Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

Jubliano/wav2vec2-large-xls-r-300m-ipa-INTERNATIONAL1.9.2WithoutSpaces

Jubliano

transformers

automatic-speech-recognition

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

talkchief/distil-whisper_distil-large-v3

talkchief

Entry not found

Naturen/Naturen

Naturen

--- # 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]

kevin009/llamamathv7

kevin009

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** kevin009 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

talkchief/openai_whisper-large-v2

talkchief

Entry not found

sounana/large

sounana

Entry not found

AdamRTomkins/test_upload

AdamRTomkins

peft

--- license: mit library_name: peft tags: - axolotl - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: test_upload results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adam_beta2: 0.95 adam_epsilon: 1.0e-05 adapter: qlora base_model: microsoft/phi-1_5 dataset_prepared_path: null datasets: - path: garage-bAInd/Open-Platypus type: alpaca debug: null deepspeed: null early_stopping_patience: null evals_per_epoch: 1 flash_attention: true fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true hub_model_id: AdamRTomkins/test_upload hub_strategy: end learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 2 micro_batch_size: 1 model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: ./outputs/phi-sft-out pad_to_sequence_len: true resize_token_embeddings_to_32x: true resume_from_checkpoint: null sample_packing: true saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <|endoftext|> strict: false tokenizer_type: AutoTokenizer val_set_size: 0.05 wandb_entity: null wandb_log_model: null wandb_name: null wandb_project: null wandb_watch: null warmup_steps: 100 weight_decay: 0.1 xformers_attention: null ``` </details><br> # test_upload This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3469 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.6676 | 0.0002 | 2 | 1.3469 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.1.2+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1

vintage-lavender619/vit-base-patch16-224-finalterm

vintage-lavender619

transformers

image-classification

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finalterm results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.88125 --- <!-- 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. --> # vit-base-patch16-224-finalterm This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3547 - Accuracy: 0.8812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3999 | 1.0 | 10 | 1.1607 | 0.5094 | | 0.993 | 2.0 | 20 | 0.7807 | 0.7031 | | 0.6819 | 3.0 | 30 | 0.5753 | 0.8063 | | 0.5485 | 4.0 | 40 | 0.6475 | 0.7594 | | 0.463 | 5.0 | 50 | 0.4393 | 0.8406 | | 0.3929 | 6.0 | 60 | 0.4067 | 0.8625 | | 0.3636 | 7.0 | 70 | 0.3626 | 0.8875 | | 0.3719 | 8.0 | 80 | 0.3613 | 0.8875 | | 0.343 | 9.0 | 90 | 0.3624 | 0.8781 | | 0.3297 | 10.0 | 100 | 0.3800 | 0.8625 | | 0.2948 | 11.0 | 110 | 0.3320 | 0.8938 | | 0.33 | 12.0 | 120 | 0.3481 | 0.8781 | | 0.3281 | 13.0 | 130 | 0.3418 | 0.8875 | | 0.3 | 14.0 | 140 | 0.3425 | 0.8844 | | 0.3014 | 15.0 | 150 | 0.3547 | 0.8812 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

thangduong0509/blip_vivqa_finetuned_200

thangduong0509

Entry not found

tsavage68/UTI2_M2_1000steps_1e7rate_CSFTDPO

tsavage68

transformers

text-generation

--- license: apache-2.0 base_model: tsavage68/UTI_M2_1000steps_1e7rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: UTI2_M2_1000steps_1e7rate_CSFTDPO 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. --> # UTI2_M2_1000steps_1e7rate_CSFTDPO This model is a fine-tuned version of [tsavage68/UTI_M2_1000steps_1e7rate_SFT](https://huggingface.co/tsavage68/UTI_M2_1000steps_1e7rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5546 - Rewards/chosen: 0.0422 - Rewards/rejected: -0.2698 - Rewards/accuracies: 0.8600 - Rewards/margins: 0.3120 - Logps/rejected: -39.8957 - Logps/chosen: -19.8371 - Logits/rejected: -2.6809 - Logits/chosen: -2.6783 ## 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-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6931 | 0.3333 | 25 | 0.6921 | 0.0031 | 0.0001 | 0.1900 | 0.0030 | -39.3560 | -19.9153 | -2.6832 | -2.6806 | | 0.6879 | 0.6667 | 50 | 0.6797 | 0.0157 | -0.0141 | 0.5300 | 0.0298 | -39.3843 | -19.8902 | -2.6825 | -2.6799 | | 0.7047 | 1.0 | 75 | 0.6833 | 0.0064 | -0.0173 | 0.5300 | 0.0237 | -39.3907 | -19.9087 | -2.6821 | -2.6796 | | 0.6925 | 1.3333 | 100 | 0.6719 | 0.0150 | -0.0314 | 0.6200 | 0.0464 | -39.4189 | -19.8915 | -2.6833 | -2.6807 | | 0.6674 | 1.6667 | 125 | 0.6638 | 0.0030 | -0.0600 | 0.6900 | 0.0630 | -39.4762 | -19.9155 | -2.6817 | -2.6791 | | 0.6591 | 2.0 | 150 | 0.6356 | 0.0148 | -0.1082 | 0.8100 | 0.1230 | -39.5726 | -19.8920 | -2.6816 | -2.6790 | | 0.637 | 2.3333 | 175 | 0.6319 | 0.0113 | -0.1196 | 0.8200 | 0.1309 | -39.5954 | -19.8989 | -2.6812 | -2.6786 | | 0.6179 | 2.6667 | 200 | 0.6054 | 0.0342 | -0.1567 | 0.8300 | 0.1909 | -39.6696 | -19.8532 | -2.6821 | -2.6795 | | 0.6173 | 3.0 | 225 | 0.6032 | 0.0393 | -0.1577 | 0.8200 | 0.1970 | -39.6716 | -19.8429 | -2.6816 | -2.6790 | | 0.5873 | 3.3333 | 250 | 0.5858 | 0.0189 | -0.2169 | 0.8400 | 0.2358 | -39.7899 | -19.8837 | -2.6812 | -2.6786 | | 0.5795 | 3.6667 | 275 | 0.5877 | 0.0141 | -0.2185 | 0.8000 | 0.2326 | -39.7932 | -19.8934 | -2.6813 | -2.6787 | | 0.6008 | 4.0 | 300 | 0.5756 | 0.0356 | -0.2244 | 0.8400 | 0.2600 | -39.8049 | -19.8503 | -2.6803 | -2.6777 | | 0.57 | 4.3333 | 325 | 0.5764 | 0.0262 | -0.2323 | 0.8400 | 0.2585 | -39.8208 | -19.8692 | -2.6807 | -2.6781 | | 0.5584 | 4.6667 | 350 | 0.5605 | 0.0242 | -0.2723 | 0.8600 | 0.2964 | -39.9007 | -19.8732 | -2.6802 | -2.6776 | | 0.572 | 5.0 | 375 | 0.5604 | 0.0279 | -0.2703 | 0.8700 | 0.2982 | -39.8968 | -19.8658 | -2.6804 | -2.6778 | | 0.5811 | 5.3333 | 400 | 0.5617 | 0.0342 | -0.2607 | 0.8500 | 0.2949 | -39.8776 | -19.8531 | -2.6798 | -2.6772 | | 0.5751 | 5.6667 | 425 | 0.5648 | 0.0392 | -0.2472 | 0.8600 | 0.2865 | -39.8506 | -19.8431 | -2.6809 | -2.6783 | | 0.561 | 6.0 | 450 | 0.5624 | 0.0124 | -0.2803 | 0.8500 | 0.2927 | -39.9167 | -19.8967 | -2.6806 | -2.6781 | | 0.545 | 6.3333 | 475 | 0.5525 | 0.0448 | -0.2732 | 0.8700 | 0.3180 | -39.9025 | -19.8319 | -2.6815 | -2.6789 | | 0.6125 | 6.6667 | 500 | 0.5589 | 0.0463 | -0.2561 | 0.8700 | 0.3023 | -39.8683 | -19.8290 | -2.6811 | -2.6785 | | 0.5398 | 7.0 | 525 | 0.5612 | 0.0214 | -0.2753 | 0.8400 | 0.2966 | -39.9067 | -19.8788 | -2.6805 | -2.6779 | | 0.543 | 7.3333 | 550 | 0.5643 | 0.0400 | -0.2494 | 0.8500 | 0.2894 | -39.8549 | -19.8415 | -2.6806 | -2.6781 | | 0.5541 | 7.6667 | 575 | 0.5616 | 0.0247 | -0.2721 | 0.8500 | 0.2968 | -39.9002 | -19.8720 | -2.6813 | -2.6788 | | 0.5576 | 8.0 | 600 | 0.5650 | 0.0122 | -0.2764 | 0.8500 | 0.2886 | -39.9089 | -19.8971 | -2.6812 | -2.6786 | | 0.5543 | 8.3333 | 625 | 0.5605 | 0.0330 | -0.2649 | 0.8600 | 0.2980 | -39.8860 | -19.8555 | -2.6809 | -2.6783 | | 0.5405 | 8.6667 | 650 | 0.5648 | 0.0146 | -0.2732 | 0.8500 | 0.2878 | -39.9025 | -19.8924 | -2.6810 | -2.6784 | | 0.5535 | 9.0 | 675 | 0.5536 | 0.0354 | -0.2789 | 0.8500 | 0.3143 | -39.9140 | -19.8507 | -2.6798 | -2.6772 | | 0.5292 | 9.3333 | 700 | 0.5534 | 0.0444 | -0.2708 | 0.8600 | 0.3152 | -39.8978 | -19.8328 | -2.6808 | -2.6782 | | 0.5718 | 9.6667 | 725 | 0.5556 | 0.0429 | -0.2668 | 0.8400 | 0.3097 | -39.8898 | -19.8358 | -2.6813 | -2.6787 | | 0.585 | 10.0 | 750 | 0.5512 | 0.0392 | -0.2799 | 0.8800 | 0.3191 | -39.9159 | -19.8431 | -2.6809 | -2.6783 | | 0.5609 | 10.3333 | 775 | 0.5540 | 0.0352 | -0.2800 | 0.8600 | 0.3152 | -39.9161 | -19.8511 | -2.6808 | -2.6782 | | 0.5572 | 10.6667 | 800 | 0.5500 | 0.0424 | -0.2816 | 0.8700 | 0.3240 | -39.9193 | -19.8367 | -2.6809 | -2.6783 | | 0.5514 | 11.0 | 825 | 0.5541 | 0.0433 | -0.2698 | 0.8700 | 0.3131 | -39.8958 | -19.8350 | -2.6809 | -2.6783 | | 0.5467 | 11.3333 | 850 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | | 0.5803 | 11.6667 | 875 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | | 0.5514 | 12.0 | 900 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | | 0.5579 | 12.3333 | 925 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | | 0.5599 | 12.6667 | 950 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | | 0.5609 | 13.0 | 975 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | | 0.552 | 13.3333 | 1000 | 0.5546 | 0.0422 | -0.2698 | 0.8600 | 0.3120 | -39.8957 | -19.8371 | -2.6809 | -2.6783 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1

yaraksen/komod_no_sftmx_2_6

yaraksen

Entry not found

alexgrigore/videomae-base-finetuned-good-gestureUnitV11

alexgrigore

transformers

video-classification

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-good-gestureUnitV11 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-good-gestureUnitV11 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9310 - Loss: 0.3025 - Accuracy Gunit: 0.8571 - Accuracy Nothing: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 80 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | Accuracy Gunit | Accuracy Nothing | |:-------------:|:------:|:----:|:--------:|:---------------:|:--------------:|:----------------:| | 0.6685 | 0.2125 | 17 | 0.6071 | 0.6297 | 1.0 | 0.2903 | | 0.5348 | 1.2125 | 34 | 0.8214 | 0.4292 | 0.6 | 1.0 | | 0.3395 | 2.2125 | 51 | 0.7857 | 0.4852 | 0.88 | 0.7097 | | 0.3607 | 3.2125 | 68 | 0.8214 | 0.4507 | 0.6 | 1.0 | | 0.2436 | 4.15 | 80 | 0.7857 | 0.4433 | 0.76 | 0.8065 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

manbeast3b/KinoInferlol2

manbeast3b

Entry not found

GarciaDos/ppo-Huggy2

GarciaDos

Entry not found

Kigo1974/KigoGrader-1.0

Kigo1974

Entry not found

danigambit/test_1006

danigambit

Entry not found

jnalwa/auto

jnalwa

Entry not found

Attaboi/my_awesome_model

Attaboi

Entry not found

yaraksen/komod_no_sftmx_4_3

yaraksen

Entry not found

Jakh0103/new_llama3-8b_mcq_rag

Jakh0103

transformers

text-generation

Entry not found

tranthaihoa/llama3_evidence

tranthaihoa

transformers

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

danigambit/testxx

danigambit

Entry not found

longlivebigcat/hunheNew_qiwen7b_alp_lora2400_model

longlivebigcat

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl base_model: unsloth/Qwen2-7B-bnb-4bit --- # Uploaded model - **Developed by:** longlivebigcat - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2-7B-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

AshiqaSameem/gemma_biology_summarizer_model

AshiqaSameem

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: unsloth/gemma-7b-bnb-4bit --- # Uploaded model - **Developed by:** AshiqaSameem - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Mirman619/Realistic_3D_rendering_of_girls

Mirman619

--- license: openrail ---

kartikay101/wtimit-base-960h-normal-reduced-learning-rate-all

kartikay101

transformers

automatic-speech-recognition

--- license: apache-2.0 tags: - generated_from_trainer base_model: facebook/wav2vec2-base-960h metrics: - wer model-index: - name: wtimit-base-960h-normal-reduced-learning-rate-all 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. --> # wtimit-base-960h-normal-reduced-learning-rate-all This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3181 - Wer: 0.2132 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:------:| | 0.4297 | 2.1552 | 1000 | 0.3046 | 0.2440 | | 0.3137 | 4.3103 | 2000 | 0.2941 | 0.2240 | | 0.2578 | 6.4655 | 3000 | 0.2982 | 0.2176 | | 0.2153 | 8.6207 | 4000 | 0.3063 | 0.2166 | | 0.1998 | 10.7759 | 5000 | 0.3036 | 0.2155 | | 0.1913 | 12.9310 | 6000 | 0.3049 | 0.2122 | | 0.1836 | 15.0862 | 7000 | 0.3160 | 0.2161 | | 0.1755 | 17.2414 | 8000 | 0.3192 | 0.2152 | | 0.1681 | 19.3966 | 9000 | 0.3181 | 0.2132 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

LinearizedLLM/llama-2-7b-grouped-linear

LinearizedLLM

transformers

text-generation

--- license: llama2 language: - en ---

MFF212/alva

MFF212

Entry not found

aleoaaaa/t5-base-fr-sum-cnndm_finetuned_10_06_14_09

aleoaaaa

Entry not found

LinearizedLLM/llama-2-7b-medusa-head-grouped-linear

LinearizedLLM

transformers

Entry not found

LinearizedLLM/llama-2-7b-medusa-head-local-linear

LinearizedLLM

transformers

Entry not found

vmattoso/my-wine-classification-first-model

vmattoso

transformers

Entry not found

astarel/llama3-8b-oig-unsloth-merged

astarel

transformers

text-generation

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

SerhiiML/wav2vec2-large-mms-1b-turkish-colab

SerhiiML

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

HyperdustProtocol/ImHyperAGI-llama2-7b-813

HyperdustProtocol

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - **Developed by:** HyperdustProtocol - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

camenduru/t2v-turbo

camenduru

Entry not found

LinearizedLLM/llama-2-7b-local-linear

LinearizedLLM

transformers

text-generation

--- license: llama2 language: - en ---

jnalwa/customer_support_model

jnalwa

Entry not found

Justicescott/API-Code

Justicescott

Entry not found

Dcoolno1/Dcool1

Dcoolno1

--- license: apache-2.0 ---

astarel/llama3-8b-oig-unsloth

astarel

transformers

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

ohadfel/whisper-tiny-q

ohadfel

transformers

automatic-speech-recognition

Entry not found

vicky4s4s/openchat-8b

vicky4s4s

transformers

text-generation

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B tags: - openchat - llama3 - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">Llama 3 Version: OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.6</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #90e0ef; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">20240522</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open-source 8B Model 🏆 <br> 🚀 Outperforms Llama-3-8B-Instruct and open-source finetunes/merges 🚀 </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center; width: 110%; margin-left: -5%;"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/benchmarks-openchat-3.6-20240522.svg" style="width: 100%; border-radius: 1em"> </div> <div style="display: flex; justify-content: center; align-items: center"> <p>* Llama-3-Instruct often fails to follow the few-shot templates. See <a href="https://huggingface.co/openchat/openchat-3.6-8b-20240522/discussions/6">example</a>.</p> </div> <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. | Model | Size | Context | Weights | Serving | |-----------------------|------|---------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------| | OpenChat-3.6-20240522 | 8B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat-3.6-8b-20240522) | `python -m ochat.serving.openai_api_server --model openchat/openchat-3.6-8b-20240522` | <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.6", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` </details> ### Conversation templates 💡 **Default Mode**: Best for coding, chat and general tasks. It's a modified version of the Llama 3 Instruct template, the only difference is role names, which are either `GPT4 Correct User` or `GPT4 Correct Assistant` ``` <|start_header_id|>GPT4 Correct User<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>GPT4 Correct Assistant<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>GPT4 Correct User<|end_header_id|>\n\nHow are you today?<|eot_id|><|start_header_id|>GPT4 Correct Assistant<|end_header_id|>\n\n ``` ⚠️ **Notice:** Remember to set `<|eot_id|>` as end of generation token. The default template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) ``` ## Inference using Transformers ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "vicky4s4s/openchat-8b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "user", "content": "Explain how large language models work in detail."}, ] input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(input_ids, do_sample=True, temperature=0.5, max_new_tokens=1024 ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` <div align="center"> <h2> Limitations </h2> </div> **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing from you and collaborating on this exciting project! **Project Lead:** - Guan Wang [imonenext at gmail dot com] - [Alpay Ariyak](https://github.com/alpayariyak) [aariyak at wpi dot edu] <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ```

aleoaaaa/mT5_multilingual_XLSum_finetuned_10_06_14_25

aleoaaaa

Entry not found

tranthaihoa/mistral_evidence

tranthaihoa

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - **Developed by:** tranthaihoa - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

silent666/Qwen-Qwen1.5-1.8B-1718022339

silent666

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JiaxinGe/llama3_4_bit_hellaswag_3_shots_generated_data_anthropic_dataset

JiaxinGe

transformers

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

Anhnh27042001/new_lora_model_llama3

Anhnh27042001

text-generation

--- pipeline_tag: text-generation ---

Ilya-Nazimov/lct-ruElectra-large-ner

Ilya-Nazimov

Entry not found

silent666/Qwen-Qwen1.5-1.8B-1718022552

silent666

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

onizukal/Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1

onizukal

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1 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: 0.8548696844993141 --- <!-- 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. --> # Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5790 - Accuracy: 0.8549 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3103 | 1.0 | 914 | 0.3588 | 0.8494 | | 0.3671 | 2.0 | 1828 | 0.3382 | 0.8669 | | 0.2679 | 3.0 | 2742 | 0.4568 | 0.8491 | | 0.13 | 4.0 | 3656 | 0.7675 | 0.8595 | | 0.0539 | 5.0 | 4570 | 1.0063 | 0.8543 | | 0.0034 | 6.0 | 5484 | 1.3345 | 0.8543 | | 0.001 | 7.0 | 6398 | 1.4146 | 0.8562 | | 0.0013 | 8.0 | 7312 | 1.6343 | 0.8529 | | 0.0023 | 9.0 | 8226 | 1.5956 | 0.8486 | | 0.0001 | 10.0 | 9140 | 1.5790 | 0.8549 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

vishal324/fine_tuned_llama3_8b

vishal324

transformers

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]

LogicalPanda/Test

LogicalPanda

Entry not found

hoverinc/gestalt2_test

hoverinc

Entry not found

Dumele/Viv-final

Dumele

--- license: apache-2.0 ---

erikka-22/MuseSwallow

erikka-22

--- license: cc-by-4.0 ---

kajamo/model_16

kajamo

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: distilbert-base-uncased model-index: - name: model_16 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. --> # model_16 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: - eval_loss: 0.6194 - eval_accuracy: 0.7624 - eval_precision: 0.7632 - eval_recall: 0.7624 - eval_f1: 0.7621 - eval_runtime: 42.8182 - eval_samples_per_second: 285.977 - eval_steps_per_second: 17.89 - epoch: 14.0 - step: 42868 ## 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

M2XXX/whisper-id

M2XXX

transformers

automatic-speech-recognition

Entry not found

LucasMscFGV/results

LucasMscFGV

Entry not found

jnalwa/customer_support_tokenizer

jnalwa

Entry not found

anesabdennebi/SecFalGEN-IDS

anesabdennebi

Entry not found

Souvikrad365/outputmodel

Souvikrad365

Entry not found

tranthaihoa/gemma_context

tranthaihoa

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-bnb-4bit --- # Uploaded model - **Developed by:** tranthaihoa - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ENSTA-U2IS/tutorial-models

ENSTA-U2IS

--- license: mit --- MNIST models trained for 75 epochs with no selection.

odelz/hindi_fb1mms

odelz

transformers

automatic-speech-recognition

Entry not found

shakun42/indic-bert-finetuned-squad1.1

shakun42

Entry not found

Reihaneh/wav2vec2_fy_common_voice_34

Reihaneh

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Sjjsn/Dj

Sjjsn

Entry not found

chaewoners/LisaofBlackpink

chaewoners

--- license: unknown ---

arhamk/ppo-LunarLander-v2-2

arhamk

reinforcement-learning

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -144.04 +/- 92.67 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 5 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'arhamk/ppo-LunarLander-v2-2' 'batch_size': 512 'minibatch_size': 128} ```

tranthaihoa/llama3_context

tranthaihoa

transformers

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

mysharingorg/sharing_codebase

mysharingorg

Entry not found

sharifMunna/munna_bhai_mbbs_model_08_12_1

sharifMunna

Entry not found

llmvetter/PixelCopter

llmvetter

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 27.50 +/- 27.08 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

izmuhammadra/jobseeker-falcon-7b

izmuhammadra

--- license: openrail ---

sharifMunna/munna_bhai_mbbs_model_12_12_1

sharifMunna

Entry not found

loicloic/loic

loicloic

Entry not found

swiss-ai-center/giscup2023-deepLabV3Plus

swiss-ai-center

keras

--- license: mit ---

techcto/solodev

techcto

--- license: apache-2.0 ---

Propicto/t2p-t5-large-orfeo

Propicto

transformers

text2text-generation

--- license: apache-2.0 ---

badrabdullah/xls-r-300-cv17-bulgarian

badrabdullah

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: xls-r-300-cv17-bulgarian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: bg split: validation args: bg metrics: - name: Wer type: wer value: 0.2967878948765596 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-polish/runs/snulovqw) # xls-r-300-cv17-bulgarian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4329 - Wer: 0.2968 - Cer: 0.0726 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:----:|:---------------:|:------:|:------:| | 4.0388 | 0.6579 | 100 | 4.1422 | 1.0 | 1.0 | | 3.047 | 1.3158 | 200 | 3.0730 | 1.0 | 1.0 | | 2.7349 | 1.9737 | 300 | 2.7601 | 0.9939 | 0.9946 | | 0.6047 | 2.6316 | 400 | 0.6984 | 0.7954 | 0.1942 | | 0.3868 | 3.2895 | 500 | 0.5550 | 0.5994 | 0.1519 | | 0.3423 | 3.9474 | 600 | 0.4548 | 0.4804 | 0.1195 | | 0.1942 | 4.6053 | 700 | 0.3973 | 0.4277 | 0.1034 | | 0.1754 | 5.2632 | 800 | 0.4166 | 0.4391 | 0.1055 | | 0.1734 | 5.9211 | 900 | 0.4146 | 0.4195 | 0.1018 | | 0.1089 | 6.5789 | 1000 | 0.3859 | 0.3867 | 0.0937 | | 0.233 | 7.2368 | 1100 | 0.4183 | 0.4054 | 0.1005 | | 0.1519 | 7.8947 | 1200 | 0.4459 | 0.4151 | 0.1030 | | 0.1176 | 8.5526 | 1300 | 0.4026 | 0.3845 | 0.0937 | | 0.0997 | 9.2105 | 1400 | 0.3849 | 0.3590 | 0.0869 | | 0.1266 | 9.8684 | 1500 | 0.4281 | 0.3781 | 0.0947 | | 0.0945 | 10.5263 | 1600 | 0.4471 | 0.3983 | 0.0979 | | 0.0575 | 11.1842 | 1700 | 0.4290 | 0.3660 | 0.0897 | | 0.0854 | 11.8421 | 1800 | 0.4258 | 0.3749 | 0.0938 | | 0.0558 | 12.5 | 1900 | 0.4242 | 0.3644 | 0.0907 | | 0.0774 | 13.1579 | 2000 | 0.4339 | 0.3616 | 0.0888 | | 0.0397 | 13.8158 | 2100 | 0.4155 | 0.3581 | 0.0882 | | 0.0603 | 14.4737 | 2200 | 0.4681 | 0.3737 | 0.0943 | | 0.0723 | 15.1316 | 2300 | 0.4446 | 0.3560 | 0.0875 | | 0.0746 | 15.7895 | 2400 | 0.4430 | 0.3573 | 0.0889 | | 0.0727 | 16.4474 | 2500 | 0.4549 | 0.3470 | 0.0870 | | 0.0458 | 17.1053 | 2600 | 0.4581 | 0.3520 | 0.0873 | | 0.0694 | 17.7632 | 2700 | 0.4414 | 0.3575 | 0.0896 | | 0.0462 | 18.4211 | 2800 | 0.4235 | 0.3261 | 0.0802 | | 0.0539 | 19.0789 | 2900 | 0.4496 | 0.3329 | 0.0810 | | 0.0368 | 19.7368 | 3000 | 0.4043 | 0.3406 | 0.0846 | | 0.0347 | 20.3947 | 3100 | 0.4367 | 0.3225 | 0.0789 | | 0.019 | 21.0526 | 3200 | 0.4487 | 0.3272 | 0.0801 | | 0.0361 | 21.7105 | 3300 | 0.4272 | 0.3241 | 0.0785 | | 0.0475 | 22.3684 | 3400 | 0.4324 | 0.3191 | 0.0781 | | 0.0341 | 23.0263 | 3500 | 0.4564 | 0.3398 | 0.0847 | | 0.0454 | 23.6842 | 3600 | 0.4415 | 0.3188 | 0.0789 | | 0.0346 | 24.3421 | 3700 | 0.4187 | 0.3072 | 0.0751 | | 0.1315 | 25.0 | 3800 | 0.4480 | 0.3124 | 0.0765 | | 0.0663 | 25.6579 | 3900 | 0.4488 | 0.3151 | 0.0779 | | 0.0225 | 26.3158 | 4000 | 0.4372 | 0.3006 | 0.0739 | | 0.0382 | 26.9737 | 4100 | 0.4164 | 0.2987 | 0.0730 | | 0.0194 | 27.6316 | 4200 | 0.4190 | 0.2942 | 0.0718 | | 0.0101 | 28.2895 | 4300 | 0.4328 | 0.2960 | 0.0726 | | 0.0224 | 28.9474 | 4400 | 0.4302 | 0.2944 | 0.0720 | | 0.0174 | 29.6053 | 4500 | 0.4329 | 0.2968 | 0.0726 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

SpeechResearch/wtimit-base-960h-whisper-reduced-learning-rate-all

SpeechResearch

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LuziaMoura/2024-05-31-QeA-MMGD-unsloth_mistral_7b_bnb_4bit

LuziaMoura

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - **Developed by:** LuziaMoura - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

tranthaihoa/llama2_context

tranthaihoa

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - **Developed by:** tranthaihoa - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

teste001/Narrando_Paulo

teste001

--- license: openrail ---

b1zk1t1337/Garfield_Lorenzo_Music

b1zk1t1337

Entry not found

Propicto/t2p-nllb-200-distilled-600M-orfeo

Propicto

transformers

text2text-generation

--- license: apache-2.0 ---

manbeast3b/KinoInferLord3

manbeast3b

Entry not found

onizukal/Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1

onizukal

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1 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: 0.8495434696308058 --- <!-- 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. --> # Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5872 - Accuracy: 0.8495 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3849 | 1.0 | 632 | 0.3967 | 0.8273 | | 0.3194 | 2.0 | 1264 | 0.4043 | 0.8372 | | 0.2199 | 3.0 | 1896 | 0.4423 | 0.8503 | | 0.1532 | 4.0 | 2528 | 0.6718 | 0.8444 | | 0.0267 | 5.0 | 3160 | 0.9647 | 0.8416 | | 0.0853 | 6.0 | 3792 | 1.2277 | 0.8428 | | 0.0213 | 7.0 | 4424 | 1.4343 | 0.8491 | | 0.0008 | 8.0 | 5056 | 1.4458 | 0.8495 | | 0.0035 | 9.0 | 5688 | 1.5300 | 0.8495 | | 0.0003 | 10.0 | 6320 | 1.5872 | 0.8495 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

mndousse/naruto-lora

mndousse

Entry not found

JiaxinGe/llama3_4_bit_hellaswag_3_shots_transformed_data_anthropic_dataset

JiaxinGe

transformers

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

onizukal/Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold1

onizukal

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold1 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: 0.8414336139017106 --- <!-- 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. --> # Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold1 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1930 - Accuracy: 0.8414 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3482 | 1.0 | 924 | 0.4193 | 0.8262 | | 0.3157 | 2.0 | 1848 | 0.4359 | 0.8352 | | 0.1507 | 3.0 | 2772 | 0.6032 | 0.8403 | | 0.1694 | 4.0 | 3696 | 0.9383 | 0.8414 | | 0.0111 | 5.0 | 4620 | 1.1930 | 0.8414 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

bakanaims/falcon-7b-AG-News

bakanaims

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: tiiuae/falcon-7b metrics: - accuracy model-index: - name: falcon-7b-AG-News 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. --> # falcon-7b-AG-News This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4483 - Balanced Accuracy: 0.8911 - Accuracy: 0.8867 ## 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 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Balanced Accuracy | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:| | 1.7257 | 1.0 | 25 | 1.2132 | 0.5713 | 0.47 | | 0.8197 | 2.0 | 50 | 0.5488 | 0.8580 | 0.8367 | | 0.2867 | 3.0 | 75 | 0.4392 | 0.8726 | 0.86 | | 0.104 | 4.0 | 100 | 0.5123 | 0.8912 | 0.8833 | | 0.0393 | 5.0 | 125 | 0.4483 | 0.8911 | 0.8867 | ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

Mirman619/phoenix_offset

Mirman619

--- license: openrail ---

alexgrigore/videomae-base-finetuned-good-gestureUnitV12

alexgrigore

transformers

video-classification

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-good-gestureUnitV12 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-good-gestureUnitV12 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: 0.8966 - Loss: 0.2937 - Accuracy Gunit: 0.8333 - Accuracy Nothing: 0.9556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 160 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | Accuracy Gunit | Accuracy Nothing | |:-------------:|:------:|:----:|:--------:|:---------------:|:--------------:|:----------------:| | 0.8549 | 0.1062 | 17 | 0.5714 | 0.6774 | 0.68 | 0.4839 | | 0.6437 | 1.1062 | 34 | 0.4643 | 0.7254 | 1.0 | 0.0323 | | 0.6226 | 2.1063 | 51 | 0.6071 | 0.6527 | 0.96 | 0.3226 | | 0.5883 | 3.1063 | 68 | 0.5714 | 0.6389 | 1.0 | 0.2258 | | 0.5136 | 4.1063 | 85 | 0.6964 | 0.5330 | 0.84 | 0.5806 | | 0.4284 | 5.1063 | 102 | 0.8214 | 0.4506 | 0.84 | 0.8065 | | 0.3474 | 6.1063 | 119 | 0.8214 | 0.3974 | 0.76 | 0.8710 | | 0.2859 | 7.1063 | 136 | 0.8214 | 0.3822 | 0.64 | 0.9677 | | 0.3059 | 8.1062 | 153 | 0.8393 | 0.3763 | 0.68 | 0.9677 | | 0.2582 | 9.0437 | 160 | 0.8393 | 0.3738 | 0.68 | 0.9677 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

Nick20241/1

Nick20241

--- license: apache-2.0 ---

iloncka/exp_5_old_bg_raw-subs_1_v_5_convnext_nano.in12k_ft_in1k_ep_60

iloncka

fastai

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

vmattoso/my-wine-classification-first-model_v2

vmattoso

sklearn

tabular-classification

--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: model_main_v2_hf.joblib widget: - structuredData: alcohol: - 10.8 - 9.6 - 11.7 chlorides: - 0.171 - 0.095 - 0.063 citric acid: - 0.43 - 0.0 - 0.33 density: - 0.9982 - 0.99854 - 0.99516 fixed acidity: - 10.8 - 8.1 - 9.1 free sulfur dioxide: - 27.0 - 5.0 - 13.0 pH: - 3.17 - 3.36 - 3.26 residual sugar: - 2.1 - 4.1 - 2.05 sulphates: - 0.76 - 0.53 - 0.84 total sulfur dioxide: - 66.0 - 14.0 - 27.0 volatile acidity: - 0.47 - 0.82 - 0.29 --- # Model description This is the best model ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------| | bootstrap | True | | ccp_alpha | 0.0 | | class_weight | | | criterion | gini | | max_depth | | | max_features | sqrt | | max_leaf_nodes | | | max_samples | | | min_impurity_decrease | 0.0 | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | monotonic_cst | | | n_estimators | 100 | | n_jobs | | | oob_score | False | | random_state | 0 | | verbose | 0 | | warm_start | False | </details> ### Model Plot <style>#sk-container-id-1 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;} }#sk-container-id-1 {color: var(--sklearn-color-text); }#sk-container-id-1 pre {padding: 0; }#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; }#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); }#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative; }#sk-container-id-1 div.sk-text-repr-fallback {display: none; }div.sk-parallel-item, div.sk-serial, div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; }/* Parallel-specific style estimator block */#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; }#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; }#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column; }#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; }#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; }#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0; }/* Serial-specific style estimator block */#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is clickable and can be expanded/collapsed. - Pipeline and ColumnTransformer use this feature and define the default style - Estimators will overwrite some part of the style using the `sk-estimator` class *//* Pipeline and ColumnTransformer style (default) */#sk-container-id-1 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background); }/* Toggleable label */ #sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center; }#sk-container-id-1 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); }#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); }/* Toggleable content - dropdown */#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); }#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); }#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; }#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; }/* Pipeline/ColumnTransformer-specific style */#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); }/* Estimator-specific style *//* Colorize estimator box */ #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2); }#sk-container-id-1 div.sk-label label.sk-toggleable__label, #sk-container-id-1 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); }/* On hover, darken the color of the background */ #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); }/* Label box, darken color on hover, fitted */ #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); }/* Estimator label */#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; }#sk-container-id-1 div.sk-label-container {text-align: center; }/* Estimator-specific */ #sk-container-id-1 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); }/* on hover */ #sk-container-id-1 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, a:link.sk-estimator-doc-link, a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1); }.sk-estimator-doc-link.fitted, a:link.sk-estimator-doc-link.fitted, a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); }/* On hover */ div.sk-estimator:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover, div.sk-label-container:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover, div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }/* Span, style for the box shown on hovering the info icon */ .sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3); }.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); }.sk-estimator-doc-link:hover span {display: block; }/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-1 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; }#sk-container-id-1 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); }/* On hover */ #sk-container-id-1 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); } </style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;RandomForestClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(random_state=0)</pre></div> </div></div></div></div> ## Evaluation Results | Metric | Value | |----------|---------| | accuracy | 0.7125 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # citation_bibtex bibtex @inproceedings{...,year={2020}} # get_started_code import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) # model_card_authors skops_user # limitations This model is not ready to be used in production. # model_description This is a RandomForest Model model trained on wine classification dataset. # confusion_matrix 

silent666/Qwen-Qwen1.5-7B-1718026036

silent666

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Propicto/t2p-nllb-200-distilled-600M-commonvoice

Propicto

transformers

text2text-generation

--- license: apache-2.0 ---