 0.9216 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.1218 - Accuracy Flat-parkingdriveway: 0.0002 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.8580 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8872 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9240 - Accuracy Nature-terrain: 0.7787 - Accuracy Sky: 0.8348 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0000 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.4851 - Iou Flat-sidewalk: 0.7175 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.1207 - Iou Flat-parkingdriveway: 0.0002 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.5750 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.5599 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.7288 - Iou Nature-terrain: 0.5948 - Iou Sky: 0.7728 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0000 - Iou Void-unclear: 0.0 ## 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: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - 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 \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Flat-road \| Accuracy Flat-sidewalk \| Accuracy Flat-crosswalk \| Accuracy Flat-cyclinglane \| Accuracy Flat-parkingdriveway \| Accuracy Flat-railtrack \| Accuracy Flat-curb \| Accuracy Human-person \| Accuracy Human-rider \| Accuracy Vehicle-car \| Accuracy Vehicle-truck \| Accuracy Vehicle-bus \| Accuracy Vehicle-tramtrain \| Accuracy Vehicle-motorcycle \| Accuracy Vehicle-bicycle \| Accuracy Vehicle-caravan \| Accuracy Vehicle-cartrailer \| Accuracy Construction-building \| Accuracy Construction-door \| Accuracy Construction-wall \| Accuracy Construction-fenceguardrail \| Accuracy Construction-bridge \| Accuracy Construction-tunnel \| Accuracy Construction-stairs \| Accuracy Object-pole \| Accuracy Object-trafficsign \| Accuracy Object-trafficlight \| Accuracy Nature-vegetation \| Accuracy Nature-terrain \| Accuracy Sky \| Accuracy Void-ground \| Accuracy Void-dynamic \| Accuracy Void-static \| Accuracy Void-unclear \| Iou Unlabeled \| Iou Flat-road \| Iou Flat-sidewalk \| Iou Flat-crosswalk \| Iou Flat-cyclinglane \| Iou Flat-parkingdriveway \| Iou Flat-railtrack \| Iou Flat-curb \| Iou Human-person \| Iou Human-rider \| Iou Vehicle-car \| Iou Vehicle-truck \| Iou Vehicle-bus \| Iou Vehicle-tramtrain \| Iou Vehicle-motorcycle \| Iou Vehicle-bicycle \| Iou Vehicle-caravan \| Iou Vehicle-cartrailer \| Iou Construction-building \| Iou Construction-door \| Iou Construction-wall \| Iou Construction-fenceguardrail \| Iou Construction-bridge \| Iou Construction-tunnel \| Iou Construction-stairs \| Iou Object-pole \| Iou Object-trafficsign \| Iou Object-trafficlight \| Iou Nature-vegetation \| Iou Nature-terrain \| Iou Sky \| Iou Void-ground \| Iou Void-dynamic \| Iou Void-static \| Iou Void-unclear \| 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\| 2.9079 \| 0.1 \| 20 \| 3.1086 \| 0.0647 \| 0.1138 \| 0.5503 \| nan \| 0.0368 \| 0.8899 \| 0.0003 \| 0.0322 \| 0.0003 \| nan \| 0.0006 \| 0.0 \| 0.0 \| 0.7271 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7960 \| 0.0 \| 0.0004 \| 0.0022 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9478 \| 0.0068 \| 0.1996 \| 0.0 \| 0.0 \| 0.0005 \| 0.0 \| nan \| 0.0359 \| 0.5924 \| 0.0003 \| 0.0304 \| 0.0003 \| 0.0 \| 0.0006 \| 0.0 \| 0.0 \| 0.3287 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4669 \| 0.0 \| 0.0004 \| 0.0020 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4806 \| 0.0054 \| 0.1909 \| 0.0 \| 0.0 \| 0.0005 \| 0.0 \| \| 2.9751 \| 0.2 \| 40 \| 2.4969 \| 0.0845 \| 0.1338 \| 0.6123 \| nan \| 0.3974 \| 0.9215 \| 0.0001 \| 0.0122 \| 0.0000 \| nan \| 0.0002 \| 0.0 \| 0.0 \| 0.7324 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8570 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.9405 \| 0.1742 \| 0.2446 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.3419 \| 0.6367 \| 0.0001 \| 0.0120 \| 0.0000 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.3801 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.4767 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.5843 \| 0.1296 \| 0.2277 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 2.1243 \| 0.3 \| 60 \| 2.1662 \| 0.1055 \| 0.1523 \| 0.6477 \| nan \| 0.5778 \| 0.9287 \| 0.0 \| 0.0135 \| 0.0009 \| nan \| 0.0001 \| 0.0 \| 0.0 \| 0.8344 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8487 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.9301 \| 0.2460 \| 0.4938 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.4250 \| 0.6604 \| 0.0 \| 0.0134 \| 0.0009 \| nan \| 0.0001 \| 0.0 \| 0.0 \| 0.4751 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5074 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.6306 \| 0.1975 \| 0.4642 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 1.9666 \| 0.4 \| 80 \| 2.0080 \| 0.1217 \| 0.1704 \| 0.6691 \| nan \| 0.7097 \| 0.8904 \| 0.0 \| 0.0234 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8510 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8901 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9252 \| 0.4752 \| 0.6888 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4257 \| 0.6858 \| 0.0 \| 0.0231 \| 0.0000 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5028 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5269 \| 0.0 \| 0.0000 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6823 \| 0.3861 \| 0.6614 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.9626 \| 0.5 \| 100 \| 1.8857 \| 0.1346 \| 0.1838 \| 0.6929 \| nan \| 0.7341 \| 0.9091 \| 0.0 \| 0.0866 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8543 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8658 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9372 \| 0.6673 \| 0.8275 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.4661 \| 0.6989 \| 0.0 \| 0.0855 \| 0.0001 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5474 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5453 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7069 \| 0.5067 \| 0.7495 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 1.8737 \| 0.6 \| 120 \| 1.8184 \| 0.1389 \| 0.1906 \| 0.6957 \| nan \| 0.7647 \| 0.8845 \| 0.0 \| 0.1470 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8567 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8899 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9071 \| 0.8097 \| 0.8378 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.4546 \| 0.7034 \| 0.0 \| 0.1427 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5363 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5445 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7286 \| 0.5730 \| 0.7628 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 1.7869 \| 0.7 \| 140 \| 1.7455 \| 0.1389 \| 0.1879 \| 0.7009 \| nan \| 0.7817 \| 0.9034 \| 0.0 \| 0.1201 \| 0.0006 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8718 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8740 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9384 \| 0.6890 \| 0.8347 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.4663 \| 0.7148 \| 0.0 \| 0.1183 \| 0.0006 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5568 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5643 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7099 \| 0.5460 \| 0.7670 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 2.0396 \| 0.8 \| 160 \| 1.7097 \| 0.1399 \| 0.1899 \| 0.7039 \| nan \| 0.7784 \| 0.9053 \| 0.0 \| 0.1396 \| 0.0004 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8835 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8935 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9243 \| 0.7040 \| 0.8467 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.4758 \| 0.7193 \| 0.0 \| 0.1375 \| 0.0004 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5406 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5591 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7224 \| 0.5518 \| 0.7701 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.9288 \| 0.9 \| 180 \| 1.6806 \| 0.1415 \| 0.1903 \| 0.7070 \| nan \| 0.7716 \| 0.9175 \| 0.0 \| 0.1212 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8615 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8971 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9191 \| 0.7875 \| 0.8123 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.4829 \| 0.7173 \| 0.0 \| 0.1200 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5653 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5572 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7308 \| 0.5904 \| 0.7643 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| \| 1.6438 \| 1.0 \| 200 \| 1.6610 \| 0.1423 \| 0.1906 \| 0.7085 \| nan \| 0.7735 \| 0.9216 \| 0.0 \| 0.1218 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.8580 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.8872 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9240 \| 0.7787 \| 0.8348 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| nan \| 0.4851 \| 0.7175 \| 0.0 \| 0.1207 \| 0.0002 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.5750 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5599 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7288 \| 0.5948 \| 0.7728 \| 0.0 \| 0.0 \| 0.0000 \| 0.0 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.0+rocm5.6 - Datasets 2.20.0 - Tokenizers 0.19.1
TransLLaMA/TransLLaMA2-7B-XNLI	TransLLaMA	2024-07-02T09:17:32Z	null	[ "license:mit", "region:us" ]	null	2024-07-02T09:17:32Z	--- license: mit ---
TransLLaMA/TransLLaMA2-7B-X-CSQA	TransLLaMA	2024-07-02T09:17:52Z	null	[ "license:mit", "region:us" ]	null	2024-07-02T09:17:52Z	--- license: mit ---
Dev372/HarshDev-whisper-tiny-English_2000_new	Dev372	2024-07-02T12:05:09Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:Hani89/medical_asr_recording_dataset", "base_model:openai/whisper-small.en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-02T09:18:02Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer datasets: - Hani89/medical_asr_recording_dataset metrics: - wer model-index: - name: English Whisper Model results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Medical type: Hani89/medical_asr_recording_dataset args: 'split: test' metrics: - name: Wer type: wer value: 7.0236794171220405 --- <!-- 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. --> # English Whisper Model This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the Medical dataset. It achieves the following results on the evaluation set: - Loss: 0.1122 - Wer: 7.0237 ## 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: 3.5e-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 - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 0.0076 \| 3.0030 \| 1000 \| 0.1181 \| 7.3734 \| \| 0.0003 \| 6.0060 \| 2000 \| 0.1122 \| 7.0237 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
ScandinavianMrT/SkoleGPT	ScandinavianMrT	2024-07-02T09:18:02Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-02T09:18:02Z	--- license: apache-2.0 ---
cortexso/claude-3-sonnet-20240229	cortexso	2024-07-02T09:18:52Z	null	[ "region:us" ]	null	2024-07-02T09:18:08Z	Entry not found
multimolecule/calm	multimolecule	2024-07-02T09:19:22Z	multimolecule	[ "multimolecule", "pytorch", "safetensors", "calm", "Biology", "RNA", "fill-mask", "dna", "dataset:multimolecule/ena", "license:agpl-3.0", "region:us" ]	fill-mask	2024-07-02T09:18:15Z	--- language: dna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/ena library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "PRNP" text: "CTG<mask>AAGCGGCCCACGCGGACTGACGGGCGGGGG" output: - label: "CGG" score: 0.03824129328131676 - label: "CCG" score: 0.030699172988533974 - label: "GGG" score: 0.026188895106315613 - label: "GCG" score: 0.02376439981162548 - label: "CCC" score: 0.023052876815199852 --- # CaLM Pre-trained model on protein-coding DNA (cDNA) using a masked language modeling (MLM) objective. ## Statement _Codon language embeddings provide strong signals for use in protein engineering_ is published in [Nature Machine Intelligence](https://doi.org/10.1038/s42256-024-00791-0), which is a Closed Access / Author-Fee journal. > Machine learning has been at the forefront of the movement for free and open access to research. > > We see no role for closed access or author-fee publication in the future of machine learning research and believe the adoption of these journals as an outlet of record for the machine learning community would be a retrograde step. The MultiMolecule team is committed to the principles of open access and open science. We do NOT endorse the publication of manuscripts in Closed Access / Author-Fee journals and encourage the community to support Open Access journals. Please consider signing the [Statement on Nature Machine Intelligence](https://openaccess.engineering.oregonstate.edu). ## Disclaimer This is an UNOFFICIAL implementation of the [Codon language embeddings provide strong signals for use in protein engineering](https://doi.org/10.1101/2022.12.15.519894) by Carlos Outeiral and Charlotte M. Deane. The OFFICIAL repository of CaLM is at [oxpig/CaLM](https://github.com/oxpig/CaLM). !!! Danger "Reproducibility" The MultiMolecule team is unable to confirm that the provided model and checkpoints are producing the same intermediate representations as the original implementation. This is because The proposed method is published in a Closed Access / Author-Fee journal. The team releasing CaLM did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details CaLM is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of protein-coding DNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of DNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Model Specification \| Num Layers \| Hidden Size \| Num Heads \| Intermediate Size \| Num Parameters (M) \| FLOPs (G) \| MACs (G) \| Max Num Tokens \| \| ---------- \| ----------- \| --------- \| ----------------- \| ------------------ \| --------- \| -------- \| -------------- \| \| 12 \| 768 \| 12 \| 3072 \| 85.75 \| 22.36 \| 11.17 \| 1024 \| ### Links - Code: [multimolecule.calm](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/calm) - Weights: [multimolecule/calm](https://huggingface.co/multimolecule/calm) - Data: [European Nucleotide Archive](https://ebi.ac.uk/ena) - Paper: [Codon language embeddings provide strong signals for use in protein engineering](https://doi.org/10.1101/2022.12.15.519894) - Developed by: Carlos Outeiral, Charlotte M. Deane - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ESM](https://huggingface.co/facebook/esm2_t48_15B_UR50D) - Original Repository: [https://github.com/oxpig/CaLM](https://github.com/oxpig/CaLM) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/calm') >>> unmasker("ctg<mask>aagcggcccacgcggactgacgggcggggg") [{'score': 0.03824129328131676, 'token': 43, 'token_str': 'CGG', 'sequence': 'CUG CGG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.030699172988533974, 'token': 38, 'token_str': 'CCG', 'sequence': 'CUG CCG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.026188895106315613, 'token': 68, 'token_str': 'GGG', 'sequence': 'CUG GGG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.02376439981162548, 'token': 63, 'token_str': 'GCG', 'sequence': 'CUG GCG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.023052876815199852, 'token': 37, 'token_str': 'CCC', 'sequence': 'CUG CCC AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, CaLmModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmModel.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') output = model(input) ``` #### Sequence Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, CaLmForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmForSequencePrediction.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(input, labels=label) ``` #### Nucleotide Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, CaLmForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmForNucleotidePrediction.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, CaLmForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmForContactPrediction.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details CaLM used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 25% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The CaLM model was pre-trained coding sequences of all organisms available on the [European Nucleotide Archive (ENA)](https://ebi.ac.uk/ena). European Nucleotide Archive provides a comprehensive record of the world’s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. CaLM collected coding sequences of all organisms from ENA on April 2022, including 114,214,475 sequences. Only high level assembly information (dataclass CON) were used. Sequences matching the following criteria were filtered out: - with unknown nucleotides (`N`, `Y`, `R`) - start codon is not `ATG` - contains interstitial stop codons - number of nucleotides is not a multiple of three To reduce redundancy, CaLM grouped the entries by organism, and apply CD-HIT (CD-HIT-EST) with a cut-off at 40% sequence identity to the translated protein sequences. The final dataset contains 9,858,385 cDNA sequences. Note that the alphabet in the original implementation is RNA instead of DNA, therefore, we use [`RnaTokenizer`][multimolecule.RnaTokenizer] to tokenize the sequences. `RnaTokenizer` of `multimolecule` will convert "U"s to "T"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing CaLM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 25% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 4 NVIDIA Quadro RTX4000 GPUs with 8GiB memories. - Learning rate: 1e-4 - Optimizer: AdamW - Learning rate scheduler: cosine - Learning rate warm-up: 1,000 steps - Epochs: 14 - Batch Size: 1,000 ## Citation BibTeX: ```bibtex @article {outeiral2022coodn, author = {Outeiral, Carlos and Deane, Charlotte M.}, title = {Codon language embeddings provide strong signals for protein engineering}, elocation-id = {2022.12.15.519894}, year = {2022}, doi = {10.1101/2022.12.15.519894}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Protein representations from deep language models have yielded state-of-the-art performance across many tasks in computational protein engineering. In recent years, progress has primarily focused on parameter count, with recent models{\textquoteright} capacities surpassing the size of the very datasets they were trained on. Here, we propose an alternative direction. We show that large language models trained on codons, instead of amino acid sequences, provide high-quality representations that outperform comparable state-of-the-art models across a variety of tasks. In some tasks, like species recognition, prediction of protein and transcript abundance, or melting point estimation, we show that a language model trained on codons outperforms every other published protein language model, including some that contain over 50 times more parameters. These results suggest that, in addition to commonly studied scale and model complexity, the information content of biological data provides an orthogonal direction to improve the power of machine learning in biology.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2022/12/19/2022.12.15.519894}, eprint = {https://www.biorxiv.org/content/early/2022/12/19/2022.12.15.519894.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [CaLM paper](https://doi.org/10.1101/2022.12.15.519894) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
multimolecule/ernierna	multimolecule	2024-07-02T09:20:39Z	multimolecule	[ "multimolecule", "pytorch", "safetensors", "ernierna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/rnacentral", "license:agpl-3.0", "region:us" ]	fill-mask	2024-07-02T09:19:29Z	--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUGA" output: - label: "U" score: 0.218908429145813 - label: "A" score: 0.20248650014400482 - label: "C" score: 0.18175390362739563 - label: "-" score: 0.11062020808458328 - label: "G" score: 0.10719843208789825 --- # ERNIE-RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) by Weijie Yin, Zhaoyu Zhang, Liang He, et al. The OFFICIAL repository of ERNIE-RNA is at [Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA). !!! Success "Reproducibility" The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. The team releasing ERNIE-RNA did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details ERNIE-RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variations - [`multimolecule/ernierna`](https://huggingface.co/multimolecule/ernierna): The ERNIE-RNA model pre-trained on non-coding RNA sequences. - [`multimolecule/ernierna.ss`](https://huggingface.co/multimolecule/ernierna.ss): The ERNIE-RNA model fine-tuned on RNA secondary structure prediction. ### Model Specification \| Num Layers \| Hidden Size \| Num Heads \| Intermediate Size \| Num Parameters (M) \| FLOPs (G) \| MACs (G) \| Max Num Tokens \| \| ---------- \| ----------- \| --------- \| ----------------- \| ------------------ \| --------- \| -------- \| -------------- \| \| 12 \| 768 \| 12 \| 3072 \| 85.67 \| 22.36 \| 11.17 \| 1024 \| ### Links - Code: [multimolecule.ernierna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/ernierna) - Data: [RNAcentral](https://rnacentral.org) - Paper: [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) - Developed by: Weijie Yin, Zhaoyu Zhang, Liang He, Rui Jiang, Shuo Zhang, Gan Liu, Xuegong Zhang, Tao Qin, Zhen Xie - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ERNIE](https://huggingface.co/nghuyong/ernie-3.0-base-zh) - Original Repository: [https://github.com/Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/ernierna') >>> unmasker("uagc<mask>uaucagacugauguuga") [{'score': 0.218908429145813, 'token': 9, 'token_str': 'U', 'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'}, {'score': 0.20248650014400482, 'token': 6, 'token_str': 'A', 'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'}, {'score': 0.18175390362739563, 'token': 7, 'token_str': 'C', 'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'}, {'score': 0.11062020808458328, 'token': 25, 'token_str': '-', 'sequence': 'U A G C - U A U C A G A C U G A U G U U G A'}, {'score': 0.10719843208789825, 'token': 8, 'token_str': 'G', 'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, ErnieRnaModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaModel.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') output = model(input) ``` #### Sequence Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaForSequencePrediction.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(input, labels=label) ``` #### Nucleotide Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaForNucleotidePrediction.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaForContactPrediction.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details ERNIE-RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The ERNIE-RNA model was pre-trained on [RNAcentral](https://rnacentral.org). RNAcentral is a comprehensive database of non-coding RNA sequences from a wide range of species. It combines 47 different databases, adding up to around 34 million RNA sequences in total. ERNIE-RNA applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral, resulting 25 million unique sequences. Sequences longer than 1024 nucleotides were subsequently excluded. The final dataset contains 20.4 million non-redundant RNA sequences. ERNIE-RNA preprocessed all tokens by replacing "T"s with "S"s. Note that [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing ERNIE-RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 24 NVIDIA V100 GPUs with 32GiB memories. - Learning rate: 1e-4 - Weight decay: 0.01 - Learning rate warm-up: 20,000 steps ## Citation BibTeX: ```bibtex @article {Yin2024.03.17.585376, author = {Yin, Weijie and Zhang, Zhaoyu and He, Liang and Jiang, Rui and Zhang, Shuo and Liu, Gan and Zhang, Xuegong and Qin, Tao and Xie, Zhen}, title = {ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations}, elocation-id = {2024.03.17.585376}, year = {2024}, doi = {10.1101/2024.03.17.585376}, publisher = {Cold Spring Harbor Laboratory}, abstract = {With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.Competing Interest StatementOne patent based on the study was submitted by Z.X. and W.Y., which is entitled as "A Pre-training Approach for RNA Sequences and Its Applications"(application number, no 202410262527.5). The remaining authors declare no competing interests.}, URL = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376}, eprint = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [ERNIE-RNA paper](https://doi.org/10.1101/2024.03.17.585376) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
cortexso/claude-3-haiku-20240307	cortexso	2024-07-02T09:20:56Z	null	[ "region:us" ]	null	2024-07-02T09:19:30Z	Entry not found
atmatechai/speecht5_tts_dataset_primer_male_1000	atmatechai	2024-07-02T10:24:54Z	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]	text-to-audio	2024-07-02T09:19:41Z	Entry not found
baxtos/bartik10-4	baxtos	2024-07-02T09:22:02Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T09:19:42Z	Entry not found
Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF	Dabitron	2024-07-02T09:23:31Z	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]	text-generation	2024-07-02T09:20:28Z	--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -c 2048 ```
streamtune/624595c7-1eeb-45e0-8ba7-70f0959d552a	streamtune	2024-07-02T09:22:47Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:20:31Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: streamtune - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
multimolecule/ernierna.ss	multimolecule	2024-07-02T09:57:35Z	multimolecule	[ "multimolecule", "pytorch", "safetensors", "ernierna", "Biology", "RNA", "rna", "dataset:multimolecule/rnacentral", "base_model:multimolecule/ernierna", "license:agpl-3.0", "region:us" ]	null	2024-07-02T09:20:47Z	--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral library_name: multimolecule base_model: multimolecule/ernierna --- # ERNIE-RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) by Weijie Yin, Zhaoyu Zhang, Liang He, et al. The OFFICIAL repository of ERNIE-RNA is at [Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA). !!! Success "Reproducibility" The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. The team releasing ERNIE-RNA did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details ERNIE-RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variations - [`multimolecule/ernierna`](https://huggingface.co/multimolecule/ernierna): The ERNIE-RNA model pre-trained on non-coding RNA sequences. - [`multimolecule/ernierna.ss`](https://huggingface.co/multimolecule/ernierna.ss): The ERNIE-RNA model fine-tuned on RNA secondary structure prediction. ### Model Specification \| Num Layers \| Hidden Size \| Num Heads \| Intermediate Size \| Num Parameters (M) \| FLOPs (G) \| MACs (G) \| Max Num Tokens \| \| ---------- \| ----------- \| --------- \| ----------------- \| ------------------ \| --------- \| -------- \| -------------- \| \| 12 \| 768 \| 12 \| 3072 \| 85.67 \| 22.36 \| 11.17 \| 1024 \| ### Links - Code: [multimolecule.ernierna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/ernierna) - Data: [RNAcentral](https://rnacentral.org) - Paper: [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) - Developed by: Weijie Yin, Zhaoyu Zhang, Liang He, Rui Jiang, Shuo Zhang, Gan Liu, Xuegong Zhang, Tao Qin, Zhen Xie - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ERNIE](https://huggingface.co/nghuyong/ernie-3.0-base-zh) - Original Repository: [https://github.com/Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/ernierna.ss') >>> unmasker("uagc<mask>uaucagacugauguuga") [{'score': 0.19777926802635193, 'token': 9, 'token_str': 'U', 'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'}, {'score': 0.16415606439113617, 'token': 7, 'token_str': 'C', 'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'}, {'score': 0.15474674105644226, 'token': 8, 'token_str': 'G', 'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'}, {'score': 0.13006599247455597, 'token': 25, 'token_str': '-', 'sequence': 'U A G C - U A U C A G A C U G A U G U U G A'}, {'score': 0.1272154450416565, 'token': 6, 'token_str': 'A', 'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, ErnieRnaModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaModel.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') output = model(input) ``` #### Sequence Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaForSequencePrediction.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(input, labels=label) ``` #### Nucleotide Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaForNucleotidePrediction.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaForContactPrediction.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details ERNIE-RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The ERNIE-RNA model was pre-trained on [RNAcentral](https://rnacentral.org). RNAcentral is a comprehensive database of non-coding RNA sequences from a wide range of species. It combines 47 different databases, adding up to around 34 million RNA sequences in total. ERNIE-RNA applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral, resulting 25 million unique sequences. Sequences longer than 1024 nucleotides were subsequently excluded. The final dataset contains 20.4 million non-redundant RNA sequences. ERNIE-RNA preprocessed all tokens by replacing "T"s with "S"s. Note that [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing ERNIE-RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 24 NVIDIA V100 GPUs with 32GiB memories. - Learning rate: 1e-4 - Weight decay: 0.01 - Learning rate warm-up: 20,000 steps ## Citation BibTeX: ```bibtex @article {Yin2024.03.17.585376, author = {Yin, Weijie and Zhang, Zhaoyu and He, Liang and Jiang, Rui and Zhang, Shuo and Liu, Gan and Zhang, Xuegong and Qin, Tao and Xie, Zhen}, title = {ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations}, elocation-id = {2024.03.17.585376}, year = {2024}, doi = {10.1101/2024.03.17.585376}, publisher = {Cold Spring Harbor Laboratory}, abstract = {With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.Competing Interest StatementOne patent based on the study was submitted by Z.X. and W.Y., which is entitled as "A Pre-training Approach for RNA Sequences and Its Applications"(application number, no 202410262527.5). The remaining authors declare no competing interests.}, URL = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376}, eprint = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [ERNIE-RNA paper](https://doi.org/10.1101/2024.03.17.585376) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
Manila-8333003722/8333-00.3722	Manila-8333003722	2024-07-02T09:21:19Z	null	[ "license:mit", "region:us" ]	null	2024-07-02T09:21:19Z	--- license: mit ---
emakin02/Trendyol-Embed	emakin02	2024-07-02T09:25:53Z	null	[ "safetensors", "region:us" ]	null	2024-07-02T09:21:44Z	Entry not found
multimolecule/rinalmo	multimolecule	2024-07-02T09:35:24Z	multimolecule	[ "multimolecule", "pytorch", "safetensors", "rinalmo", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/rnacentral", "dataset:multimolecule/rfam", "dataset:multimolecule/ensembl-genome-browser", "dataset:multimolecule/nucleotide", "license:agpl-3.0", "region:us" ]	fill-mask	2024-07-02T09:22:21Z	--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral - multimolecule/rfam - multimolecule/ensembl-genome-browser - multimolecule/nucleotide library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUGA" output: - label: "A" score: 0.28896641731262207 - label: "U" score: 0.27602624893188477 - label: "X" score: 0.18329711258411407 - label: "C" score: 0.1668907254934311 - label: "G" score: 0.08479981869459152 --- # RiNALMo Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://doi.org/10.48550/arXiv.2403.00043) by Rafael Josip Penić, et al. The OFFICIAL repository of RiNALMo is at [lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo). !!! Success "Reproducibility" The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. The team releasing RiNALMo did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details RiNALMo is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Model Specification \| Num Layers \| Hidden Size \| Num Heads \| Intermediate Size \| Num Parameters (M) \| FLOPs (G) \| MACs (G) \| Max Num Tokens \| \| ---------- \| ----------- \| --------- \| ----------------- \| ------------------ \| --------- \| -------- \| -------------- \| \| 33 \| 1280 \| 20 \| 5120 \| 650.88 \| 168.92 \| 84.43 \| 1022 \| ### Links - Code: [multimolecule.rinalmo](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rinalmo) - Weights: [`multimolecule/rinalmo`](https://huggingface.co/multimolecule/rinalmo) - Data: [RNAcentral](https://rnacentral.org) - Paper: [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://doi.org/10.48550/arXiv.2403.00043) - Developed by: Rafael Josip Penić, Tin Vlašić, Roland G. Huber, Yue Wan, Mile Šikić - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - Original Repository: [https://github.com/lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/rinalmo') >>> unmasker("uagc<mask>uaucagacugauguuga") [{'score': 0.28896641731262207, 'token': 6, 'token_str': 'A', 'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'}, {'score': 0.27602624893188477, 'token': 9, 'token_str': 'U', 'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'}, {'score': 0.18329711258411407, 'token': 12, 'token_str': 'X', 'sequence': 'U A G C X U A U C A G A C U G A U G U U G A'}, {'score': 0.1668907254934311, 'token': 7, 'token_str': 'C', 'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'}, {'score': 0.08479981869459152, 'token': 8, 'token_str': 'G', 'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, RiNALMoModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoModel.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') output = model(input) ``` #### Sequence Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RiNALMoForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoForSequencePrediction.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(input, labels=label) ``` #### Nucleotide Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RiNALMoForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoForNucleotidePrediction.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RiNALMoForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoForContactPrediction.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details RiNALMo used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The RiNALMo model was pre-trained on a cocktail of databases including [RNAcentral](https://rnacentral.org), [Rfam](https://rfam.org), [Ensembl Genome Browser](https://ensembl.org), and [Nucleotide](https://ncbi.nlm.nih.gov/nucleotide). The training data contains 36 million unique ncRNA sequences. To ensure sequence diversity in each training batch, RiNALMo clustered the sequences with [MMSeqs2](https://github.com/soedinglab/MMseqs2) into 17 million clusters and then sampled each sequence in the batch from a different cluster. RiNALMo preprocessed all tokens by replacing "U"s with "T"s. Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing RiNALMo used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 7 NVIDIA A100 GPUs with 80GiB memories. - Learning rate: 5e-5 - Learning rate scheduler: cosine - Learning rate warm-up: 2,000 steps - Learning rate minimum: 1e-5 - Epochs: 6 - Batch Size: 1344 - Dropout: 0.1 ## Citation BibTeX: ```bibtex @article{penic2024rinalmo, title={RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks}, author={Penić, Rafael Josip and Vlašić, Tin and Huber, Roland G. and Wan, Yue and Šikić, Mile}, journal={arXiv preprint arXiv:2403.00043}, year={2024} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [RiNALMo paper](https://doi.org/10.48550/arXiv.2403.00043) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
Peacoc/37_best_t_12_1	Peacoc	2024-07-02T09:25:20Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-02T09:22:35Z	--- 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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ssoypark/fine_tuned_clothing_model_b7	ssoypark	2024-07-02T09:24:21Z	null	[ "region:us" ]	null	2024-07-02T09:24:16Z	Entry not found
baxtos/bartik12-4	baxtos	2024-07-02T09:27:40Z	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-02T09:25:18Z	Entry not found
BobbBuilder/openai-whisper-tiny	BobbBuilder	2024-07-02T09:25:34Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:25:32Z	--- 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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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]
TheNight12/medical_llama3	TheNight12	2024-07-02T09:26:00Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:25:49Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: TheNight12 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
NghiemAbe/PhoBert-Base-v2-NewVocab	NghiemAbe	2024-07-02T09:26:52Z	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-07-02T09:26:29Z	--- 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]
KhiTuKi/SAVE_TRAIN	KhiTuKi	2024-07-02T09:26:31Z	null	[ "region:us" ]	null	2024-07-02T09:26:31Z	Entry not found
akashAD/bart-large-mnli-onnx	akashAD	2024-07-02T09:26:52Z	null	[ "region:us" ]	null	2024-07-02T09:26:51Z	Entry not found
cortexso/claude-3-5-sonnet-20240620	cortexso	2024-07-02T09:28:32Z	null	[ "region:us" ]	null	2024-07-02T09:27:35Z	Entry not found
TensorNTU/Risk_Empirical	TensorNTU	2024-07-02T09:27:53Z	null	[ "region:us" ]	null	2024-07-02T09:27:53Z	Entry not found
Chonlasitk/ASR-fine-tuning	Chonlasitk	2024-07-02T09:29:38Z	null	[ "license:mit", "region:us" ]	null	2024-07-02T09:29:38Z	--- license: mit ---
ZZPENG/3f_Supermacy_draft1	ZZPENG	2024-07-02T09:37:43Z	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-02T09:29:56Z	Entry not found
pinguG/BRKsEDU	pinguG	2024-07-02T09:31:51Z	null	[ "region:us" ]	null	2024-07-02T09:30:55Z	Entry not found
KasuleTrevor/test_20	KasuleTrevor	2024-07-02T09:30:59Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-02T09:30:58Z	--- 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. 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]

baxtos/bartik03-4

baxtos

2024-07-02T08:41:35Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:39:03Z

Entry not found

DokHee/llama3-ko-bemoi-8b-VBioLLM1000

DokHee

2024-07-02T08:41:07Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:39:16Z

--- base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** DokHee - **License:** apache-2.0 - **Finetuned from model :** beomi/Llama-3-Open-Ko-8B-Instruct-preview This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

yashss/diaratechHf_llama35506e3a-cd9b-475f-95dd-7005c87a2a94

yashss

2024-07-02T08:41:15Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "text-generation", "conversational", "dataset:generator", "base_model:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]

text-generation

2024-07-02T08:39:48Z

--- base_model: microsoft/Phi-3-mini-128k-instruct datasets: - generator library_name: peft license: mit pipeline_tag: text-generation tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama35506e3a-cd9b-475f-95dd-7005c87a2a94 results: [] ---  # diaratechHf_llama35506e3a-cd9b-475f-95dd-7005c87a2a94 This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the generator 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Mluleki/dyu-fr-translation

Mluleki

2024-07-02T10:44:15Z

0

transformers

[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-07-02T08:42:04Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: Mluleki/dyu-fr-translation results: [] ---  # Mluleki/dyu-fr-translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0678 - Validation Loss: 2.8734 - Epoch: 9 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.6875 | 3.2490 | 0 | | 3.4758 | 3.1470 | 1 | | 3.3749 | 3.0798 | 2 | | 3.3153 | 3.0285 | 3 | | 3.2551 | 2.9931 | 4 | | 3.2077 | 2.9603 | 5 | | 3.1696 | 2.9331 | 6 | | 3.1311 | 2.9081 | 7 | | 3.0996 | 2.8899 | 8 | | 3.0678 | 2.8734 | 9 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.15.0 - Datasets 2.14.2 - Tokenizers 0.13.3

mayarmostafa/videomae-base-finetuned-bleeding-exp_3

mayarmostafa

2024-07-02T11:12:19Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2024-07-02T08:42:12Z

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-bleeding-exp_3 results: [] ---  # videomae-base-finetuned-bleeding-exp_3 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) 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: 8 - 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_ratio: 0.1 - training_steps: 600 ### Framework versions - Transformers 4.40.2 - Pytorch 1.12.0 - Datasets 2.19.1 - Tokenizers 0.19.1

sit-justin/whisper-small-test

sit-justin

2024-07-02T09:44:39Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:custom_datset", "base_model:Kathernie/whisper-small-all", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T08:43:06Z

--- language: - zh base_model: Kathernie/whisper-small-all tags: - generated_from_trainer datasets: - custom_datset model-index: - name: Whisper Small Chinese MOE Response results: [] ---  # Whisper Small Chinese MOE Response This model is a fine-tuned version of [Kathernie/whisper-small-all](https://huggingface.co/Kathernie/whisper-small-all) on the MOE Response Chinese dataset. It achieves the following results on the evaluation set: - Loss: 0.2640 - Cer: 11.0180 ## 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-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 250 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | 0.2252 | 1.0811 | 200 | 0.2339 | 12.4230 | | 0.1268 | 2.1622 | 400 | 0.2223 | 10.9194 | | 0.056 | 3.2432 | 600 | 0.2242 | 10.8701 | | 0.023 | 4.3243 | 800 | 0.2387 | 11.3384 | | 0.01 | 5.4054 | 1000 | 0.2546 | 11.2645 | | 0.0044 | 6.4865 | 1200 | 0.2515 | 11.2891 | | 0.0028 | 7.5676 | 1400 | 0.2552 | 10.9440 | | 0.0017 | 8.6486 | 1600 | 0.2623 | 11.2645 | | 0.0017 | 9.7297 | 1800 | 0.2624 | 10.9933 | | 0.001 | 10.8108 | 2000 | 0.2640 | 11.0180 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.0 - Datasets 2.20.0 - Tokenizers 0.19.1

dusrb37/dpo-NIPA2

dusrb37

2024-07-02T08:43:54Z

0

null

[ "region:us" ]

null

2024-07-02T08:43:54Z

Entry not found

SidXXD/3-only_cos-person-eps_10-person

SidXXD

2024-07-02T09:00:49Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-07-02T08:44:03Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_10-person These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

SidXXD/3-only_cos-person-eps_50-person

SidXXD

2024-07-02T09:01:15Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-07-02T08:44:17Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_50-person These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

weifar/FTAudit-mistral-7b-mix-v0.1

weifar

2024-07-02T08:46:51Z

0

transformers

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

text-generation

2024-07-02T08:44:33Z

--- library_name: transformers tags: - unsloth --- # 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]

DokHee/KO_AI

DokHee

2024-07-02T09:26:11Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:44:33Z

--- base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** DokHee - **License:** apache-2.0 - **Finetuned from model :** MLP-KTLim/llama-3-Korean-Bllossom-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

SidXXD/3-only_cos-person-eps_99-person

SidXXD

2024-07-02T09:01:14Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-07-02T08:44:35Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_99-person These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

baxtos/bartik04-4

baxtos

2024-07-02T08:47:12Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:44:49Z

Entry not found

RyanLee1229/Llama3_Model_V3.0

RyanLee1229

2024-07-02T08:45:08Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-07-02T08:45:08Z

--- license: apache-2.0 ---

Yntec/BetterPonyDiffusion

Yntec

2024-07-02T11:30:36Z

0

diffusers

[ "diffusers", "safetensors", "Base Model", "Anime", "Photorealistic", "Furry", "diffusionfanatic1173", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-07-02T08:45:52Z

--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - Base Model - Anime - Photorealistic - Furry - diffusionfanatic1173 - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image inference: true --- # Better Pony Diffusion V6 For SD 1.5 v1 Stable Diffusion 1.5 finetuned with the SDXL Pony V6 Dataset and then with ~5000 hand-selected images by diffusionfanatic1173 to improve its aesthetics so you can use its tags like score_9, show accurate, source_anime and others I won't mention so you can check the original page at: https://civitai.com/models/544876?modelVersionId=605949 Samples and prompts: ![Free online image generator Better Pony Diffusion](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/9JyTdNKVZcFFPkfT4_gde.png) (Click for larger) Top left: source_anime, highquality, masterpiece, 1girl, Chi-Chi, :D, close up, smile, arms up, pink helmet, black hair, black eyes, blush, white bikini armor, aqua cape, pink gloves, pink boots, cleavage. cave, rock, mountain. blue collar. CHIBI Top right: score_9, show accurate, cute pony portrait, beach background Bottom left: uploaded on e621, ((by Cleon Peterson, by Sonia Delaunay, by Tomer Hanuka, by Dagasi, traditional media (artwork))), solo female ((toony judy hopps, grey body, blue eyes, white short t-shirt, dark blue short pants, small breasts)), shoulder bag, ((three-quarter portrait, three-quarter view,)) Bottom right: Highly detailed, High Quality, Masterpiece, beautiful, cute anime girl as toon link, teal headwear, Zelda

Columbia-NLP/LION-LLaMA-3-8b-sft-v1.0

Columbia-NLP

2024-07-02T08:56:32Z

0

transformers

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

text-generation

2024-07-02T08:46:15Z

--- 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]

Columbia-NLP/LION-Gemma-2b-sft-v1.0

Columbia-NLP

2024-07-02T08:58:39Z

0

transformers

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

text-generation

2024-07-02T08:46:52Z

--- 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]

symphonia16456/Imp-v1.5-3B-196-q3f16_1

symphonia16456

2024-07-02T09:02:56Z

0

null

[ "region:us" ]

null

2024-07-02T08:48:09Z

Entry not found

whizzzzkid/whizzzzkid_402_2

whizzzzkid

2024-07-02T08:48:30Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:48:10Z

Entry not found

Temo27Anas/videomae-base-finetuned-ucf101-subset-200f-fixed

Temo27Anas

2024-07-02T08:50:18Z

0

null

[ "region:us" ]

null

2024-07-02T08:50:18Z

Entry not found

baxtos/bartik05-4

baxtos

2024-07-02T08:53:46Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:50:27Z

Entry not found

anushkamantri/llama-2-stock-sentiment-merged

anushkamantri

2024-07-02T09:20:08Z

0

transformers

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

text-generation

2024-07-02T08:50:32Z

--- license: llama2 ---

adamfendri/distilTestToDelete

adamfendri

2024-07-02T08:51:28Z

0

transformers

[ "transformers", "safetensors", "distilbert", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

question-answering

2024-07-02T08:50:56Z

--- 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]

gguichard/NuExtract_finetuned_kind_of

gguichard

2024-07-02T08:52:43Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "trl", "sft", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-07-02T08:51:17Z

--- library_name: transformers tags: - trl - sft --- # 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]

MesTruck/norwegian-gpt2

MesTruck

2024-07-02T08:51:40Z

0

null

[ "region:us" ]

null

2024-07-02T08:51:40Z

Entry not found

zilla0717/ChatTTS-SG2300x

zilla0717

2024-07-02T09:04:18Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-07-02T08:51:46Z

--- license: apache-2.0 ---

IreNkweke/bert-finetuned-ner-conll2003

IreNkweke

2024-07-02T09:06:08Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-02T08:52:42Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-conll2003 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9347898047004303 - name: Recall type: recall value: 0.9505217098619994 - name: F1 type: f1 value: 0.9425901201602136 - name: Accuracy type: accuracy value: 0.9871813739918761 ---  # bert-finetuned-ner-conll2003 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0597 - Precision: 0.9348 - Recall: 0.9505 - F1: 0.9426 - Accuracy: 0.9872 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.078 | 1.0 | 1756 | 0.0798 | 0.8891 | 0.9233 | 0.9059 | 0.9789 | | 0.035 | 2.0 | 3512 | 0.0640 | 0.9290 | 0.9468 | 0.9378 | 0.9856 | | 0.0222 | 3.0 | 5268 | 0.0597 | 0.9348 | 0.9505 | 0.9426 | 0.9872 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

nhidinh2/xlm-roberta-base-finetuned-ner-thesis-dseb

nhidinh2

2024-07-02T08:55:43Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-02T08:52:45Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: xlm-roberta-base-finetuned-ner-thesis-dseb results: [] ---  # xlm-roberta-base-finetuned-ner-thesis-dseb This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1471 - Precision: 0.7995 - Recall: 0.9088 - F1: 0.8506 - Accuracy: 0.9605 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.7775 | 1.0 | 31 | 0.3746 | 0.6199 | 0.6839 | 0.6503 | 0.8978 | | 0.1886 | 2.0 | 62 | 0.0734 | 0.9590 | 0.9301 | 0.9444 | 0.9875 | | 0.0821 | 3.0 | 93 | 0.0413 | 0.9697 | 0.9651 | 0.9674 | 0.9928 | | 0.0427 | 4.0 | 124 | 0.0400 | 0.9491 | 0.9635 | 0.9562 | 0.9911 | | 0.0352 | 5.0 | 155 | 0.0397 | 0.9421 | 0.9571 | 0.9496 | 0.9899 | | 0.0315 | 6.0 | 186 | 0.0410 | 0.9371 | 0.9579 | 0.9474 | 0.9895 | | 0.0344 | 7.0 | 217 | 0.0386 | 0.9612 | 0.9643 | 0.9627 | 0.9922 | | 0.0292 | 8.0 | 248 | 0.0383 | 0.9574 | 0.9651 | 0.9612 | 0.9921 | | 0.0286 | 9.0 | 279 | 0.0387 | 0.9543 | 0.9619 | 0.9581 | 0.9913 | | 0.0259 | 10.0 | 310 | 0.0415 | 0.9430 | 0.9595 | 0.9512 | 0.9901 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

yuvimor24/whisper-small-hi

yuvimor24

2024-07-02T08:53:09Z

0

null

[ "region:us" ]

null

2024-07-02T08:53:09Z

Entry not found

manbeast3b/ZZZZZZZZdriver132

manbeast3b

2024-07-02T08:55:15Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:53:20Z

Entry not found

streamtune/e24cad19-5047-4f6d-b062-4e6df17b5f4d

streamtune

2024-07-02T08:57:21Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T08:55:00Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** streamtune - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

GGuGGuCat/roberta-base-finetuned-sts-f1_

GGuGGuCat

2024-07-02T09:11:16Z

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T08:55:16Z

Entry not found

Temo27Anas/videomae-base-finetuned-fixed-subset-200f

Temo27Anas

2024-07-02T08:55:49Z

0

null

[ "region:us" ]

null

2024-07-02T08:55:49Z

Entry not found

suji1575/llm-mistral-100

suji1575

2024-07-02T09:00:54Z

0

transformers

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

text-generation

2024-07-02T08:56:53Z

--- 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]

baxtos/bartik06-4

baxtos

2024-07-02T08:59:21Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T08:57:01Z

Entry not found

klea28f/klea

klea28f

2024-07-02T08:57:53Z

0

null

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

null

2024-07-02T08:57:06Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **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]

yemen2016/dfm_1_NCST

yemen2016

2024-07-02T10:44:36Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T08:57:29Z

--- base_model: KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align tags: - generated_from_trainer model-index: - name: dfm_1_NCST results: [] ---  # dfm_1_NCST This model is a fine-tuned version of [KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align](https://huggingface.co/KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8597 - F1-score: 0.5828 ## 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-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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1-score | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7084 | 1.0 | 528 | 0.6993 | 0.5614 | | 0.621 | 2.0 | 1056 | 0.7156 | 0.5726 | | 0.4739 | 3.0 | 1584 | 0.8597 | 0.5828 | | 0.2725 | 4.0 | 2112 | 1.3816 | 0.5533 | | 0.1412 | 5.0 | 2640 | 2.1550 | 0.5506 | | 0.0732 | 6.0 | 3168 | 2.9031 | 0.5677 | | 0.0351 | 7.0 | 3696 | 3.3674 | 0.5634 | | 0.0188 | 8.0 | 4224 | 3.4715 | 0.5702 | | 0.0119 | 9.0 | 4752 | 3.6579 | 0.5611 | | 0.0044 | 10.0 | 5280 | 3.7318 | 0.5607 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Makkoen/whisper-large-cit-synth-do015-wd0-lr1e-06-1000

Makkoen

2024-07-02T12:09:52Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T08:58:34Z

--- language: - en license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: ./whisper-large-cit-synth-do015-wd0-lr1e-06-1000 results: [] ---  # ./whisper-large-cit-synth-do015-wd0-lr1e-06-1000 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the SF 1000 dataset. It achieves the following results on the evaluation set: - Loss: 0.3706 - Wer: 23.6647 ## 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-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - 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: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | No log | 0.4444 | 25 | 0.7983 | 35.9064 | | 0.967 | 0.8889 | 50 | 0.6724 | 32.3977 | | 0.967 | 1.3333 | 75 | 0.5459 | 30.7602 | | 0.6804 | 1.7778 | 100 | 0.4692 | 27.4854 | | 0.6804 | 2.2222 | 125 | 0.4341 | 26.3548 | | 0.5145 | 2.6667 | 150 | 0.4143 | 25.5361 | | 0.5145 | 3.1111 | 175 | 0.4019 | 25.4191 | | 0.4614 | 3.5556 | 200 | 0.3914 | 25.0292 | | 0.4614 | 4.0 | 225 | 0.3879 | 24.4444 | | 0.3891 | 4.4444 | 250 | 0.3835 | 24.6784 | | 0.3891 | 4.8889 | 275 | 0.3794 | 24.6004 | | 0.3765 | 5.3333 | 300 | 0.3772 | 24.0156 | | 0.3765 | 5.7778 | 325 | 0.3745 | 23.4308 | | 0.3511 | 6.2222 | 350 | 0.3726 | 23.5478 | | 0.3511 | 6.6667 | 375 | 0.3713 | 23.5867 | | 0.3307 | 7.1111 | 400 | 0.3706 | 23.4308 | | 0.3307 | 7.5556 | 425 | 0.3699 | 23.1189 | | 0.3176 | 8.0 | 450 | 0.3706 | 23.3918 | | 0.3176 | 8.4444 | 475 | 0.3708 | 23.6647 | | 0.31 | 8.8889 | 500 | 0.3706 | 23.6647 | ### Framework versions - Transformers 4.42.3 - Pytorch 1.13.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

tz3/finetune_v6

tz3

2024-07-02T09:30:08Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T08:59:00Z

--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: finetune_v6 results: [] ---  # finetune_v6 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3628 - Wer: 24.6544 ## 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: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | No log | 6.6667 | 10 | 0.2278 | 15.2074 | | No log | 13.3333 | 20 | 0.3188 | 20.2765 | | No log | 20.0 | 30 | 0.3442 | 29.7235 | | No log | 26.6667 | 40 | 0.3628 | 24.6544 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.0 - Datasets 2.20.0 - Tokenizers 0.19.1

ClementineBleuze/deberta_prefix_cont_lr_SEP

ClementineBleuze

2024-07-02T12:01:28Z

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T09:00:35Z

--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: deberta_prefix_cont_lr_SEP results: [] ---  # deberta_prefix_cont_lr_SEP This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1159 - F1 Weighted: 0.8781 - F1 Samples: 0.8866 - F1 Macro: 0.7534 - F1 Micro: 0.8813 - Accuracy: 0.8552 ## 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-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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Weighted | F1 Samples | F1 Macro | F1 Micro | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:-----------:|:----------:|:--------:|:--------:|:--------:| | 0.2985 | 0.3381 | 500 | 0.2049 | 0.6509 | 0.6434 | 0.3553 | 0.6781 | 0.6245 | | 0.1922 | 0.6761 | 1000 | 0.1577 | 0.7286 | 0.7408 | 0.4025 | 0.7643 | 0.7152 | | 0.1628 | 1.0142 | 1500 | 0.1490 | 0.7580 | 0.7712 | 0.5103 | 0.7854 | 0.7483 | | 0.1429 | 1.3523 | 2000 | 0.1351 | 0.7795 | 0.7818 | 0.5737 | 0.7995 | 0.7598 | | 0.1401 | 1.6903 | 2500 | 0.1356 | 0.8044 | 0.8124 | 0.6459 | 0.8136 | 0.7774 | | 0.1298 | 2.0284 | 3000 | 0.1358 | 0.8172 | 0.8152 | 0.6646 | 0.8223 | 0.7828 | | 0.115 | 2.3665 | 3500 | 0.1297 | 0.8223 | 0.8253 | 0.6671 | 0.8315 | 0.7957 | | 0.1089 | 2.7045 | 4000 | 0.1281 | 0.8321 | 0.8394 | 0.6787 | 0.8389 | 0.8126 | | 0.1064 | 3.0426 | 4500 | 0.1164 | 0.8445 | 0.8501 | 0.7046 | 0.8510 | 0.8214 | | 0.0892 | 3.3807 | 5000 | 0.1175 | 0.8491 | 0.8558 | 0.7012 | 0.8570 | 0.8261 | | 0.0859 | 3.7187 | 5500 | 0.1298 | 0.8345 | 0.8387 | 0.6752 | 0.8355 | 0.8024 | | 0.0877 | 4.0568 | 6000 | 0.1140 | 0.8517 | 0.8594 | 0.7180 | 0.8577 | 0.8288 | | 0.0733 | 4.3949 | 6500 | 0.1126 | 0.8587 | 0.8693 | 0.7196 | 0.8642 | 0.8383 | | 0.0716 | 4.7329 | 7000 | 0.1194 | 0.8612 | 0.8691 | 0.7221 | 0.8656 | 0.8363 | | 0.0718 | 5.0710 | 7500 | 0.1170 | 0.8616 | 0.8700 | 0.7188 | 0.8669 | 0.8437 | | 0.0644 | 5.4091 | 8000 | 0.1114 | 0.8619 | 0.8674 | 0.7173 | 0.8653 | 0.8342 | | 0.0576 | 5.7471 | 8500 | 0.1205 | 0.8637 | 0.8714 | 0.7378 | 0.8663 | 0.8383 | | 0.0536 | 6.0852 | 9000 | 0.1151 | 0.8676 | 0.8758 | 0.7246 | 0.8694 | 0.8451 | | 0.0499 | 6.4233 | 9500 | 0.1184 | 0.8687 | 0.8782 | 0.7410 | 0.8732 | 0.8437 | | 0.0489 | 6.7613 | 10000 | 0.1159 | 0.8781 | 0.8866 | 0.7534 | 0.8813 | 0.8552 | | 0.0468 | 7.0994 | 10500 | 0.1211 | 0.8729 | 0.8799 | 0.7645 | 0.8760 | 0.8478 | | 0.0407 | 7.4375 | 11000 | 0.1234 | 0.8762 | 0.8843 | 0.7679 | 0.8779 | 0.8532 | | 0.0415 | 7.7755 | 11500 | 0.1251 | 0.8679 | 0.8722 | 0.7628 | 0.8689 | 0.8430 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

streamtune/8bd15f1d-431f-4e00-abc3-4198fd22b2fd

streamtune

2024-07-02T09:03:59Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T09:01:26Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** streamtune - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

SCnetPTY/CHatBot

SCnetPTY

2024-07-02T09:02:52Z

0

null

[ "en", "af", "region:us" ]

null

2024-07-02T09:01:59Z

--- language: - en - af ---

baxtos/bartik07-4

baxtos

2024-07-02T09:05:00Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T09:02:37Z

Entry not found

Nitss/onxx_test_model

Nitss

2024-07-02T09:18:29Z

0

transformers

[ "transformers", "onnx", "bert", "feature-extraction", "endpoints_compatible", "text-embeddings-inference", "region:us" ]

feature-extraction

2024-07-02T09:04:02Z

Entry not found

Piece-Of-Schmidt/LocNER_model_v1

Piece-Of-Schmidt

2024-07-02T09:06:48Z

0

transformers

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

null

2024-07-02T09:06:30Z

--- 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]

Hemanth1729/SentimentAnalysis_modelv2

Hemanth1729

2024-07-02T09:07:33Z

0

transformers

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

null

2024-07-02T09:07:32Z

--- 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]

baxtos/bartik08-4

baxtos

2024-07-02T09:10:47Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T09:08:14Z

Entry not found

waylandzhang/whisper-small-hi

waylandzhang

2024-07-02T11:33:28Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T09:11:19Z

Entry not found

MichaelBr/realDataFineTune

MichaelBr

2024-07-02T12:54:33Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]

null

2024-07-02T09:11:49Z

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct model-index: - name: realDataFineTune results: [] ---  # realDataFineTune This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.0+cpu - Datasets 2.19.0 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2

suji1575/llm-mistral-40

suji1575

2024-07-02T09:22:03Z

0

transformers

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

text-generation

2024-07-02T09:12:12Z

--- 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]

John6666/3010nc-xx-mixpony-v16-sdxl

John6666

2024-07-02T09:17:22Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-07-02T09:12:15Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - pony --- Original model is [here](https://civitai.com/models/548205/3010nc-xx-mixpony?modelVersionId=613617).

johnwee1/peft-pythoncoder-it

johnwee1

2024-07-03T00:36:41Z

0

null

[ "safetensors", "region:us" ]

null

2024-07-02T09:12:46Z

Entry not found

oz1115/meta_llama_peft

oz1115

2024-07-02T09:12:51Z

0

transformers

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

null

2024-07-02T09:12:48Z

--- 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]

maxseats/SungBeom-whisper-small-ko-set15

maxseats

2024-07-02T09:13:18Z

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "speech-recognition", "ko", "dataset:maxseats/aihub-464-preprocessed-680GB-set-15", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T09:12:57Z

--- language: ko tags: - whisper - speech-recognition datasets: - maxseats/aihub-464-preprocessed-680GB-set-15 metrics: - cer --- # Model Name : maxseats/SungBeom-whisper-small-ko-set14 # Description - 파인튜닝 데이터셋 : maxseats/aihub-464-preprocessed-680GB-set-15 # 설명 - AI hub의 주요 영역별 회의 음성 데이터셋을 학습 중이에요. - 680GB 중 set_0~14 데이터(150GB)까지 파인튜닝한 모델을 불러와서, set_15 데이터(10GB)를 학습한 모델입니다. - 링크 : https://huggingface.co/datasets/maxseats/aihub-464-preprocessed-680GB-set-15

John6666/ely-pony-xl-v1-sdxl

John6666

2024-07-02T09:22:31Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-07-02T09:13:15Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - pony --- Original model is [here](https://civitai.com/models/551640/elyponyxl?modelVersionId=613842).

Reihaneh/wav2vec2_fy_nl_en_common_voice_54

Reihaneh

2024-07-02T09:13:23Z

0

transformers

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

null

2024-07-02T09:13:22Z

--- 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]

SidXXD/3-only_cos-person-eps_99-alpha_5e-1

SidXXD

2024-07-02T09:33:18Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-07-02T09:13:27Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_99-alpha_5e-1 These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

SidXXD/3-only_cos-person-eps_99-alpha_5e-2

SidXXD

2024-07-02T09:33:19Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-07-02T09:13:39Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/3-only_cos-person-eps_99-alpha_5e-2 These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

finn03091993/naschainv76

finn03091993

2024-07-02T13:52:30Z

0

null

[ "region:us" ]

null

2024-07-02T09:13:55Z

Entry not found

baxtos/bartik09-4

baxtos

2024-07-02T09:16:24Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T09:14:03Z

Entry not found

streamtune/162c9955-c68a-4880-a7b5-a577592a1a52

streamtune

2024-07-02T09:17:34Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T09:14:29Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** streamtune - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

John6666/agenda-mix-pdxl-v15-sdxl

John6666

2024-07-02T09:20:26Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-07-02T09:14:32Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - pony --- Original model is [here](https://civitai.com/models/434919/agenda-mix-pdxl?modelVersionId=613794).

qsy71/none_quantization_medical_Gemma-1.1-7B-Chat

qsy71

2024-07-02T16:39:55Z

0

transformers

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

text-generation

2024-07-02T09:15:36Z

--- library_name: transformers tags: - llama-factory --- # 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]

ZZPENG/3f_Lottery_draft1

ZZPENG

2024-07-02T09:23:18Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T09:15:53Z

Entry not found

CHARKA/Mistral-7B-Instruct-v0.3tmaroc_edu

CHARKA

2024-07-02T09:17:03Z

0

transformers

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

null

2024-07-02T09:16:18Z

--- 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]

wanib26/finetuningllama2

wanib26

2024-07-02T09:16:43Z

0

null

[ "region:us" ]

null

2024-07-02T09:16:43Z

Entry not found

zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF

zhentaoyu

2024-07-02T09:17:01Z

0

null

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-2", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]

text-generation

2024-07-02T09:16:43Z

--- base_model: meta-llama/Llama-2-7b-chat-hf language: - en license: llama2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 - llama-cpp - gguf-my-repo extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: "### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\"Agreement\" means\ \ the terms and conditions for use, reproduction, distribution and modification\ \ of the Llama Materials set forth herein. \n\"Documentation\" means the specifications,\ \ manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/.\ \ \n\"Licensee\" or \"you\" means you, or your employer or any other person or\ \ entity (if you are entering into this Agreement on such person or entity's behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf. \n\"Llama 2\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.\n\"Llama\ \ Materials\" means, collectively, Meta's proprietary Llama 2 and documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). \n\ \nBy clicking \"I Accept\" below or by using or distributing any portion or element\ \ of the Llama Materials, you agree to be bound by this Agreement.\n1. License Rights\ \ and Redistribution. \na. Grant of Rights. You are granted a non-exclusive, worldwide,\ \ non- transferable and royalty-free limited license under Meta's intellectual property\ \ or other rights owned by Meta embodied in the Llama Materials to use, reproduce,\ \ distribute, copy, create derivative works of, and make modifications to the Llama\ \ Materials. \nb. Redistribution and Use.\ni. If you distribute or make the Llama\ \ Materials, or any derivative works thereof, available to a third party, you shall\ \ provide a copy of this Agreement to such third party. \nii. If you receive Llama\ \ Materials, or any derivative works thereof, from a Licensee as part of an integrated\ \ end user product, then Section 2 of this Agreement will not apply to you. \n\ iii. You must retain in all copies of the Llama Materials that you distribute the\ \ following attribution notice within a \"Notice\" text file distributed as a part\ \ of such copies: \"Llama 2 is licensed under the LLAMA 2 Community License, Copyright\ \ (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials\ \ must comply with applicable laws and regulations (including trade compliance\ \ laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials\ \ (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated\ \ by reference into this Agreement.\nv. You will not use the Llama Materials or\ \ any output or results of the Llama Materials to improve any other large language\ \ model (excluding Llama 2 or derivative works thereof). \n\n2. Additional Commercial\ \ Terms. If, on the Llama 2 version release date, the monthly active users of the\ \ products or services made available by or for Licensee, or Licensee's affiliates,\ \ is greater than 700 million monthly active users in the preceding calendar month,\ \ you must request a license from Meta, which Meta may grant to you in its sole\ \ discretion, and you are not authorized to exercise any of the rights under this\ \ Agreement unless or until Meta otherwise expressly grants you such rights.\n\ 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS\ \ AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\" BASIS, WITHOUT\ \ WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION,\ \ ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A\ \ PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS\ \ OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\ \ WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation\ \ of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY\ \ OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE,\ \ ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL,\ \ CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS\ \ AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n\n\ 5. Intellectual Property.\na. No trademark licenses are granted under this Agreement,\ \ and in connection with the Llama Materials, neither Meta nor Licensee may use\ \ any name or mark owned by or associated with the other or any of its affiliates,\ \ except as required for reasonable and customary use in describing and redistributing\ \ the Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives\ \ made by or for Meta, with respect to any derivative works and modifications of\ \ the Llama Materials that are made by you, as between you and Meta, you are and\ \ will be the owner of such derivative works and modifications.\nc. If you institute\ \ litigation or other proceedings against Meta or any entity (including a cross-claim\ \ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs\ \ or results, or any portion of any of the foregoing, constitutes infringement\ \ of intellectual property or other rights owned or licensable by you, then any\ \ licenses granted to you under this Agreement shall terminate as of the date such\ \ litigation or claim is filed or instituted. You will indemnify and hold harmless\ \ Meta from and against any claim by any third party arising out of or related \ \ to your use or distribution of the Llama Materials.\n6. Term and Termination.\ \ The term of this Agreement will commence upon your acceptance of this Agreement\ \ or access to the Llama Materials and will continue in full force and effect until\ \ terminated in accordance with the terms and conditions herein. Meta may terminate\ \ this Agreement if you are in breach of any term or condition of this Agreement.\ \ Upon termination of this Agreement, you shall delete and cease use of the Llama\ \ Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\ \ \n7. Governing Law and Jurisdiction. This Agreement will be governed and construed\ \ under the laws of the State of California without regard to choice of law principles,\ \ and the UN Convention on Contracts for the International Sale of Goods does not\ \ apply to this Agreement. The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement. \n### Llama 2 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy\ \ (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).\n\ #### Prohibited Uses\nWe want everyone to use Llama 2 safely and responsibly. You\ \ agree you will not use, or allow others to use, Llama 2 to:\n1. Violate the law\ \ or others’ rights, including to:\n 1. Engage in, promote, generate, contribute\ \ to, encourage, plan, incite, or further illegal or unlawful activity or content,\ \ such as: \n 1. Violence or terrorism \n 2. Exploitation or harm\ \ to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4.\ \ The illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6.\ \ Any other criminal activity\n 2. Engage in, promote, incite, or facilitate\ \ the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n \ \ 4. Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices \n 5. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 6. Engage in or facilitate any\ \ action or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama 2 Materials\n 7. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system \n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Llama 2 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Llama 2 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Llama 2 or outputs are human-generated\n\ \ 6. Generating or facilitating false online engagement, including fake reviews\ \ and other means of fake online engagement \n 4. Fail to appropriately disclose\ \ to end users any known dangers of your AI system \nPlease report any violation\ \ of this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means: \n * Reporting issues with\ \ the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\n\ \ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ \ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\ \ \n * Reporting violations of the Acceptable Use Policy or unlicensed uses of\ \ Llama: [[email protected]](mailto:[email protected])" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF This model was converted to GGUF format from [`meta-llama/Llama-2-7b-chat-hf`](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zhentaoyu/Llama-2-7b-chat-hf-Q4_K_S-GGUF --hf-file llama-2-7b-chat-hf-q4_k_s.gguf -c 2048 ```

Makkoen/whisper-medium-cit-do015-wd0-lr1e-06-1000

Makkoen

2024-07-02T09:57:10Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T09:16:48Z

Entry not found

shin7965977/test

shin7965977

2024-07-02T09:17:02Z

0

null

[ "license:mit", "region:us" ]

null

2024-07-02T09:17:01Z

--- license: mit ---

Bramwel/segformer-b0-finetuned-segments-sidewalk-2

Bramwel

2024-07-02T12:14:57Z

0

transformers

[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2024-07-02T09:17:09Z

--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-sidewalk-2 results: [] ---  # segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.6610 - Mean Iou: 0.1423 - Mean Accuracy: 0.1906 - Overall Accuracy: 0.7085 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.7735 - Accuracy Flat-sidewalk: 0.9216 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.1218 - Accuracy Flat-parkingdriveway: 0.0002 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.8580 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8872 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9240 - Accuracy Nature-terrain: 0.7787 - Accuracy Sky: 0.8348 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0000 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.4851 - Iou Flat-sidewalk: 0.7175 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.1207 - Iou Flat-parkingdriveway: 0.0002 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.5750 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.5599 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.7288 - Iou Nature-terrain: 0.5948 - Iou Sky: 0.7728 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0000 - Iou Void-unclear: 0.0 ## 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: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - 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 | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | 2.9079 | 0.1 | 20 | 3.1086 | 0.0647 | 0.1138 | 0.5503 | nan | 0.0368 | 0.8899 | 0.0003 | 0.0322 | 0.0003 | nan | 0.0006 | 0.0 | 0.0 | 0.7271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7960 | 0.0 | 0.0004 | 0.0022 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9478 | 0.0068 | 0.1996 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.0359 | 0.5924 | 0.0003 | 0.0304 | 0.0003 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.3287 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4669 | 0.0 | 0.0004 | 0.0020 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4806 | 0.0054 | 0.1909 | 0.0 | 0.0 | 0.0005 | 0.0 | | 2.9751 | 0.2 | 40 | 2.4969 | 0.0845 | 0.1338 | 0.6123 | nan | 0.3974 | 0.9215 | 0.0001 | 0.0122 | 0.0000 | nan | 0.0002 | 0.0 | 0.0 | 0.7324 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8570 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9405 | 0.1742 | 0.2446 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3419 | 0.6367 | 0.0001 | 0.0120 | 0.0000 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.3801 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4767 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.5843 | 0.1296 | 0.2277 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.1243 | 0.3 | 60 | 2.1662 | 0.1055 | 0.1523 | 0.6477 | nan | 0.5778 | 0.9287 | 0.0 | 0.0135 | 0.0009 | nan | 0.0001 | 0.0 | 0.0 | 0.8344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8487 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9301 | 0.2460 | 0.4938 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4250 | 0.6604 | 0.0 | 0.0134 | 0.0009 | nan | 0.0001 | 0.0 | 0.0 | 0.4751 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5074 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6306 | 0.1975 | 0.4642 | 0.0 | 0.0 | 0.0000 | 0.0 | | 1.9666 | 0.4 | 80 | 2.0080 | 0.1217 | 0.1704 | 0.6691 | nan | 0.7097 | 0.8904 | 0.0 | 0.0234 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8510 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8901 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9252 | 0.4752 | 0.6888 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4257 | 0.6858 | 0.0 | 0.0231 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5028 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5269 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6823 | 0.3861 | 0.6614 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9626 | 0.5 | 100 | 1.8857 | 0.1346 | 0.1838 | 0.6929 | nan | 0.7341 | 0.9091 | 0.0 | 0.0866 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8543 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8658 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9372 | 0.6673 | 0.8275 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4661 | 0.6989 | 0.0 | 0.0855 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.5474 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5453 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7069 | 0.5067 | 0.7495 | 0.0 | 0.0 | 0.0000 | 0.0 | | 1.8737 | 0.6 | 120 | 1.8184 | 0.1389 | 0.1906 | 0.6957 | nan | 0.7647 | 0.8845 | 0.0 | 0.1470 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.8567 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8899 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9071 | 0.8097 | 0.8378 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4546 | 0.7034 | 0.0 | 0.1427 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.5363 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5445 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7286 | 0.5730 | 0.7628 | 0.0 | 0.0 | 0.0000 | 0.0 | | 1.7869 | 0.7 | 140 | 1.7455 | 0.1389 | 0.1879 | 0.7009 | nan | 0.7817 | 0.9034 | 0.0 | 0.1201 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.8718 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8740 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9384 | 0.6890 | 0.8347 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4663 | 0.7148 | 0.0 | 0.1183 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.5568 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5643 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7099 | 0.5460 | 0.7670 | 0.0 | 0.0 | 0.0000 | 0.0 | | 2.0396 | 0.8 | 160 | 1.7097 | 0.1399 | 0.1899 | 0.7039 | nan | 0.7784 | 0.9053 | 0.0 | 0.1396 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.8835 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8935 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9243 | 0.7040 | 0.8467 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4758 | 0.7193 | 0.0 | 0.1375 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.5406 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5591 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7224 | 0.5518 | 0.7701 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9288 | 0.9 | 180 | 1.6806 | 0.1415 | 0.1903 | 0.7070 | nan | 0.7716 | 0.9175 | 0.0 | 0.1212 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8615 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8971 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9191 | 0.7875 | 0.8123 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4829 | 0.7173 | 0.0 | 0.1200 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.5653 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5572 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7308 | 0.5904 | 0.7643 | 0.0 | 0.0 | 0.0000 | 0.0 | | 1.6438 | 1.0 | 200 | 1.6610 | 0.1423 | 0.1906 | 0.7085 | nan | 0.7735 | 0.9216 | 0.0 | 0.1218 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.8580 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8872 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9240 | 0.7787 | 0.8348 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4851 | 0.7175 | 0.0 | 0.1207 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.5750 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5599 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7288 | 0.5948 | 0.7728 | 0.0 | 0.0 | 0.0000 | 0.0 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.0+rocm5.6 - Datasets 2.20.0 - Tokenizers 0.19.1

TransLLaMA/TransLLaMA2-7B-XNLI

TransLLaMA

2024-07-02T09:17:32Z

0

null

[ "license:mit", "region:us" ]

null

2024-07-02T09:17:32Z

--- license: mit ---

TransLLaMA/TransLLaMA2-7B-X-CSQA

TransLLaMA

2024-07-02T09:17:52Z

0

null

[ "license:mit", "region:us" ]

null

2024-07-02T09:17:52Z

--- license: mit ---

Dev372/HarshDev-whisper-tiny-English_2000_new

Dev372

2024-07-02T12:05:09Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:Hani89/medical_asr_recording_dataset", "base_model:openai/whisper-small.en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-02T09:18:02Z

--- language: - en license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer datasets: - Hani89/medical_asr_recording_dataset metrics: - wer model-index: - name: English Whisper Model results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Medical type: Hani89/medical_asr_recording_dataset args: 'split: test' metrics: - name: Wer type: wer value: 7.0236794171220405 ---  # English Whisper Model This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the Medical dataset. It achieves the following results on the evaluation set: - Loss: 0.1122 - Wer: 7.0237 ## 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: 3.5e-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 - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 0.0076 | 3.0030 | 1000 | 0.1181 | 7.3734 | | 0.0003 | 6.0060 | 2000 | 0.1122 | 7.0237 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1

ScandinavianMrT/SkoleGPT

ScandinavianMrT

2024-07-02T09:18:02Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-07-02T09:18:02Z

--- license: apache-2.0 ---

cortexso/claude-3-sonnet-20240229

cortexso

2024-07-02T09:18:52Z

0

null

[ "region:us" ]

null

2024-07-02T09:18:08Z

Entry not found

multimolecule/calm

multimolecule

2024-07-02T09:19:22Z

0

multimolecule

[ "multimolecule", "pytorch", "safetensors", "calm", "Biology", "RNA", "fill-mask", "dna", "dataset:multimolecule/ena", "license:agpl-3.0", "region:us" ]

fill-mask

2024-07-02T09:18:15Z

--- language: dna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/ena library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "PRNP" text: "CTG<mask>AAGCGGCCCACGCGGACTGACGGGCGGGGG" output: - label: "CGG" score: 0.03824129328131676 - label: "CCG" score: 0.030699172988533974 - label: "GGG" score: 0.026188895106315613 - label: "GCG" score: 0.02376439981162548 - label: "CCC" score: 0.023052876815199852 --- # CaLM Pre-trained model on protein-coding DNA (cDNA) using a masked language modeling (MLM) objective. ## Statement _Codon language embeddings provide strong signals for use in protein engineering_ is published in [Nature Machine Intelligence](https://doi.org/10.1038/s42256-024-00791-0), which is a Closed Access / Author-Fee journal. > Machine learning has been at the forefront of the movement for free and open access to research. > > We see no role for closed access or author-fee publication in the future of machine learning research and believe the adoption of these journals as an outlet of record for the machine learning community would be a retrograde step. The MultiMolecule team is committed to the principles of open access and open science. We do NOT endorse the publication of manuscripts in Closed Access / Author-Fee journals and encourage the community to support Open Access journals. Please consider signing the [Statement on Nature Machine Intelligence](https://openaccess.engineering.oregonstate.edu). ## Disclaimer This is an UNOFFICIAL implementation of the [Codon language embeddings provide strong signals for use in protein engineering](https://doi.org/10.1101/2022.12.15.519894) by Carlos Outeiral and Charlotte M. Deane. The OFFICIAL repository of CaLM is at [oxpig/CaLM](https://github.com/oxpig/CaLM). !!! Danger "Reproducibility" The MultiMolecule team is unable to confirm that the provided model and checkpoints are producing the same intermediate representations as the original implementation. This is because The proposed method is published in a Closed Access / Author-Fee journal. **The team releasing CaLM did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details CaLM is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of protein-coding DNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of DNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Model Specification | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens | | ---------- | ----------- | --------- | ----------------- | ------------------ | --------- | -------- | -------------- | | 12 | 768 | 12 | 3072 | 85.75 | 22.36 | 11.17 | 1024 | ### Links - **Code**: [multimolecule.calm](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/calm) - **Weights**: [multimolecule/calm](https://huggingface.co/multimolecule/calm) - **Data**: [European Nucleotide Archive](https://ebi.ac.uk/ena) - **Paper**: [Codon language embeddings provide strong signals for use in protein engineering](https://doi.org/10.1101/2022.12.15.519894) - **Developed by**: Carlos Outeiral, Charlotte M. Deane - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ESM](https://huggingface.co/facebook/esm2_t48_15B_UR50D) - **Original Repository**: [https://github.com/oxpig/CaLM](https://github.com/oxpig/CaLM) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/calm') >>> unmasker("ctg<mask>aagcggcccacgcggactgacgggcggggg") [{'score': 0.03824129328131676, 'token': 43, 'token_str': 'CGG', 'sequence': 'CUG CGG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.030699172988533974, 'token': 38, 'token_str': 'CCG', 'sequence': 'CUG CCG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.026188895106315613, 'token': 68, 'token_str': 'GGG', 'sequence': 'CUG GGG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.02376439981162548, 'token': 63, 'token_str': 'GCG', 'sequence': 'CUG GCG AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}, {'score': 0.023052876815199852, 'token': 37, 'token_str': 'CCC', 'sequence': 'CUG CCC AAG CGG CCC ACG CGG ACU GAC GGG CGG GGG'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, CaLmModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmModel.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') output = model(**input) ``` #### Sequence Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, CaLmForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmForSequencePrediction.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Nucleotide Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, CaLmForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmForNucleotidePrediction.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, CaLmForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/calm') model = CaLmForContactPrediction.from_pretrained('multimolecule/calm') text = "GCCAGTCGCTGACAGCCGCGG" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details CaLM used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 25% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The CaLM model was pre-trained coding sequences of all organisms available on the [European Nucleotide Archive (ENA)](https://ebi.ac.uk/ena). European Nucleotide Archive provides a comprehensive record of the world’s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. CaLM collected coding sequences of all organisms from ENA on April 2022, including 114,214,475 sequences. Only high level assembly information (dataclass CON) were used. Sequences matching the following criteria were filtered out: - with unknown nucleotides (`N`, `Y`, `R`) - start codon is not `ATG` - contains interstitial stop codons - number of nucleotides is not a multiple of three To reduce redundancy, CaLM grouped the entries by organism, and apply CD-HIT (CD-HIT-EST) with a cut-off at 40% sequence identity to the translated protein sequences. The final dataset contains 9,858,385 cDNA sequences. Note that the alphabet in the original implementation is RNA instead of DNA, therefore, we use [`RnaTokenizer`][multimolecule.RnaTokenizer] to tokenize the sequences. `RnaTokenizer` of `multimolecule` will convert "U"s to "T"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing CaLM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 25% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 4 NVIDIA Quadro RTX4000 GPUs with 8GiB memories. - Learning rate: 1e-4 - Optimizer: AdamW - Learning rate scheduler: cosine - Learning rate warm-up: 1,000 steps - Epochs: 14 - Batch Size: 1,000 ## Citation **BibTeX**: ```bibtex @article {outeiral2022coodn, author = {Outeiral, Carlos and Deane, Charlotte M.}, title = {Codon language embeddings provide strong signals for protein engineering}, elocation-id = {2022.12.15.519894}, year = {2022}, doi = {10.1101/2022.12.15.519894}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Protein representations from deep language models have yielded state-of-the-art performance across many tasks in computational protein engineering. In recent years, progress has primarily focused on parameter count, with recent models{\textquoteright} capacities surpassing the size of the very datasets they were trained on. Here, we propose an alternative direction. We show that large language models trained on codons, instead of amino acid sequences, provide high-quality representations that outperform comparable state-of-the-art models across a variety of tasks. In some tasks, like species recognition, prediction of protein and transcript abundance, or melting point estimation, we show that a language model trained on codons outperforms every other published protein language model, including some that contain over 50 times more parameters. These results suggest that, in addition to commonly studied scale and model complexity, the information content of biological data provides an orthogonal direction to improve the power of machine learning in biology.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2022/12/19/2022.12.15.519894}, eprint = {https://www.biorxiv.org/content/early/2022/12/19/2022.12.15.519894.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [CaLM paper](https://doi.org/10.1101/2022.12.15.519894) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```

multimolecule/ernierna

multimolecule

2024-07-02T09:20:39Z

0

multimolecule

[ "multimolecule", "pytorch", "safetensors", "ernierna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/rnacentral", "license:agpl-3.0", "region:us" ]

fill-mask

2024-07-02T09:19:29Z

--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUGA" output: - label: "U" score: 0.218908429145813 - label: "A" score: 0.20248650014400482 - label: "C" score: 0.18175390362739563 - label: "-" score: 0.11062020808458328 - label: "G" score: 0.10719843208789825 --- # ERNIE-RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) by Weijie Yin, Zhaoyu Zhang, Liang He, et al. The OFFICIAL repository of ERNIE-RNA is at [Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA). !!! Success "Reproducibility" The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing ERNIE-RNA did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details ERNIE-RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variations - **[`multimolecule/ernierna`](https://huggingface.co/multimolecule/ernierna)**: The ERNIE-RNA model pre-trained on non-coding RNA sequences. - **[`multimolecule/ernierna.ss`](https://huggingface.co/multimolecule/ernierna.ss)**: The ERNIE-RNA model fine-tuned on RNA secondary structure prediction. ### Model Specification | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens | | ---------- | ----------- | --------- | ----------------- | ------------------ | --------- | -------- | -------------- | | 12 | 768 | 12 | 3072 | 85.67 | 22.36 | 11.17 | 1024 | ### Links - **Code**: [multimolecule.ernierna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/ernierna) - **Data**: [RNAcentral](https://rnacentral.org) - **Paper**: [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) - **Developed by**: Weijie Yin, Zhaoyu Zhang, Liang He, Rui Jiang, Shuo Zhang, Gan Liu, Xuegong Zhang, Tao Qin, Zhen Xie - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ERNIE](https://huggingface.co/nghuyong/ernie-3.0-base-zh) - **Original Repository**: [https://github.com/Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/ernierna') >>> unmasker("uagc<mask>uaucagacugauguuga") [{'score': 0.218908429145813, 'token': 9, 'token_str': 'U', 'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'}, {'score': 0.20248650014400482, 'token': 6, 'token_str': 'A', 'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'}, {'score': 0.18175390362739563, 'token': 7, 'token_str': 'C', 'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'}, {'score': 0.11062020808458328, 'token': 25, 'token_str': '-', 'sequence': 'U A G C - U A U C A G A C U G A U G U U G A'}, {'score': 0.10719843208789825, 'token': 8, 'token_str': 'G', 'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, ErnieRnaModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaModel.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') output = model(**input) ``` #### Sequence Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaForSequencePrediction.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Nucleotide Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaForNucleotidePrediction.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna') model = ErnieRnaForContactPrediction.from_pretrained('multimolecule/ernierna') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details ERNIE-RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The ERNIE-RNA model was pre-trained on [RNAcentral](https://rnacentral.org). RNAcentral is a comprehensive database of non-coding RNA sequences from a wide range of species. It combines 47 different databases, adding up to around 34 million RNA sequences in total. ERNIE-RNA applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral, resulting 25 million unique sequences. Sequences longer than 1024 nucleotides were subsequently excluded. The final dataset contains 20.4 million non-redundant RNA sequences. ERNIE-RNA preprocessed all tokens by replacing "T"s with "S"s. Note that [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing ERNIE-RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 24 NVIDIA V100 GPUs with 32GiB memories. - Learning rate: 1e-4 - Weight decay: 0.01 - Learning rate warm-up: 20,000 steps ## Citation **BibTeX**: ```bibtex @article {Yin2024.03.17.585376, author = {Yin, Weijie and Zhang, Zhaoyu and He, Liang and Jiang, Rui and Zhang, Shuo and Liu, Gan and Zhang, Xuegong and Qin, Tao and Xie, Zhen}, title = {ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations}, elocation-id = {2024.03.17.585376}, year = {2024}, doi = {10.1101/2024.03.17.585376}, publisher = {Cold Spring Harbor Laboratory}, abstract = {With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.Competing Interest StatementOne patent based on the study was submitted by Z.X. and W.Y., which is entitled as "A Pre-training Approach for RNA Sequences and Its Applications"(application number, no 202410262527.5). The remaining authors declare no competing interests.}, URL = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376}, eprint = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [ERNIE-RNA paper](https://doi.org/10.1101/2024.03.17.585376) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```

cortexso/claude-3-haiku-20240307

cortexso

2024-07-02T09:20:56Z

0

null

[ "region:us" ]

null

2024-07-02T09:19:30Z

Entry not found

atmatechai/speecht5_tts_dataset_primer_male_1000

atmatechai

2024-07-02T10:24:54Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]

text-to-audio

2024-07-02T09:19:41Z

Entry not found

baxtos/bartik10-4

baxtos

2024-07-02T09:22:02Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T09:19:42Z

Entry not found

Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF

Dabitron

2024-07-02T09:23:31Z

0

null

[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:natong19/Qwen2-7B-Instruct-abliterated", "license:apache-2.0", "region:us" ]

text-generation

2024-07-02T09:20:28Z

--- base_model: natong19/Qwen2-7B-Instruct-abliterated language: - en license: apache-2.0 pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF This model was converted to GGUF format from [`natong19/Qwen2-7B-Instruct-abliterated`](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Dabitron/Qwen2-7B-Instruct-abliterated-Q5_K_S-GGUF --hf-file qwen2-7b-instruct-abliterated-q5_k_s.gguf -c 2048 ```

streamtune/624595c7-1eeb-45e0-8ba7-70f0959d552a

streamtune

2024-07-02T09:22:47Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T09:20:31Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** streamtune - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

multimolecule/ernierna.ss

multimolecule

2024-07-02T09:57:35Z

0

multimolecule

[ "multimolecule", "pytorch", "safetensors", "ernierna", "Biology", "RNA", "rna", "dataset:multimolecule/rnacentral", "base_model:multimolecule/ernierna", "license:agpl-3.0", "region:us" ]

null

2024-07-02T09:20:47Z

--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral library_name: multimolecule base_model: multimolecule/ernierna --- # ERNIE-RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) by Weijie Yin, Zhaoyu Zhang, Liang He, et al. The OFFICIAL repository of ERNIE-RNA is at [Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA). !!! Success "Reproducibility" The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing ERNIE-RNA did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details ERNIE-RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variations - **[`multimolecule/ernierna`](https://huggingface.co/multimolecule/ernierna)**: The ERNIE-RNA model pre-trained on non-coding RNA sequences. - **[`multimolecule/ernierna.ss`](https://huggingface.co/multimolecule/ernierna.ss)**: The ERNIE-RNA model fine-tuned on RNA secondary structure prediction. ### Model Specification | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens | | ---------- | ----------- | --------- | ----------------- | ------------------ | --------- | -------- | -------------- | | 12 | 768 | 12 | 3072 | 85.67 | 22.36 | 11.17 | 1024 | ### Links - **Code**: [multimolecule.ernierna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/ernierna) - **Data**: [RNAcentral](https://rnacentral.org) - **Paper**: [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376) - **Developed by**: Weijie Yin, Zhaoyu Zhang, Liang He, Rui Jiang, Shuo Zhang, Gan Liu, Xuegong Zhang, Tao Qin, Zhen Xie - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ERNIE](https://huggingface.co/nghuyong/ernie-3.0-base-zh) - **Original Repository**: [https://github.com/Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/ernierna.ss') >>> unmasker("uagc<mask>uaucagacugauguuga") [{'score': 0.19777926802635193, 'token': 9, 'token_str': 'U', 'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'}, {'score': 0.16415606439113617, 'token': 7, 'token_str': 'C', 'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'}, {'score': 0.15474674105644226, 'token': 8, 'token_str': 'G', 'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'}, {'score': 0.13006599247455597, 'token': 25, 'token_str': '-', 'sequence': 'U A G C - U A U C A G A C U G A U G U U G A'}, {'score': 0.1272154450416565, 'token': 6, 'token_str': 'A', 'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, ErnieRnaModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaModel.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') output = model(**input) ``` #### Sequence Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaForSequencePrediction.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Nucleotide Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaForNucleotidePrediction.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, ErnieRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna.ss') model = ErnieRnaForContactPrediction.from_pretrained('multimolecule/ernierna.ss') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details ERNIE-RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The ERNIE-RNA model was pre-trained on [RNAcentral](https://rnacentral.org). RNAcentral is a comprehensive database of non-coding RNA sequences from a wide range of species. It combines 47 different databases, adding up to around 34 million RNA sequences in total. ERNIE-RNA applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral, resulting 25 million unique sequences. Sequences longer than 1024 nucleotides were subsequently excluded. The final dataset contains 20.4 million non-redundant RNA sequences. ERNIE-RNA preprocessed all tokens by replacing "T"s with "S"s. Note that [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing ERNIE-RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 24 NVIDIA V100 GPUs with 32GiB memories. - Learning rate: 1e-4 - Weight decay: 0.01 - Learning rate warm-up: 20,000 steps ## Citation **BibTeX**: ```bibtex @article {Yin2024.03.17.585376, author = {Yin, Weijie and Zhang, Zhaoyu and He, Liang and Jiang, Rui and Zhang, Shuo and Liu, Gan and Zhang, Xuegong and Qin, Tao and Xie, Zhen}, title = {ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations}, elocation-id = {2024.03.17.585376}, year = {2024}, doi = {10.1101/2024.03.17.585376}, publisher = {Cold Spring Harbor Laboratory}, abstract = {With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.Competing Interest StatementOne patent based on the study was submitted by Z.X. and W.Y., which is entitled as "A Pre-training Approach for RNA Sequences and Its Applications"(application number, no 202410262527.5). The remaining authors declare no competing interests.}, URL = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376}, eprint = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [ERNIE-RNA paper](https://doi.org/10.1101/2024.03.17.585376) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```

Manila-8333003722/8333-00.3722

Manila-8333003722

2024-07-02T09:21:19Z

0

null

[ "license:mit", "region:us" ]

null

2024-07-02T09:21:19Z

--- license: mit ---

emakin02/Trendyol-Embed

emakin02

2024-07-02T09:25:53Z

0

null

[ "safetensors", "region:us" ]

null

2024-07-02T09:21:44Z

Entry not found

multimolecule/rinalmo

multimolecule

2024-07-02T09:35:24Z

0

multimolecule

[ "multimolecule", "pytorch", "safetensors", "rinalmo", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/rnacentral", "dataset:multimolecule/rfam", "dataset:multimolecule/ensembl-genome-browser", "dataset:multimolecule/nucleotide", "license:agpl-3.0", "region:us" ]

fill-mask

2024-07-02T09:22:21Z

--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral - multimolecule/rfam - multimolecule/ensembl-genome-browser - multimolecule/nucleotide library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUGA" output: - label: "A" score: 0.28896641731262207 - label: "U" score: 0.27602624893188477 - label: "X" score: 0.18329711258411407 - label: "C" score: 0.1668907254934311 - label: "G" score: 0.08479981869459152 --- # RiNALMo Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://doi.org/10.48550/arXiv.2403.00043) by Rafael Josip Penić, et al. The OFFICIAL repository of RiNALMo is at [lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo). !!! Success "Reproducibility" The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing RiNALMo did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details RiNALMo is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Model Specification | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens | | ---------- | ----------- | --------- | ----------------- | ------------------ | --------- | -------- | -------------- | | 33 | 1280 | 20 | 5120 | 650.88 | 168.92 | 84.43 | 1022 | ### Links - **Code**: [multimolecule.rinalmo](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rinalmo) - **Weights**: [`multimolecule/rinalmo`](https://huggingface.co/multimolecule/rinalmo) - **Data**: [RNAcentral](https://rnacentral.org) - **Paper**: [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://doi.org/10.48550/arXiv.2403.00043) - **Developed by**: Rafael Josip Penić, Tin Vlašić, Roland G. Huber, Yue Wan, Mile Šikić - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - **Original Repository**: [https://github.com/lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='multimolecule/rinalmo') >>> unmasker("uagc<mask>uaucagacugauguuga") [{'score': 0.28896641731262207, 'token': 6, 'token_str': 'A', 'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'}, {'score': 0.27602624893188477, 'token': 9, 'token_str': 'U', 'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'}, {'score': 0.18329711258411407, 'token': 12, 'token_str': 'X', 'sequence': 'U A G C X U A U C A G A C U G A U G U U G A'}, {'score': 0.1668907254934311, 'token': 7, 'token_str': 'C', 'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'}, {'score': 0.08479981869459152, 'token': 8, 'token_str': 'G', 'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, RiNALMoModel tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoModel.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') output = model(**input) ``` #### Sequence Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RiNALMoForSequencePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoForSequencePrediction.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Nucleotide Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RiNALMoForNucleotidePrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoForNucleotidePrediction.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RiNALMoForContactPrediction tokenizer = RnaTokenizer.from_pretrained('multimolecule/rinalmo') model = RiNALMoForContactPrediction.from_pretrained('multimolecule/rinalmo') text = "UAGCUUAUCAGACUGAUGUUGA" input = tokenizer(text, return_tensors='pt') label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details RiNALMo used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The RiNALMo model was pre-trained on a cocktail of databases including [RNAcentral](https://rnacentral.org), [Rfam](https://rfam.org), [Ensembl Genome Browser](https://ensembl.org), and [Nucleotide](https://ncbi.nlm.nih.gov/nucleotide). The training data contains 36 million unique ncRNA sequences. To ensure sequence diversity in each training batch, RiNALMo clustered the sequences with [MMSeqs2](https://github.com/soedinglab/MMseqs2) into 17 million clusters and then sampled each sequence in the batch from a different cluster. RiNALMo preprocessed all tokens by replacing "U"s with "T"s. Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing RiNALMo used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### PreTraining The model was trained on 7 NVIDIA A100 GPUs with 80GiB memories. - Learning rate: 5e-5 - Learning rate scheduler: cosine - Learning rate warm-up: 2,000 steps - Learning rate minimum: 1e-5 - Epochs: 6 - Batch Size: 1344 - Dropout: 0.1 ## Citation **BibTeX**: ```bibtex @article{penic2024rinalmo, title={RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks}, author={Penić, Rafael Josip and Vlašić, Tin and Huber, Roland G. and Wan, Yue and Šikić, Mile}, journal={arXiv preprint arXiv:2403.00043}, year={2024} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [RiNALMo paper](https://doi.org/10.48550/arXiv.2403.00043) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```

Peacoc/37_best_t_12_1

Peacoc

2024-07-02T09:25:20Z

0

transformers

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

text-generation

2024-07-02T09:22:35Z

--- 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]

ssoypark/fine_tuned_clothing_model_b7

ssoypark

2024-07-02T09:24:21Z

0

null

[ "region:us" ]

null

2024-07-02T09:24:16Z

Entry not found

baxtos/bartik12-4

baxtos

2024-07-02T09:27:40Z

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-02T09:25:18Z

Entry not found

BobbBuilder/openai-whisper-tiny

BobbBuilder

2024-07-02T09:25:34Z

0

transformers

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

null

2024-07-02T09:25:32Z

--- 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]

TheNight12/medical_llama3

TheNight12

2024-07-02T09:26:00Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-02T09:25:49Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** TheNight12 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

NghiemAbe/PhoBert-Base-v2-NewVocab

NghiemAbe

2024-07-02T09:26:52Z

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-07-02T09:26:29Z

--- 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]

KhiTuKi/SAVE_TRAIN

KhiTuKi

2024-07-02T09:26:31Z

0

null

[ "region:us" ]

null

2024-07-02T09:26:31Z

Entry not found

akashAD/bart-large-mnli-onnx

akashAD

2024-07-02T09:26:52Z

0

null

[ "region:us" ]

null

2024-07-02T09:26:51Z

Entry not found

cortexso/claude-3-5-sonnet-20240620

cortexso

2024-07-02T09:28:32Z

0

null

[ "region:us" ]

null

2024-07-02T09:27:35Z

Entry not found

TensorNTU/Risk_Empirical

TensorNTU

2024-07-02T09:27:53Z

0

null

[ "region:us" ]

null

2024-07-02T09:27:53Z

Entry not found

Chonlasitk/ASR-fine-tuning

Chonlasitk

2024-07-02T09:29:38Z

0

null

[ "license:mit", "region:us" ]

null

2024-07-02T09:29:38Z

--- license: mit ---

ZZPENG/3f_Supermacy_draft1

ZZPENG

2024-07-02T09:37:43Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-02T09:29:56Z

Entry not found

pinguG/BRKsEDU

pinguG

2024-07-02T09:31:51Z

0

null

[ "region:us" ]

null

2024-07-02T09:30:55Z

Entry not found

KasuleTrevor/test_20

KasuleTrevor

2024-07-02T09:30:59Z

0

transformers

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

null

2024-07-02T09:30:58Z

--- 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]