Datasets:

librarian-bots

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model_cards_with_metadata_test

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yepyepyo/Minji_NewJeans

yepyepyo

Entry not found

mattzhang/idefics-9b-doodles

mattzhang

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]

mttgermano/q-FrozenLake-v1-4x4-noSlippery

mttgermano

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="mttgermano/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

mttgermano/q-Taxi-v3

mttgermano

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 name: mean_reward verified: false --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="mttgermano/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

vakev/TEST

vakev

--- license: apache-2.0 ---

Andy1124233/Capstone_Forecaster

Andy1124233

--- license: mit ---

wanghx47/sdxl_perturbed_attention_guidance

wanghx47

unconditional-image-generation

--- language: - en pipeline_tag: unconditional-image-generation tags: - Diffusion Models - Stable Diffusion - Perturbed-Attention Guidance - PAG --- # Perturbed-Attention Guidance for SDXL <div style="display:flex"> <video width=50% autoplay loop controls> <source src="https://huggingface.co/multimodalart/sdxl_perturbed_attention_guidance/resolve/main/pag_sdxl.mp4" type="video/mp4"> </video> <video width=50% autoplay loop controls> <source src="https://huggingface.co/multimodalart/sdxl_perturbed_attention_guidance/resolve/main/pag_uncond.mp4" type="video/mp4"> </video> </div> The original Perturbed-Attention Guidance for unconditional models and SD1.5 by [Hyoungwon Cho](https://huggingface.co/hyoungwoncho) is availiable at [hyoungwoncho/sd_perturbed_attention_guidance](https://huggingface.co/hyoungwoncho/sd_perturbed_attention_guidance) [Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance) This repository is just a simple SDXL implementation of the Perturbed-Attention Guidance (PAG) on Stable Diffusion XL (SDXL) for the 🧨 diffusers library. ## Quickstart Loading Custom Pipeline: ```py from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance", torch_dtype=torch.float16 ) device="cuda" pipe = pipe.to(device) ``` Unconditional sampling with PAG:  ```py output = pipe( "", num_inference_steps=50, guidance_scale=0.0, pag_scale=5.0, pag_applied_layers=['mid'] ).images ``` Sampling with PAG and CFG:  ```py output = pipe( "the spirit of a tamagotchi wandering in the city of Vienna", num_inference_steps=25, guidance_scale=4.0, pag_scale=3.0, pag_applied_layers=['mid'] ).images ``` ## Parameters `guidance_scale` : guidance scale of CFG (ex: `7.5`) `pag_scale` : guidance scale of PAG (ex: `4.0`) `pag_applied_layers`: layer to apply perturbation (ex: ['mid']) `pag_applied_layers_index` : index of the layers to apply perturbation (ex: ['m0', 'm1']) ## Stable Diffusion XL Demo [Try it here](https://huggingface.co/spaces/multimodalart/perturbed-attention-guidance-sdxl)

Abosteet/openai-whisper-large-v3-Arabic

Abosteet

transformers

automatic-speech-recognition

--- library_name: transformers datasets: - mozilla-foundation/common_voice_17_0 language: - ar pipeline_tag: automatic-speech-recognition license: apache-2.0 --- # 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]

bmehrba/Meta-Llama-3-8B-Instruct-fine-tuned-adapters_Llama3_8b_rephrasetesting2_50epochs

bmehrba

Entry not found

bmehrba/Meta-Llama-3-8B-Instruct-fine-tuned-adapters_Llama3_8b_rephrasetesting2_100epochs

bmehrba

Entry not found

nihil117/QJab_v.03

nihil117

transformers

text-generation

--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** nihil117 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-v0.3-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)

vdavidr/CodeLlama-13b-Instruct-hf_En__translations_size_104_epochs_10_2024-06-22_07-00-27_3558000

vdavidr

--- license: llama2 base_model: codellama/CodeLlama-13b-Instruct-hf tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: CodeLlama-13b-Instruct-hf_En__translations_size_104_epochs_10_2024-06-22_07-00-27_3558000 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. --> # CodeLlama-13b-Instruct-hf_En__translations_size_104_epochs_10_2024-06-22_07-00-27_3558000 This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7291 - Accuracy: 0.053 - Chrf: 0.654 - Bleu: 0.554 - Sacrebleu: 0.6 - Rouge1: 0.61 - Rouge2: 0.367 - Rougel: 0.556 - Rougelsum: 0.603 - Meteor: 0.542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.2903 | 4.0 | 104 | 2.0336 | 0.052 | 0.541 | 0.417 | 0.4 | 0.524 | 0.268 | 0.48 | 0.519 | 0.477 | | 0.2305 | 8.0 | 208 | 2.2999 | 0.05 | 0.513 | 0.412 | 0.4 | 0.495 | 0.26 | 0.454 | 0.489 | 0.453 | | 0.1581 | 12.0 | 312 | 2.0944 | 0.054 | 0.566 | 0.475 | 0.5 | 0.548 | 0.313 | 0.502 | 0.543 | 0.52 | | 0.6402 | 16.0 | 416 | 1.9048 | 0.05 | 0.601 | 0.482 | 0.5 | 0.568 | 0.32 | 0.521 | 0.562 | 0.544 | | 0.1487 | 20.0 | 520 | 2.0162 | 0.054 | 0.591 | 0.487 | 0.5 | 0.567 | 0.324 | 0.516 | 0.556 | 0.472 | | 0.1857 | 24.0 | 624 | 2.0241 | 0.055 | 0.572 | 0.481 | 0.5 | 0.545 | 0.304 | 0.499 | 0.542 | 0.509 | | 0.718 | 28.0 | 728 | 1.7555 | 0.054 | 0.63 | 0.529 | 0.5 | 0.595 | 0.345 | 0.549 | 0.588 | 0.532 | | 0.1329 | 32.0 | 832 | 1.7785 | 0.053 | 0.651 | 0.554 | 0.6 | 0.619 | 0.379 | 0.567 | 0.614 | 0.536 | | 0.2488 | 36.0 | 936 | 1.7203 | 0.052 | 0.657 | 0.556 | 0.6 | 0.608 | 0.371 | 0.558 | 0.599 | 0.549 | | 0.1337 | 40.0 | 1040 | 1.7291 | 0.053 | 0.654 | 0.554 | 0.6 | 0.61 | 0.367 | 0.556 | 0.603 | 0.542 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

mltang2/my-test-llm-michael

mltang2

--- license: llama3 ---

Fudan-FUXI/llm-conditioned-diffusion-v1.0

Fudan-FUXI

text-to-image

--- license: apache-2.0 language: - en - zh pipeline_tag: text-to-image ---

magnifi/parser_user_v8-0621-epoch7-0.002_systempromptv3_trainonly

magnifi

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** magnifi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-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)

hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand10-aligned_unaugmentation

hchcsuim

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand10-aligned_unaugmentation 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: 0.9927594429960584 - name: Precision type: precision value: 0.9939030199104506 - name: Recall type: recall value: 0.9981139553636103 - name: F1 type: f1 value: 0.9960040368774208 --- <!-- 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. --> # batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand10-aligned_unaugmentation This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0211 - Accuracy: 0.9928 - Precision: 0.9939 - Recall: 0.9981 - F1: 0.9960 - Roc Auc: 0.9990 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | 0.0526 | 0.9994 | 1264 | 0.0211 | 0.9928 | 0.9939 | 0.9981 | 0.9960 | 0.9990 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1

fudan-generative-ai/hallo

fudan-generative-ai

diffusers

--- license: mit --- <h1 align='Center'>Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation</h1> <div align='Center'> <a href='https://github.com/xumingw' target='_blank'>Mingwang Xu</a>^1*&emsp; <a href='https://github.com/crystallee-ai' target='_blank'>Hui Li</a>^1*&emsp; <a href='https://github.com/subazinga' target='_blank'>Qingkun Su</a>^1*&emsp; <a href='https://github.com/NinoNeumann' target='_blank'>Hanlin Shang</a>¹&emsp; <a href='https://github.com/AricGamma' target='_blank'>Liwei Zhang</a>¹&emsp; <a href='https://github.com/cnexah' target='_blank'>Ce Liu</a>³&emsp; </div> <div align='center'> <a href='https://jingdongwang2017.github.io/' target='_blank'>Jingdong Wang</a>²&emsp; <a href='https://yoyo000.github.io/' target='_blank'>Yao Yao</a>⁴&emsp; <a href='https://sites.google.com/site/zhusiyucs/home' target='_blank'>Siyu Zhu</a>¹&emsp; </div> <div align='Center'> ¹Fudan University&emsp; ²Baidu Inc&emsp; ³ETH Zurich&emsp; ⁴Nanjing University </div> <br> <div align='center'> <a href='https://github.com/fudan-generative-vision/hallo'><img src='https://img.shields.io/github/stars/fudan-generative-vision/hallo?style=social'></a> <a href='https://fudan-generative-vision.github.io/hallo/#/'><img src='https://img.shields.io/badge/Project-HomePage-Green'></a> <a href='https://arxiv.org/pdf/2406.08801'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/fudan-generative-ai/hallo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-yellow'></a> <a href='assets/wechat.jpeg'><img src='https://badges.aleen42.com/src/wechat.svg'></a> </div> <br> # Social Risks and Mitigations The development of portrait image animation technologies driven by audio inputs poses social risks, such as the ethical implications of creating realistic portraits that could be misused for deepfakes. To mitigate these risks, it is crucial to establish ethical guidelines and responsible use practices. Privacy and consent concerns also arise from using individuals' images and voices. Addressing these involves transparent data usage policies, informed consent, and safeguarding privacy rights. By addressing these risks and implementing mitigations, the research aims to ensure the responsible and ethical development of this technology.

Coolwowsocoolwow/Morshu_11

Coolwowsocoolwow

--- license: openrail ---

bmehrba/Meta-Llama-3-8B-Instruct-fine-tuned-adapters_Llama3_8b_rephrasetesting2_20epochs

bmehrba

Entry not found

davidyu2023/Qwen-Qwen1.5-7B-1719029444

davidyu2023

peft

--- base_model: Qwen/Qwen1.5-7B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

davidyu2023/google-gemma-2b-1719029522

davidyu2023

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

hasininawoda/3d-icon-sdxl-lora

hasininawoda

Entry not found

bmehrba/Meta-Llama-3-8B-Instruct-fine-tuned-adapters_Llama3_8b_rephrasetesting2_30epochs

bmehrba

Entry not found

Wawaworker/exsswimsuit

Wawaworker

Entry not found

AdamSayyed/my_awesome_model

AdamSayyed

Entry not found

xummer/adversarial_qa_dbert_based_on

xummer

transformers

text2text-generation

--- license: apache-2.0 base_model: google-t5/t5-large tags: - generated_from_trainer metrics: - bleu model-index: - name: fft-t5-large/adversarial_qa_dbert_based_on 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. --> # fft-t5-large/adversarial_qa_dbert_based_on This model is a fine-tuned version of [google-t5/t5-large](https://huggingface.co/google-t5/t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1381 - Exact Match: 0.3467 - Bleu: 0.3083 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:| | 1.0162 | 1.0 | 63 | 0.7607 | 0.2754 | 0.2749 | | 0.3929 | 2.0 | 126 | 0.7943 | 0.2959 | 0.2412 | | 0.1542 | 3.0 | 189 | 1.0053 | 0.3018 | 0.2720 | | 0.0544 | 4.0 | 252 | 1.1005 | 0.3457 | 0.3185 | | 0.0239 | 5.0 | 315 | 1.1381 | 0.3467 | 0.3083 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

RajuEEE/GPT2_FineTunedModel_GPTData_ThreeLabel

RajuEEE

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]

synap-du/kocamel-13b-inst

synap-du

Entry not found

arabic-translation-prompt-engineering/atpe-notebooks

arabic-translation-prompt-engineering

--- license: openrail ---

nishantup/example-model

nishantup

--- license: mit ---

geraldabrhm/llama-3-8b-regulardataset-simplecontext-32lora

geraldabrhm

Entry not found

01N02/StudioVip

01N02

--- license: unknown ---

samrocksc/learngod

samrocksc

token-classification

--- license: mit datasets: - OpenGVLab/ShareGPT-4o language: - en metrics: - accuracy - character pipeline_tag: token-classification ---

ParZiVal04/lora_model_1

ParZiVal04

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ParZiVal04 - **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)

klandtech/kland_name3

klandtech

Entry not found

cxfajar197/bert-base-multilingual-cased-finetuned-imdb

cxfajar197

transformers

fill-mask

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-multilingual-cased-finetuned-imdb 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. --> # bert-base-multilingual-cased-finetuned-imdb This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3958 | 1.0 | 50 | 2.2363 | | 1.5939 | 2.0 | 100 | 1.9750 | | 1.8532 | 3.0 | 150 | 2.1309 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

shiromiya/program

shiromiya

Entry not found

xummer/adversarial_qa_dbert_answer_the_following_q

xummer

transformers

text2text-generation

Entry not found

Dahepix/models

Dahepix

Entry not found

habberrih/olive-branch

habberrih

--- license: apache-2.0 ---

ankunjin/test

ankunjin

peft

--- base_model: beomi/KoAlpaca-Polyglot-5.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_unaugmentation

hchcsuim

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: batch-size16_FFPP-raw_opencv-1FPS_unaugmentation 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: 0.9033487951125693 - name: Precision type: precision value: 0.8971202577231833 - name: Recall type: recall value: 0.990071106486299 - name: F1 type: f1 value: 0.9413066030930314 --- <!-- 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. --> # batch-size16_FFPP-raw_opencv-1FPS_unaugmentation This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2388 - Accuracy: 0.9033 - Precision: 0.8971 - Recall: 0.9901 - F1: 0.9413 - Roc Auc: 0.9623 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | 0.2365 | 0.9998 | 1381 | 0.2388 | 0.9033 | 0.8971 | 0.9901 | 0.9413 | 0.9623 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1

Hanbitchan/CoreMLModels

Hanbitchan

Entry not found

Ketansomewhere/sd-naruto-model

Ketansomewhere

Entry not found

HyperdustProtocol/HyperAuto-cog-llama2-7b-4555

HyperdustProtocol

transformers

--- base_model: unsloth/llama-2-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # 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)

bmehrba/Meta-Llama-3-8B-Instruct-fine-tuned-adapters_Llama3_8b_rephrasetesting2_200epochs

bmehrba

Entry not found

frankperdomo1947/Videocool

frankperdomo1947

--- license: apache-2.0 ---

AliGhiasvand86/last_checkpoint

AliGhiasvand86

transformers

text2text-generation

--- license: mit ---

sosnaji/mohammd

sosnaji

Entry not found

wallaceblaia/wisper-icm-17v

wallaceblaia

Entry not found

hiudy/Stella

hiudy

--- license: other license_name: nzn license_link: https://painel.nodzhost.com.br/license ---

Imran1/embadding

Imran1

transformers

--- license: mit --- # Model using ```python from transformers import AutoConfig, AutoTokenizer from torch import nn import torch.nn.functional as F import torch # First, define your custom model class again class HFCustomBertModel(nn.Module): def __init__(self, config): super().__init__() self.bert = BertModel(config) self.pooler = nn.Sequential( nn.Linear(config.hidden_size, config.hidden_size), nn.Tanh() ) def forward(self, input_ids, attention_mask=None, token_type_ids=None): outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) pooled_output = self.pooler(outputs.pooler_output) return pooled_output def load_custom_model_and_tokenizer(model_path): # Load the config config = AutoConfig.from_pretrained(model_path) # Initialize the custom model with the config model = HFCustomBertModel(config) # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_path) return model, tokenizer # Usage model_path = "Imran1/embadding" model, tokenizer = load_custom_model_and_tokenizer(model_path) queries = ["how much protein should a female eat"] documents = ["As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day."] model.eval() # Set the model to evaluation mode with torch.no_grad(): # Tokenize and encode the queries and documents query_inputs = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") document_inputs = tokenizer(documents, padding=True, truncation=True, return_tensors="pt") # Get embeddings query_embeddings = model(**query_inputs) document_embeddings = model(**document_inputs) # Normalize embeddings query_embeddings = F.normalize(query_embeddings, p=2, dim=1) document_embeddings = F.normalize(document_embeddings, p=2, dim=1) # Calculate cosine similarity scores = torch.matmul(query_embeddings, document_embeddings.transpose(0, 1)) print(f"Similarity score: {scores.item():.4f}") Similarity score: 0.9605 ```

Zzainy/Z.comic

Zzainy

--- license: apache-2.0 ---

bmehrba/Llama-2-7b-chat-hf-fine-tuned-adapters_Llama3_8b_rephrasetesting2_150epochs

bmehrba

Entry not found

v0dkapapi/falcon-7b-sharded-bf16-finetuned-mental-health-conversational

v0dkapapi

--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-sharded-bf16-finetuned-mental-health-conversational 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-sharded-bf16-finetuned-mental-health-conversational This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.13.1 - Tokenizers 0.13.3

RajuEEE/LLama_FineTunedModel_GPTEDataThreeLabel

RajuEEE

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]

vdavidr/CodeLlama-7b-Instruct-hf_En__translations_size_104_epochs_10_2024-06-22_08-44-43_3558001

vdavidr

Entry not found

VKapseln475/Nexalyn59

VKapseln475

# Nexalyn Danmark Anmeldelser - Nexalyn Dose & Intake Works Officiel hjemmeside Køb nu Nexalyn Anmeldelser Dosis og Virker At forbrænde fedt i besværlige områder er en udfordring for mange mennesker på deres vægttabsrejse. Dette stædige kropsfedt kan være frustrerende og svært at målrette mod med kost og motion alene. Nexaslim-tillægget kan dog give den løsning, du har ledt efter. ## **[Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark)** ## Fordele du får ved at bruge Nexalyn Me Supplement Piller - Forventede resultater ### Øget libido En øget libido er resultatet af Nexalyns forbedring af ophidselse og lyst. Der kan opstå en mærkbar stigning i brugernes ønske om nærhed, hvilket fører til større iver og entusiasme for at have samleje med deres partnere. ### Forbedret erektil funktion Med Nexalyn kan brugerne forvente erektioner, der er stærkere, mere komplekse og varer længere. I soveværelset fører denne forbedring af erektil funktion til mere tilfredsstillende og behagelige romantiske møder, som øger selvtilliden og selvværdet. ### Forbedret virilitet Brugere af Nexalyn føler sig mere magtfulde og maskuline, da stoffet tilskynder til større virilitet. Nexalyn hjælper mænd med at projicere selvtillid og styrke under intime interaktioner ved at fremme hormonbalancen og forbedre den romantiske præstation. ### Mere intense orgasmer Nexalyn kan få brugere til at få mere potente og intense orgasmer. Tilskuddet arbejder for at øge følelsen af ​​følsomhed og nydelse under samleje, hvilket resulterer i forbedrede oplevelser og øget klimakstilfredshed. ### Øget energiniveau Nexalyn giver kunderne øget energi, så de kan blive sent oppe. Forbedret udholdenhed og udholdenhed gør det muligt for brugerne at deltage i længerevarende romantiske aktiviteter uden at opleve træthed eller udmattelse, hvilket letter mere behagelige og givende møder. ## Hvem er Nexalyn for? Kan kvinder også bruge Nexalyn? Den primære målgruppe for Nexalyn er mænd, der ønsker at forbedre deres præstationer, sundhed og præstationer. Mænd i alle aldre, der kan have problemer såsom erektil dysfunktion, dårlig libido eller nedsat romantisk tilfredshed, bør tage dette vitamin. Nexalyn er en sikker og effektiv måde at forbedre mænds fysiske sundhed på, uanset dine mål – forbedre kvaliteten af ​​din erektion, øge din lyst eller øge din generelle romantiske vitalitet. Mænd, der ønsker at føle sig mere tilfredse og selvsikre under samleje, kan opleve, at Nexalyn hjælper. Nexalyn kan få dig til at føle dig mere selvsikker og tilfreds i dine intime forhold, uanset om du er langtidsdating med nogen eller lige er startet. Mænd kan opleve mere tilfredsstillende og glædelige romantiske møder med Nexalyn, hvilket øger nærhed og lykke i forhold ved at fremme mange facetter af mænds intime sundhed. ## **[Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark)**

Krish778/gpt-neo-2.7B-custom

Krish778

Entry not found

Seikaijyu/RWKV-x060-World-3B-v2.1-Chat-Anthropomorphic

Seikaijyu

--- license: mit language: - zh --- ### 模型说明 #### 基于RWKV6-v2.1-3B基底模型微调的进行pissa微调的chat模型，此模型开箱即用，无需过多准备 #### 使用ChatGLM4基于[优美的中国话](https://huggingface.co/datasets/Seikaijyu/Beautiful-Chinese)语料进行异构，使单轮语料成为多轮语料，并进行了大量清洗后得到了2632条多轮对话数据集后训练的模型 #### 此模型仅微调了Assistant对话，无任何额外数据微调，但是经过测试，语料对其它角色有一定的泛化作用 #### 效果如下：    推荐参数如下： ##### Temperature=1-3之间 ##### Top_P=0.55-0.65之间 ##### Presence Penalty=0.4-0之间 ##### Frequency Penalty=0.4-1.2之间

hussnainahmedsaqib/xlnet_ner_res_pipeline

hussnainahmedsaqib

Entry not found

Minutor/new-dummy-model

Minutor

Entry not found

ymoslem/whisper-medium-ga2en-v6.3.2-15k-r

ymoslem

transformers

automatic-speech-recognition

--- language: - ga - en license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - ymoslem/IWSLT2023-GA-EN - ymoslem/FLEURS-GA-EN - ymoslem/BitesizeIrish-GA-EN - ymoslem/SpokenWords-GA-EN-MTed - ymoslem/Tatoeba-Speech-Irish - ymoslem/Wikimedia-Speech-Irish - ymoslem/EUbookshop-Speech-Irish metrics: - bleu - wer model-index: - name: Whisper Medium GA-EN Speech Translation results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop type: ymoslem/IWSLT2023-GA-EN metrics: - name: Bleu type: bleu value: 34.85 - name: Wer type: wer value: 60.91850517784781 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Medium GA-EN Speech Translation This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset. It achieves the following results on the evaluation set: - Loss: 1.2038 - Bleu: 34.85 - Chrf: 54.43 - Wer: 60.9185 ## 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: 0.03 - training_steps: 15000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |:-------------:|:------:|:-----:|:-----:|:-----:|:---------------:|:--------:| | 2.5219 | 0.0138 | 100 | 0.44 | 10.48 | 2.1106 | 107.2490 | | 2.4608 | 0.0276 | 200 | 3.3 | 20.43 | 2.1816 | 179.1535 | | 2.3008 | 0.0414 | 300 | 3.66 | 21.59 | 2.0587 | 206.4836 | | 2.2095 | 0.0552 | 400 | 8.79 | 27.66 | 1.9459 | 100.3602 | | 2.0454 | 0.0690 | 500 | 8.14 | 27.36 | 1.8681 | 122.1522 | | 1.9937 | 0.0828 | 600 | 11.05 | 30.26 | 1.8717 | 97.2535 | | 1.868 | 0.0966 | 700 | 9.14 | 29.03 | 1.7917 | 129.0410 | | 1.9924 | 0.1103 | 800 | 12.62 | 33.2 | 1.7170 | 89.6443 | | 1.8646 | 0.1241 | 900 | 11.98 | 30.77 | 1.7252 | 97.8838 | | 1.7644 | 0.1379 | 1000 | 10.87 | 31.0 | 1.6832 | 109.1851 | | 1.692 | 0.1517 | 1100 | 13.05 | 34.46 | 1.6837 | 93.3814 | | 1.7044 | 0.1655 | 1200 | 20.95 | 37.42 | 1.5527 | 75.2364 | | 1.6824 | 0.1793 | 1300 | 14.91 | 35.56 | 1.5611 | 92.6159 | | 1.6557 | 0.1931 | 1400 | 14.0 | 36.54 | 1.5554 | 99.8199 | | 1.5456 | 0.2069 | 1500 | 19.72 | 39.81 | 1.5058 | 83.5660 | | 1.3755 | 0.2207 | 1600 | 18.04 | 37.95 | 1.5039 | 82.9806 | | 1.3959 | 0.2345 | 1700 | 17.01 | 39.5 | 1.4374 | 85.2319 | | 1.5012 | 0.2483 | 1800 | 14.93 | 39.24 | 1.4242 | 114.4079 | | 1.4278 | 0.2621 | 1900 | 23.85 | 42.69 | 1.3904 | 73.0302 | | 1.3285 | 0.2759 | 2000 | 17.7 | 37.23 | 1.4493 | 83.8811 | | 1.2655 | 0.2897 | 2100 | 20.1 | 40.32 | 1.3661 | 79.7839 | | 1.2074 | 0.3034 | 2200 | 24.45 | 43.79 | 1.3387 | 72.9851 | | 1.1893 | 0.3172 | 2300 | 21.45 | 42.61 | 1.3308 | 82.3953 | | 1.1236 | 0.3310 | 2400 | 22.77 | 44.17 | 1.3050 | 77.3075 | | 1.0934 | 0.3448 | 2500 | 25.54 | 46.32 | 1.2793 | 72.2647 | | 1.06 | 0.3586 | 2600 | 28.27 | 47.32 | 1.2396 | 65.6911 | | 1.0327 | 0.3724 | 2700 | 28.45 | 47.01 | 1.2577 | 67.3570 | | 1.1623 | 0.3862 | 2800 | 24.54 | 47.43 | 1.2194 | 73.6155 | | 1.0215 | 0.4 | 2900 | 27.4 | 49.6 | 1.2039 | 69.2481 | | 0.9185 | 0.4138 | 3000 | 27.04 | 49.24 | 1.1724 | 67.8973 | | 0.9003 | 0.4276 | 3100 | 31.08 | 50.11 | 1.1674 | 63.8001 | | 0.9839 | 0.4414 | 3200 | 30.24 | 50.63 | 1.1580 | 64.5655 | | 0.9396 | 0.4552 | 3300 | 30.79 | 51.72 | 1.1202 | 64.9257 | | 0.9051 | 0.4690 | 3400 | 30.34 | 53.08 | 1.1180 | 66.4566 | | 0.8621 | 0.4828 | 3500 | 33.3 | 53.86 | 1.1042 | 60.7834 | | 0.8236 | 0.4966 | 3600 | 32.77 | 53.21 | 1.1070 | 62.0441 | | 0.829 | 0.5103 | 3700 | 32.49 | 54.21 | 1.0771 | 62.5844 | | 0.8375 | 0.5241 | 3800 | 32.27 | 53.98 | 1.0780 | 63.0797 | | 0.8206 | 0.5379 | 3900 | 33.26 | 55.07 | 1.0615 | 61.6389 | | 0.8059 | 0.5517 | 4000 | 33.24 | 55.16 | 1.0552 | 61.5038 | | 0.9133 | 0.5655 | 4100 | 29.38 | 49.22 | 1.2218 | 66.0964 | | 1.051 | 0.5793 | 4200 | 25.12 | 46.01 | 1.2304 | 71.8145 | | 0.954 | 0.5931 | 4300 | 25.47 | 45.88 | 1.2501 | 75.3715 | | 0.939 | 0.6069 | 4400 | 29.19 | 47.63 | 1.2204 | 66.9068 | | 0.9887 | 0.6207 | 4500 | 27.99 | 47.01 | 1.2099 | 67.7172 | | 1.0044 | 0.6345 | 4600 | 23.77 | 45.33 | 1.2080 | 73.3904 | | 0.9881 | 0.6483 | 4700 | 26.46 | 47.36 | 1.2188 | 68.5277 | | 0.9674 | 0.6621 | 4800 | 26.11 | 45.92 | 1.2296 | 68.3026 | | 0.8845 | 0.6759 | 4900 | 27.3 | 46.08 | 1.2347 | 68.0324 | | 0.8297 | 0.6897 | 5000 | 29.48 | 48.96 | 1.2108 | 64.6105 | | 0.9065 | 0.7034 | 5100 | 29.81 | 49.94 | 1.1873 | 64.2503 | | 0.8096 | 0.7172 | 5200 | 28.5 | 46.93 | 1.2122 | 66.2314 | | 0.8077 | 0.7310 | 5300 | 29.26 | 48.21 | 1.1945 | 64.4755 | | 0.8227 | 0.7448 | 5400 | 26.82 | 48.43 | 1.2310 | 71.4093 | | 0.7587 | 0.7586 | 5500 | 29.45 | 49.03 | 1.2067 | 65.3309 | | 0.7206 | 0.7724 | 5600 | 29.89 | 49.33 | 1.2114 | 65.5561 | | 0.8088 | 0.7862 | 5700 | 31.88 | 51.4 | 1.1689 | 64.2954 | | 0.693 | 0.8 | 5800 | 27.23 | 48.11 | 1.1644 | 68.7078 | | 0.7099 | 0.8138 | 5900 | 31.01 | 49.42 | 1.1852 | 63.3949 | | 0.7564 | 0.8276 | 6000 | 28.3 | 50.34 | 1.1554 | 71.0941 | | 0.584 | 0.8414 | 6100 | 34.79 | 51.69 | 1.1566 | 59.0725 | | 0.6817 | 0.8552 | 6200 | 34.08 | 51.95 | 1.1245 | 59.8829 | | 0.5968 | 0.8690 | 6300 | 32.4 | 51.59 | 1.1475 | 62.9896 | | 0.6092 | 0.8828 | 6400 | 32.83 | 52.82 | 1.1250 | 62.5844 | | 0.6325 | 0.8966 | 6500 | 29.29 | 51.68 | 1.1108 | 69.1130 | | 0.6002 | 0.9103 | 6600 | 27.64 | 52.7 | 1.0993 | 71.0941 | | 0.6247 | 0.9241 | 6700 | 28.39 | 52.4 | 1.0898 | 68.3026 | | 0.6257 | 0.9379 | 6800 | 28.54 | 52.33 | 1.0863 | 70.9140 | | 0.6719 | 0.9517 | 6900 | 31.43 | 53.53 | 1.0891 | 66.1414 | | 0.4994 | 0.9655 | 7000 | 33.81 | 52.77 | 1.1066 | 61.0986 | | 0.5469 | 0.9793 | 7100 | 30.52 | 53.13 | 1.0891 | 67.3570 | | 0.6031 | 0.9931 | 7200 | 33.16 | 54.03 | 1.0933 | 62.1792 | | 0.2469 | 1.0069 | 7300 | 33.76 | 52.38 | 1.1426 | 62.8546 | | 0.2572 | 1.0207 | 7400 | 33.16 | 51.71 | 1.1292 | 64.8807 | | 0.2762 | 1.0345 | 7500 | 34.76 | 54.28 | 1.1090 | 60.7384 | | 0.2332 | 1.0483 | 7600 | 30.95 | 52.28 | 1.1073 | 66.1864 | | 0.2069 | 1.0621 | 7700 | 32.39 | 53.08 | 1.0999 | 65.5561 | | 0.2417 | 1.0759 | 7800 | 31.3 | 53.87 | 1.1008 | 65.1058 | | 0.2403 | 1.0897 | 7900 | 32.18 | 53.3 | 1.1053 | 66.4566 | | 0.208 | 1.1034 | 8000 | 32.0 | 52.48 | 1.1067 | 66.7717 | | 0.3328 | 1.1172 | 8100 | 28.92 | 49.12 | 1.2137 | 68.4376 | | 0.4045 | 1.1310 | 8200 | 28.47 | 51.53 | 1.2165 | 68.3926 | | 0.4175 | 1.1448 | 8300 | 26.88 | 47.57 | 1.2790 | 74.5160 | | 0.3976 | 1.1586 | 8400 | 21.56 | 44.64 | 1.3060 | 84.1513 | | 0.4026 | 1.1724 | 8500 | 25.22 | 47.73 | 1.2476 | 73.1202 | | 0.4088 | 1.1862 | 8600 | 26.03 | 48.08 | 1.2387 | 72.8050 | | 0.4245 | 1.2 | 8700 | 29.8 | 49.69 | 1.2136 | 67.4021 | | 0.4083 | 1.2138 | 8800 | 26.26 | 48.23 | 1.2784 | 73.4804 | | 0.3832 | 1.2276 | 8900 | 29.06 | 49.36 | 1.2527 | 66.4115 | | 0.4335 | 1.2414 | 9000 | 30.11 | 49.24 | 1.2772 | 67.2670 | | 0.4056 | 1.2552 | 9100 | 32.51 | 50.18 | 1.3013 | 63.3048 | | 0.3877 | 1.2690 | 9200 | 26.91 | 47.47 | 1.2897 | 71.5894 | | 0.3787 | 1.2828 | 9300 | 1.2430| 30.16 | 50.61 | 65.1058 | | 0.3947 | 1.2966 | 9400 | 1.2318| 29.9 | 50.77 | 66.0964 | | 0.3908 | 1.3103 | 9500 | 1.1927| 30.7 | 51.62 | 64.6105 | | 0.405 | 1.3241 | 9600 | 1.2249| 26.56 | 49.05 | 71.7695 | | 0.3847 | 1.3379 | 9700 | 1.2105| 33.22 | 51.98 | 61.8640 | | 0.3674 | 1.3517 | 9800 | 1.2545| 30.93 | 50.34 | 65.6011 | | 0.3642 | 1.3655 | 9900 | 1.2443| 25.23 | 47.97 | 77.9379 | | 0.3636 | 1.3793 | 10000 | 1.2796| 26.78 | 48.07 | 73.6155 | | 0.329 | 1.3931 | 10100 | 1.2373| 29.06 | 49.55 | 66.4566 | | 0.4195 | 1.4069 | 10200 | 1.2187| 29.11 | 50.65 | 66.2314 | | 0.4244 | 1.4207 | 10300 | 1.2346| 27.97 | 49.86 | 69.0680 | | 0.3338 | 1.4345 | 10400 | 1.2239| 29.96 | 50.45 | 66.0063 | | 0.3401 | 1.4483 | 10500 | 1.2501| 29.84 | 51.0 | 65.6911 | | 0.3792 | 1.4621 | 10600 | 1.2353| 28.38 | 49.19 | 69.1130 | | 0.3549 | 1.4759 | 10700 | 1.2178| 28.63 | 49.73 | 68.5727 | | 0.3326 | 1.4897 | 10800 | 1.1936| 29.57 | 51.1 | 64.4755 | | 0.3418 | 1.5034 | 10900 | 1.1741| 33.06 | 52.86 | 60.9185 | | 0.3143 | 1.5172 | 11000 | 1.2046| 31.49 | 50.4 | 63.5750 | | 0.3245 | 1.5310 | 11100 | 1.2145| 30.9 | 50.17 | 64.6105 | | 0.3268 | 1.5448 | 11200 | 1.2119| 33.5 | 53.0 | 60.2431 | | 0.2894 | 1.5586 | 11300 | 1.2126| 32.01 | 52.17 | 61.0986 | | 0.2702 | 1.5724 | 11400 | 1.2213| 31.33 | 50.89 | 63.7551 | | 0.2876 | 1.5862 | 11500 | 1.2126| 31.44 | 51.28 | 63.1697 | | 0.2759 | 1.6 | 11600 | 1.2283| 30.49 | 51.02 | 64.7456 | | 0.2902 | 1.6138 | 11700 | 1.2205| 32.33 | 50.53 | 63.2148 | | 0.2638 | 1.6276 | 11800 | 1.2097| 31.89 | 51.14 | 62.6745 | | 0.2605 | 1.6414 | 11900 | 1.2129| 31.35 | 50.63 | 63.3048 | | 0.2374 | 1.6552 | 12000 | 1.2319| 31.48 | 51.73 | 63.4849 | | 0.2436 | 1.6690 | 12100 | 1.2219| 30.43 | 50.92 | 65.5110 | | 0.2366 | 1.6828 | 12200 | 1.2367| 31.64 | 51.14 | 64.7006 | | 0.218 | 1.6966 | 12300 | 1.2142| 30.8 | 51.63 | 64.1153 | | 0.2313 | 1.7103 | 12400 | 1.1877| 30.8 | 50.63 | 64.5655 | | 0.2307 | 1.7241 | 12500 | 1.1817| 32.22 | 51.41 | 63.3498 | | 0.2638 | 1.7379 | 12600 | 1.1514| 33.74 | 52.11 | 60.6033 | | 0.2211 | 1.7517 | 12700 | 1.1563| 30.71 | 52.07 | 64.5655 | | 0.197 | 1.7655 | 12800 | 1.1941| 32.22 | 52.9 | 62.8546 | | 0.2307 | 1.7793 | 12900 | 1.1771| 32.83 | 52.96 | 62.7645 | | 0.198 | 1.7931 | 13000 | 1.1908| 32.16 | 51.85 | 63.9352 | | 0.1716 | 1.8069 | 13100 | 1.2065| 31.91 | 51.37 | 62.6294 | | 0.2031 | 1.8207 | 13200 | 1.1745| 31.83 | 51.86 | 64.0252 | | 0.1785 | 1.8345 | 13300 | 1.1607| 31.33 | 52.57 | 64.7006 | | 0.2013 | 1.8483 | 13400 | 1.1785| 33.29 | 53.34 | 62.6745 | | 0.1842 | 1.8621 | 13500 | 1.1723| 34.41 | 54.31 | 60.0630 | | 0.2015 | 1.8759 | 13600 | 1.1859| 32.88 | 53.07 | 62.2692 | | 0.1848 | 1.8897 | 13700 | 1.1668| 33.62 | 53.75 | 62.8095 | | 0.1394 | 1.9034 | 13800 | 1.1734| 34.33 | 54.03 | 61.2787 | | 0.1774 | 1.9172 | 13900 | 1.1735| 32.63 | 53.37 | 62.8996 | | 0.1506 | 1.9310 | 14000 | 1.1768| 35.17 | 54.34 | 59.4327 | | 0.1399 | 1.9448 | 14100 | 1.1827| 33.68 | 53.8 | 62.1792 | | 0.1434 | 1.9586 | 14200 | 1.1721| 34.62 | 54.24 | 60.9185 | | 0.1203 | 1.9724 | 14300 | 1.1733| 34.08 | 53.75 | 61.8190 | | 0.1417 | 1.9862 | 14400 | 1.1615| 33.98 | 54.19 | 62.1792 | | 0.1458 | 2.0 | 14500 | 1.1739| 33.65 | 53.31 | 62.9896 | | 0.07 | 2.0138 | 14600 | 1.1916| 33.98 | 53.96 | 61.9090 | | 0.051 | 2.0276 | 14700 | 1.1967| 34.13 | 54.36 | 61.1887 | | 0.0481 | 2.0414 | 14800 | 1.2024| 34.06 | 54.38 | 61.4588 | | 0.0574 | 2.0552 | 14900 | 1.2038| 34.23 | 54.08 | 61.2787 | | 0.0621 | 2.0690 | 15000 | 1.2038| 34.85 | 54.43 | 60.9185 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

ShapeKapseln33/BPZone34

ShapeKapseln33

BP Zone USA Reviews BP Zone is a dietary supplement to help keep blood pressure healthy by lowering oxidative stress using various organic, non-harmful ingredients. The official website says that the BP Zone supplement makes it possible for the body to fully absorb the powerful components and start immediately to speed up the process of making nitric oxide. **[Click here to buy now from official website of BP Zone](https://capsules24x7.com/bp-zone)** ##BP Zone Ingredient? BP zone ingredients are the goodwill ambassador of this natural supplement. Naturally enriched herbs help blood pressure patients to feel comfortable and enjoy boosted energy, particularly in old age. Dr. Ryan Shylton at the Zenith Labs did a great effort to formulate this advanced formula. Following are the ingredients that have been playing a vital role in controlling high and low blood pressure. ##Ingredients ##Saffron: BP zone ingredients are the goodwill ambassador of this natural supplement. Naturally enriched herbs help blood pressure patients to feel comfortable and enjoy boosted energy, particularly in old age. Following are the ingredients that have been playing a vital role in controlling high and low blood pressure. ##Garlic: Garlic is a source of all, which is anti-oxidative stress. Allin has fiber that has the potential to reduce oxidative stress. Researches show great impact in flexing the tightened platelets. Its various advantages for your body are evident, particularly for cardiac rehabilitation. ##Hawthorn: Inflammatory has adverse impacts on arteries and veins, maintaining blood supply—increased inflammation in arteries weakening heart. Hawthorn has unique anti-inflammatory properties. Hence, Hawthorn not only reduces inflammation from veins and arteries but increases the efficiency of oxygen in the blood. ##Ginger: A powerful medicinal property that has been using in a series of medicine formulas. BP zone supplement has gingerol properties, which are useful to reduce inflammation and oxidants from your body. It is helpful in smooth blood flow. Its antiseptic properties eliminate harmful chemicals and bacteria inside of your body. ##Danshen: Known as a red sage in medical history is a useful herb in treating heart and blood vessels. It plays a vital role in opening arteries and veins. Unblocking blood supply to the heart boosts blood supply and maintains the flow of blood in your body. It also empowers the body to combat oxidative stress and maintain blood pressure. **[Click here to buy now from official website of BP Zone](https://capsules24x7.com/bp-zone)** ##Calcium: The antagonist’s effect of calcium helps to maintain blood pressure. Calcium plays an imperative role in removing blockades to the cardiovascular. Its natural properties hit enzymes and reduce inflammation in your body. It treats diastolic blood pressure and resists hypertension. ##Arjuna: Known as cardiotonic in heart failure. Incredible herb with a series of benefits for human health has a long medical history. Arjuna helps to treat cardiomyopathy and stimulates blood circulation in your body. It helps arteries and vessels supply blood to the heart, where the heart pumps blood to normalize blood pressure. ##Magnesium: A wonderful natural remedy to regulate the blood pressure in your body. It influences the reaction of the enzyme in your body and supporting the immune system to protect your body from disease. Pumpkins, cashew spinach, and almonds are rich foods having magnesium properties. ##CoQ10: Coenzyme Q10 has been supporting heart treatments for a long time. It is a particular ingredient that deals with congestive heart failure and blood pressure. Studies show that CoQ10 has an important role in lowering systolic blood pressure. Systolic is a top reading. Coenzyme Q10 also controls the diastolic pressure by 10mm Hg without significant side effects. ##Berberine HCL: A series of plans include barberry, Oregon grape, and tree t4urmeric provide berberine HCL. The extracts of berberine are useful to control high levels of cholesterol and high blood pressure. A vital extract of plants is also anti-inflammatory. **[Click here to buy now from official website of BP Zone](https://capsules24x7.com/bp-zone)**

geraldabrhm/llama-2-13b-regulardataset-simplecontext-16lora

geraldabrhm

Entry not found

jddllwqa/Qwen-Qwen1.5-1.8B-1719036380

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

jddllwqa/Qwen-Qwen1.5-7B-1719036460

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-7B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

shiromiya/public-models

shiromiya

Entry not found

jddllwqa/google-gemma-2b-1719036503

jddllwqa

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Jada1/distilbert-base-uncased-finetuned-cola

Jada1

Entry not found

jddllwqa/Qwen-Qwen1.5-0.5B-1719036629

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

jddllwqa/Qwen-Qwen1.5-1.8B-1719036687

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

jddllwqa/Qwen-Qwen1.5-7B-1719036766

jddllwqa

Entry not found

jddllwqa/google-gemma-2b-1719036806

jddllwqa

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

El-chapoo/segmind_tiny-stable_diffusion_q8.ov

El-chapoo

Entry not found

zandercoffman/SatTutor

zandercoffman

--- license: mit ---

happyneishon/git

happyneishon

Entry not found

Eka-Korn/git-base-ru

Eka-Korn

Entry not found

slelab/AES3

slelab

Entry not found

north/scandinavian_linguistic_classifier_bert

north

transformers

text-classification

--- license: mit --- # Scandinavian Linguistic Classifier NB-BERT Trained using code from [ComsmoPedia[https://github.com/huggingface/cosmopedia/tree/main/classification], but with the [nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) as starting point. The [data](https://huggingface.co/datasets/north/scandinavian-llama3-annotations) used in classification is from [GlotCC](https://huggingface.co/datasets/cis-lmu/GlotCC-V1) and have been annotated using Gemini 1.5 Flash. The following command where used for training. Please note that `train_regressor_bert.py` has a few minor changes to the original `train_edu_bert.py`: ``` python train_regressor_bert.py --base_model_name="NbAiLab/nb-bert-base" --dataset_name="north/scandinavian-linguistic-annotations" --target_column="score" --checkpoint_dir="/home/pere/checkpoints/scandinavian_bert/" ``` We also provide an evaluation script. This is targetted toward the same dataset. ``` python eval_regressor_bert.py --checkpoint_dir="/home/pere/checkpoints/scandinavian_bert/final/" --dataset_name="north/scandinavian-linguistic-annotations" ``` For convenience we also provide the `run_regressor_bert.py` script. This is also based on `run_edu_bert.py` from Cosmopedia. You can modify this script to annotate HuggingFace datasets directly. Cosmopedia also provides slurm-scripts here. We have not included these since we have had the opportunity to test them. ## Classification Report | Class | Precision | Recall | F1-score | Support | |-------|-----------|--------|----------|---------| | 0 | 0.84 | 0.60 | 0.70 | 12209 | | 1 | 0.70 | 0.72 | 0.71 | 24316 | | 2 | 0.41 | 0.49 | 0.44 | 10499 | | 3 | 0.38 | 0.51 | 0.43 | 5833 | | 4 | 0.10 | 0.24 | 0.14 | 1342 | | 5 | 0.87 | 0.39 | 0.54 | 5656 | ### Overall Metrics | Metric | Value | |---------------|-------| | Accuracy | 0.59 | | Macro Avg | | | - Precision | 0.55 | | - Recall | 0.49 | | - F1-score | 0.50 | | Weighted Avg | | | - Precision | 0.65 | | - Recall | 0.59 | | - F1-score | 0.61 | | Support | 59855 | ## Confusion Matrix | | Predicted 0 | Predicted 1 | Predicted 2 | Predicted 3 | Predicted 4 | Predicted 5 | |-------|--------------|--------------|--------------|--------------|--------------|--------------| | Actual 0 | 7318 | 4278 | 529 | 63 | 19 | 2 | | Actual 1 | 1364 | 17602 | 4414 | 785 | 135 | 16 | | Actual 2 | 38 | 2615 | 5130 | 2289 | 369 | 58 | | Actual 3 | 10 | 333 | 1726 | 2952 | 664 | 148 | | Actual 4 | 3 | 83 | 350 | 476 | 324 | 106 | | Actual 5 | 6 | 98 | 479 | 1205 | 1639 | 2229 | ## Evaluation Metrics | Metric | Value | |--------------------------|-----------------------| | Eval Loss | 0.673861563205719 | | Eval Precision | 0.5502142676492386 | | Eval Recall | 0.49225148166352145 | | Eval F1 Macro | 0.49616318856882935 | | Eval Accuracy | 0.5940188789574806 | | Eval Runtime | 285.9726 | | Eval Samples per Second | 209.303 | | Eval Steps per Second | 3.273 | | Epoch | 19.96 | ## Training Runtime | Metric | Value | |--------------------------|-----------------------| | Train Runtime | 105056.8322 | | Train Samples per Second | 102.552 | | Train Steps per Second | 1.603 | | Train Loss | 0.6785072675819606 | | Epoch | 20.0 | ### Run Summary | Metric | Value | |----------------------------|-----------------------| | Eval Accuracy | 0.59402 | | Eval F1 Macro | 0.49616 | | Eval Loss | 0.67386 | | Eval Precision | 0.55021 | | Eval Recall | 0.49225 | | Eval Runtime | 285.9726 | | Eval Samples per Second | 209.303 | | Eval Steps per Second | 3.273 | | Total FLOPs | 2.8346790572921083e+18| | Train Epoch | 20.0 | | Train Global Step | 168360 | | Train Grad Norm | 2.77268 | | Train Learning Rate | 0.0 | | Train Loss | 0.6201 | | Train Loss (Final) | 0.67851 | | Train Runtime | 105056.8322 | | Train Samples per Second | 102.552 | | Train Steps per Second | 1.603 |

Lululaudu/Cv

Lululaudu

--- license: apache-2.0 ---

whoisotna/imas

whoisotna

--- license: openrail ---

BLAGODEL/Hub

BLAGODEL

--- license: apache-2.0 ---

vorstcavry/onclite

vorstcavry

Entry not found

jddllwqa/Qwen-Qwen1.5-0.5B-1719037966

jddllwqa

Entry not found

acd23/new-model

acd23

First time

jddllwqa/Qwen-Qwen1.5-0.5B-1719038330

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

teowu/longvideodb

teowu

Entry not found

jddllwqa/Qwen-Qwen1.5-1.8B-1719038406

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

sh9/face-mask-cls

sh9

--- license: gpl-3.0 ---

jddllwqa/google-gemma-2b-1719038456

jddllwqa

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

jddllwqa/Qwen-Qwen1.5-0.5B-1719038712

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

jddllwqa/Qwen-Qwen1.5-1.8B-1719038781

jddllwqa

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Alefsouza72/Vortix

Alefsouza72

Introdução à Aliança Sombra Apresentação do grupo de vilões e seus planos contra Vortix. Investigação de Vortix Daniel detecta atividades suspeitas e investiga como Vortix. Confronto no Armazém Vortix confronta a Aliança Sombra e enfrenta seus membros. Intervenção de Dr. Nova Dr. Nova entra em cena e luta contra Vortix. Batalha Intensa A batalha entre Vortix e Dr. Nova se intensifica. Retirada e Promessa de Vingança Dr. Nova e a Aliança Sombra recuam, prometendo voltar.

jddllwqa/google-gemma-2b-1719038828

jddllwqa

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Ohrfeige/test

Ohrfeige

mlx

--- license: apache-2.0 datasets: - google/MusicCaps language: - de - en library_name: mlx ---

AI-Wheelz/AriesV2

AI-Wheelz

--- license: openrail ---

dalietng/yolov10l-visdrone

dalietng

Entry not found

nerdthingz/rl_course_vizdoom_health_gathering_supreme

nerdthingz

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.20 +/- 5.61 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r nerdthingz/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

Hafees/emotion-llm

Hafees

Entry not found