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kuax/ChatGLM3_lora_checkpoint-3000

kuax

Entry not found

steven-0419/LLaMoCo

steven-0419

--- license: bsd-3-clause --- This is a fine-tuning framework for adapting general LLMs as optimizers.

csukuangfj/sherpa-mms-torchaudio

csukuangfj

--- license: cc-by-nc-4.0 --- # Introduction The model in this directory is converted from https://github.com/pytorch/audio/blob/d5a94336c2f32a31629d49d2b9d417e66e0e4e6c/src/torchaudio/pipelines/_wav2vec2/impl.py#L1657

asun17904/anliR3-bert-base-uncased-alum

asun17904

pytorch

--- language: en license: mit library_name: pytorch --- # Knowledge Continuity Regularized Network Dataset: ANLI Round: None Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 16 - `gradient_accumulation_steps` = 1 - `weight_decay` = 1e-09 - `seed` = 42 Regularization Hyperparameters - `numerical stability denominator constant` = 1.0 - `lambda` = 1.0 - `alpha` = 1.0 - `beta` = 1.0 Extended Logs: |eval_loss|eval_accuracy|epoch| |--|--|--| |1.096|0.427|1.0| |1.090|0.432|2.0| |1.077|0.455|3.0| |1.087|0.443|4.0| |1.082|0.453|5.0| |1.085|0.444|6.0| |1.082|0.448|7.0| |1.084|0.446|8.0| |1.088|0.448|9.0| |1.091|0.444|10.0| |1.089|0.455|11.0| |1.084|0.461|12.0| |1.082|0.458|13.0| |1.080|0.461|14.0| |1.080|0.461|15.0| |1.081|0.454|16.0| |1.078|0.463|17.0| |1.078|0.461|18.0| |1.076|0.469|19.0| **Test Accuracy: 0.463**

nanasse/gpt2-squad

nanasse

Entry not found

Extrabass/llama-2-13b-chat-hf-light-and-ice-qa

Extrabass

transformers

text-generation

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

k-seungri/k_whisper_processor

k-seungri

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]

k-seungri/k_whisper_tokenizer

k-seungri

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]

kakojuvenkat/mistral-7b-finetuned

kakojuvenkat

--- license: apache-2.0 ---

yaoandy107/whisper-medium.en-moba

yaoandy107

transformers

automatic-speech-recognition

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

LoreTuc/prova

LoreTuc

Entry not found

GabiRayman/melodea_lamini_test

GabiRayman

Entry not found

Milad1b/MLM_finetuned_on_BBBP_lr_5e-5_epochs-20

Milad1b

Entry not found

prashant123/pyannote-speaker-diarization

prashant123

--- license: apache-2.0 ---

shivi23/inti-28-24

shivi23

transformers

image-text-to-text

Entry not found

mayur2639/inv_ven

mayur2639

--- license: unknown ---

himanshue2e/sdxl-pokemon-model

himanshue2e

Entry not found

kavitaShingare/CTS_AI_tasks

kavitaShingare

Entry not found

aduduhaha/ddpm-sclm-0-128

aduduhaha

Entry not found

ElDestructo/my_awesome_model2

ElDestructo

Entry not found

PixelRage/MacWhiz

PixelRage

--- license: apache-2.0 ---

soslolxox/output_model

soslolxox

Entry not found

DanishMehar/Danish

DanishMehar

Entry not found

daviani/mistral

daviani

Entry not found

kkknikodem/czcz

kkknikodem

Entry not found

AI-Wheelz/Hugo_PlagueV2

AI-Wheelz

--- license: openrail ---

davanstrien/flair

davanstrien

--- license: apache-2.0 tags: - medical --- ## FLAIR: A Foundation LAnguage Image model of the Retina <img src="https://github.com/jusiro/FLAIR/raw/main/documents/flair.png" width = "1000" alt="" align=center /> <br/> <b>A Foundation LAnguage Image model of the Retina (FLAIR):</b> <br/> <b>Encoding expert knowledge in text supervision</b> <br/> [Julio Silva-Rodriguez¹](https://scholar.google.es/citations?user=1UMYgHMAAAAJ&hl), [Hadi Chakor²](https://scholar.google.ca/citations?user=0Njg-cQAAAAJ&hl), [Riadh Kobbi²](https://ca.linkedin.com/in/riadh-kobbi), [Jose Dolz¹](https://scholar.google.es/citations?user=yHQIFFMAAAAJ&hl), [Ismail Ben Ayed¹](https://scholar.google.es/citations?user=29vyUccAAAAJ&hl) <br/> [¹LIVIA - ETS Montreal](https://liviamtl.ca/), [²DIAGNOS Inc.](https://www.diagnos.com/)<br/> | [Project](https://jusiro.github.io/projects/flair) | [Paper](https://arxiv.org/pdf/2308.07898.pdf) | [Code](https://github.com/jusiro/FLAIR) | [Tutorials](https://colab.research.google.com/drive/1LE50MQmsEQxMM-qvytXGeJ9WAu09w1MR?usp=sharing) | **note** this is a duplicate of the model card at https://github.com/jusiro/FLAIR/blob/main/readme.md updated to support loading from the Hugging Face Hub. ## Install FLAIR * Install in your enviroment a compatible torch version with your GPU. For example: ``` conda create -n flair_env python=3.8 -y conda activate flair_env conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge ``` * Install FLAIR library. ``` pip install git+https://github.com/jusiro/FLAIR.git ``` * Install the huggingface_hub library. ``` pip install huggingface_hub ``` ## Usage ``` from PIL import Image import numpy as np # Import FLAIR from flair import FLAIRModel rom huggingface_hub import hf_hub_download # Set model model_path = hf_hub_download(repo_id="davanstrien/flair", filename="flair_resnet.pth") model = FLAIRModel.load_from_pretrained(model_path) # Load image and set target categories # (if the repo is not cloned, download the image and change the path!) image = np.array(Image.open("./documents/sample_macular_hole.png")) text = ["normal", "healthy", "macular edema", "diabetic retinopathy", "glaucoma", "macular hole", "lesion", "lesion in the macula"] # Forward FLAIR model to compute similarities probs, logits = model(image, text) print("Image-Text similarities:") print(logits.round(3)) # [[-0.32 -2.782 3.164 4.388 5.919 6.639 6.579 10.478]] print("Probabilities:") print(probs.round(3)) # [[0. 0. 0.001 0.002 0.01 0.02 0.019 0.948]] ``` ## **Note**: problems during automatic **pre-trained weights download** If you encounter any issue while downloading the **pre-trained weights** (i.e. `from_checkpoint=True`), you can manually download the weights from the following links (see Table), unzip the file, and store them at: `./flair/modeling/flair_pretrained_weights/[ID].pth`. | Backbone | ID | | |-----------|:------------:|:---------------------------------------------------------------------------------------------:| | ResNet-50 | flair_resnet | [LINK](https://drive.google.com/file/d/1l24_2IzwQdnaa034I0zcyDLs_zMujsbR/view?usp=drive_link) | ## Pre-training and transferability In the following, we present the scripts for model pre-training and transferability. To use them, we recommend cloning the whole repository. ``` git clone https://github.com/jusiro/FLAIR.git cd FLAIR pip install -r requirements.txt ``` ### 📦 Foundation model pre-training * Define the relative paths for datasets and dataframes in `./local_data/constants.py`. * Prepare the FUNDUS assembly dataset - check `./local_data/prepare_partitions.py` to prepare the dataframes. | | | | | | | |---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|-----------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------| | [01_EYEPACS](https://www.kaggle.com/datasets/mariaherrerot/eyepacspreprocess) | [08_ODIR-5K](https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k) | [15_APTOS](https://www.kaggle.com/competitions/aptos2019-blindness-detection/data) | [22_HEI-MED](https://github.com/lgiancaUTH/HEI-MED) | [29_AIROGS](https://zenodo.org/record/5793241#.ZDi2vNLMJH5) | [36_ACRIMA](https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-019-0649-y) | | [02_MESIDOR](https://www.adcis.net/en/third-party/messidor2/) | [09_PAPILA](https://figshare.com/articles/dataset/PAPILA/14798004/1) | [16_FUND-OCT](https://data.mendeley.com/datasets/trghs22fpg/3) | [23_HRF](http://www5.cs.fau.de/research/data/fundus-images/) | [30_SUSTech-SYSU](https://figshare.com/articles/dataset/The_SUSTech-SYSU_dataset_for_automated_exudate_detection_and_diabetic_retinopathy_grading/12570770/1) | [37_DeepDRiD](https://github.com/deepdrdoc/DeepDRiD) | | [03_IDRID](https://idrid.grand-challenge.org/Rules/) | [10_PARAGUAY](https://zenodo.org/record/4647952#.ZBT5xXbMJD9) | [17_DiaRetDB1](https://www.it.lut.fi/project/imageret/diaretdb1_v2_1/) | [24_ORIGA](https://pubmed.ncbi.nlm.nih.gov/21095735/) | [31_JICHI](https://figshare.com/articles/figure/Davis_Grading_of_One_and_Concatenated_Figures/4879853/1) | | | [04_RFMid](https://ieee-dataport.org/documents/retinal-fundus-multi-disease-image-dataset-rfmid-20) | [11_STARE](https://cecas.clemson.edu/~ahoover/stare/) | [18_DRIONS-DB](http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html) | [25_REFUGE](https://refuge.grand-challenge.org/) | [32_CHAKSU](https://figshare.com/articles/dataset/Ch_k_u_A_glaucoma_specific_fundus_image_database/20123135?file=38944805) | | | [05_1000x39](https://www.nature.com/articles/s41467-021-25138-w#Sec16) | [12_ARIA](https://www.damianjjfarnell.com/?page_id=276) | [19_Drishti-GS1](http://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/Home.php) | [26_ROC](http://webeye.ophth.uiowa.edu/ROC/) | [33_DR1-2](https://figshare.com/articles/dataset/Advancing_Bag_of_Visual_Words_Representations_for_Lesion_Classification_in_Retinal_Images/953671?file=6502302) | | | [06_DEN](https://github.com/Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation) | [13_FIVES](https://figshare.com/articles/figure/FIVES_A_Fundus_Image_Dataset_for_AI-based_Vessel_Segmentation/19688169/1) | [20_E-ophta](https://www.adcis.net/en/third-party/e-ophtha/) | [27_BRSET](https://physionet.org/content/brazilian-ophthalmological/1.0.0/) | [34_Cataract](https://www.kaggle.com/datasets/jr2ngb/cataractdataset) | | | [07_LAG](https://github.com/smilell/AG-CNN) | [14_AGAR300](https://ieee-dataport.org/open-access/diabetic-retinopathy-fundus-image-datasetagar300) | [21_G1020](https://arxiv.org/abs/2006.09158) | [28_OIA-DDR](https://github.com/nkicsl/DDR-dataset) | [35_ScarDat](https://github.com/li-xirong/fundus10k) | | * Vision-Language Pre-training. ``` python main_pretrain.py --augment_description True --balance True --epochs 15 --batch_size 128 --num_workers 6 ``` ### 📦 Transferability to downstream tasks/domains * Define the relative paths for datasets and dataframes in `./local_data/constants.py`. * Prepare the experiment setting for the target dataset - we used `./local_data/experiments.py` to store them. ``` if experiment == "02_MESSIDOR": setting = {"dataframe": PATH_DATAFRAME_TRANSFERABILITY_CLASSIFICATION + "02_MESSIDOR.csv", "task": "classification", "targets": {"no diabetic retinopathy": 0, "mild diabetic retinopathy": 1, "moderate diabetic retinopathy": 2, "severe diabetic retinopathy": 3, "proliferative diabetic retinopathy": 4}} ``` * Zero-shot (no adaptation). ``` python main_transferability.py --experiment 02_MESSIDOR --method zero_shot --load_weights True --domain_knowledge True --shots_train 0% --shots_test 100% --project_features True --norm_features True --folds 1 ``` * Linear Probing. ``` python main_transferability.py --experiment 02_MESSIDOR --method lp --load_weights True --shots_train 80% --shots_test 20% --project_features False --norm_features False --folds 5 ``` # Citation If you find this repository useful, please consider citing this paper: ``` @article{FLAIR2023, title={A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision}, author={Julio Silva-Rodriguez and Hadi Chakor and Riadh Kobbi and Jose Dolz and Ismail Ben Ayed}, journal={ArXiv Preprint}, year={2023} } ``` # License - **Code and Model Weights** are released under [Apache 2.0 license](LICENSE)

Palistha/distilbert-base-uncased-finetuned-imdb

Palistha

Entry not found

tirohan0310/zephyr-7b-beta-cloned

tirohan0310

--- license: mit ---

vikranth1111/sign_language_detection

vikranth1111

Entry not found

wcyat/whisper-small-yue-hk-mdcc-retrained-2

wcyat

transformers

automatic-speech-recognition

Entry not found

doll774/animagine-3.0-iLECO

doll774

Entry not found

abhi5hekjangid/phi-2-finetuned-abhishek

abhi5hekjangid

peft

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi-2-finetuned-abhishek 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. --> # phi-2-finetuned-abhishek This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3078 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3009 | 1.0 | 779 | 1.3078 | ### Framework versions - PEFT 0.8.0 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

padeoe/test

padeoe

Entry not found

Deepakkori45/New_prompt_test

Deepakkori45

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]

8glabs/alpaca-bitcoin-tweets-sentiment

8glabs

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]

ninagroot/tiny-bert-sst2-distilled

ninagroot

Entry not found

moulichand/LLMED

moulichand

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

mzbac/Kunpeng-4x7B-mistral-hf-4bit-mlx

mzbac

transformers

text-generation

--- license: apache-2.0 ---

mzbac/Kunpeng-4x7B-mistral-hf-4bit-mlx-adapters

mzbac

--- license: mit --- adapter file for the model `mzbac/Kunpeng-4x7B-mistral-hf-4bit-mlx-adapters` qlora finetuning

harakiriartist/Mike_Patton_Mondo_Cane

harakiriartist

--- license: unknown ---

mycode-lucky321/llama-2-7b-RS

mycode-lucky321

--- license: mit ---

harakiriartist/Mike_Patton_The_Real_Thing

harakiriartist

--- license: unknown ---

DominikPtaszek231643/testing_dreambooth_with_lora

DominikPtaszek231643

Entry not found

enry8giano/assets-registry-model

enry8giano

Entry not found

Freddie-Dassin/Daniel_Balavoine

Freddie-Dassin

Entry not found

ssarkar4445/OpenHathi-7B-v0.1-instruct

ssarkar4445

peft

--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: sarvamai/OpenHathi-7B-Hi-v0.1-Base model-index: - name: OpenHathi-7B-v0.1-instruct 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. --> # OpenHathi-7B-v0.1-instruct This model is a fine-tuned version of [sarvamai/OpenHathi-7B-Hi-v0.1-Base](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.0218 ## 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: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1606 | 0.97 | 21 | 1.0843 | | 0.9479 | 1.93 | 42 | 1.0218 | ### Framework versions - PEFT 0.8.2.dev0 - Transformers 4.38.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

SoooSlooow/bert-base-cnn-dailymail

SoooSlooow

transformers

fill-mask

Entry not found

vishalrgupta/samplefin

vishalrgupta

Entry not found

Jxctcv/CardiB

Jxctcv

--- license: apache-2.0 ---

Vishal24/BCG_adapter_v3

Vishal24

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # 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 0.8.2.dev0

catpawdev/my_awesome_qa_model

catpawdev

Entry not found

MohamadElnomrossie/ppo-LunarLander-v2

MohamadElnomrossie

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 259.98 +/- 19.86 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

nunggari/KSG

nunggari

Entry not found

M-Bilal/first-model

M-Bilal

Entry not found

DanielFi/mistra7b_finetuned_df

DanielFi

Entry not found

sahilkadge/whisper-small-en

sahilkadge

transformers

automatic-speech-recognition

--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - sahilkadge/medical_audio_dataset model-index: - name: Whisper Small en - sahil_Kamran results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small en - sahil_Kamran This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

thatguymarth/F_lora

thatguymarth

Entry not found

asun17904/anliR3-gpt2-alum

asun17904

pytorch

--- language: en license: mit library_name: pytorch --- # Knowledge Continuity Regularized Network Dataset: ANLI Round: None Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 8 - `gradient_accumulation_steps` = 2 - `weight_decay` = 1e-09 - `seed` = 42 Regularization Hyperparameters - `numerical stability denominator constant` = 1.0 - `lambda` = 1.0 - `alpha` = 1.0 - `beta` = 1.0 Extended Logs: |eval_loss|eval_accuracy|epoch| |--|--|--| |1.083|0.438|1.0| |1.077|0.446|2.0| |1.082|0.443|3.0| |1.081|0.450|4.0| |1.082|0.443|5.0| |1.083|0.447|6.0| |1.076|0.458|7.0| |1.088|0.446|8.0| |1.096|0.434|9.0| |1.086|0.441|10.0| |1.083|0.457|11.0| |1.088|0.445|12.0| |1.085|0.448|13.0| |1.085|0.447|14.0| **Test Accuracy: 0.446**

Nabarajsub/finetuning

Nabarajsub

--- license: apache-2.0 ---

dsbhuvanneubrain/mistral-finetuned-alpaca

dsbhuvanneubrain

Entry not found

ceciliasr/language-detection-fine-tuned-on-xlm-roberta-base

ceciliasr

Entry not found

paisanx/Reinforce-Pixelcopter-PLE-v6

paisanx

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v6 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.50 +/- 14.27 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

rafationgson/realcartoon-xl-v6

rafationgson

Entry not found

ViewpointWatcher/Melon22

ViewpointWatcher

Entry not found

TeamUNIVA/Komodo_7B_v1.0.1

TeamUNIVA

transformers

text-generation

--- license: apache-2.0 language: - ko - en --- # Base Model mistralai/Mistral-7B-v0.1

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

deependu

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 playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="deependu/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"]) ```

HaniaIm/Test

HaniaIm

Entry not found

benzebra/hugging

benzebra

Entry not found

YURIJ24/SeanJohnson

YURIJ24

--- license: openrail ---

ideepankarsharma2003/AI_GenImageClassifier_MidJourney

ideepankarsharma2003

# **Not a MODEL, just a practice repo**

pradeep239/wipro_700_10epoch

pradeep239

transformers

image-text-to-text

--- license: mit ---

Albe/laforme_test_10

Albe

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: illustration of city tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Albe/laforme_test_10 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on illustration of city using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

deependu/Taxi-V3

deependu

Entry not found

jingasd/my_awesome_billsum_model

jingasd

Entry not found

yleo/monacan-translator-fr-mon-1m

yleo

transformers

text-generation

Entry not found

AaronGross/AaronGross53

AaronGross

--- license: mit ---

rafationgson/pixelwave-xl-v7

rafationgson

Entry not found

Trelis/CodeLlama-70b-Instruct-hf-function-calling-v3

Trelis

transformers

text-generation

--- language: - code datasets: - Trelis/function_calling_v3 license: llama2 pipeline_tag: text-generation tags: - llama-2 - function-calling - function calling extra_gated_prompt: "Purchase access to this repo [HERE](https://buy.stripe.com/dR6fZK6xT5TJ6wE15r)" --- # Function Calling Fine-tuned CodeLlama 70B Purchase access to this model [here](https://buy.stripe.com/dR6fZK6xT5TJ6wE15r). This model is fine-tuned for function calling. - The function metadata format is the same as used for OpenAI. - The model is suitable for commercial use. - GGUF available on request. - AWQ is in the awq branch. Check out other fine-tuned function calling models [here](https://trelis.com/function-calling/). ## Quick Server Setup Runpod one click templates: (You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model.) - [TGI 4bit AWQ - recommended](https://runpod.io/gsc?template=sgeg8whvbf&ref=jmfkcdio) - [TGI bitsandbytes-nf4](https://runpod.io/gsc?template=tew7ssv91h&ref=jmfkcdio). Runpod Affiliate [Link](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel). ## Inference Scripts See below for sample prompt format. Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/): - Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages) - Automate catching, handling and chaining of function calls. ## Prompt Format ``` B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n" B_INST, E_INST = "Source: user\n\n ", " <step> Source: assistant\nDestination: user\n\n " # Code Llama 70B prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n" ``` ### Using tokenizer.apply_chat_template For an easier application of the prompt, you can set up as follows: Set up `messages`: ``` [ { "role": "function_metadata", "content": "FUNCTION_METADATA" }, { "role": "user", "content": "What is the current weather in London?" }, { "role": "function_call", "content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}" }, { "role": "function_response", "content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}" }, { "role": "assistant", "content": "The current weather in London is Cloudy with a temperature of 15 Celsius" } ] ``` with `FUNCTION_METADATA` as: ``` [ { "type": "function", "function": { "name": "get_current_weather", "description": "This function gets the current weather in a given city", "parameters": { "type": "object", "properties": { "city": { "type": "string", "description": "The city, e.g., San Francisco" }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use." } }, "required": ["city"] } } }, { "type": "function", "function": { "name": "get_clothes", "description": "This function provides a suggestion of clothes to wear based on the current weather", "parameters": { "type": "object", "properties": { "temperature": { "type": "string", "description": "The temperature, e.g., 15 C or 59 F" }, "condition": { "type": "string", "description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'" } }, "required": ["temperature", "condition"] } } } ] ``` and then apply the chat template to get a formatted prompt: ``` tokenizer = AutoTokenizer.from_pretrained('Trelis/CodeLlama-70b-Instruct-hf-function-calling-v3', trust_remote_code=True) prompt = tokenizer.apply_chat_template(prompt, tokenize=False) ``` If you are using a gated model, you need to first run: ``` pip install huggingface_hub huggingface-cli login ``` ### Manual Prompt: ``` Source: user You have access to the following functions. Use them if required: [ { "type": "function", "function": { "name": "get_stock_price", "description": "Get the stock price of an array of stocks", "parameters": { "type": "object", "properties": { "names": { "type": "array", "items": { "type": "string" }, "description": "An array of stocks" } }, "required": [ "names" ] } } }, { "type": "function", "function": { "name": "get_big_stocks", "description": "Get the names of the largest N stocks by market cap", "parameters": { "type": "object", "properties": { "number": { "type": "integer", "description": "The number of largest stocks to get the names of, e.g. 25" }, "region": { "type": "string", "description": "The region to consider, can be \"US\" or \"World\"." } }, "required": [ "number" ] } } } ] Get the names of the five largest stocks by market cap <step> Source: assistant Destination: user { "name": "get_stock_price", "arguments": { "names": [ "AAPL" ] } }<step> ``` # Dataset See [Trelis/function_calling_v3](https://huggingface.co/datasets/Trelis/function_calling_v3). # License This model may be used commercially for inference according to the terms of the Llama license. Further, users may not re-publish or re-sell this model in the same or derivative form (including fine-tunes). ** The original model card follows below: ** # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | | 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) | Model capabilities: - [x] Code completion. - [ ] Infilling. - [x] Instructions / chat. - [ ] Python specialist. ## Model Use Install `transformers` ```bash pip install transformers accelerate ``` **Chat use:** The 70B Instruct model uses a [different prompt template](#chat_prompt) than the smaller versions. To use it with `transformers`, we recommend you use the built-in chat template: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "codellama/CodeLlama-70b-Instruct-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto", ) chat = [ {"role": "system", "content": "You are a helpful and honest code assistant expert in JavaScript. Please, provide all answers to programming questions in JavaScript"}, {"role": "user", "content": "Write a function that computes the set of sums of all contiguous sublists of a given list."}, ] inputs = tokenizer.apply_chat_template(chat, return_tensors="pt").to("cuda") output = model.generate(input_ids=inputs, max_new_tokens=200) output = output[0].to("cpu") print(tokenizer.decode(output)) ``` You can also use the model for **text or code completion**. This examples uses transformers' `pipeline` interface: ```py from transformers import AutoTokenizer import transformers import torch model_id = "codellama/CodeLlama-70b-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) pipeline = transformers.pipeline( "text-generation", model=model_id, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'def fibonacci(', do_sample=True, temperature=0.2, top_p=0.9, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=100, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` <a name="chat_prompt"></a> ## Chat prompt CodeLlama 70B Instruct uses a different format for the chat prompt than previous Llama 2 or CodeLlama models. As mentioned above, the easiest way to use it is with the help of the tokenizer's chat template. If you need to build the string or tokens, manually, here's how to do it. We'll do our tests with the following made-up dialog: ```py chat = [ {"role": "system", "content": "System prompt "}, {"role": "user", "content": "First user query"}, {"role": "assistant", "content": "Model response to first query"}, {"role": "user", "content": "Second user query"}, ] ``` First, let's see what the prompt looks like if we use the chat template: ```py tokenizer.apply_chat_template(chat, tokenize=False) ``` ``` '<s>Source: system\n\n System prompt <step> Source: user\n\n First user query <step> Source: assistant\n\n Model response to first query <step> Source: user\n\n Second user query <step> Source: assistant\nDestination: user\n\n ' ``` So each turn of the conversation has a `Source` (`system`, `user`, or `assistant`), and then the content appears after two newlines and a space. Turns are separated with the special token ` <step> `. After the last turn (which must necessarily come from the `user`), we invite the model to respond by using the special syntax `Source: assistant\nDestination: user\n\n `. Let's see how we can build the same string ourselves: ```py output = "<s>" for m in chat: output += f"Source: {m['role']}\n\n {m['content'].strip()}" output += " <step> " output += "Source: assistant\nDestination: user\n\n " output ``` ``` '<s>Source: system\n\n System prompt <step> Source: user\n\n First user query <step> Source: assistant\n\n Model response to first query <step> Source: user\n\n Second user query <step> Source: assistant\nDestination: user\n\n ' ``` To verify that we got it right, we'll compare against the [reference code in the original GitHub repo](https://github.com/facebookresearch/codellama/blob/1af62e1f43db1fa5140fa43cb828465a603a48f3/llama/generation.py#L506). We used the same dialog and tokenized it with the `dialog_prompt_tokens` function and got the following tokens: ```py reference_tokens = [1, 7562, 29901, 1788, 13, 13, 2184, 9508, 32015, 7562, 29901, 1404, 13, 13, 3824, 1404, 2346, 32015, 7562, 29901, 20255, 13, 13, 8125, 2933, 304, 937, 2346, 32015, 7562, 29901, 1404, 13, 13, 6440, 1404, 2346, 32015, 7562, 29901, 20255, 13, 14994, 3381, 29901, 1404, 13, 13, 29871] ``` Let's see what we get with the string we built using our Python loop. Note that we don't add "special tokens" because the string already starts with `<s>`, the beginning of sentence token: ```py tokens = tokenizer.encode(output, add_special_tokens=False) assert reference_tokens == tokens ``` Similarly, let's verify that the chat template produces the same token sequence: ```py assert reference_tokens == tokenizer.apply_chat_template(chat) ``` As a final detail, please note that if the dialog does not start with a `system` turn, the [original code will insert one with an empty content string](https://github.com/facebookresearch/codellama/blob/1af62e1f43db1fa5140fa43cb828465a603a48f3/llama/generation.py#L418). ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. **This repository contains the Instruct version of the 70B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens. **Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).

Wajid333/Reinforce

Wajid333

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

MasterMinons/Makima

MasterMinons

Entry not found

ostapeno/library-stablelm-5-experts-flan_3ep

ostapeno

Number of experts present in the library: 5 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | c0o5_5e | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating,app_reviews_categorize_rating_using_review,paws_wiki_1_1_0,cos_e_v1_11_question_description_option_text,math_dataset_algebra__linear_1d_1_0_0,dbpedia_14_pick_one_category_for_the_following_text,dbpedia_14_given_a_choice_of_categories_,app_reviews_convert_to_rating,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,glue_qqp_2_0_0,dream_baseline,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,qasc_qa_with_separated_facts_1,cot_creak_ii,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,qasc_is_correct_1,cot_sensemaking_ii,glue_mnli_2_0_0,qasc_is_correct_2,duorc_SelfRC_generate_question,definite_pronoun_resolution_1_1_0,glue_wnli_2_0_0,glue_mrpc_2_0_0,cot_strategyqa_ii | lora | | c2o5_5e | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text,anli_r3_0_1_0,cos_e_v1_11_question_option_description_id,cos_e_v1_11_question_description_option_id,cos_e_v1_11_description_question_option_id,adversarial_qa_dbidaf_answer_the_following_q | lora | | c3o5_5e | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,duorc_ParaphraseRC_decide_worth_it,duorc_SelfRC_question_answering,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,anli_r2_0_1_0,anli_r1_0_1_0,glue_stsb_2_0_0,duorc_SelfRC_extract_answer,duorc_SelfRC_movie_director | lora | | c1o5_5e | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/glue_sst2_2_0_0,natural_questions_open_1_0_0,kilt_tasks_hotpotqa_final_exam,ag_news_subset_1_0_0,adversarial_qa_droberta_tell_what_it_is,cot_gsm8k_ii,kilt_tasks_hotpotqa_combining_facts,kilt_tasks_hotpotqa_straighforward_qa,cot_strategyqa,cot_ecqa_ii,adversarial_qa_droberta_based_on,adversarial_qa_dbert_answer_the_following_q,kilt_tasks_hotpotqa_complex_question,adversarial_qa_droberta_question_context_answer,glue_qnli_2_0_0,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,kilt_tasks_hotpotqa_formulate,cosmos_qa_1_0_0,cot_esnli_ii,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,cot_qasc,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora | | c4o5_5e | stabilityai/stablelm-3b-4e1t | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question,cot_esnli,cot_gsm8k,cot_sensemaking,cos_e_v1_11_rationale,duorc_SelfRC_generate_question_by_answer,lambada_1_0_0,dream_generate_last_utterance,glue_cola_2_0_0,gem_dart_1_1_0,cos_e_v1_11_generate_explanation_given_text,gem_common_gen_1_1_0,cot_creak,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_generate_question,adversarial_qa_dbidaf_generate_question,cos_e_v1_11_i_think,fix_punct,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,gigaword_1_2_0,gem_web_nlg_en_1_1_0,cos_e_v1_11_explain_why_human,app_reviews_generate_review,cot_ecqa,cos_e_v1_11_aligned_with_common_sense,dream_generate_first_utterance,gem_e2e_nlg_1_1_0,para_crawl_enes,adversarial_qa_dbert_generate_question | lora | Last updated on: 2024-01-31 12:19:40+00:00

pfb30/test2

pfb30

Entry not found

xddtc48jo/CodeLlama-70B-Python-Q6_K-GGUF

xddtc48jo

text-generation

--- license: llama2 pipeline_tag: text-generation tags: - llama-2 --- <!-- description start --> ## Description Converted to f16 using llama_cpp convert.py script, then quantized to q6_K using quantize from the same llama_cpp repository.<br> Resulting file was split into 2 parts. <br><br> **Note**: HF does not support uploading files larger than 50GB.<br> <!-- description end --> ### File require joining To join the files, do the following: <br> cat codellama-70b-python-q6_K.gguf-split-* > codellama-70b-python-q6_K.gguf && rm codellama-70b-python-q6_K.gguf-split-*

jingasd/summarize_test

jingasd

Entry not found

Dekirem/rosalia

Dekirem

--- license: other license_name: rosalia license_link: LICENSE ---

aalaaa/distilbert-base-uncased-finetuned-squad

aalaaa

Entry not found

TvERn/my-pet-cat

TvERn

Entry not found

yotasr/Smart_Tour_GizaVersion1.01

yotasr

transformers

image-classification

--- base_model: yotasr/Smart_TourGuide tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Smart_Tour_GizaVersion1.01 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- 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. --> # Smart_Tour_GizaVersion1.01 This model is a fine-tuned version of [yotasr/Smart_TourGuide](https://huggingface.co/yotasr/Smart_TourGuide) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0491 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 4.4777 | 0.0 | | No log | 2.0 | 2 | 0.0777 | 1.0 | | No log | 3.0 | 3 | 0.0510 | 1.0 | | No log | 4.0 | 4 | 0.0491 | 1.0 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Syedian123/GapsDetection

Syedian123

--- license: other license_name: gapssegmented license_link: LICENSE ---

CaaSS/obesegirlsconcept

CaaSS

Entry not found

Lalith16/Zephyr7B-10epoch-CC_dataset

Lalith16

--- license: mit base_model: HuggingFaceH4/zephyr-7b-beta tags: - trl - sft - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7065 ## 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: 0.00025 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7779 | 0.35 | 100 | 1.0474 | | 0.6553 | 0.69 | 200 | 0.8922 | | 0.6339 | 1.04 | 300 | 0.7221 | | 0.6018 | 1.39 | 400 | 0.7020 | | 0.5853 | 1.74 | 500 | 0.6908 | | 0.445 | 2.08 | 600 | 0.6887 | | 0.4875 | 2.43 | 700 | 0.6783 | | 0.4938 | 2.78 | 800 | 0.6883 | | 0.3598 | 3.12 | 900 | 0.6893 | | 0.3549 | 3.47 | 1000 | 0.6763 | | 0.3624 | 3.82 | 1100 | 0.6971 | | 0.344 | 4.17 | 1200 | 0.7348 | | 0.3393 | 4.51 | 1300 | 0.7502 | | 0.3823 | 4.86 | 1400 | 0.6861 | | 0.3534 | 5.21 | 1500 | 0.6849 | | 0.3632 | 5.56 | 1600 | 0.6585 | | 0.3634 | 5.9 | 1700 | 0.6414 | | 0.3002 | 6.25 | 1800 | 0.6662 | | 0.3126 | 6.6 | 1900 | 0.6864 | | 0.3129 | 6.94 | 2000 | 0.6638 | | 0.259 | 7.29 | 2100 | 0.6967 | | 0.27 | 7.64 | 2200 | 0.7059 | | 0.3063 | 7.99 | 2300 | 0.6582 | | 0.2814 | 8.33 | 2400 | 0.7205 | | 0.3005 | 8.68 | 2500 | 0.7334 | | 0.2862 | 9.03 | 2600 | 0.6839 | | 0.3092 | 9.38 | 2700 | 0.6929 | | 0.3078 | 9.72 | 2800 | 0.7065 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

abdouaziiz/llama2_merged

abdouaziiz

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]

mattwin/test1

mattwin

--- license: mit ---

jokuka/Landscape

jokuka

Entry not found

MasterMinons/Astra

MasterMinons

Entry not found

mysterious-pie/ViT-finetuned-lora-10epochs-22classes

mysterious-pie

Entry not found

manu780/xlnet

manu780

Entry not found

NBS777/ppo-LunarLander-v2

NBS777

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO - StableBaselines3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.69 +/- 23.71 name: mean_reward verified: false --- # **PPO - StableBaselines3** Agent playing **LunarLander-v2** This is a trained model of a **PPO - StableBaselines3** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

meyderun/harry

meyderun

--- license: openrail ---