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maoweiwei/whisper-hakka-medium-1
maoweiwei
2023-12-15T13:58:13Z
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2023-12-15T13:58:13Z
Entry not found
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Vital65/Pirat
Vital65
2023-12-15T14:00:12Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-15T13:58:20Z
--- license: openrail ---
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vigneshgs7/segformer-b0-finetuned-segments-docboundary-dec-15_kaggletrial1
vigneshgs7
2023-12-15T14:02:54Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:02:54Z
Entry not found
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InferencetrainingAI/SER
InferencetrainingAI
2023-12-16T02:24:45Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-15T14:03:23Z
--- license: mit ---
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owanr/ghc-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T06:33:44Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T14:05:24Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: ghc-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # ghc-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0335 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8994 | 1.0 | 11020 | 2.9253 | | 2.915 | 2.0 | 22040 | 3.0335 | | 2.8825 | 3.0 | 33060 | 3.0335 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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Jeevesh8/bt__qqp_ft_only_data_shuff_10
Jeevesh8
2023-12-15T16:50:37Z
0
0
transformers
[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T14:05:29Z
Entry not found
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Jeevesh8/bt__qqp_ft_only_data_shuff_11
Jeevesh8
2023-12-15T16:49:05Z
0
0
transformers
[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T14:05:29Z
Entry not found
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hkivancoral/smids_5x_beit_base_sgd_001_fold2
hkivancoral
2023-12-15T15:25:30Z
0
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-15T14:06:48Z
--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_beit_base_sgd_001_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8851913477537438 --- <!-- 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. --> # smids_5x_beit_base_sgd_001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2987 - Accuracy: 0.8852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6816 | 1.0 | 375 | 0.6833 | 0.7338 | | 0.5586 | 2.0 | 750 | 0.5192 | 0.7987 | | 0.4343 | 3.0 | 1125 | 0.4591 | 0.8037 | | 0.3314 | 4.0 | 1500 | 0.4185 | 0.8270 | | 0.3478 | 5.0 | 1875 | 0.4048 | 0.8369 | | 0.3776 | 6.0 | 2250 | 0.3805 | 0.8336 | | 0.3391 | 7.0 | 2625 | 0.3595 | 0.8403 | | 0.3304 | 8.0 | 3000 | 0.3500 | 0.8469 | | 0.2847 | 9.0 | 3375 | 0.3354 | 0.8602 | | 0.2987 | 10.0 | 3750 | 0.3303 | 0.8519 | | 0.2929 | 11.0 | 4125 | 0.3216 | 0.8669 | | 0.3369 | 12.0 | 4500 | 0.3243 | 0.8569 | | 0.2344 | 13.0 | 4875 | 0.3140 | 0.8619 | | 0.2532 | 14.0 | 5250 | 0.3121 | 0.8669 | | 0.2563 | 15.0 | 5625 | 0.3059 | 0.8769 | | 0.2186 | 16.0 | 6000 | 0.3073 | 0.8652 | | 0.3036 | 17.0 | 6375 | 0.3090 | 0.8669 | | 0.2499 | 18.0 | 6750 | 0.3061 | 0.8719 | | 0.2571 | 19.0 | 7125 | 0.2980 | 0.8752 | | 0.287 | 20.0 | 7500 | 0.3028 | 0.8719 | | 0.2417 | 21.0 | 7875 | 0.2951 | 0.8819 | | 0.2578 | 22.0 | 8250 | 0.2968 | 0.8769 | | 0.2485 | 23.0 | 8625 | 0.3021 | 0.8752 | | 0.2288 | 24.0 | 9000 | 0.2899 | 0.8852 | | 0.1978 | 25.0 | 9375 | 0.3001 | 0.8785 | | 0.2856 | 26.0 | 9750 | 0.2985 | 0.8819 | | 0.2426 | 27.0 | 10125 | 0.2936 | 0.8852 | | 0.222 | 28.0 | 10500 | 0.2944 | 0.8852 | | 0.2193 | 29.0 | 10875 | 0.2940 | 0.8802 | | 0.2438 | 30.0 | 11250 | 0.2935 | 0.8802 | | 0.1916 | 31.0 | 11625 | 0.2931 | 0.8735 | | 0.2423 | 32.0 | 12000 | 0.2890 | 0.8869 | | 0.207 | 33.0 | 12375 | 0.2979 | 0.8769 | | 0.1912 | 34.0 | 12750 | 0.3002 | 0.8885 | | 0.2247 | 35.0 | 13125 | 0.2940 | 0.8819 | | 0.1554 | 36.0 | 13500 | 0.2936 | 0.8819 | | 0.1927 | 37.0 | 13875 | 0.2998 | 0.8769 | | 0.1704 | 38.0 | 14250 | 0.2978 | 0.8835 | | 0.2119 | 39.0 | 14625 | 0.3017 | 0.8769 | | 0.1926 | 40.0 | 15000 | 0.2970 | 0.8819 | | 0.1898 | 41.0 | 15375 | 0.3000 | 0.8785 | | 0.2087 | 42.0 | 15750 | 0.2990 | 0.8819 | | 0.1865 | 43.0 | 16125 | 0.2976 | 0.8835 | | 0.2027 | 44.0 | 16500 | 0.3000 | 0.8819 | | 0.1873 | 45.0 | 16875 | 0.2993 | 0.8835 | | 0.2551 | 46.0 | 17250 | 0.2983 | 0.8819 | | 0.227 | 47.0 | 17625 | 0.2985 | 0.8852 | | 0.2135 | 48.0 | 18000 | 0.3011 | 0.8785 | | 0.2131 | 49.0 | 18375 | 0.2986 | 0.8852 | | 0.2054 | 50.0 | 18750 | 0.2987 | 0.8852 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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hkivancoral/smids_5x_beit_base_sgd_0001_fold2
hkivancoral
2023-12-15T15:25:47Z
0
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-15T14:07:14Z
--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_beit_base_sgd_0001_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8036605657237936 --- <!-- 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. --> # smids_5x_beit_base_sgd_0001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4724 - Accuracy: 0.8037 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0748 | 1.0 | 375 | 1.2296 | 0.3677 | | 1.052 | 2.0 | 750 | 1.1460 | 0.3943 | | 0.9644 | 3.0 | 1125 | 1.0650 | 0.4326 | | 0.8415 | 4.0 | 1500 | 0.9877 | 0.4942 | | 0.786 | 5.0 | 1875 | 0.9192 | 0.5557 | | 0.7841 | 6.0 | 2250 | 0.8580 | 0.6323 | | 0.7782 | 7.0 | 2625 | 0.8061 | 0.6689 | | 0.7211 | 8.0 | 3000 | 0.7620 | 0.6889 | | 0.6883 | 9.0 | 3375 | 0.7245 | 0.7138 | | 0.6641 | 10.0 | 3750 | 0.6925 | 0.7371 | | 0.6683 | 11.0 | 4125 | 0.6665 | 0.7404 | | 0.7093 | 12.0 | 4500 | 0.6445 | 0.7454 | | 0.5818 | 13.0 | 4875 | 0.6259 | 0.7604 | | 0.5841 | 14.0 | 5250 | 0.6091 | 0.7604 | | 0.5811 | 15.0 | 5625 | 0.5946 | 0.7637 | | 0.5799 | 16.0 | 6000 | 0.5819 | 0.7704 | | 0.5841 | 17.0 | 6375 | 0.5719 | 0.7720 | | 0.5531 | 18.0 | 6750 | 0.5621 | 0.7770 | | 0.5613 | 19.0 | 7125 | 0.5532 | 0.7820 | | 0.5733 | 20.0 | 7500 | 0.5445 | 0.7837 | | 0.538 | 21.0 | 7875 | 0.5380 | 0.7887 | | 0.5305 | 22.0 | 8250 | 0.5326 | 0.7903 | | 0.5558 | 23.0 | 8625 | 0.5259 | 0.7887 | | 0.5149 | 24.0 | 9000 | 0.5202 | 0.7903 | | 0.5317 | 25.0 | 9375 | 0.5157 | 0.7903 | | 0.5391 | 26.0 | 9750 | 0.5118 | 0.7937 | | 0.557 | 27.0 | 10125 | 0.5073 | 0.7920 | | 0.4911 | 28.0 | 10500 | 0.5033 | 0.7970 | | 0.4985 | 29.0 | 10875 | 0.5001 | 0.7937 | | 0.5262 | 30.0 | 11250 | 0.4968 | 0.7937 | | 0.4712 | 31.0 | 11625 | 0.4944 | 0.7970 | | 0.5163 | 32.0 | 12000 | 0.4912 | 0.7953 | | 0.4489 | 33.0 | 12375 | 0.4890 | 0.7987 | | 0.4565 | 34.0 | 12750 | 0.4870 | 0.8003 | | 0.484 | 35.0 | 13125 | 0.4849 | 0.7987 | | 0.4879 | 36.0 | 13500 | 0.4832 | 0.8020 | | 0.48 | 37.0 | 13875 | 0.4815 | 0.8053 | | 0.4581 | 38.0 | 14250 | 0.4797 | 0.8053 | | 0.4627 | 39.0 | 14625 | 0.4783 | 0.8070 | | 0.475 | 40.0 | 15000 | 0.4772 | 0.8053 | | 0.4851 | 41.0 | 15375 | 0.4762 | 0.8037 | | 0.4434 | 42.0 | 15750 | 0.4753 | 0.8037 | | 0.4381 | 43.0 | 16125 | 0.4746 | 0.8037 | | 0.506 | 44.0 | 16500 | 0.4740 | 0.8037 | | 0.4383 | 45.0 | 16875 | 0.4734 | 0.8037 | | 0.4915 | 46.0 | 17250 | 0.4730 | 0.8037 | | 0.4925 | 47.0 | 17625 | 0.4727 | 0.8037 | | 0.4423 | 48.0 | 18000 | 0.4725 | 0.8037 | | 0.4853 | 49.0 | 18375 | 0.4724 | 0.8037 | | 0.5129 | 50.0 | 18750 | 0.4724 | 0.8037 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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Mairati/eliri
Mairati
2023-12-15T14:19:32Z
0
0
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-12-15T14:07:49Z
--- # 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. 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 <!-- Provide a longer summary of what this model is. --> - **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]
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owanr/Sentiment-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T06:51:57Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T14:12:11Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: Sentiment-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # Sentiment-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9525 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.066 | 1.0 | 5628 | 3.9396 | | 3.9721 | 2.0 | 11256 | 3.9525 | | 3.7446 | 3.0 | 16884 | 3.9525 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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marcchew/LaMini-Flan-T5-248M-Orca-12.5K-4bit
marcchew
2023-12-15T14:14:05Z
0
0
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]
text2text-generation
2023-12-15T14:12:50Z
Entry not found
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zhengr/zephyr-7b-sft-lora
zhengr
2023-12-15T14:13:09Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:13:09Z
Entry not found
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joshmittal/phi-2-finetuned
joshmittal
2023-12-15T14:13:42Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:13:42Z
Entry not found
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gkallergis/bbbp_60_hp_opt
gkallergis
2023-12-15T14:26:58Z
0
0
null
[ "license:cc-by-nc-nd-4.0", "region:us" ]
null
2023-12-15T14:13:56Z
--- license: cc-by-nc-nd-4.0 ---
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cgus/Solar-10.7B-SLERP-exl2
cgus
2023-12-15T20:22:02Z
0
0
transformers
[ "transformers", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-15T14:14:09Z
--- inference: false license: apache-2.0 language: - en --- <!-- header start --> <!-- 200823 --> ## Solar-10.7B-SLERP-EXL2 Model: [Solar-10.7B-SLERP](https://huggingface.co/jan-hq/Solar-10.7B-SLERP) Made by: [jan-hq](https://huggingface.co/jan-hq) Based on original model: [SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) Created by: [upstage](https://huggingface.co/upstage) ## List of quants: [4bpw h8 (main)](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/main) [4.65bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/4.65bpw-h8) [5bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/5bpw-h8) [6bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/6bpw-h8) [8bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/8bpw-h8) ## Quantization notes Made with exllamav2-0.0.10, calibrated with [wmt-da-human-evaluation.parquet](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation) dataset. The reason this dataset was used because the model worked very well with non-English contexts and texts, similar to Xwin models. So I decided to try a dataset that uses multiple different languages. This model uses a lot less memory compared to Llama2-13B models and it *almost* fits 12GB VRAM even at 8bpw. But with 8-bit cache it even uses just a tiny bit less than 12GB VRAM. ## How to run This quantization method uses GPU and requires Exllamav2 loader which can be found in following applications: [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) [KoboldAI](https://github.com/henk717/KoboldAI) [ExUI](https://github.com/turboderp/exui) # Original model card: <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a > - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `Slerp` merge method from the best models on 14th Dec on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 2. [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - base model: [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) The yaml config file for this model is here: ```yaml slices: - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [0, 48] - model: janhq/Pandora-v1-10.7B layer_range: [0, 48] merge_method: slerp base_model: upstage/SOLAR-10.7B-Instruct-v1.0 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` # Prompt template - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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gkallergis/bbbp_80_hp_opt
gkallergis
2023-12-15T14:27:10Z
0
0
null
[ "license:cc-by-nc-nd-4.0", "region:us" ]
null
2023-12-15T14:14:50Z
--- license: cc-by-nc-nd-4.0 ---
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gutimatemalaysia/GutimateMalaysia
gutimatemalaysia
2023-12-15T14:17:46Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:16:05Z
Gutimate: Malaysia, harga, ulasan, suplemen, Faedah, bahan-bahan, aduan, kos, asal! Gutimate adalah bahan semulajadi yang berfungsi untuk melegakan sakit sendi di samping membantu pengguna memulihkan rawan yang rosak di dalam badan mereka. Gutimate Malaysia <a href="https://www.nutritionsee.com/GutiMala">Gutimate Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a> Laman web rasmi: <a href="https://www.nutritionsee.com/GutiMala">www.Gutimate.com</a> <p><a href="https://www.nutritionsee.com/GutiMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd_hPIKRph7b2aC6CRraIAkALQzg2WZzdQfkPmKaLO_8P8mZoKa8UW5y6ep8Az9mkOMj3i7V0vrUpImCMhYqqXZv1KfoAMGCYEWPB8Su8gDrZKUlpzNcoRFmU25610Z0mmH3XX5TglmP4EQ0Hl_mvi1xZXFShfOnz0_Xjb5jA7XjctF2xLnILMQrr3MfE/w633-h425/Gutimate%20Malaysia.png" alt="enter image description here"> </a></p> ➢Nama Produk — Gutimate ➢Kategori –Kesihatan Sendi ➢Faedah Utama — Memberi anda lebih keselesaan sendi & menjadikan hidup anda lebih bahagia dan lebih aktif. ➢ Komposisi — Sebatian Organik Semulajadi ➢ Kesan Sampingan—NA ➢Penilaian Akhir: — 4.8 ➢ Ketersediaan — Dalam Talian ➢Tawaran & Diskaun; JIMAT HARI INI! BELI SEKARANG UNTUK MEMBELI TAWARAN ISTIMEWA!!! <a href="https://www.nutritionsee.com/GutiMala">Gutimate Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a> Laman web rasmi: <a href="https://www.nutritionsee.com/GutiMala">www.Gutimate.com</a> <p><a href="https://www.nutritionsee.com/GutiMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd_hPIKRph7b2aC6CRraIAkALQzg2WZzdQfkPmKaLO_8P8mZoKa8UW5y6ep8Az9mkOMj3i7V0vrUpImCMhYqqXZv1KfoAMGCYEWPB8Su8gDrZKUlpzNcoRFmU25610Z0mmH3XX5TglmP4EQ0Hl_mvi1xZXFShfOnz0_Xjb5jA7XjctF2xLnILMQrr3MfE/w633-h425/Gutimate%20Malaysia.png" alt="enter image description here"> </a></p> Apakah Gutimate? Gutimate ialah formula baharu revolusioner yang membantu menangani isu asas yang menyebabkan sakit sendi kronik. Gutimate komponen melegakan kekakuan sendi, sakit pada bahu dan lutut, dan bengkak pada sendi. Orang ramai menggunakan formula ini untuk melegakan toksin dan kesakitan dalam badan. Melegakan kesakitan dan peningkatan penghantaran nutrien hanyalah dua daripada banyak kelebihan mengambil produk ini. https://www.nutritionsee.com/GutiMala https://www.facebook.com/Gutimate.Malaysia https://sites.google.com/view/gutimate/home https://healthtoned.blogspot.com/2023/12/gutimate-malaysia.html https://www.weddingwire.com/website/gutimate-and-malaysia https://medium.com/@healthytalk24x7/gutimate-malaysia-f43517d64223 https://medium.com/@healthytalk24x7/gutimate-malaysia-harga-ulasan-suplemen-faedah-bahan-bahan-aduan-kos-asal-cde1968ea9ca https://infogram.com/gutimate-malaysia-1hmr6g7pjznxo6n?live https://softisenilspain.hashnode.dev/gutimate-malaysia https://sway.office.com/gRYjOG9V7xjzs24Q https://gutimatemalaysia.company.site/ https://gamma.app/docs/Gutimate-Malaysia-8rktzjkzb0ajuzj?mode=doc https://healthytalk24x7.wixsite.com/sunshine/post/gutimate-malaysia https://groups.google.com/g/snshine/c/NQS3HZ_dXcg https://www.sunflower-cissp.com/glossary/cissp/7313/gutimate-malaysia https://community.thebatraanumerology.com/user/gutimatemalaysia https://community.thebatraanumerology.com/post/gutimate-malaysia-harga-ulasan-suplemen-faedah-bahan-bahan-aduan-kos-asal-657c5e57e991b9039ac2c4bd https://nepal.tribe.so/post/gutimate-malaysia-657c5e8458837d87d6352cb4 https://replit.com/@gutimatemalaysi https://soundcloud.com/gutimate/gutimate-malaysia?si=fadfea1dcf324de4806b551368a3c168&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing https://www.deviantart.com/gutimate/art/Gutimate-Malaysia-1002185383
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sanmaro6803/llama2-ko-7b-ds-qlora-sft-merged-a100-6epochs
sanmaro6803
2023-12-15T14:19:38Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-15T14:16:33Z
Entry not found
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marcchew/LaMini-T5-61M-8bit
marcchew
2023-12-15T14:17:35Z
0
0
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]
text2text-generation
2023-12-15T14:17:28Z
Entry not found
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Emma5099/runs_prefix_100_bias
Emma5099
2023-12-15T14:18:49Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:18:49Z
Entry not found
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ac0hik/finetuned_model
ac0hik
2023-12-15T14:19:48Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:19:48Z
Entry not found
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LavaPlanet/Venus-120b-v1.1-exl2_2-2.64bpw
LavaPlanet
2023-12-15T20:07:09Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-15T14:19:54Z
--- license: llama2 --- This should fit on 2x3090s on windows with a 18-19,24 gpu split with 6k-8k context. Uses the new exlv2 quant
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Mairati/elirai
Mairati
2023-12-15T14:27:11Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-15T14:23:15Z
--- license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657c5d693160b534e505cfc8/bDqfQRZ7TR71woPdOGTG4.png)
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sauravns/roberta-base-finetuned-squadv2
sauravns
2023-12-15T15:34:40Z
0
0
transformers
[ "transformers", "tf", "roberta", "question-answering", "generated_from_keras_callback", "base_model:roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2023-12-15T14:28:41Z
--- license: mit base_model: roberta-base tags: - generated_from_keras_callback model-index: - name: sauravns/roberta-base-finetuned-squadv2 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. --> # sauravns/roberta-base-finetuned-squadv2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0576 - Train End Logits Accuracy: 0.3438 - Train Start Logits Accuracy: 0.3438 - Validation Loss: 2.6074 - Validation End Logits Accuracy: 0.4660 - Validation Start Logits Accuracy: 0.4660 - Epoch: 1 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 124, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 4.3704 | 0.1946 | 0.1885 | 2.9534 | 0.4660 | 0.4660 | 0 | | 3.0576 | 0.3438 | 0.3438 | 2.6074 | 0.4660 | 0.4660 | 1 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.15.0 - Tokenizers 0.15.0
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owanr/Sentiment-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T07:12:42Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T14:33:00Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: Sentiment-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05 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. --> # Sentiment-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0987 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.4525 | 1.0 | 5628 | 2.2193 | | 1.9608 | 2.0 | 11256 | 2.0987 | | 1.9252 | 3.0 | 16884 | 2.0987 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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JustinSEO/AutoGPTNew
JustinSEO
2023-12-15T14:34:43Z
0
0
null
[ "license:cc-by-4.0", "region:us" ]
null
2023-12-15T14:34:43Z
--- license: cc-by-4.0 ---
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tokyos/CNN-prueba
tokyos
2023-12-15T14:36:02Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-15T14:36:02Z
--- license: mit ---
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owanr/SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T06:16:54Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T14:36:21Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5250 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.3787 | 1.0 | 12516 | 2.3244 | | 2.4722 | 2.0 | 25032 | 2.5250 | | 2.6552 | 3.0 | 37548 | 2.5250 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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farshadafx/LEDA-GPT2-v1
farshadafx
2023-12-15T14:36:48Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:36:47Z
Entry not found
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ADISH007/Aws_donut_10k_incremental_1_Epoch_28
ADISH007
2023-12-15T14:38:05Z
0
0
transformers
[ "transformers", "safetensors", "vision-encoder-decoder", "endpoints_compatible", "region:us" ]
null
2023-12-15T14:37:46Z
Entry not found
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hkivancoral/smids_5x_deit_tiny_sgd_0001_fold3
hkivancoral
2023-12-15T15:33:57Z
0
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-15T14:39:44Z
--- license: apache-2.0 base_model: facebook/deit-tiny-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_sgd_0001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8283333333333334 --- <!-- 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. --> # smids_5x_deit_tiny_sgd_0001_fold3 This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4818 - Accuracy: 0.8283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1768 | 1.0 | 375 | 1.1673 | 0.3967 | | 1.0999 | 2.0 | 750 | 1.1000 | 0.4267 | | 1.0421 | 3.0 | 1125 | 1.0522 | 0.4717 | | 0.9929 | 4.0 | 1500 | 1.0115 | 0.5 | | 0.9542 | 5.0 | 1875 | 0.9748 | 0.5233 | | 0.8817 | 6.0 | 2250 | 0.9389 | 0.5417 | | 0.8788 | 7.0 | 2625 | 0.9041 | 0.57 | | 0.8581 | 8.0 | 3000 | 0.8691 | 0.59 | | 0.7544 | 9.0 | 3375 | 0.8356 | 0.6033 | | 0.7744 | 10.0 | 3750 | 0.8037 | 0.6317 | | 0.7141 | 11.0 | 4125 | 0.7743 | 0.66 | | 0.7231 | 12.0 | 4500 | 0.7462 | 0.6733 | | 0.7108 | 13.0 | 4875 | 0.7217 | 0.7033 | | 0.6884 | 14.0 | 5250 | 0.6985 | 0.7217 | | 0.6146 | 15.0 | 5625 | 0.6770 | 0.7333 | | 0.573 | 16.0 | 6000 | 0.6579 | 0.7467 | | 0.6638 | 17.0 | 6375 | 0.6415 | 0.76 | | 0.653 | 18.0 | 6750 | 0.6259 | 0.775 | | 0.5464 | 19.0 | 7125 | 0.6119 | 0.7833 | | 0.5679 | 20.0 | 7500 | 0.6000 | 0.79 | | 0.5005 | 21.0 | 7875 | 0.5883 | 0.795 | | 0.5248 | 22.0 | 8250 | 0.5790 | 0.8017 | | 0.5865 | 23.0 | 8625 | 0.5701 | 0.8017 | | 0.5913 | 24.0 | 9000 | 0.5617 | 0.8033 | | 0.542 | 25.0 | 9375 | 0.5539 | 0.8033 | | 0.5193 | 26.0 | 9750 | 0.5470 | 0.8033 | | 0.5655 | 27.0 | 10125 | 0.5403 | 0.805 | | 0.59 | 28.0 | 10500 | 0.5340 | 0.8117 | | 0.4649 | 29.0 | 10875 | 0.5287 | 0.815 | | 0.4871 | 30.0 | 11250 | 0.5240 | 0.8133 | | 0.5615 | 31.0 | 11625 | 0.5192 | 0.815 | | 0.5652 | 32.0 | 12000 | 0.5148 | 0.8167 | | 0.5595 | 33.0 | 12375 | 0.5110 | 0.8183 | | 0.4782 | 34.0 | 12750 | 0.5072 | 0.8167 | | 0.4 | 35.0 | 13125 | 0.5039 | 0.8183 | | 0.427 | 36.0 | 13500 | 0.5006 | 0.82 | | 0.4646 | 37.0 | 13875 | 0.4979 | 0.82 | | 0.4598 | 38.0 | 14250 | 0.4954 | 0.8217 | | 0.4848 | 39.0 | 14625 | 0.4932 | 0.8233 | | 0.4928 | 40.0 | 15000 | 0.4911 | 0.8267 | | 0.4256 | 41.0 | 15375 | 0.4892 | 0.8267 | | 0.4494 | 42.0 | 15750 | 0.4875 | 0.8267 | | 0.5087 | 43.0 | 16125 | 0.4862 | 0.8267 | | 0.4788 | 44.0 | 16500 | 0.4849 | 0.8267 | | 0.4663 | 45.0 | 16875 | 0.4839 | 0.8267 | | 0.4385 | 46.0 | 17250 | 0.4831 | 0.8267 | | 0.4878 | 47.0 | 17625 | 0.4825 | 0.8283 | | 0.4959 | 48.0 | 18000 | 0.4821 | 0.8283 | | 0.4851 | 49.0 | 18375 | 0.4819 | 0.8283 | | 0.4569 | 50.0 | 18750 | 0.4818 | 0.8283 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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idontgoddamn/NahidaRVC.zip
idontgoddamn
2023-12-15T14:42:05Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:41:31Z
Entry not found
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JapGuy/MichalMalatny_vALL
JapGuy
2023-12-15T14:59:27Z
0
0
null
[ "music", "rvc", "michal", "malatny", "chinaski", "model", "audio-to-audio", "cs", "license:openrail", "region:us" ]
audio-to-audio
2023-12-15T14:41:52Z
--- license: openrail language: - cs pipeline_tag: audio-to-audio tags: - music - rvc - michal - malatny - chinaski - model --- ![image.jpg](https://vltava.rozhlas.cz/sites/default/files/images/02991993.jpeg) # Michal Malátný ( Chinaski ) [CZ] (vALL) # 882 Epochs - RVC V2 - rmvpe Trained on 57 minutes 29 seconds of isolated acapellas using UVR (Voc FT + Reverb HQ) and Audacity to remove parts with double vocals and vocals from others (+Noise Gate)
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alexx122/idk
alexx122
2023-12-15T14:42:36Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:42:36Z
Entry not found
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riho1710/CBQAModel-2s5p-wasabi-2
riho1710
2023-12-17T14:21:30Z
0
0
transformers
[ "transformers", "pytorch", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-12-15T14:42:46Z
Entry not found
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aorams99/nyeh
aorams99
2023-12-15T15:05:00Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:43:49Z
Entry not found
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owanr/ghc-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T07:12:50Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T14:46:27Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: ghc-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # ghc-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7394 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.343 | 1.0 | 11020 | 3.9205 | | 3.5405 | 2.0 | 22040 | 3.7394 | | 3.4352 | 3.0 | 33060 | 3.7394 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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jlbaker361/sd-wikiart
jlbaker361
2023-12-15T14:47:01Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:47:01Z
Entry not found
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jlbaker361/sd-wikiart-lora
jlbaker361
2023-12-15T14:47:16Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:47:16Z
Entry not found
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tscstudios/20W0X7uBLySfQWJAJbAcWs4PffJ2
tscstudios
2023-12-15T14:49:53Z
0
0
null
[ "region:us" ]
null
2023-12-15T14:48:51Z
Entry not found
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marcweibel/Reinforce-Pixelcopter
marcweibel
2023-12-15T17:50:13Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-15T14:49:40Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 34.00 +/- 26.08 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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LETHEmusic/YUNGBLUD
LETHEmusic
2023-12-15T14:52:32Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-15T14:51:28Z
--- license: openrail ---
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owanr/Sentiment-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T07:33:22Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T14:53:00Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: Sentiment-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # Sentiment-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7208 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.7243 | 1.0 | 5628 | 4.7497 | | 4.5455 | 2.0 | 11256 | 4.7208 | | 4.4727 | 3.0 | 16884 | 4.7208 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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s-gladkykh/sky-diffusion-128-lr2-5
s-gladkykh
2023-12-15T16:25:28Z
0
0
null
[ "tensorboard", "region:us" ]
null
2023-12-15T14:54:03Z
Entry not found
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zgce/SUS-Chat-34B-roleplay-lora
zgce
2023-12-15T15:07:39Z
0
0
null
[ "safetensors", "license:mit", "region:us" ]
null
2023-12-15T14:58:02Z
--- license: mit --- Although it was aligned, the role play effect was very poor, so I tried to use the roleplay classification in the spicy data set, and also used Google Translate to Chinese to make two copies of the Chinese and English data. This LoRA is based on [unalign](https://huggingface.co/zgce/SUS-Chat-34B-unalign-lora).
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Lin25/wav2vec2-large-mms-1b-england-1215-adapter-test
Lin25
2023-12-15T16:54:20Z
0
0
transformers
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-12-15T14:58:03Z
Entry not found
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beamaia/openhermes-mistral-dpo-gptq
beamaia
2023-12-15T15:02:15Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:02:15Z
Entry not found
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Minmin61/sungmin
Minmin61
2023-12-15T15:15:28Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-15T15:05:11Z
--- license: openrail ---
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Waflon/sii_model
Waflon
2023-12-16T07:54:46Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_es", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-12-15T15:11:10Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad_es model-index: - name: sii_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sii_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_es dataset. It achieves the following results on the evaluation set: - Loss: 2.2384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6308 | 1.0 | 4380 | 2.4981 | | 2.226 | 2.0 | 8760 | 2.2684 | | 2.0631 | 3.0 | 13140 | 2.2384 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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owanr/Sentiment-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T07:53:18Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T15:13:39Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: Sentiment-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # Sentiment-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0929 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3093 | 1.0 | 5628 | 3.0476 | | 3.0432 | 2.0 | 11256 | 3.0929 | | 2.9238 | 3.0 | 16884 | 3.0929 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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asherisaac/testmodel2
asherisaac
2023-12-15T15:15:54Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:prajjwal1/bert-tiny", "region:us" ]
null
2023-12-15T15:15:53Z
--- library_name: peft base_model: prajjwal1/bert-tiny --- # 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. --> - **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] ### Framework versions - PEFT 0.7.1
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kaerusan/Text_block_classification
kaerusan
2023-12-15T15:15:56Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-15T15:15:56Z
--- license: apache-2.0 ---
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HemaPro/HemaPro
HemaPro
2023-12-15T15:17:00Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:16:02Z
HemaPro: Hema Pro Turkey, Krem, jel, Balsam, İncelemeler, Fiyat, İçindekiler, İşler, eczane! HemaPro, basur veya hemoroit ile ilişkili ağrı ve rahatsızlıktan kurtulma sağlayan bir hemoroid sağlık ürünüdür. Hema Pro Turkey <a href="https://www.excaliburnutrition.com/HemaPTur">HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/HemaPTur">www.HemaPro.com</a> <p><a href="https://www.excaliburnutrition.com/HemaPTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBdrzblDaZvDL5MJhtCq2je-iCfWmXRWMjN5HL8r-yHzjeqg062Yt72-9rzTyd9uel11GWGU-22NtBtNFSH59NTrus3SxLO2f57ylPNd9bsti4pux0uMJnmFn2XBHMLyn7XKsjSkNpPvz1LpCkUv4zzID ØÜrün Adı — HemaPro ØKategori - hemoroid Ø Bileşimi — Doğal Organik Bileşik ➢ Yan Etkiler—YOK ØNihai Derecelendirme: — 4,8 ➢ Kullanılabilirlik — Çevrimiçi ➢Teklifler ve İndirimler; BUGÜN KAYDET! ÖZEL TEKLİF satın almak için ŞİMDİ ALIŞVERİŞ YAPIN!!! HemaPro nedir? Hema Pro Turkey, basur veya hemoroid semptomlarını hafifletmeye yardımcı olduğu bilinen bitkisel bileşenlerin bir karışımıyla yapılan doğal bir formüldür. Ürün içten dışa doğru çalışacak, iltihabı azaltmaya, ağrıyı hafifletmeye ve sağlıklı kan dolaşımını teşvik etmeye yardımcı olacak şekilde formüle edilmiştir. HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !! <a href="https://www.excaliburnutrition.com/HemaPTur">HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/HemaPTur">www.HemaPro.com</a> <p><a href="https://www.excaliburnutrition.com/HemaPTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBdrzblDaZvDL5MJhtCq2je-iCfWmXRWMjN5HL8r-yHzjeqg062Yt72-9rzTyd9uel11GWGU-22NtBtNFSH59NTrus3SxLO2f57ylPNd9bsti4pux0uMJnmFn2XBHMLyn7XKsjSkNpPvz1LpCkUv4zzID HemaPro HemaPro Krem HemaPro jel HemaPro Balsam HemaPro Fiyat HemaPro İncelemeler HemaPro İçindekiler HemaPro Faydalar HemaPro Yan etkiler HemaPro Krem fiyatı HemaPro Krem yorumları HemaPro Kompozisyon HemaPro Şikayet HemaPro Nereden Alınır HemaPro Nasıl kullanılır HemaPro maliyet HemaPro İşler HemaPro forum HemaPro orijinal HemaPro eczane HemaPro nasıl çalışır? HemaPro basur veya basurun temel nedenini hedef alarak çalışır. Kapsüllerdeki doğal bileşenlerin karışımı, kan dolaşımını iyileştirmek, iltihabı azaltmak ve anüsteki damarların duvarlarını güçlendirmek için birlikte çalışır. Bu, şişliği ve rahatsızlığı azaltmaya yardımcı olur ve hatta yeni hemoroid oluşumunun önlenmesine bile yardımcı olabilir. HemaPro'ün kullanımı güvenli midir? Her üründe olduğu gibi, özellikle altta yatan sağlık sorunlarınız varsa veya reçeteli ilaçlar kullanıyorsanız, HemaPro'ü almadan önce sağlık uzmanınızla konuşmanız önemlidir. Ayrıca bitkisel takviyeler FDA tarafından düzenlenmediğinden bunların güvenliği ve etkinliği her zaman garanti edilmez. <a href="https://www.excaliburnutrition.com/HemaPTur">HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/HemaPTur">www.HemaPro.com</a> <p><a href="https://www.excaliburnutrition.com/HemaPTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBdrzblDaZvDL5MJhtCq2je-iCfWmXRWMjN5HL8r-yHzjeqg062Yt72-9rzTyd9uel11GWGU-22NtBtNFSH59NTrus3SxLO2f57ylPNd9bsti4pux0uMJnmFn2XBHMLyn7XKsjSkNpPvz1LpCkUv4zzID HemaPro nereden satın alınır? HemaPro'ü satın almakla ilgileniyorsanız, araştırmanızı yapmanız ve saygın ve güvenilir bir satıcı bulmanız önemlidir. Çevrimiçi perakendecileri arayın Şüphe uyandıracak derecede düşük fiyatlara veya ürünün etkinliğine ilişkin abartılı iddialara karşı dikkatli olun. Her zaman satıcının bu ürünü satma yetkisine sahip olduğundan emin olun. HemaPro HemaPro Krem HemaPro jel HemaPro Balsam HemaPro Fiyat HemaPro İncelemeler HemaPro İçindekiler HemaPro Faydalar HemaPro Yan etkiler HemaPro Krem fiyatı HemaPro Krem yorumları HemaPro Kompozisyon HemaPro Şikayet HemaPro Nereden Alınır HemaPro Nasıl kullanılır HemaPro maliyet HemaPro İşler HemaPro forum HemaPro orijinal HemaPro eczane https://www.excaliburnutrition.com/HemaPTur https://sites.google.com/view/hemapro-krem/home https://healthtoned.blogspot.com/2023/12/hemapro-turkey.html https://medium.com/@hemaprofiyat/hemapro-turkey-badef4e9cb9a https://medium.com/@hemaprofiyat/hemapro-hema-pro-turkey-krem-jel-balsam-i%CC%87ncelemeler-fiyat-i%CC%87%C3%A7indekiler-i%CC%87%C5%9Fler-eczane-766025869d79 https://www.weddingwire.com/website/hemapro-and-turkey-2026-10-13 https://infogram.com/hemapro-turkey-1h0n25y5p1g8z6p?live https://softisenilspain.hashnode.dev/hemapro-turkey https://sway.office.com/wg7XkZPqv58B92Fk https://hemaprofiyat.company.site/ https://gamma.app/docs/HemaPro-Turkey-tf8ourgb3z8k63p?mode=doc https://healthytalk24x7.wixsite.com/sunshine/post/hemapro-hema-pro-turkey-krem-jel-balsam-i%CC%87ncelemeler-fiyat-i%CC%87%C3%A7indekiler-i%CC%87%C5%9Fler-eczane https://groups.google.com/g/snshine/c/-fenIpujJkU https://www.sunflower-cissp.com/glossary/cissp/7314/hemapro-turkey https://replit.com/@hemaprofiyat
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owanr/SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T06:43:07Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T15:19:36Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3168 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2405 | 1.0 | 12516 | 2.3180 | | 2.3278 | 2.0 | 25032 | 2.3168 | | 2.0887 | 3.0 | 37548 | 2.3168 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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aisingsV2/Jizuu_mangio_550Epochs
aisingsV2
2023-12-15T15:22:25Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-15T15:20:40Z
--- license: openrail ---
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owanr/ghc-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T07:51:40Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T15:20:41Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: ghc-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05 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. --> # ghc-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4268 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.6784 | 1.0 | 11020 | 0.4245 | | 0.3594 | 2.0 | 22040 | 0.4268 | | 0.4199 | 3.0 | 33060 | 0.4268 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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PowerInfer/ReluFalcon-40B-PowerInfer-GGUF
PowerInfer
2023-12-16T09:04:23Z
0
0
null
[ "gguf", "en", "license:apache-2.0", "region:us" ]
null
2023-12-15T15:22:06Z
--- license: apache-2.0 language: - en --- # ReluFalcon-40B-PowerInfer-GGUF - Original model: [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B) - Converted & distributed by: [PowerInfer](https://huggingface.co/PowerInfer) This model is the downstream distribution of [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B) in PowerInfer GGUF format consisting of the LLM model weights and predictor weights.
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Shaleen123/mistrallite_medical_full
Shaleen123
2023-12-15T15:23:10Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:01-ai/Yi-6B-200K", "region:us" ]
null
2023-12-15T15:23:08Z
--- library_name: peft base_model: 01-ai/Yi-6B-200K --- # 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. --> - **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] ### Framework versions - PEFT 0.7.1
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ppsingh/setfit_1_sector_mlbl_dense
ppsingh
2023-12-15T15:24:32Z
0
0
setfit
[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-mpnet-base-v2", "model-index", "region:us" ]
text-classification
2023-12-15T15:24:17Z
--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy - f1-micro - f1-samples - f1-weighted widget: - text: Implementation of the CCAP will encompass activities geared towards building the resilience of households, communities, vulnerable groups, enterprises, sectors and ultimately the nation. Implementation measures are being identified at the national and community levels, with regional and international support and backstopping provided through agreed modalities. - text: Short term. Strengthen the National Biodiversity Strategy and Action Plan. South Sudan will declare commitment to the National Biodiversity Strategy and Action Plan (2018–2027) by showcasing its current implementation status and a roadmap for future implementation. Build more comprehensive and strengthened action plans for conserving the biodiversity of South Sudan. The National Biodiversity Strategy and Action Plan has been developed for South Sudan. Short term. - text: Integrate the climate change and disaster risk reduction into the National Development Plan by 2015. Revise capital budget appraisal guidelines to incorporate comprehensive hazard and risk management (CHARM) and vulnerability and adaptation (VA) assessments by 2015. There is a need to increase the resourcing of adaptation and mitigation measures. Short Term (up to 2 years). Explore climate change financing modalities by 2015. Medium Term (3 to 5 years). - text: Implement a policy framework to promote more efficient vehicles and alternative fuels/blends through incorporation of fuel economy labels; emissions testing; fuel economy standards, limitations and emissions- based taxes/feebates for imported vehicles by 2025. Action. - text: Work with stakeholders in the tourism sector to develop a strategic plan, which incorporates Climate Change considerations and appropriate measures such as water conservation programmes, as well as, general sustainability concerns. Health and wellbeing. Inform, sensitize and educate health personnel and the public-at-large about Climate Change related health matters including but not limited to heat stress, vector borne diseases and impacts on the vulnerable sub sector of society. pipeline_tag: text-classification inference: false base_model: sentence-transformers/all-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.464 name: Accuracy - type: f1-micro value: 0.5454545454545454 name: F1-Micro - type: f1-samples value: 0.5722666666666667 name: F1-Samples - type: f1-weighted value: 0.5359564860415268 name: F1-Weighted --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 384 tokens - **Number of Classes:** 15 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Evaluation ### Metrics | Label | Accuracy | F1-Micro | F1-Samples | F1-Weighted | |:--------|:---------|:---------|:-----------|:------------| | **all** | 0.464 | 0.5455 | 0.5723 | 0.5360 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("ppsingh/setfit_1_sector_mlbl_dense") # Run inference preds = model("Implement a policy framework to promote more efficient vehicles and alternative fuels/blends through incorporation of fuel economy labels; emissions testing; fuel economy standards, limitations and emissions- based taxes/feebates for imported vehicles by 2025. Action.") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 19 | 61.4937 | 258 | ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (1, 8) - max_steps: -1 - sampling_strategy: undersampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0010 | 1 | 0.2408 | - | | 0.1001 | 100 | 0.237 | 0.1678 | | 0.2002 | 200 | 0.1691 | 0.1742 | | 0.0010 | 1 | 0.0409 | - | | 0.1001 | 100 | 0.1416 | 0.1855 | | 0.2002 | 200 | 0.055 | 0.1805 | | 0.3003 | 300 | 0.096 | 0.1655 | | 0.4004 | 400 | 0.1486 | 0.1647 | | 0.5005 | 500 | 0.1175 | 0.1633 | | 0.6006 | 600 | 0.0556 | 0.1652 | | 0.7007 | 700 | 0.1104 | 0.1711 | | 0.8008 | 800 | 0.0194 | 0.1671 | | 0.9009 | 900 | 0.0173 | 0.1654 | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.1 - Sentence Transformers: 2.2.2 - Transformers: 4.35.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.15.0 - Tokenizers: 0.15.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
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ajithks2024/Llama-2-7b-chat-finetune
ajithks2024
2023-12-15T15:34:49Z
0
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-15T15:24:32Z
Entry not found
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Mischka123/GeheimesModell2
Mischka123
2023-12-15T15:34:19Z
0
0
sklearn
[ "sklearn", "joblib", "text-classification", "de", "arxiv:1910.09700", "license:cc0-1.0", "region:us" ]
text-classification
2023-12-15T15:24:45Z
--- license: cc0-1.0 language: - de pipeline_tag: text-classification library_name: sklearn --- # 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. 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 <!-- Provide a longer summary of what this model is. --> - **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]
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Jeevesh8/bt__qqp_ft_only_data_shuff_12
Jeevesh8
2023-12-15T17:59:57Z
0
0
transformers
[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T15:24:58Z
Entry not found
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PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF
PowerInfer
2023-12-16T09:02:51Z
0
0
null
[ "gguf", "en", "license:llama2", "region:us" ]
null
2023-12-15T15:25:24Z
--- license: llama2 language: - en --- # ReluLLaMA-13B-PowerInfer-GGUF - Original model: [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B) - Converted & distributed by: [PowerInfer](https://huggingface.co/PowerInfer) This model is the downstream distribution of [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B) in PowerInfer GGUF format consisting of the LLM model weights and predictor weights.
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hkivancoral/smids_5x_beit_base_sgd_001_fold3
hkivancoral
2023-12-15T16:45:57Z
0
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-15T15:26:36Z
--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_beit_base_sgd_001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.91 --- <!-- 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. --> # smids_5x_beit_base_sgd_001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2734 - Accuracy: 0.91 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6727 | 1.0 | 375 | 0.6864 | 0.7317 | | 0.6041 | 2.0 | 750 | 0.5073 | 0.8033 | | 0.4189 | 3.0 | 1125 | 0.4416 | 0.8267 | | 0.3943 | 4.0 | 1500 | 0.3997 | 0.8433 | | 0.4289 | 5.0 | 1875 | 0.3785 | 0.855 | | 0.3775 | 6.0 | 2250 | 0.3585 | 0.8617 | | 0.3696 | 7.0 | 2625 | 0.3398 | 0.8767 | | 0.3454 | 8.0 | 3000 | 0.3318 | 0.8767 | | 0.3235 | 9.0 | 3375 | 0.3277 | 0.8717 | | 0.3129 | 10.0 | 3750 | 0.3193 | 0.8733 | | 0.2807 | 11.0 | 4125 | 0.3194 | 0.8833 | | 0.2877 | 12.0 | 4500 | 0.3054 | 0.8867 | | 0.2952 | 13.0 | 4875 | 0.3044 | 0.89 | | 0.2764 | 14.0 | 5250 | 0.3000 | 0.89 | | 0.2449 | 15.0 | 5625 | 0.2977 | 0.8933 | | 0.2466 | 16.0 | 6000 | 0.2964 | 0.8983 | | 0.2903 | 17.0 | 6375 | 0.2961 | 0.8883 | | 0.2759 | 18.0 | 6750 | 0.2922 | 0.895 | | 0.2465 | 19.0 | 7125 | 0.2899 | 0.9 | | 0.2405 | 20.0 | 7500 | 0.2859 | 0.8967 | | 0.1951 | 21.0 | 7875 | 0.2887 | 0.9 | | 0.2066 | 22.0 | 8250 | 0.2843 | 0.9017 | | 0.2962 | 23.0 | 8625 | 0.2838 | 0.9033 | | 0.2403 | 24.0 | 9000 | 0.2814 | 0.9033 | | 0.2279 | 25.0 | 9375 | 0.2878 | 0.9033 | | 0.2748 | 26.0 | 9750 | 0.2833 | 0.9017 | | 0.2265 | 27.0 | 10125 | 0.2865 | 0.905 | | 0.2583 | 28.0 | 10500 | 0.2821 | 0.9083 | | 0.1555 | 29.0 | 10875 | 0.2828 | 0.905 | | 0.2329 | 30.0 | 11250 | 0.2785 | 0.9117 | | 0.235 | 31.0 | 11625 | 0.2766 | 0.9067 | | 0.245 | 32.0 | 12000 | 0.2760 | 0.91 | | 0.2626 | 33.0 | 12375 | 0.2763 | 0.9067 | | 0.2391 | 34.0 | 12750 | 0.2794 | 0.905 | | 0.1618 | 35.0 | 13125 | 0.2760 | 0.9117 | | 0.1815 | 36.0 | 13500 | 0.2756 | 0.9067 | | 0.1967 | 37.0 | 13875 | 0.2796 | 0.9033 | | 0.1639 | 38.0 | 14250 | 0.2740 | 0.91 | | 0.2156 | 39.0 | 14625 | 0.2769 | 0.9083 | | 0.1866 | 40.0 | 15000 | 0.2767 | 0.915 | | 0.1684 | 41.0 | 15375 | 0.2758 | 0.9083 | | 0.194 | 42.0 | 15750 | 0.2754 | 0.9067 | | 0.2588 | 43.0 | 16125 | 0.2756 | 0.9083 | | 0.213 | 44.0 | 16500 | 0.2746 | 0.91 | | 0.1893 | 45.0 | 16875 | 0.2745 | 0.9083 | | 0.2111 | 46.0 | 17250 | 0.2742 | 0.9083 | | 0.2091 | 47.0 | 17625 | 0.2740 | 0.91 | | 0.2023 | 48.0 | 18000 | 0.2739 | 0.9083 | | 0.1852 | 49.0 | 18375 | 0.2735 | 0.91 | | 0.197 | 50.0 | 18750 | 0.2734 | 0.91 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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hkivancoral/smids_5x_beit_base_sgd_0001_fold3
hkivancoral
2023-12-15T16:45:53Z
0
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-15T15:26:49Z
--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_beit_base_sgd_0001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8283333333333334 --- <!-- 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. --> # smids_5x_beit_base_sgd_0001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4604 - Accuracy: 0.8283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1529 | 1.0 | 375 | 1.2380 | 0.355 | | 1.0823 | 2.0 | 750 | 1.1537 | 0.38 | | 0.966 | 3.0 | 1125 | 1.0702 | 0.43 | | 0.8969 | 4.0 | 1500 | 0.9930 | 0.4833 | | 0.8438 | 5.0 | 1875 | 0.9234 | 0.5467 | | 0.8108 | 6.0 | 2250 | 0.8618 | 0.6067 | | 0.781 | 7.0 | 2625 | 0.8085 | 0.6533 | | 0.7359 | 8.0 | 3000 | 0.7640 | 0.695 | | 0.6561 | 9.0 | 3375 | 0.7269 | 0.7067 | | 0.687 | 10.0 | 3750 | 0.6956 | 0.72 | | 0.6327 | 11.0 | 4125 | 0.6692 | 0.7333 | | 0.6325 | 12.0 | 4500 | 0.6472 | 0.7433 | | 0.6338 | 13.0 | 4875 | 0.6288 | 0.7567 | | 0.6265 | 14.0 | 5250 | 0.6103 | 0.76 | | 0.5767 | 15.0 | 5625 | 0.5960 | 0.7667 | | 0.5529 | 16.0 | 6000 | 0.5830 | 0.7717 | | 0.6057 | 17.0 | 6375 | 0.5711 | 0.7817 | | 0.572 | 18.0 | 6750 | 0.5610 | 0.7817 | | 0.5708 | 19.0 | 7125 | 0.5513 | 0.7833 | | 0.5256 | 20.0 | 7500 | 0.5427 | 0.7867 | | 0.4697 | 21.0 | 7875 | 0.5351 | 0.795 | | 0.5346 | 22.0 | 8250 | 0.5286 | 0.7983 | | 0.5543 | 23.0 | 8625 | 0.5218 | 0.8017 | | 0.5387 | 24.0 | 9000 | 0.5157 | 0.8033 | | 0.5031 | 25.0 | 9375 | 0.5106 | 0.8017 | | 0.5567 | 26.0 | 9750 | 0.5058 | 0.805 | | 0.5071 | 27.0 | 10125 | 0.5014 | 0.805 | | 0.5467 | 28.0 | 10500 | 0.4973 | 0.8083 | | 0.4516 | 29.0 | 10875 | 0.4933 | 0.8083 | | 0.497 | 30.0 | 11250 | 0.4901 | 0.8133 | | 0.5249 | 31.0 | 11625 | 0.4866 | 0.8133 | | 0.5174 | 32.0 | 12000 | 0.4834 | 0.8167 | | 0.5605 | 33.0 | 12375 | 0.4805 | 0.8183 | | 0.5131 | 34.0 | 12750 | 0.4779 | 0.82 | | 0.4459 | 35.0 | 13125 | 0.4756 | 0.8217 | | 0.4556 | 36.0 | 13500 | 0.4735 | 0.8217 | | 0.4756 | 37.0 | 13875 | 0.4716 | 0.8233 | | 0.4606 | 38.0 | 14250 | 0.4696 | 0.8233 | | 0.5275 | 39.0 | 14625 | 0.4682 | 0.825 | | 0.4638 | 40.0 | 15000 | 0.4668 | 0.8233 | | 0.4141 | 41.0 | 15375 | 0.4657 | 0.8233 | | 0.4983 | 42.0 | 15750 | 0.4646 | 0.8283 | | 0.5146 | 43.0 | 16125 | 0.4634 | 0.8267 | | 0.4784 | 44.0 | 16500 | 0.4625 | 0.8267 | | 0.4516 | 45.0 | 16875 | 0.4618 | 0.8283 | | 0.496 | 46.0 | 17250 | 0.4612 | 0.8283 | | 0.5144 | 47.0 | 17625 | 0.4609 | 0.8283 | | 0.501 | 48.0 | 18000 | 0.4606 | 0.8283 | | 0.4693 | 49.0 | 18375 | 0.4604 | 0.8283 | | 0.4848 | 50.0 | 18750 | 0.4604 | 0.8283 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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Casual111/OliverNorthVM
Casual111
2023-12-15T15:30:36Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-15T15:27:53Z
--- license: openrail ---
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ewof/koishi-8x7b-qlora-gptq
ewof
2023-12-15T16:09:56Z
0
0
transformers
[ "transformers", "mixtral", "text-generation", "dataset:ewof/koishi-instruct-metharme", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-15T15:29:12Z
--- datasets: - ewof/koishi-instruct-metharme --- ## GPTQ 2048 sequence length VMware/open-instruct dataset ## Training [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training on a 4x nvidia a100 gpu cluster. the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree). trained on koishi commit 6e675d1 for one epoch ## Base Model rank 16 qlora tune of mistralai/Mixtral-8x7B-v0.1 (all modules, merged) ## Prompting The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
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LMario/narradores
LMario
2023-12-15T15:30:01Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:29:33Z
Entry not found
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robinsyihab/Indonesian-wa-chat-lora-v0.1
robinsyihab
2023-12-17T12:20:25Z
0
1
null
[ "safetensors", "license:apache-2.0", "region:us" ]
null
2023-12-15T15:30:09Z
--- license: apache-2.0 --- The Lora Adapter designed for Natural Language Understanding (NLU) in Indonesian chat-style language. It's trained with Indonesian WhatsApp dialogues and uses the [Sidrap-7B-v2](https://huggingface.co/robinsyihab/Sidrap-7B-v2) as the foundational model.
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IKHOR/LCM_models
IKHOR
2023-12-15T15:36:01Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:30:42Z
Entry not found
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NikitaO/15122023_headshot_V1
NikitaO
2023-12-15T15:36:50Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:31:04Z
Entry not found
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thegrowingdude/falcon-7b-instruct-ft-adapters
thegrowingdude
2023-12-15T15:31:31Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "region:us" ]
null
2023-12-15T15:31:29Z
--- library_name: peft base_model: vilsonrodrigues/falcon-7b-instruct-sharded --- # 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. --> - **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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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] ### Framework versions - PEFT 0.7.2.dev0
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hkivancoral/smids_5x_deit_tiny_sgd_0001_fold4
hkivancoral
2023-12-15T16:29:31Z
0
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-15T15:34:40Z
--- license: apache-2.0 base_model: facebook/deit-tiny-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_sgd_0001_fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8 --- <!-- 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. --> # smids_5x_deit_tiny_sgd_0001_fold4 This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4936 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1564 | 1.0 | 375 | 1.1576 | 0.3767 | | 1.0853 | 2.0 | 750 | 1.0913 | 0.4 | | 0.9971 | 3.0 | 1125 | 1.0409 | 0.44 | | 0.9972 | 4.0 | 1500 | 0.9979 | 0.4683 | | 0.9211 | 5.0 | 1875 | 0.9595 | 0.4967 | | 0.885 | 6.0 | 2250 | 0.9224 | 0.535 | | 0.8576 | 7.0 | 2625 | 0.8878 | 0.5583 | | 0.8551 | 8.0 | 3000 | 0.8542 | 0.5783 | | 0.8253 | 9.0 | 3375 | 0.8221 | 0.6017 | | 0.8198 | 10.0 | 3750 | 0.7908 | 0.6283 | | 0.6752 | 11.0 | 4125 | 0.7615 | 0.645 | | 0.6508 | 12.0 | 4500 | 0.7343 | 0.6767 | | 0.6556 | 13.0 | 4875 | 0.7097 | 0.69 | | 0.7132 | 14.0 | 5250 | 0.6866 | 0.7083 | | 0.6057 | 15.0 | 5625 | 0.6661 | 0.7183 | | 0.5722 | 16.0 | 6000 | 0.6478 | 0.7283 | | 0.5982 | 17.0 | 6375 | 0.6328 | 0.7333 | | 0.5686 | 18.0 | 6750 | 0.6177 | 0.735 | | 0.5939 | 19.0 | 7125 | 0.6046 | 0.7417 | | 0.5225 | 20.0 | 7500 | 0.5938 | 0.7483 | | 0.5314 | 21.0 | 7875 | 0.5829 | 0.7567 | | 0.5367 | 22.0 | 8250 | 0.5746 | 0.765 | | 0.506 | 23.0 | 8625 | 0.5665 | 0.77 | | 0.5218 | 24.0 | 9000 | 0.5589 | 0.7717 | | 0.5608 | 25.0 | 9375 | 0.5520 | 0.7767 | | 0.5255 | 26.0 | 9750 | 0.5459 | 0.78 | | 0.5248 | 27.0 | 10125 | 0.5406 | 0.78 | | 0.496 | 28.0 | 10500 | 0.5353 | 0.78 | | 0.4514 | 29.0 | 10875 | 0.5308 | 0.785 | | 0.4878 | 30.0 | 11250 | 0.5266 | 0.785 | | 0.4791 | 31.0 | 11625 | 0.5226 | 0.785 | | 0.4601 | 32.0 | 12000 | 0.5192 | 0.785 | | 0.527 | 33.0 | 12375 | 0.5161 | 0.7867 | | 0.4682 | 34.0 | 12750 | 0.5130 | 0.785 | | 0.4268 | 35.0 | 13125 | 0.5104 | 0.7917 | | 0.4602 | 36.0 | 13500 | 0.5080 | 0.795 | | 0.4456 | 37.0 | 13875 | 0.5057 | 0.7983 | | 0.4657 | 38.0 | 14250 | 0.5038 | 0.7983 | | 0.5191 | 39.0 | 14625 | 0.5021 | 0.7983 | | 0.5029 | 40.0 | 15000 | 0.5005 | 0.8 | | 0.4811 | 41.0 | 15375 | 0.4991 | 0.8 | | 0.4466 | 42.0 | 15750 | 0.4979 | 0.8 | | 0.4615 | 43.0 | 16125 | 0.4969 | 0.8017 | | 0.4147 | 44.0 | 16500 | 0.4960 | 0.8 | | 0.4484 | 45.0 | 16875 | 0.4953 | 0.8 | | 0.4471 | 46.0 | 17250 | 0.4947 | 0.8 | | 0.4839 | 47.0 | 17625 | 0.4942 | 0.8 | | 0.4773 | 48.0 | 18000 | 0.4939 | 0.8 | | 0.4334 | 49.0 | 18375 | 0.4937 | 0.8 | | 0.4329 | 50.0 | 18750 | 0.4936 | 0.8 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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albertoparrasmoreno/check-api-service
albertoparrasmoreno
2023-12-15T15:34:52Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:34:52Z
Entry not found
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vdo/AnimateDiff
vdo
2023-12-15T15:42:15Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:40:42Z
Entry not found
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imagepipeline/LoRA-LCM-SDXL
imagepipeline
2023-12-15T15:43:06Z
0
0
null
[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-12-15T15:41:52Z
--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## LoRA-LCM-SDXL <img src="https://f005.backblazeb2.com/b2api/v2/b2_download_file_by_id?fileId=4_zfdf0a8ed59e8666b89b10713_f102e27ef30cbc5ad_d20231202_m035349_c005_v0501009_t0041_u01701489229022" alt="Generated by Image Pipeline" style="border-radius: 10px;"> **This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)** Model details - For use with LCMScheduler [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/LoRA-LCM-SDXL?id=90525551-24b5-43fe-9dfc-4c6d9cffe157/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sdxl/text2image/v1/run" payload = json.dumps({ "model_id": "sdxl", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "90525551-24b5-43fe-9dfc-4c6d9cffe157", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sdxl/text2image/v1 ``` | Headers | Type | Description | |:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------| | `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) | | `Content-Type` | `str` | application/json - content type of the request body | | Parameter | Type | Description | | :-------- | :------- | :------------------------- | | `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own| | `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips | | `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) | | `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 | | `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page | | `lora_weights` | `str, array` | Strength of the LoRA effect | --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
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NothingSeen/firstLLM
NothingSeen
2023-12-15T15:41:58Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:41:58Z
Entry not found
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ac0hik/my_awesome_model
ac0hik
2023-12-15T16:04:32Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T15:42:11Z
Entry not found
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owanr/ghc-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
owanr
2023-12-16T08:15:33Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-15T15:44:11Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: ghc-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05 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. --> # ghc-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4190 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0572 | 1.0 | 11020 | 2.1141 | | 2.283 | 2.0 | 22040 | 2.4190 | | 2.3257 | 3.0 | 33060 | 2.4190 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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Over9012/IEEE-Chatbot
Over9012
2023-12-15T15:46:20Z
0
0
null
[ "safetensors", "autotrain", "text-generation", "license:other", "region:us" ]
text-generation
2023-12-15T15:44:40Z
--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
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ahlamali/hello
ahlamali
2023-12-15T15:45:35Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:45:35Z
Entry not found
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jackspparrow/freya
jackspparrow
2023-12-15T15:48:57Z
0
0
allennlp
[ "allennlp", "en", "dataset:HuggingFaceH4/no_robots", "dataset:Open-Orca/SlimOrca", "license:apache-2.0", "region:us" ]
null
2023-12-15T15:46:34Z
--- license: apache-2.0 datasets: - HuggingFaceH4/no_robots - Open-Orca/SlimOrca language: - en metrics: - character library_name: allennlp ---
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BDubbs/jjj
BDubbs
2023-12-15T15:48:46Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:48:46Z
Entry not found
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darkc0de/Zordon
darkc0de
2023-12-15T15:49:15Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:49:14Z
Entry not found
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thorirhrafn/llama2_lora_test
thorirhrafn
2023-12-15T16:08:05Z
0
0
null
[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]
null
2023-12-15T15:50:15Z
--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: llama2_lora_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama2_lora_test This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7435 | 0.02 | 500 | 2.6775 | | 2.6637 | 0.04 | 1000 | 2.6209 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.1
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VishalMysore/cookgptsmall
VishalMysore
2023-12-15T15:51:00Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-15T15:51:00Z
--- license: apache-2.0 ---
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vdo/i2vgen-xl
vdo
2023-12-15T15:56:04Z
0
1
null
[ "arxiv:2312.09109", "arxiv:2308.06571", "arxiv:2311.04145", "license:mit", "region:us" ]
null
2023-12-15T15:51:28Z
--- license: mit --- # VGen ![figure1](source/VGen.jpg "figure1") VGen is an open-source video synthesis codebase developed by the Tongyi Lab of Alibaba Group, featuring state-of-the-art video generative models. This repository includes implementations of the following methods: - [I2VGen-xl: High-quality image-to-video synthesis via cascaded diffusion models](https://i2vgen-xl.github.io/) - [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://videocomposer.github.io/) - [Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation](https://higen-t2v.github.io/) - [A Recipe for Scaling up Text-to-Video Generation with Text-free Videos]() - [InstructVideo: Instructing Video Diffusion Models with Human Feedback]() - [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://dreamvideo-t2v.github.io/) - [VideoLCM: Video Latent Consistency Model](https://arxiv.org/abs/2312.09109) - [Modelscope text-to-video technical report](https://arxiv.org/abs/2308.06571) VGen can produce high-quality videos from the input text, images, desired motion, desired subjects, and even the feedback signals provided. It also offers a variety of commonly used video generation tools such as visualization, sampling, training, inference, join training using images and videos, acceleration, and more. <a href='https://i2vgen-xl.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2311.04145'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/XUi0y7dxqEQ) <a href='https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441039979087.mp4'><img src='source/logo.png'></a> ## 🔥News!!! - __[2023.12]__ We release the high-efficiency video generation method [VideoLCM](https://arxiv.org/abs/2312.09109) - __[2023.12]__ We release the code and model of I2VGen-XL and the ModelScope T2V - __[2023.12]__ We release the T2V method [HiGen](https://higen-t2v.github.io) and customizing T2V method [DreamVideo](https://dreamvideo-t2v.github.io). - __[2023.12]__ We write an [introduction docment](doc/introduction.pdf) for VGen and compare I2VGen-XL with SVD. - __[2023.11]__ We release a high-quality I2VGen-XL model, please refer to the [Webpage](https://i2vgen-xl.github.io) ## TODO - [x] Release the technical papers and webpage of [I2VGen-XL](doc/i2vgen-xl.md) - [x] Release the code and pretrained models that can generate 1280x720 videos - [ ] Release models optimized specifically for the human body and faces - [ ] Updated version can fully maintain the ID and capture large and accurate motions simultaneously - [ ] Release other methods and the corresponding models ## Preparation The main features of VGen are as follows: - Expandability, allowing for easy management of your own experiments. - Completeness, encompassing all common components for video generation. - Excellent performance, featuring powerful pre-trained models in multiple tasks. ### Installation ``` conda create -n vgen python=3.8 conda activate vgen pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113 pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` ### Datasets We have provided a **demo dataset** that includes images and videos, along with their lists in ``data``. *Please note that the demo images used here are for testing purposes and were not included in the training.* ### Clone codeb ``` git clone https://github.com/damo-vilab/i2vgen-xl.git cd i2vgen-xl ``` ## Getting Started with VGen ### (1) Train your text-to-video model Executing the following command to enable distributed training is as easy as that. ``` python train_net.py --cfg configs/t2v_train.yaml ``` In the `t2v_train.yaml` configuration file, you can specify the data, adjust the video-to-image ratio using `frame_lens`, and validate your ideas with different Diffusion settings, and so on. - Before the training, you can download any of our open-source models for initialization. Our codebase supports custom initialization and `grad_scale` settings, all of which are included in the `Pretrain` item in yaml file. - During the training, you can view the saved models and intermediate inference results in the `workspace/experiments/t2v_train`directory. After the training is completed, you can perform inference on the model using the following command. ``` python inference.py --cfg configs/t2v_infer.yaml ``` Then you can find the videos you generated in the `workspace/experiments/test_img_01` directory. For specific configurations such as data, models, seed, etc., please refer to the `t2v_infer.yaml` file. <!-- <table> <center> <tr> <td ><center> <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4"></video> </center></td> <td ><center> <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4"></video> </center></td> </tr> </center> </table> </center> --> <table> <center> <tr> <td ><center> <image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01Ya2I5I25utrJwJ9Jf_!!6000000007587-2-tps-1280-720.png"></image> </center></td> <td ><center> <image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01CrmYaz1zXBetmg3dd_!!6000000006723-2-tps-1280-720.png"></image> </center></td> </tr> <tr> <td ><center> <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4">HRER</a> to view the generated video.</p> </center></td> <td ><center> <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4">HRER</a> to view the generated video.</p> </center></td> </tr> </center> </table> </center> ### (2) Run the I2VGen-XL model (i) Download model and test data: ``` !pip install modelscope from modelscope.hub.snapshot_download import snapshot_download model_dir = snapshot_download('damo/I2VGen-XL', cache_dir='models/', revision='v1.0.0') ``` (ii) Run the following command: ``` python inference.py --cfg configs/i2vgen_xl_infer.yaml ``` In a few minutes, you can retrieve the high-definition video you wish to create from the `workspace/experiments/test_img_01` directory. At present, we find that the current model performs inadequately on **anime images** and **images with a black background** due to the lack of relevant training data. We are consistently working to optimize it. <span style="color:red">Due to the compression of our video quality in GIF format, please click 'HRER' below to view the original video.</span> <center> <table> <center> <tr> <td ><center> <image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01CCEq7K1ZeLpNQqrWu_!!6000000003219-0-tps-1280-720.jpg"></image> </center></td> <td ><center> <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4"></video> --> <image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01hIQcvG1spmQMLqBo0_!!6000000005816-1-tps-1280-704.gif"></image> </center></td> </tr> <tr> <td ><center> <p>Input Image</p> </center></td> <td ><center> <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4">HRER</a> to view the generated video.</p> </center></td> </tr> <tr> <td ><center> <image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01ZXY7UN23K8q4oQ3uG_!!6000000007236-2-tps-1280-720.png"></image> </center></td> <td ><center> <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4"></video> --> <image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01iaSiiv1aJZURUEY53_!!6000000003309-1-tps-1280-704.gif"></image> </center></td> </tr> <tr> <td ><center> <p>Input Image</p> </center></td> <td ><center> <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4">HRER</a> to view the generated video.</p> </center></td> </tr> <tr> <td ><center> <image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01NHpVGl1oat4H54Hjf_!!6000000005242-2-tps-1280-720.png"></image> </center></td> <td ><center> <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4"></video> --> <!-- <image muted="true" height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image> --> <image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image> </center></td> </tr> <tr> <td ><center> <p>Input Image</p> </center></td> <td ><center> <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4">HRER</a> to view the generated video.</p> </center></td> </tr> <tr> <td ><center> <image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01odS61s1WW9tXen21S_!!6000000002795-0-tps-1280-720.jpg"></image> </center></td> <td ><center> <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4"></video> --> <image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01Jyk1HT28JkZtpAtY6_!!6000000007912-1-tps-1280-704.gif"></image> </center></td> </tr> <tr> <td ><center> <p>Input Image</p> </center></td> <td ><center> <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4">HRER</a> to view the generated video.</p> </center></td> </tr> </center> </table> </center> ### (3) Other methods In preparation. ## Customize your own approach Our codebase essentially supports all the commonly used components in video generation. You can manage your experiments flexibly by adding corresponding registration classes, including `ENGINE, MODEL, DATASETS, EMBEDDER, AUTO_ENCODER, DISTRIBUTION, VISUAL, DIFFUSION, PRETRAIN`, and can be compatible with all our open-source algorithms according to your own needs. If you have any questions, feel free to give us your feedback at any time. ## BibTeX If this repo is useful to you, please cite our corresponding technical paper. ```bibtex @article{2023i2vgenxl, title={I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models}, author={Zhang, Shiwei and Wang, Jiayu and Zhang, Yingya and Zhao, Kang and Yuan, Hangjie and Qing, Zhiwu and Wang, Xiang and Zhao, Deli and Zhou, Jingren}, booktitle={arXiv preprint arXiv:2311.04145}, year={2023} } @article{2023videocomposer, title={VideoComposer: Compositional Video Synthesis with Motion Controllability}, author={Wang, Xiang and Yuan, Hangjie and Zhang, Shiwei and Chen, Dayou and Wang, Jiuniu, and Zhang, Yingya, and Shen, Yujun, and Zhao, Deli and Zhou, Jingren}, booktitle={arXiv preprint arXiv:2306.02018}, year={2023} } @article{wang2023modelscope, title={Modelscope text-to-video technical report}, author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei}, journal={arXiv preprint arXiv:2308.06571}, year={2023} } @article{dreamvideo, title={DreamVideo: Composing Your Dream Videos with Customized Subject and Motion}, author={Wei, Yujie and Zhang, Shiwei and Qing, Zhiwu and Yuan, Hangjie and Liu, Zhiheng and Liu, Yu and Zhang, Yingya and Zhou, Jingren and Shan, Hongming}, journal={arXiv preprint arXiv:2312.04433}, year={2023} } @article{qing2023higen, title={Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation}, author={Qing, Zhiwu and Zhang, Shiwei and Wang, Jiayu and Wang, Xiang and Wei, Yujie and Zhang, Yingya and Gao, Changxin and Sang, Nong }, journal={arXiv preprint arXiv:2312.04483}, year={2023} } @article{wang2023videolcm, title={VideoLCM: Video Latent Consistency Model}, author={Wang, Xiang and Zhang, Shiwei and Zhang, Han and Liu, Yu and Zhang, Yingya and Gao, Changxin and Sang, Nong }, journal={arXiv preprint arXiv:2312.09109}, year={2023} } ``` ## Disclaimer This open-source model is trained with using [WebVid-10M](https://m-bain.github.io/webvid-dataset/) and [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/) datasets and is intended for <strong>RESEARCH/NON-COMMERCIAL USE ONLY</strong>.
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ragnartd/lucas
ragnartd
2023-12-15T15:52:37Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-15T15:51:46Z
--- license: openrail ---
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lombardata/dino-large-2023_12_15-with_custom_head
lombardata
2023-12-16T07:54:16Z
0
0
transformers
[ "transformers", "pytorch", "dinov2", "multilabel-image-classification", "multilabel", "generated_from_trainer", "eng", "base_model:facebook/dinov2-large", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-12-15T15:53:15Z
--- language: - eng license: apache-2.0 base_model: facebook/dinov2-large tags: - multilabel-image-classification - multilabel - generated_from_trainer metrics: - accuracy model-index: - name: dino-large-2023_12_15-with_custom_head 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. --> # dino-large-2023_12_15-with_custom_head This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the multilabel_complete_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1159 - F1 Micro: 0.8297 - F1 Macro: 0.7800 - Roc Auc: 0.8880 - Accuracy: 0.5435 - Learning Rate: 0.001 ## 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: 0.01 - 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:-----:| | No log | 1.0 | 268 | 0.1749 | 0.6911 | 0.5979 | 0.7869 | 0.4355 | 0.01 | | 0.2108 | 2.0 | 536 | 0.1887 | 0.6448 | 0.5545 | 0.7473 | 0.4330 | 0.01 | | 0.2108 | 3.0 | 804 | 0.1639 | 0.7291 | 0.5960 | 0.8089 | 0.4223 | 0.01 | | 0.1888 | 4.0 | 1072 | 0.1676 | 0.7426 | 0.6583 | 0.8357 | 0.4416 | 0.01 | | 0.1888 | 5.0 | 1340 | 0.1619 | 0.7133 | 0.6050 | 0.7932 | 0.4527 | 0.01 | | 0.1877 | 6.0 | 1608 | 0.1532 | 0.7497 | 0.6624 | 0.8279 | 0.4691 | 0.01 | | 0.1877 | 7.0 | 1876 | 0.1690 | 0.7409 | 0.6522 | 0.8402 | 0.3758 | 0.01 | | 0.1845 | 8.0 | 2144 | 0.1461 | 0.7830 | 0.6987 | 0.8601 | 0.4770 | 0.01 | | 0.1845 | 9.0 | 2412 | 0.1533 | 0.7629 | 0.7143 | 0.8392 | 0.4659 | 0.01 | | 0.1845 | 10.0 | 2680 | 0.1703 | 0.7335 | 0.6227 | 0.8306 | 0.4234 | 0.01 | | 0.1845 | 11.0 | 2948 | 0.1714 | 0.7191 | 0.6140 | 0.8054 | 0.4455 | 0.01 | | 0.1871 | 12.0 | 3216 | 0.1538 | 0.7677 | 0.6816 | 0.8427 | 0.4562 | 0.01 | | 0.1871 | 13.0 | 3484 | 0.1533 | 0.7673 | 0.6923 | 0.8508 | 0.4573 | 0.01 | | 0.1881 | 14.0 | 3752 | 0.1572 | 0.7550 | 0.6424 | 0.8395 | 0.4584 | 0.01 | | 0.1768 | 15.0 | 4020 | 0.1300 | 0.8118 | 0.7646 | 0.8766 | 0.5248 | 0.001 | | 0.1768 | 16.0 | 4288 | 0.1237 | 0.8229 | 0.7727 | 0.8838 | 0.5277 | 0.001 | | 0.1499 | 17.0 | 4556 | 0.1218 | 0.8285 | 0.7891 | 0.8933 | 0.5348 | 0.001 | | 0.1499 | 18.0 | 4824 | 0.1193 | 0.8285 | 0.7831 | 0.8902 | 0.5284 | 0.001 | | 0.1424 | 19.0 | 5092 | 0.1182 | 0.8298 | 0.7778 | 0.8849 | 0.5445 | 0.001 | | 0.1424 | 20.0 | 5360 | 0.1182 | 0.8286 | 0.7698 | 0.8827 | 0.5395 | 0.001 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
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igorktech/TALANT_saiga_7b-lora
igorktech
2023-12-15T15:55:07Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:IlyaGusev/saiga_mistral_7b_merged", "region:us" ]
null
2023-12-15T15:55:00Z
--- library_name: peft base_model: IlyaGusev/saiga_mistral_7b_merged --- # 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. --> - **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] ### Framework versions - PEFT 0.7.2.dev0
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annavvv/path-to-save-model
annavvv
2023-12-15T15:58:08Z
0
0
null
[ "region:us" ]
null
2023-12-15T15:58:08Z
Entry not found
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gkallergis/bbbp_100_hp_opt
gkallergis
2023-12-15T16:11:05Z
0
0
null
[ "license:cc-by-nc-nd-4.0", "region:us" ]
null
2023-12-15T15:58:34Z
--- license: cc-by-nc-nd-4.0 ---
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realryo1/nva-miku16th_0.1
realryo1
2023-12-15T16:00:44Z
0
0
null
[ "region:us" ]
null
2023-12-15T16:00:39Z
Entry not found
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elizabetvaganova/speech-to-speech-translation-vaganovaeli
elizabetvaganova
2023-12-15T16:20:54Z
0
0
null
[ "region:us" ]
null
2023-12-15T16:03:19Z
--- title: Speech To Speech Translation emoji: 🏆 colorFrom: pink colorTo: indigo sdk: gradio sdk_version: 3.36.1 app_file: app.py pinned: false --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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Djck/kimeru
Djck
2023-12-15T16:20:02Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-15T16:05:00Z
--- license: apache-2.0 ---
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Samara1/distilbert-base-uncased-lora-text-classification
Samara1
2023-12-15T16:06:10Z
0
0
null
[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "region:us" ]
null
2023-12-15T16:06:06Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9937 - Accuracy: {'accuracy': 0.878} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:| | No log | 1.0 | 250 | 0.5858 | {'accuracy': 0.839} | | 0.4248 | 2.0 | 500 | 0.6538 | {'accuracy': 0.832} | | 0.4248 | 3.0 | 750 | 0.5316 | {'accuracy': 0.88} | | 0.2198 | 4.0 | 1000 | 0.6755 | {'accuracy': 0.875} | | 0.2198 | 5.0 | 1250 | 0.8534 | {'accuracy': 0.867} | | 0.0838 | 6.0 | 1500 | 0.8499 | {'accuracy': 0.88} | | 0.0838 | 7.0 | 1750 | 0.8912 | {'accuracy': 0.885} | | 0.0257 | 8.0 | 2000 | 0.9443 | {'accuracy': 0.879} | | 0.0257 | 9.0 | 2250 | 0.9789 | {'accuracy': 0.88} | | 0.0018 | 10.0 | 2500 | 0.9937 | {'accuracy': 0.878} | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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Jeevesh8/bt__qqp_ft_only_data_shuff_14
Jeevesh8
2023-12-15T18:52:15Z
0
0
transformers
[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T16:07:35Z
Entry not found
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Jeevesh8/bt__qqp_ft_only_data_shuff_15
Jeevesh8
2023-12-15T18:58:46Z
0
0
transformers
[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T16:07:35Z
Entry not found
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Jeevesh8/bt__qqp_ft_only_data_shuff_13
Jeevesh8
2023-12-15T18:48:33Z
0
0
transformers
[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-15T16:07:35Z
Entry not found
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