librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
odedregev/Llama-2-7b-chat-hf-science-rejection-sampling	odedregev	2024-07-01T14:51:15Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T14:43:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ilyass31/results	ilyass31	2024-07-01T16:41:15Z	0	null	[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-07-01T14:44:27Z	--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - 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 [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.13.3
josedonoso/blip2-ecg-khan	josedonoso	2024-07-01T14:48:27Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T14:48:24Z	--- 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]
bitmind/deepfake-detector-base	bitmind	2024-07-01T14:51:54Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T14:50:05Z	--- license: mit ---
rashid996958/pix2pix_exp42	rashid996958	2024-07-01T14:50:53Z	0	null	[ "region:us" ]	null	2024-07-01T14:50:48Z	Entry not found
HieuBeo/Ho_Chi_Minh-LoRa	HieuBeo	2024-07-01T14:50:57Z	0	null	[ "region:us" ]	null	2024-07-01T14:50:57Z	Entry not found
Pyszczysko/swendamocnaboli	Pyszczysko	2024-07-01T14:51:27Z	0	null	[ "license:unknown", "region:us" ]	null	2024-07-01T14:51:26Z	--- license: unknown ---
cheng-cherry/my_awesome_opus_books_model	cheng-cherry	2024-07-01T15:28:30Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-07-01T14:51:31Z	Entry not found
yuchuantian/IPG_rep	yuchuantian	2024-07-01T14:55:50Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T14:52:12Z	--- license: apache-2.0 ---
hmpm00/bul-id-bulas-final	hmpm00	2024-07-01T14:52:21Z	0	null	[ "region:us" ]	null	2024-07-01T14:52:21Z	Entry not found
Smabbler/Multiclass-Disease-Diagnosis-Model	Smabbler	2024-07-01T18:43:56Z	0	null	[ "text-classification", "en", "dataset:duxprajapati/symptom-disease-dataset", "region:us" ]	text-classification	2024-07-01T14:53:25Z	--- datasets: - duxprajapati/symptom-disease-dataset language: - en metrics: - accuracy - f1 pipeline_tag: text-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description A predictive machine learning model was developed that can classify data points into distinct categories based on symptoms using diseases data. - Developed by: Priyanka Kamila - Model type: RandomForestClassifier, SVC - Language(s) (NLP): EN ## 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. --> This model can be directly used for disease diagnosis based on binary encoded medical features. By inputting patient symptoms in the form of binary vectors, the model predicts the likely medical condition. Here’s how you can utilize the model: Prepare Input Data: Ensure that the input data is formatted as a binary matrix, where each row represents a patient and each column represents a symptom or feature. The target variable should be a categorical label representing the medical condition. Load the Model: Load the trained Random Forest Classifier or SVM Classifier from the repository. You can use libraries like joblib or pickle in Python to load the pre-trained model. Make Predictions: Use the loaded model to make predictions on new input data. For instance, in Python: python Copy code import joblib model = joblib.load('path_to_model.pkl') predictions = model.predict(new_input_data) Interpret Results: The model will output the predicted medical condition for each input row. These predictions can be used by healthcare professionals to assist in diagnosing patients. This model is intended for direct use in clinical decision support systems or healthcare applications where quick and accurate disease diagnosis is critical. It can be integrated into electronic health records (EHR) systems, patient management software, or used as a standalone diagnostic tool. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> This model is designed specifically for diagnosing diseases based on binary encoded medical features. It is important to recognize the limitations and potential misuse of the model: Non-Medical Applications: The model is not suitable for non-medical applications or any use cases outside of healthcare diagnostics. Using this model for unrelated classification tasks will yield inaccurate and irrelevant results. Incomplete or Inaccurate Input Data: The model relies on precise binary encoding of medical symptoms. Providing incomplete, inaccurate, or improperly formatted data can lead to incorrect diagnoses. It is crucial to ensure that input data is complete and correctly formatted according to the binary encoding schema used during model training. Real-Time Critical Decisions: While the model can aid in diagnosis, it should not be solely relied upon for real-time critical medical decisions without human oversight. Healthcare professionals should verify the model’s predictions and consider additional clinical information and diagnostics before making final decisions. Malicious Use: The model should not be used to intentionally misdiagnose or manipulate medical diagnoses for fraudulent purposes. Ensuring ethical use of the model is paramount, and it should only be used to assist in improving patient care. Diagnostic Scope Limitation: The model is trained on specific diseases included in the dataset. It may not perform well in diagnosing conditions outside the scope of its training data. For diseases not represented in the training data, the model might default to predicting "other," which should be interpreted with caution. General Population Screening: This model is not intended for general population screening or predicting disease prevalence in broad, non-clinical populations. It is designed for use with patients already presenting symptoms or those in a clinical setting. By understanding these limitations and potential misuse scenarios, users can ensure that the model is applied appropriately and ethically in relevant healthcare contexts. ## 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. --> The training data used for this model consists of a custom dataset with binary encoded medical features. Each row in the dataset represents a patient's symptoms encoded as binary values, and the corresponding label represents the diagnosed disease. The dataset includes a wide range of medical conditions, with the aim of providing a comprehensive diagnostic tool. Source of Data: The dataset was compiled from https://huggingface.co/datasets/duxprajapati/symptom-disease-dataset from huggingface which was then processed in terms of data-labeling using Smabbler's QueryLab platform ensuring a accurate representation of data-labels for common and rare diseases. Pre-processing: Data was pre-processed to ensure consistency and accuracy. This involved cleaning the data, handling missing values, and normalizing the binary encoding. Each symptom was converted into a binary feature (0 or 1), indicating its absence or presence respectively. The labels were mapped to specific diseases using a detailed mapping file to ensure accurate representation. Label Mapping: The labels in the dataset correspond to various diseases. A mapping file (mapping.json) was used to translate encoded labels to human-readable disease names. Top labels include diseases like Psoriasis, Malaria, Bronchial Asthma, Dengue, Arthritis, Heart Attack, and many more. Additional Documentation: Detailed documentation on data pre-processing and filtering steps is provided to ensure reproducibility and transparency. The dataset card includes information on the data sources, pre-processing steps, and any additional filtering or transformations applied. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> The training procedure for this model involves several key steps to ensure robust and accurate disease diagnosis using Random Forest and SVM classifiers. Below are the detailed steps and technical specifications related to the training procedure: Data Splitting: The dataset was split into training and testing sets using an 80-20 split ratio. The training set was used to train the classifiers, while the testing set was used to evaluate the model’s performance. Feature Selection: Binary encoded features representing the presence or absence of symptoms were selected as input features. The target variable was the disease label, which was mapped from encoded integers to human-readable disease names. Model Initialization: Two classifiers were initialized: Random Forest Classifier and Support Vector Machine (SVM) Classifier. Both classifiers were initialized with default parameters and a fixed random state to ensure reproducibility. Training the Models: Random Forest Classifier: The Random Forest model was trained on the training data using the fit method. Hyperparameters such as the number of trees and depth were tuned to optimize performance. SVM Classifier: The SVM model was similarly trained using the fit method. Kernel type, regularization parameters, and other hyperparameters were adjusted for optimal classification. ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> The performance of both models was evaluated on the testing set. Metrics such as accuracy, precision, recall, and f1-score were calculated to assess model performance. Confusion matrices were generated to visualize the performance of each classifier in predicting the correct disease labels. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6682b3f72b1f40e83883f999/zzVYejajU3qtj6dlDUS6g.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6682b3f72b1f40e83883f999/yK7UpL0dtCGz8Nq3q2ylN.png) ### Results ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6682b3f72b1f40e83883f999/58ZffNH4K8hgweNRqAHMC.png) #### Summary This model utilizes both Random Forest and SVM classifiers to accurately diagnose a variety of diseases based on binary encoded medical features. The training involved data pre-processing, feature selection, model training, and extensive evaluation to ensure reliability. Designed for healthcare applications, it aids professionals in making informed diagnostic decisions efficiently. ## Model Card Authors Priyanka Kamila
tsavage68/Summary4500_M2_1000steps_1e8rate_SFT	tsavage68	2024-07-01T14:59:03Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T14:55:11Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_1000steps_1e8rate_SFT 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. --> # Summary4500_M2_1000steps_1e8rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9563 \| 0.0447 \| 50 \| 1.9699 \| \| 1.9559 \| 0.0895 \| 100 \| 1.9696 \| \| 1.9636 \| 0.1342 \| 150 \| 1.9675 \| \| 1.9608 \| 0.1790 \| 200 \| 1.9666 \| \| 1.9525 \| 0.2237 \| 250 \| 1.9654 \| \| 1.9514 \| 0.2685 \| 300 \| 1.9645 \| \| 1.9704 \| 0.3132 \| 350 \| 1.9644 \| \| 1.9596 \| 0.3579 \| 400 \| 1.9639 \| \| 1.9558 \| 0.4027 \| 450 \| 1.9641 \| \| 1.9481 \| 0.4474 \| 500 \| 1.9635 \| \| 1.945 \| 0.4922 \| 550 \| 1.9639 \| \| 1.9532 \| 0.5369 \| 600 \| 1.9634 \| \| 1.955 \| 0.5817 \| 650 \| 1.9642 \| \| 1.9589 \| 0.6264 \| 700 \| 1.9635 \| \| 1.9638 \| 0.6711 \| 750 \| 1.9632 \| \| 1.9679 \| 0.7159 \| 800 \| 1.9634 \| \| 1.9484 \| 0.7606 \| 850 \| 1.9634 \| \| 1.9593 \| 0.8054 \| 900 \| 1.9634 \| \| 1.9598 \| 0.8501 \| 950 \| 1.9634 \| \| 1.9584 \| 0.8949 \| 1000 \| 1.9634 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
MichaelBui/Collection	MichaelBui	2024-07-02T08:15:05Z	0	null	[ "region:us" ]	null	2024-07-01T14:56:09Z	Entry not found
sekeun/EchoFM	sekeun	2024-07-01T14:56:35Z	0	null	[ "region:us" ]	null	2024-07-01T14:56:35Z	Entry not found
davelotito/donut_experiment_bayesian_trial_17	davelotito	2024-07-01T16:02:31Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-07-01T14:57:18Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer metrics: - bleu - wer model-index: - name: donut_experiment_bayesian_trial_17 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. --> # donut_experiment_bayesian_trial_17 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4635 - Bleu: 0.0675 - Precisions: [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307] - Brevity Penalty: 0.0895 - Length Ratio: 0.2930 - Translation Length: 477 - Reference Length: 1628 - Cer: 0.7603 - Wer: 0.8297 ## 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: 0.00018015728878154226 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Precisions \| Brevity Penalty \| Length Ratio \| Translation Length \| Reference Length \| Cer \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------------------------------------------------------------------------------:\|:---------------:\|:------------:\|:------------------:\|:----------------:\|:------:\|:------:\| \| 0.8044 \| 1.0 \| 253 \| 0.7112 \| 0.0610 \| [0.7535641547861507, 0.6497695852534562, 0.5809018567639257, 0.5125] \| 0.0987 \| 0.3016 \| 491 \| 1628 \| 0.7647 \| 0.8548 \| \| 0.3513 \| 2.0 \| 506 \| 0.5640 \| 0.0632 \| [0.7908902691511387, 0.7089201877934272, 0.6449864498644986, 0.5801282051282052] \| 0.0934 \| 0.2967 \| 483 \| 1628 \| 0.7549 \| 0.8416 \| \| 0.2101 \| 3.0 \| 759 \| 0.4754 \| 0.0666 \| [0.8198757763975155, 0.744131455399061, 0.6802168021680217, 0.6217948717948718] \| 0.0934 \| 0.2967 \| 483 \| 1628 \| 0.7508 \| 0.8282 \| \| 0.0756 \| 4.0 \| 1012 \| 0.4635 \| 0.0675 \| [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307] \| 0.0895 \| 0.2930 \| 477 \| 1628 \| 0.7603 \| 0.8297 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.19.1
habulaj/152328129573	habulaj	2024-07-01T14:57:47Z	0	null	[ "region:us" ]	null	2024-07-01T14:57:44Z	Entry not found
RymHrizi/lora_Llema38bsideeffect	RymHrizi	2024-07-01T16:19:05Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T14:57:45Z	--- 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: RymHrizi - 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)
vincent-espitalier/candle-beit	vincent-espitalier	2024-07-01T22:23:50Z	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-07-01T14:58:11Z	--- license: cc-by-nc-4.0 --- This repo contains the pre-trained weights for the [Beit model](https://github.com/microsoft/unilm/tree/master/beit) converted in a format that can be used by [candle](https://github.com/huggingface/candle). ## Citing DINOv2 As per their [GitHub repository](https://github.com/microsoft/unilm/tree/master/beit): ``` @misc{bao2022beitbertpretrainingimage, title={BEiT: BERT Pre-Training of Image Transformers}, author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei}, year={2022}, } ```
bobtk/mlx-communityLlama-3-Swallow-8B-Instruct-v0.1-8bit	bobtk	2024-07-01T14:58:20Z	0	null	[ "region:us" ]	null	2024-07-01T14:58:20Z	Entry not found
sail/data-mixture-doremi-1b	sail	2024-07-01T14:58:55Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T14:58:55Z	--- license: mit ---
sail/data-mixture-regmix-1b	sail	2024-07-01T14:59:06Z	0	null	[ "region:us" ]	null	2024-07-01T14:59:06Z	Entry not found
sail/data-mixture-human-1b	sail	2024-07-01T14:59:22Z	0	null	[ "region:us" ]	null	2024-07-01T14:59:22Z	Entry not found
sail/data-mixture-pile-cc-1b	sail	2024-07-01T14:59:35Z	0	null	[ "region:us" ]	null	2024-07-01T14:59:35Z	Entry not found
mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit	mlx-community	2024-07-01T15:10:46Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "ja", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T15:00:05Z	--- language: - en - ja license: llama3 library_name: transformers tags: - mlx pipeline_tag: text-generation model_type: llama --- # mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit The Model [mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit](https://huggingface.co/mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit) was converted to MLX format from [tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
veela4/ELDEN-RING-MOD	veela4	2024-07-02T07:21:12Z	0	null	[ "region:us" ]	null	2024-07-01T15:00:12Z	Entry not found
shine1607/masked_language_model	shine1607	2024-07-01T15:00:35Z	0	null	[ "region:us" ]	null	2024-07-01T15:00:35Z	Entry not found
ethedeltae/llama3-8b-oig-unsloth-iitg-final	ethedeltae	2024-07-01T15:01:20Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:01:01Z	--- 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: ethedeltae - 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)
yuchuantian/Instruct-IPT-single	yuchuantian	2024-07-01T15:06:59Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T15:01:39Z	--- license: apache-2.0 ---
yuchuantian/IPG_Tiny	yuchuantian	2024-07-01T15:19:18Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T15:03:22Z	--- license: apache-2.0 ---
sfgefgetg/tytu	sfgefgetg	2024-07-01T15:03:51Z	0	null	[ "region:us" ]	null	2024-07-01T15:03:51Z	Entry not found
rashid996958/pix2pix_exp43	rashid996958	2024-07-01T15:06:09Z	0	null	[ "region:us" ]	null	2024-07-01T15:06:04Z	Entry not found
tsavage68/Summary4500_M2_1000steps_1e7rate_SFT	tsavage68	2024-07-01T15:12:13Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T15:06:39Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_1000steps_1e7rate_SFT 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. --> # Summary4500_M2_1000steps_1e7rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4442 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8849 \| 0.0447 \| 50 \| 1.8726 \| \| 1.4871 \| 0.0895 \| 100 \| 1.4453 \| \| 0.8608 \| 0.1342 \| 150 \| 0.7955 \| \| 0.4432 \| 0.1790 \| 200 \| 0.4648 \| \| 0.4269 \| 0.2237 \| 250 \| 0.4556 \| \| 0.424 \| 0.2685 \| 300 \| 0.4519 \| \| 0.4417 \| 0.3132 \| 350 \| 0.4497 \| \| 0.4253 \| 0.3579 \| 400 \| 0.4481 \| \| 0.4247 \| 0.4027 \| 450 \| 0.4470 \| \| 0.4152 \| 0.4474 \| 500 \| 0.4461 \| \| 0.4116 \| 0.4922 \| 550 \| 0.4453 \| \| 0.4174 \| 0.5369 \| 600 \| 0.4448 \| \| 0.4201 \| 0.5817 \| 650 \| 0.4446 \| \| 0.423 \| 0.6264 \| 700 \| 0.4444 \| \| 0.4243 \| 0.6711 \| 750 \| 0.4441 \| \| 0.4325 \| 0.7159 \| 800 \| 0.4442 \| \| 0.4128 \| 0.7606 \| 850 \| 0.4441 \| \| 0.4207 \| 0.8054 \| 900 \| 0.4441 \| \| 0.424 \| 0.8501 \| 950 \| 0.4442 \| \| 0.4219 \| 0.8949 \| 1000 \| 0.4442 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
Aiden163/TiaTeste01	Aiden163	2024-07-01T15:08:41Z	0	null	[ "region:us" ]	null	2024-07-01T15:08:41Z	Entry not found
itay-nakash/model_387dff9370_sweep_expert-oath-1165	itay-nakash	2024-07-01T15:08:58Z	0	null	[ "region:us" ]	null	2024-07-01T15:08:58Z	Entry not found
net31/naschainv145	net31	2024-07-02T21:23:55Z	0	null	[ "region:us" ]	null	2024-07-01T15:10:43Z	Entry not found
itay-nakash/model_387dff9370_sweep_drawn-butterfly-1166	itay-nakash	2024-07-01T15:11:10Z	0	null	[ "region:us" ]	null	2024-07-01T15:11:10Z	Entry not found
gkngm/llama-financial-sentiment-analysis-peft	gkngm	2024-07-01T15:12:33Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:12:27Z	--- 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]
gkngm/finllm-financial-sentiment-analysis-peft	gkngm	2024-07-01T15:12:40Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:12:34Z	--- 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]
Behshadsheikhi/Morteza	Behshadsheikhi	2024-07-01T15:18:08Z	0	null	[ "license:openrail", "region:us" ]	null	2024-07-01T15:14:52Z	--- license: openrail ---
habulaj/66719121840	habulaj	2024-07-01T15:17:16Z	0	null	[ "region:us" ]	null	2024-07-01T15:17:14Z	Entry not found
Arjuna17/results	Arjuna17	2024-07-01T15:19:05Z	0	null	[ "region:us" ]	null	2024-07-01T15:19:05Z	Entry not found
LuluXML/lora_model	LuluXML	2024-07-01T15:19:44Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:19:22Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: LuluXML - 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)
jaimeazevedo/HQs	jaimeazevedo	2024-07-01T15:19:42Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T15:19:42Z	--- license: mit ---
nikest/nps-ft	nikest	2024-07-01T15:34:56Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T15:23:42Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
necrobradley/face_predict_emotion	necrobradley	2024-07-01T15:27:29Z	0	null	[ "region:us" ]	null	2024-07-01T15:27:29Z	Entry not found
luisrguerra/test	luisrguerra	2024-07-01T15:29:36Z	0	null	[ "region:us" ]	null	2024-07-01T15:29:36Z	Entry not found
sanamosuk93/news_sum	sanamosuk93	2024-07-01T15:41:59Z	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-07-01T15:32:55Z	Entry not found
ermannocavalli/face_of_FedericaFedeSala	ermannocavalli	2024-07-01T15:34:09Z	0	null	[ "region:us" ]	null	2024-07-01T15:34:09Z	Entry not found
EthanRhys/Greta-Masters-EX	EthanRhys	2024-07-01T15:34:52Z	0	null	[ "license:openrail++", "region:us" ]	null	2024-07-01T15:34:20Z	--- license: openrail++ ---
camillop/phi-mini-company-classification-adapters	camillop	2024-07-01T15:51:18Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:34:45Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: camillop - 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)
messawey/historyqa_model	messawey	2024-07-01T15:35:17Z	0	null	[ "region:us" ]	null	2024-07-01T15:35:17Z	Entry not found
js-kim/llama2-qlora-finetuned-french	js-kim	2024-07-01T15:35:34Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:35:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sushanthr/tinyLlama-1.1B-Chat-v1.0-fp16-webnn	sushanthr	2024-07-01T15:45:13Z	0	null	[ "region:us" ]	null	2024-07-01T15:37:57Z	A special port of https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0, in FP16 so that weights are loadable with WebNN. See https://sushanthr.github.io/RapidChat/ https://github.com/sushanthr/RapidChat
bartowski/Qwen2-7B-Multilingual-RP-exl2	bartowski	2024-07-01T15:38:07Z	0	null	[ "text-generation", "en", "ko", "ja", "zh", "es", "license:apache-2.0", "region:us" ]	text-generation	2024-07-01T15:38:06Z	--- license: apache-2.0 language: - en - ko - ja - zh - es quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Qwen2-7B-Multilingual-RP Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.6">turboderp's ExLlamaV2 v0.1.6</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/maywell/Qwen2-7B-Multilingual-RP ## Prompt format ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2 Qwen2-7B-Multilingual-RP-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Qwen2-7B-Multilingual-RP-exl2 --revision 6_5 --local-dir Qwen2-7B-Multilingual-RP-exl2-6_5 ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Qwen2-7B-Multilingual-RP-exl2 --revision 6_5 --local-dir Qwen2-7B-Multilingual-RP-exl2-6.5 ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
nftwp/maxart	nftwp	2024-07-02T15:33:19Z	0	null	[ "region:us" ]	null	2024-07-01T15:42:49Z	Entry not found
Simple979/ElmoBaby	Simple979	2024-07-01T17:13:28Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:43:36Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: Simple979 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jaxmetaverse/clay	jaxmetaverse	2024-07-01T15:44:42Z	0	null	[ "region:us" ]	null	2024-07-01T15:44:42Z	Entry not found
HikariLight/Mistral-SUFT-RL	HikariLight	2024-07-01T15:45:40Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T15:45:19Z	--- 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]
tsavage68/Summary4500_M2_200steps_1e7rate_SFT	tsavage68	2024-07-01T16:19:53Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T15:45:26Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_200steps_1e7rate_SFT 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. --> # Summary4500_M2_200steps_1e7rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9787 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8849 \| 0.0447 \| 50 \| 1.8726 \| \| 1.4871 \| 0.0895 \| 100 \| 1.4453 \| \| 1.0265 \| 0.1342 \| 150 \| 1.0225 \| \| 0.9518 \| 0.1790 \| 200 \| 0.9787 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
fokyoum9/test_model	fokyoum9	2024-07-01T15:50:51Z	0	null	[ "region:us" ]	null	2024-07-01T15:50:51Z	Entry not found
skyconnectiva/sky	skyconnectiva	2024-07-01T15:50:58Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T15:50:58Z	--- license: mit ---
shuyuej/MedLLaMA3-70B-base-INT4-G2048-GPTQ	shuyuej	2024-07-01T20:05:58Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T15:56:46Z	--- license: apache-2.0 ---
KeroroK66/Yoruichi	KeroroK66	2024-07-01T15:57:09Z	0	null	[ "license:openrail", "region:us" ]	null	2024-07-01T15:56:47Z	--- license: openrail ---
LinxuanPastel/parapparappa	LinxuanPastel	2024-07-01T16:27:14Z	0	null	[ "region:us" ]	null	2024-07-01T15:56:49Z	Entry not found
chunyeow/gemma-Code-Instruct-Finetune-test	chunyeow	2024-07-01T16:03:25Z	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T15:56:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
leduyson2603/autotrain-2dpe1-u5jfx	leduyson2603	2024-07-01T16:02:02Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "autotrain", "base_model:google-bert/bert-base-german-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-01T15:58:39Z	--- tags: - autotrain - token-classification base_model: google-bert/bert-base-german-cased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Token Classification ## Validation Metrics loss: 2.344400405883789 precision: 0.09302325581395349 recall: 0.25 f1: 0.13559322033898305 accuracy: 0.5533980582524272
tsavage68/Summary4500_M2_400steps_1e8rate_SFT	tsavage68	2024-07-01T16:06:17Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T16:02:10Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_400steps_1e8rate_SFT 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. --> # Summary4500_M2_400steps_1e8rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9563 \| 0.0447 \| 50 \| 1.9699 \| \| 1.9559 \| 0.0895 \| 100 \| 1.9696 \| \| 1.9642 \| 0.1342 \| 150 \| 1.9686 \| \| 1.9621 \| 0.1790 \| 200 \| 1.9673 \| \| 1.9548 \| 0.2237 \| 250 \| 1.9676 \| \| 1.9541 \| 0.2685 \| 300 \| 1.9678 \| \| 1.9743 \| 0.3132 \| 350 \| 1.9675 \| \| 1.964 \| 0.3579 \| 400 \| 1.9675 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
davelotito/donut_experiment_bayesian_trial_18	davelotito	2024-07-01T16:36:52Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:02:32Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer metrics: - bleu - wer model-index: - name: donut_experiment_bayesian_trial_18 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. --> # donut_experiment_bayesian_trial_18 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5643 - Bleu: 0.0698 - Precisions: [0.8340248962655602, 0.7741176470588236, 0.7309782608695652, 0.6784565916398714] - Brevity Penalty: 0.0928 - Length Ratio: 0.2961 - Translation Length: 482 - Reference Length: 1628 - Cer: 0.7496 - Wer: 0.8244 ## 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: 1.7803961202565393e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Precisions \| Brevity Penalty \| Length Ratio \| Translation Length \| Reference Length \| Cer \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------------------------------------------------------------------------------:\|:---------------:\|:------------:\|:------------------:\|:----------------:\|:------:\|:------:\| \| 0.0287 \| 1.0 \| 253 \| 0.5097 \| 0.0722 \| [0.8374485596707819, 0.7762237762237763, 0.7338709677419355, 0.6888888888888889] \| 0.0954 \| 0.2985 \| 486 \| 1628 \| 0.7506 \| 0.8208 \| \| 0.0159 \| 2.0 \| 506 \| 0.5583 \| 0.0697 \| [0.8319502074688797, 0.7741176470588236, 0.7282608695652174, 0.6784565916398714] \| 0.0928 \| 0.2961 \| 482 \| 1628 \| 0.7496 \| 0.8232 \| \| 0.0118 \| 3.0 \| 759 \| 0.5643 \| 0.0698 \| [0.8340248962655602, 0.7741176470588236, 0.7309782608695652, 0.6784565916398714] \| 0.0928 \| 0.2961 \| 482 \| 1628 \| 0.7496 \| 0.8244 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.19.1
cassador/4bs8lr2	cassador	2024-07-01T16:03:03Z	1	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6915", "loss:SoftmaxLoss", "id", "dataset:afaji/indonli", "arxiv:1908.10084", "base_model:indobenchmark/indobert-base-p2", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-07-01T16:02:38Z	--- base_model: indobenchmark/indobert-base-p2 datasets: - afaji/indonli language: - id library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6915 - loss:SoftmaxLoss widget: - source_sentence: Pesta Olahraga Asia Tenggara atau Southeast Asian Games, biasa disingkat SEA Games, adalah ajang olahraga yang diadakan setiap dua tahun dan melibatkan 11 negara Asia Tenggara. sentences: - Sekarang tahun 2017. - Warna kulit tidak mempengaruhi waktu berjemur yang baik untuk mengatifkan pro-vitamin D3. - Pesta Olahraga Asia Tenggara diadakan setiap tahun. - source_sentence: Menjalani aktivitas Ramadhan di tengah wabah Corona tentunya tidak mudah. sentences: - Tidak ada observasi yang pernah dilansir oleh Business Insider. - Wabah Corona membuat aktivitas Ramadhan tidak mudah dijalani. - Piala Sudirman pertama digelar pada tahun 1989. - source_sentence: Dalam bidang politik, partai ini memperjuangkan agar kekuasaan sepenuhnya berada di tangan rakyat. sentences: - Galileo tidak berhasil mengetes hasil dari Hukum Inert. - Kudeta 14 Februari 1946 gagal merebut kekuasaan Belanda. - Partai ini berusaha agar kekuasaan sepenuhnya berada di tangan rakyat. - source_sentence: Keluarga mendiang Prince menuduh layanan musik streaming Tidal memasukkan karya milik sang penyanyi legendaris tanpa izin . sentences: - Rosier adalah pelayan setia Lord Voldemort. - Bangunan ini digunakan untuk penjualan. - Keluarga mendiang Prince sudah memberi izin kepada TImbal untuk menggunakan lagu milik Prince. - source_sentence: Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum. sentences: - Pembuat Rooms hanya bisa membuat meeting yang terbuka. - Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC. - Eminem dirasa tidak akan memulai kembali kariernya tahun ini. model-index: - name: SentenceTransformer based on indobenchmark/indobert-base-p2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.596170613538296 name: Pearson Cosine - type: spearman_cosine value: 0.5861883707539226 name: Spearman Cosine - type: pearson_manhattan value: 0.5845731839861422 name: Pearson Manhattan - type: spearman_manhattan value: 0.5782563614870986 name: Spearman Manhattan - type: pearson_euclidean value: 0.5900038609486801 name: Pearson Euclidean - type: spearman_euclidean value: 0.5795936352515776 name: Spearman Euclidean - type: pearson_dot value: 0.5995818925993402 name: Pearson Dot - type: spearman_dot value: 0.5930379614276564 name: Spearman Dot - type: pearson_max value: 0.5995818925993402 name: Pearson Max - type: spearman_max value: 0.5930379614276564 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.32544389544371366 name: Pearson Cosine - type: spearman_cosine value: 0.29994363722612716 name: Spearman Cosine - type: pearson_manhattan value: 0.2875495017479062 name: Pearson Manhattan - type: spearman_manhattan value: 0.2810442265188576 name: Spearman Manhattan - type: pearson_euclidean value: 0.29788552102363436 name: Pearson Euclidean - type: spearman_euclidean value: 0.28248957351462056 name: Spearman Euclidean - type: pearson_dot value: 0.34645175745533086 name: Pearson Dot - type: spearman_dot value: 0.3331449893649715 name: Spearman Dot - type: pearson_max value: 0.34645175745533086 name: Pearson Max - type: spearman_max value: 0.3331449893649715 name: Spearman Max --- # SentenceTransformer based on indobenchmark/indobert-base-p2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity - Training Dataset: - [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) - Language: id <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("cassador/4bs8lr2") # Run inference sentences = [ 'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.', 'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.', 'Pembuat Rooms hanya bisa membuat meeting yang terbuka.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.5962 \| \| spearman_cosine \| 0.5862 \| \| pearson_manhattan \| 0.5846 \| \| spearman_manhattan \| 0.5783 \| \| pearson_euclidean \| 0.59 \| \| spearman_euclidean \| 0.5796 \| \| pearson_dot \| 0.5996 \| \| spearman_dot \| 0.593 \| \| pearson_max \| 0.5996 \| \| spearman_max \| 0.593 \| #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.3254 \| \| spearman_cosine \| 0.2999 \| \| pearson_manhattan \| 0.2875 \| \| spearman_manhattan \| 0.281 \| \| pearson_euclidean \| 0.2979 \| \| spearman_euclidean \| 0.2825 \| \| pearson_dot \| 0.3465 \| \| spearman_dot \| 0.3331 \| \| pearson_max \| 0.3465 \| \| spearman_max \| 0.3331 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### afaji/indonli * Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) * Size: 6,915 training samples * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| premise \| hypothesis \| label \| \|:--------\|:------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:------------------------------------------------\| \| type \| string \| string \| int \| \| details \| <ul><li>min: 12 tokens</li><li>mean: 29.26 tokens</li><li>max: 135 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.13 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>0: ~51.00%</li><li>1: ~49.00%</li></ul> \| * Samples: \| premise \| hypothesis \| label \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------\|:---------------\| \| <code>Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.</code> \| <code>Prediksi akhir wabah tidak disampaikan Jokowi.</code> \| <code>0</code> \| \| <code>Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.</code> \| <code>Masker sekali pakai banyak dipakai di tingkat rumah tangga.</code> \| <code>1</code> \| \| <code>Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa.</code> \| <code>Paket internet sahur tidak ditujukan untuk saat sahur.</code> \| <code>0</code> \| * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Evaluation Dataset #### afaji/indonli * Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) * Size: 1,556 evaluation samples * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| premise \| hypothesis \| label \| \|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:------------------------------------------------\| \| type \| string \| string \| int \| \| details \| <ul><li>min: 9 tokens</li><li>mean: 28.07 tokens</li><li>max: 179 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.15 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>0: ~47.90%</li><li>1: ~52.10%</li></ul> \| * Samples: \| premise \| hypothesis \| label \| \|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------\|:---------------\| \| <code>Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).</code> \| <code>Manuskrip tersebut tidak mencatat laporan kematian.</code> \| <code>0</code> \| \| <code>Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.</code> \| <code>Tidak ada observasi yang pernah dilansir oleh Business Insider.</code> \| <code>0</code> \| \| <code>Seorang wanita asal New York mengaku sangat benci air putih.</code> \| <code>Tidak ada orang dari New York yang membenci air putih.</code> \| <code>0</code> \| * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| loss \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \| \|:------:\|:----:\|:-------------:\|:------:\|:-----------------------:\|:------------------------:\| \| 0 \| 0 \| - \| - \| 0.1277 \| - \| \| 0.1156 \| 100 \| 0.6805 \| - \| - \| - \| \| 0.2312 \| 200 \| 0.5137 \| - \| - \| - \| \| 0.3468 \| 300 \| 0.5108 \| - \| - \| - \| \| 0.4624 \| 400 \| 0.5113 \| - \| - \| - \| \| 0.5780 \| 500 \| 0.5102 \| - \| - \| - \| \| 0.6936 \| 600 \| 0.5212 \| - \| - \| - \| \| 0.8092 \| 700 \| 0.5035 \| - \| - \| - \| \| 0.9249 \| 800 \| 0.472 \| - \| - \| - \| \| 1.0 \| 865 \| - \| 0.4468 \| 0.5249 \| - \| \| 1.0405 \| 900 \| 0.4193 \| - \| - \| - \| \| 1.1561 \| 1000 \| 0.3509 \| - \| - \| - \| \| 1.2717 \| 1100 \| 0.3709 \| - \| - \| - \| \| 1.3873 \| 1200 \| 0.3538 \| - \| - \| - \| \| 1.5029 \| 1300 \| 0.3619 \| - \| - \| - \| \| 1.6185 \| 1400 \| 0.388 \| - \| - \| - \| \| 1.7341 \| 1500 \| 0.3657 \| - \| - \| - \| \| 1.8497 \| 1600 \| 0.3577 \| - \| - \| - \| \| 1.9653 \| 1700 \| 0.4149 \| - \| - \| - \| \| 2.0 \| 1730 \| - \| 0.4535 \| 0.5503 \| - \| \| 2.0809 \| 1800 \| 0.3037 \| - \| - \| - \| \| 2.1965 \| 1900 \| 0.2213 \| - \| - \| - \| \| 2.3121 \| 2000 \| 0.2531 \| - \| - \| - \| \| 2.4277 \| 2100 \| 0.2281 \| - \| - \| - \| \| 2.5434 \| 2200 \| 0.2684 \| - \| - \| - \| \| 2.6590 \| 2300 \| 0.2154 \| - \| - \| - \| \| 2.7746 \| 2400 \| 0.2556 \| - \| - \| - \| \| 2.8902 \| 2500 \| 0.2515 \| - \| - \| - \| \| 3.0 \| 2595 \| - \| 0.6295 \| 0.5799 \| - \| \| 3.0058 \| 2600 \| 0.2158 \| - \| - \| - \| \| 3.1214 \| 2700 \| 0.1445 \| - \| - \| - \| \| 3.2370 \| 2800 \| 0.1191 \| - \| - \| - \| \| 3.3526 \| 2900 \| 0.1514 \| - \| - \| - \| \| 3.4682 \| 3000 \| 0.1223 \| - \| - \| - \| \| 3.5838 \| 3100 \| 0.1581 \| - \| - \| - \| \| 3.6994 \| 3200 \| 0.112 \| - \| - \| - \| \| 3.8150 \| 3300 \| 0.1396 \| - \| - \| - \| \| 3.9306 \| 3400 \| 0.1568 \| - \| - \| - \| \| 4.0 \| 3460 \| - \| 0.8635 \| 0.5862 \| 0.2999 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers and SoftmaxLoss ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
PointPresse/phi3-mini-news-analysis-fr-lora	PointPresse	2024-07-01T16:07:55Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:04:24Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: PointPresse - 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)
Iqbaliswinning/results	Iqbaliswinning	2024-07-01T16:07:42Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-07-01T16:07:25Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder 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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
rinogrego/biomedlm-2.7b-finetuned-medmcqa	rinogrego	2024-07-03T01:28:42Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:08:31Z	--- 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]
xiangruowen/ruoxiang_test	xiangruowen	2024-07-01T16:22:00Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T16:09:46Z	--- license: mit ---
bvrc1518/vosk	bvrc1518	2024-07-01T16:09:49Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T16:09:49Z	--- license: mit ---
lucyknada/amxl-reupload	lucyknada	2024-07-01T16:21:36Z	0	null	[ "region:us" ]	null	2024-07-01T16:20:00Z	Entry not found
cmmann/q-FrozenLake-v1-4x4-noSlippery	cmmann	2024-07-01T16:23:32Z	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-07-01T16:23:30Z	--- 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="cmmann/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"]) ```
samSepiol101/newRepo	samSepiol101	2024-07-01T17:02:21Z	0	null	[ "region:us" ]	null	2024-07-01T16:24:17Z	Entry not found
cmmann/q-taxi	cmmann	2024-07-01T16:42:21Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-07-01T16:27:13Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: -35.46 +/- 52.99 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="cmmann/q-taxi", 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"]) ```
sunilswain/llama2-7b-chat-EssplTravelPolicy3.7k-epoch6	sunilswain	2024-07-01T16:39:39Z	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T16:30:39Z	Entry not found
habulaj/142653118795	habulaj	2024-07-01T16:35:18Z	0	null	[ "region:us" ]	null	2024-07-01T16:35:06Z	Entry not found
allison1221/t5-small-finetuned-xsum	allison1221	2024-07-02T01:44:35Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-07-01T16:35:12Z	Entry not found
davelotito/donut_experiment_bayesian_trial_19	davelotito	2024-07-01T17:11:12Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:36:53Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer metrics: - bleu - wer model-index: - name: donut_experiment_bayesian_trial_19 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. --> # donut_experiment_bayesian_trial_19 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5754 - Bleu: 0.0724 - Precisions: [0.8450413223140496, 0.7892271662763466, 0.7486486486486487, 0.7028753993610224] - Brevity Penalty: 0.0941 - Length Ratio: 0.2973 - Translation Length: 484 - Reference Length: 1628 - Cer: 0.7493 - Wer: 0.8177 ## 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: 1.0668629620167924e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Precisions \| Brevity Penalty \| Length Ratio \| Translation Length \| Reference Length \| Cer \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------------------------------------------------------------------------------:\|:---------------:\|:------------:\|:------------------:\|:----------------:\|:------:\|:------:\| \| 0.0069 \| 1.0 \| 253 \| 0.5825 \| 0.0710 \| [0.8423236514522822, 0.7858823529411765, 0.7418478260869565, 0.6977491961414791] \| 0.0928 \| 0.2961 \| 482 \| 1628 \| 0.7509 \| 0.8197 \| \| 0.0113 \| 2.0 \| 506 \| 0.5684 \| 0.0703 \| [0.841995841995842, 0.785377358490566, 0.7411444141689373, 0.6935483870967742] \| 0.0921 \| 0.2955 \| 481 \| 1628 \| 0.7505 \| 0.8199 \| \| 0.0074 \| 3.0 \| 759 \| 0.5754 \| 0.0724 \| [0.8450413223140496, 0.7892271662763466, 0.7486486486486487, 0.7028753993610224] \| 0.0941 \| 0.2973 \| 484 \| 1628 \| 0.7493 \| 0.8177 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.19.1
lielbin/BabyBERTa-wikipedia-french-without-Masking-finetuned-Fr-SQuAD	lielbin	2024-07-01T17:17:43Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2024-07-01T16:38:48Z	--- tags: - generated_from_trainer model-index: - name: BabyBERTa-wikipedia-french-without-Masking-finetuned-Fr-SQuAD 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. --> # BabyBERTa-wikipedia-french-without-Masking-finetuned-Fr-SQuAD This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
BTX24/deit_birads_classifier	BTX24	2024-07-01T16:38:52Z	0	null	[ "region:us" ]	null	2024-07-01T16:38:52Z	Entry not found
VoxAI/Hermes-2-Theta-Llama-3-8B-DriveThru-ORPO-v1-master-0.707-adapter	VoxAI	2024-07-01T16:44:12Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:40:02Z	--- 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]
vincentantu/computer_vision	vincentantu	2024-07-01T16:41:23Z	0	null	[ "region:us" ]	null	2024-07-01T16:41:23Z	Entry not found
veronica08041991/naschainv249	veronica08041991	2024-07-02T03:18:30Z	0	null	[ "region:us" ]	null	2024-07-01T16:41:39Z	Entry not found
hishamcse/DQN-MsPacman-v4	hishamcse	2024-07-01T16:43:57Z	0	null	[ "MsPacman-v4", "dqn", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-07-01T16:41:40Z	--- tags: - MsPacman-v4 - dqn - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: DQN-MsPacman-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: MsPacman-v4 type: MsPacman-v4 metrics: - type: mean_reward value: 249.00 +/- 129.26 name: mean_reward verified: false --- # DQN Agent playing MsPacman-v4 Details see: https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-extra-unit-3-mspacmandqn-scratch
manbeast3b/ZZZZZZZZdriver121c	manbeast3b	2024-07-01T16:45:30Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-01T16:42:47Z	Entry not found
ra9hu/ra9hu	ra9hu	2024-07-01T16:44:24Z	0	null	[ "region:us" ]	null	2024-07-01T16:44:24Z	Entry not found
gjonesQ02/StatementOfWork_Generator_Omega_BS_512_2	gjonesQ02	2024-07-01T20:05:35Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T16:45:44Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: distilgpt2 model-index: - name: StatementOfWork_Generator_Omega_BS_512_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # StatementOfWork_Generator_Omega_BS_512_2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8120 ## 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: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 4 \| 0.9839 \| \| No log \| 2.0 \| 8 \| 0.9786 \| \| No log \| 3.0 \| 12 \| 0.9767 \| \| No log \| 4.0 \| 16 \| 0.9757 \| \| No log \| 5.0 \| 20 \| 0.9716 \| \| No log \| 6.0 \| 24 \| 0.9670 \| \| No log \| 7.0 \| 28 \| 0.9663 \| \| No log \| 8.0 \| 32 \| 0.9627 \| \| No log \| 9.0 \| 36 \| 0.9571 \| \| No log \| 10.0 \| 40 \| 0.9573 \| \| No log \| 11.0 \| 44 \| 0.9520 \| \| No log \| 12.0 \| 48 \| 0.9511 \| \| No log \| 13.0 \| 52 \| 0.9486 \| \| No log \| 14.0 \| 56 \| 0.9425 \| \| No log \| 15.0 \| 60 \| 0.9440 \| \| No log \| 16.0 \| 64 \| 0.9392 \| \| No log \| 17.0 \| 68 \| 0.9357 \| \| No log \| 18.0 \| 72 \| 0.9368 \| \| No log \| 19.0 \| 76 \| 0.9333 \| \| No log \| 20.0 \| 80 \| 0.9284 \| \| No log \| 21.0 \| 84 \| 0.9260 \| \| No log \| 22.0 \| 88 \| 0.9244 \| \| No log \| 23.0 \| 92 \| 0.9228 \| \| No log \| 24.0 \| 96 \| 0.9192 \| \| No log \| 25.0 \| 100 \| 0.9163 \| \| No log \| 26.0 \| 104 \| 0.9164 \| \| No log \| 27.0 \| 108 \| 0.9135 \| \| No log \| 28.0 \| 112 \| 0.9107 \| \| No log \| 29.0 \| 116 \| 0.9105 \| \| No log \| 30.0 \| 120 \| 0.9068 \| \| No log \| 31.0 \| 124 \| 0.9050 \| \| No log \| 32.0 \| 128 \| 0.9034 \| \| No log \| 33.0 \| 132 \| 0.9012 \| \| No log \| 34.0 \| 136 \| 0.8966 \| \| No log \| 35.0 \| 140 \| 0.8968 \| \| No log \| 36.0 \| 144 \| 0.8953 \| \| No log \| 37.0 \| 148 \| 0.8920 \| \| No log \| 38.0 \| 152 \| 0.8920 \| \| No log \| 39.0 \| 156 \| 0.8912 \| \| No log \| 40.0 \| 160 \| 0.8877 \| \| No log \| 41.0 \| 164 \| 0.8871 \| \| No log \| 42.0 \| 168 \| 0.8857 \| \| No log \| 43.0 \| 172 \| 0.8800 \| \| No log \| 44.0 \| 176 \| 0.8789 \| \| No log \| 45.0 \| 180 \| 0.8831 \| \| No log \| 46.0 \| 184 \| 0.8794 \| \| No log \| 47.0 \| 188 \| 0.8757 \| \| No log \| 48.0 \| 192 \| 0.8760 \| \| No log \| 49.0 \| 196 \| 0.8730 \| \| No log \| 50.0 \| 200 \| 0.8726 \| \| No log \| 51.0 \| 204 \| 0.8719 \| \| No log \| 52.0 \| 208 \| 0.8689 \| \| No log \| 53.0 \| 212 \| 0.8691 \| \| No log \| 54.0 \| 216 \| 0.8679 \| \| No log \| 55.0 \| 220 \| 0.8633 \| \| No log \| 56.0 \| 224 \| 0.8623 \| \| No log \| 57.0 \| 228 \| 0.8624 \| \| No log \| 58.0 \| 232 \| 0.8610 \| \| No log \| 59.0 \| 236 \| 0.8601 \| \| No log \| 60.0 \| 240 \| 0.8586 \| \| No log \| 61.0 \| 244 \| 0.8583 \| \| No log \| 62.0 \| 248 \| 0.8564 \| \| No log \| 63.0 \| 252 \| 0.8552 \| \| No log \| 64.0 \| 256 \| 0.8545 \| \| No log \| 65.0 \| 260 \| 0.8526 \| \| No log \| 66.0 \| 264 \| 0.8513 \| \| No log \| 67.0 \| 268 \| 0.8508 \| \| No log \| 68.0 \| 272 \| 0.8501 \| \| No log \| 69.0 \| 276 \| 0.8484 \| \| No log \| 70.0 \| 280 \| 0.8479 \| \| No log \| 71.0 \| 284 \| 0.8465 \| \| No log \| 72.0 \| 288 \| 0.8464 \| \| No log \| 73.0 \| 292 \| 0.8452 \| \| No log \| 74.0 \| 296 \| 0.8442 \| \| No log \| 75.0 \| 300 \| 0.8443 \| \| No log \| 76.0 \| 304 \| 0.8425 \| \| No log \| 77.0 \| 308 \| 0.8410 \| \| No log \| 78.0 \| 312 \| 0.8402 \| \| No log \| 79.0 \| 316 \| 0.8394 \| \| No log \| 80.0 \| 320 \| 0.8385 \| \| No log \| 81.0 \| 324 \| 0.8380 \| \| No log \| 82.0 \| 328 \| 0.8380 \| \| No log \| 83.0 \| 332 \| 0.8369 \| \| No log \| 84.0 \| 336 \| 0.8356 \| \| No log \| 85.0 \| 340 \| 0.8351 \| \| No log \| 86.0 \| 344 \| 0.8343 \| \| No log \| 87.0 \| 348 \| 0.8326 \| \| No log \| 88.0 \| 352 \| 0.8331 \| \| No log \| 89.0 \| 356 \| 0.8328 \| \| No log \| 90.0 \| 360 \| 0.8306 \| \| No log \| 91.0 \| 364 \| 0.8310 \| \| No log \| 92.0 \| 368 \| 0.8314 \| \| No log \| 93.0 \| 372 \| 0.8295 \| \| No log \| 94.0 \| 376 \| 0.8287 \| \| No log \| 95.0 \| 380 \| 0.8286 \| \| No log \| 96.0 \| 384 \| 0.8276 \| \| No log \| 97.0 \| 388 \| 0.8270 \| \| No log \| 98.0 \| 392 \| 0.8262 \| \| No log \| 99.0 \| 396 \| 0.8251 \| \| No log \| 100.0 \| 400 \| 0.8241 \| \| No log \| 101.0 \| 404 \| 0.8231 \| \| No log \| 102.0 \| 408 \| 0.8225 \| \| No log \| 103.0 \| 412 \| 0.8235 \| \| No log \| 104.0 \| 416 \| 0.8234 \| \| No log \| 105.0 \| 420 \| 0.8225 \| \| No log \| 106.0 \| 424 \| 0.8219 \| \| No log \| 107.0 \| 428 \| 0.8209 \| \| No log \| 108.0 \| 432 \| 0.8204 \| \| No log \| 109.0 \| 436 \| 0.8195 \| \| No log \| 110.0 \| 440 \| 0.8191 \| \| No log \| 111.0 \| 444 \| 0.8191 \| \| No log \| 112.0 \| 448 \| 0.8193 \| \| No log \| 113.0 \| 452 \| 0.8197 \| \| No log \| 114.0 \| 456 \| 0.8191 \| \| No log \| 115.0 \| 460 \| 0.8179 \| \| No log \| 116.0 \| 464 \| 0.8176 \| \| No log \| 117.0 \| 468 \| 0.8173 \| \| No log \| 118.0 \| 472 \| 0.8172 \| \| No log \| 119.0 \| 476 \| 0.8174 \| \| No log \| 120.0 \| 480 \| 0.8171 \| \| No log \| 121.0 \| 484 \| 0.8169 \| \| No log \| 122.0 \| 488 \| 0.8168 \| \| No log \| 123.0 \| 492 \| 0.8162 \| \| No log \| 124.0 \| 496 \| 0.8161 \| \| 0.3706 \| 125.0 \| 500 \| 0.8160 \| \| 0.3706 \| 126.0 \| 504 \| 0.8156 \| \| 0.3706 \| 127.0 \| 508 \| 0.8145 \| \| 0.3706 \| 128.0 \| 512 \| 0.8143 \| \| 0.3706 \| 129.0 \| 516 \| 0.8143 \| \| 0.3706 \| 130.0 \| 520 \| 0.8145 \| \| 0.3706 \| 131.0 \| 524 \| 0.8147 \| \| 0.3706 \| 132.0 \| 528 \| 0.8142 \| \| 0.3706 \| 133.0 \| 532 \| 0.8136 \| \| 0.3706 \| 134.0 \| 536 \| 0.8136 \| \| 0.3706 \| 135.0 \| 540 \| 0.8138 \| \| 0.3706 \| 136.0 \| 544 \| 0.8139 \| \| 0.3706 \| 137.0 \| 548 \| 0.8140 \| \| 0.3706 \| 138.0 \| 552 \| 0.8138 \| \| 0.3706 \| 139.0 \| 556 \| 0.8134 \| \| 0.3706 \| 140.0 \| 560 \| 0.8130 \| \| 0.3706 \| 141.0 \| 564 \| 0.8128 \| \| 0.3706 \| 142.0 \| 568 \| 0.8127 \| \| 0.3706 \| 143.0 \| 572 \| 0.8126 \| \| 0.3706 \| 144.0 \| 576 \| 0.8124 \| \| 0.3706 \| 145.0 \| 580 \| 0.8123 \| \| 0.3706 \| 146.0 \| 584 \| 0.8121 \| \| 0.3706 \| 147.0 \| 588 \| 0.8120 \| \| 0.3706 \| 148.0 \| 592 \| 0.8120 \| \| 0.3706 \| 149.0 \| 596 \| 0.8120 \| \| 0.3706 \| 150.0 \| 600 \| 0.8120 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
GloryKuo/llama2_medical_qlora	GloryKuo	2024-07-01T16:46:10Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:DavidLanz/Llama2-tw-7B-v2.0.1-chat", "region:us" ]	null	2024-07-01T16:45:45Z	--- base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat 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
sharathprasaath/Phi-3-mini	sharathprasaath	2024-07-01T16:48:24Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:48:02Z	--- 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]
sharathprasaath/Phi-3-min	sharathprasaath	2024-07-01T16:48:22Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T16:48:02Z	--- 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]
kr-manish/distilgpt2-finetuned-rawHrPolicy	kr-manish	2024-07-01T16:50:14Z	0	transformers	[ "transformers", "tf", "tensorboard", "gpt2", "text-generation", "generated_from_keras_callback", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T16:49:15Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_keras_callback model-index: - name: kr-manish/distilgpt2-finetuned-rawHrPolicy results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # kr-manish/distilgpt2-finetuned-rawHrPolicy This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0134 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 4.0134 \| 0 \| ### Framework versions - Transformers 4.41.2 - TensorFlow 2.15.0 - Datasets 2.20.0 - Tokenizers 0.19.1
hainc2/llmhainc	hainc2	2024-07-02T09:10:13Z	0	null	[ "license:llama3", "region:us" ]	null	2024-07-01T16:51:22Z	--- license: llama3 ---
Chairles-alex/mistral-two	Chairles-alex	2024-07-01T16:52:34Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-07-01T16:51:23Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: mistralai/Mistral-7B-Instruct-v0.3 widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
robinhub/robin_model	robinhub	2024-07-02T08:21:53Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:taide/TAIDE-LX-7B-Chat", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-01T16:54:19Z	--- base_model: taide/TAIDE-LX-7B-Chat language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: robinhub - License: apache-2.0 - Finetuned from model : taide/TAIDE-LX-7B-Chat 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)
Adzka/test-reward-model	Adzka	2024-07-01T21:30:05Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:w11wo/indonesian-roberta-base-sentiment-classifier", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-01T16:54:29Z	--- license: mit base_model: w11wo/indonesian-roberta-base-sentiment-classifier tags: - generated_from_trainer metrics: - accuracy model-index: - name: test-reward-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-reward-model This model is a fine-tuned version of [w11wo/indonesian-roberta-base-sentiment-classifier](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2784 - Accuracy: 0.8817 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7179 \| 0.67 \| 50 \| 0.6866 \| 0.6237 \| \| 0.6866 \| 1.33 \| 100 \| 0.6661 \| 0.7742 \| \| 0.6546 \| 2.0 \| 150 \| 0.6039 \| 0.8280 \| \| 0.5421 \| 2.67 \| 200 \| 0.4624 \| 0.8172 \| \| 0.3965 \| 3.33 \| 250 \| 0.3958 \| 0.8280 \| \| 0.3244 \| 4.0 \| 300 \| 0.3502 \| 0.8495 \| \| 0.251 \| 4.67 \| 350 \| 0.4012 \| 0.8602 \| \| 0.1579 \| 5.33 \| 400 \| 0.3184 \| 0.8602 \| \| 0.135 \| 6.0 \| 450 \| 0.3141 \| 0.8710 \| \| 0.1114 \| 6.67 \| 500 \| 0.3474 \| 0.8495 \| \| 0.0929 \| 7.33 \| 550 \| 0.2931 \| 0.8495 \| \| 0.0829 \| 8.0 \| 600 \| 0.2757 \| 0.8710 \| \| 0.0834 \| 8.67 \| 650 \| 0.2889 \| 0.8817 \| \| 0.057 \| 9.33 \| 700 \| 0.2810 \| 0.8925 \| \| 0.0503 \| 10.0 \| 750 \| 0.2800 \| 0.8817 \| \| 0.062 \| 10.67 \| 800 \| 0.2806 \| 0.8817 \| \| 0.0303 \| 11.33 \| 850 \| 0.2971 \| 0.8817 \| \| 0.0246 \| 12.0 \| 900 \| 0.2784 \| 0.8817 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2
starnet/11-star-07-01-02	starnet	2024-07-01T16:57:30Z	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-01T16:54:32Z	Entry not found

odedregev/Llama-2-7b-chat-hf-science-rejection-sampling

odedregev

2024-07-01T14:51:15Z

0

transformers

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

text-generation

2024-07-01T14:43:30Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ilyass31/results

ilyass31

2024-07-01T16:41:15Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "region:us" ]

null

2024-07-01T14:44:27Z

--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.13.3

josedonoso/blip2-ecg-khan

josedonoso

2024-07-01T14:48:27Z

0

transformers

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

null

2024-07-01T14:48:24Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

bitmind/deepfake-detector-base

bitmind

2024-07-01T14:51:54Z

0

null

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

null

2024-07-01T14:50:05Z

--- license: mit ---

rashid996958/pix2pix_exp42

rashid996958

2024-07-01T14:50:53Z

0

null

[ "region:us" ]

null

2024-07-01T14:50:48Z

Entry not found

HieuBeo/Ho_Chi_Minh-LoRa

HieuBeo

2024-07-01T14:50:57Z

0

null

[ "region:us" ]

null

2024-07-01T14:50:57Z

Entry not found

Pyszczysko/swendamocnaboli

Pyszczysko

2024-07-01T14:51:27Z

0

null

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

null

2024-07-01T14:51:26Z

--- license: unknown ---

cheng-cherry/my_awesome_opus_books_model

cheng-cherry

2024-07-01T15:28:30Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-07-01T14:51:31Z

Entry not found

yuchuantian/IPG_rep

yuchuantian

2024-07-01T14:55:50Z

0

null

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

null

2024-07-01T14:52:12Z

--- license: apache-2.0 ---

hmpm00/bul-id-bulas-final

hmpm00

2024-07-01T14:52:21Z

0

null

[ "region:us" ]

null

2024-07-01T14:52:21Z

Entry not found

Smabbler/Multiclass-Disease-Diagnosis-Model

Smabbler

2024-07-01T18:43:56Z

0

null

[ "text-classification", "en", "dataset:duxprajapati/symptom-disease-dataset", "region:us" ]

text-classification

2024-07-01T14:53:25Z

--- datasets: - duxprajapati/symptom-disease-dataset language: - en metrics: - accuracy - f1 pipeline_tag: text-classification --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description A predictive machine learning model was developed that can classify data points into distinct categories based on symptoms using diseases data. - **Developed by:** Priyanka Kamila - **Model type:** RandomForestClassifier, SVC - **Language(s) (NLP):** EN ## Uses  ### Direct Use  This model can be directly used for disease diagnosis based on binary encoded medical features. By inputting patient symptoms in the form of binary vectors, the model predicts the likely medical condition. Here’s how you can utilize the model: Prepare Input Data: Ensure that the input data is formatted as a binary matrix, where each row represents a patient and each column represents a symptom or feature. The target variable should be a categorical label representing the medical condition. Load the Model: Load the trained Random Forest Classifier or SVM Classifier from the repository. You can use libraries like joblib or pickle in Python to load the pre-trained model. Make Predictions: Use the loaded model to make predictions on new input data. For instance, in Python: python Copy code import joblib model = joblib.load('path_to_model.pkl') predictions = model.predict(new_input_data) Interpret Results: The model will output the predicted medical condition for each input row. These predictions can be used by healthcare professionals to assist in diagnosing patients. This model is intended for direct use in clinical decision support systems or healthcare applications where quick and accurate disease diagnosis is critical. It can be integrated into electronic health records (EHR) systems, patient management software, or used as a standalone diagnostic tool. ### Out-of-Scope Use  This model is designed specifically for diagnosing diseases based on binary encoded medical features. It is important to recognize the limitations and potential misuse of the model: Non-Medical Applications: The model is not suitable for non-medical applications or any use cases outside of healthcare diagnostics. Using this model for unrelated classification tasks will yield inaccurate and irrelevant results. Incomplete or Inaccurate Input Data: The model relies on precise binary encoding of medical symptoms. Providing incomplete, inaccurate, or improperly formatted data can lead to incorrect diagnoses. It is crucial to ensure that input data is complete and correctly formatted according to the binary encoding schema used during model training. Real-Time Critical Decisions: While the model can aid in diagnosis, it should not be solely relied upon for real-time critical medical decisions without human oversight. Healthcare professionals should verify the model’s predictions and consider additional clinical information and diagnostics before making final decisions. Malicious Use: The model should not be used to intentionally misdiagnose or manipulate medical diagnoses for fraudulent purposes. Ensuring ethical use of the model is paramount, and it should only be used to assist in improving patient care. Diagnostic Scope Limitation: The model is trained on specific diseases included in the dataset. It may not perform well in diagnosing conditions outside the scope of its training data. For diseases not represented in the training data, the model might default to predicting "other," which should be interpreted with caution. General Population Screening: This model is not intended for general population screening or predicting disease prevalence in broad, non-clinical populations. It is designed for use with patients already presenting symptoms or those in a clinical setting. By understanding these limitations and potential misuse scenarios, users can ensure that the model is applied appropriately and ethically in relevant healthcare contexts. ## Training Details ### Training Data  The training data used for this model consists of a custom dataset with binary encoded medical features. Each row in the dataset represents a patient's symptoms encoded as binary values, and the corresponding label represents the diagnosed disease. The dataset includes a wide range of medical conditions, with the aim of providing a comprehensive diagnostic tool. Source of Data: The dataset was compiled from https://huggingface.co/datasets/duxprajapati/symptom-disease-dataset from huggingface which was then processed in terms of data-labeling using Smabbler's QueryLab platform ensuring a accurate representation of data-labels for common and rare diseases. Pre-processing: Data was pre-processed to ensure consistency and accuracy. This involved cleaning the data, handling missing values, and normalizing the binary encoding. Each symptom was converted into a binary feature (0 or 1), indicating its absence or presence respectively. The labels were mapped to specific diseases using a detailed mapping file to ensure accurate representation. Label Mapping: The labels in the dataset correspond to various diseases. A mapping file (mapping.json) was used to translate encoded labels to human-readable disease names. Top labels include diseases like Psoriasis, Malaria, Bronchial Asthma, Dengue, Arthritis, Heart Attack, and many more. Additional Documentation: Detailed documentation on data pre-processing and filtering steps is provided to ensure reproducibility and transparency. The dataset card includes information on the data sources, pre-processing steps, and any additional filtering or transformations applied. ### Training Procedure  The training procedure for this model involves several key steps to ensure robust and accurate disease diagnosis using Random Forest and SVM classifiers. Below are the detailed steps and technical specifications related to the training procedure: Data Splitting: The dataset was split into training and testing sets using an 80-20 split ratio. The training set was used to train the classifiers, while the testing set was used to evaluate the model’s performance. Feature Selection: Binary encoded features representing the presence or absence of symptoms were selected as input features. The target variable was the disease label, which was mapped from encoded integers to human-readable disease names. Model Initialization: Two classifiers were initialized: Random Forest Classifier and Support Vector Machine (SVM) Classifier. Both classifiers were initialized with default parameters and a fixed random state to ensure reproducibility. Training the Models: Random Forest Classifier: The Random Forest model was trained on the training data using the fit method. Hyperparameters such as the number of trees and depth were tuned to optimize performance. SVM Classifier: The SVM model was similarly trained using the fit method. Kernel type, regularization parameters, and other hyperparameters were adjusted for optimal classification. ## Evaluation  The performance of both models was evaluated on the testing set. Metrics such as accuracy, precision, recall, and f1-score were calculated to assess model performance. Confusion matrices were generated to visualize the performance of each classifier in predicting the correct disease labels. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6682b3f72b1f40e83883f999/zzVYejajU3qtj6dlDUS6g.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6682b3f72b1f40e83883f999/yK7UpL0dtCGz8Nq3q2ylN.png) ### Results ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6682b3f72b1f40e83883f999/58ZffNH4K8hgweNRqAHMC.png) #### Summary This model utilizes both Random Forest and SVM classifiers to accurately diagnose a variety of diseases based on binary encoded medical features. The training involved data pre-processing, feature selection, model training, and extensive evaluation to ensure reliability. Designed for healthcare applications, it aids professionals in making informed diagnostic decisions efficiently. ## Model Card Authors Priyanka Kamila

tsavage68/Summary4500_M2_1000steps_1e8rate_SFT

tsavage68

2024-07-01T14:59:03Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T14:55:11Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_1000steps_1e8rate_SFT results: [] ---  # Summary4500_M2_1000steps_1e8rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.9563 | 0.0447 | 50 | 1.9699 | | 1.9559 | 0.0895 | 100 | 1.9696 | | 1.9636 | 0.1342 | 150 | 1.9675 | | 1.9608 | 0.1790 | 200 | 1.9666 | | 1.9525 | 0.2237 | 250 | 1.9654 | | 1.9514 | 0.2685 | 300 | 1.9645 | | 1.9704 | 0.3132 | 350 | 1.9644 | | 1.9596 | 0.3579 | 400 | 1.9639 | | 1.9558 | 0.4027 | 450 | 1.9641 | | 1.9481 | 0.4474 | 500 | 1.9635 | | 1.945 | 0.4922 | 550 | 1.9639 | | 1.9532 | 0.5369 | 600 | 1.9634 | | 1.955 | 0.5817 | 650 | 1.9642 | | 1.9589 | 0.6264 | 700 | 1.9635 | | 1.9638 | 0.6711 | 750 | 1.9632 | | 1.9679 | 0.7159 | 800 | 1.9634 | | 1.9484 | 0.7606 | 850 | 1.9634 | | 1.9593 | 0.8054 | 900 | 1.9634 | | 1.9598 | 0.8501 | 950 | 1.9634 | | 1.9584 | 0.8949 | 1000 | 1.9634 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

MichaelBui/Collection

MichaelBui

2024-07-02T08:15:05Z

0

null

[ "region:us" ]

null

2024-07-01T14:56:09Z

Entry not found

sekeun/EchoFM

sekeun

2024-07-01T14:56:35Z

0

null

[ "region:us" ]

null

2024-07-01T14:56:35Z

Entry not found

davelotito/donut_experiment_bayesian_trial_17

davelotito

2024-07-01T16:02:31Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-07-01T14:57:18Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer metrics: - bleu - wer model-index: - name: donut_experiment_bayesian_trial_17 results: [] ---  # donut_experiment_bayesian_trial_17 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4635 - Bleu: 0.0675 - Precisions: [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307] - Brevity Penalty: 0.0895 - Length Ratio: 0.2930 - Translation Length: 477 - Reference Length: 1628 - Cer: 0.7603 - Wer: 0.8297 ## 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: 0.00018015728878154226 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| | 0.8044 | 1.0 | 253 | 0.7112 | 0.0610 | [0.7535641547861507, 0.6497695852534562, 0.5809018567639257, 0.5125] | 0.0987 | 0.3016 | 491 | 1628 | 0.7647 | 0.8548 | | 0.3513 | 2.0 | 506 | 0.5640 | 0.0632 | [0.7908902691511387, 0.7089201877934272, 0.6449864498644986, 0.5801282051282052] | 0.0934 | 0.2967 | 483 | 1628 | 0.7549 | 0.8416 | | 0.2101 | 3.0 | 759 | 0.4754 | 0.0666 | [0.8198757763975155, 0.744131455399061, 0.6802168021680217, 0.6217948717948718] | 0.0934 | 0.2967 | 483 | 1628 | 0.7508 | 0.8282 | | 0.0756 | 4.0 | 1012 | 0.4635 | 0.0675 | [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307] | 0.0895 | 0.2930 | 477 | 1628 | 0.7603 | 0.8297 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.19.1

habulaj/152328129573

habulaj

2024-07-01T14:57:47Z

0

null

[ "region:us" ]

null

2024-07-01T14:57:44Z

Entry not found

RymHrizi/lora_Llema38bsideeffect

RymHrizi

2024-07-01T16:19:05Z

0

transformers

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

null

2024-07-01T14:57:45Z

--- 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:** RymHrizi - **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)

vincent-espitalier/candle-beit

vincent-espitalier

2024-07-01T22:23:50Z

0

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

2024-07-01T14:58:11Z

--- license: cc-by-nc-4.0 --- This repo contains the pre-trained weights for the [Beit model](https://github.com/microsoft/unilm/tree/master/beit) converted in a format that can be used by [candle](https://github.com/huggingface/candle). ## Citing DINOv2 As per their [GitHub repository](https://github.com/microsoft/unilm/tree/master/beit): ``` @misc{bao2022beitbertpretrainingimage, title={BEiT: BERT Pre-Training of Image Transformers}, author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei}, year={2022}, } ```

bobtk/mlx-communityLlama-3-Swallow-8B-Instruct-v0.1-8bit

bobtk

2024-07-01T14:58:20Z

0

null

[ "region:us" ]

null

2024-07-01T14:58:20Z

Entry not found

sail/data-mixture-doremi-1b

sail

2024-07-01T14:58:55Z

0

null

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

null

2024-07-01T14:58:55Z

--- license: mit ---

sail/data-mixture-regmix-1b

sail

2024-07-01T14:59:06Z

0

null

[ "region:us" ]

null

2024-07-01T14:59:06Z

Entry not found

sail/data-mixture-human-1b

sail

2024-07-01T14:59:22Z

0

null

[ "region:us" ]

null

2024-07-01T14:59:22Z

Entry not found

sail/data-mixture-pile-cc-1b

sail

2024-07-01T14:59:35Z

0

null

[ "region:us" ]

null

2024-07-01T14:59:35Z

Entry not found

mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit

mlx-community

2024-07-01T15:10:46Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "ja", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T15:00:05Z

--- language: - en - ja license: llama3 library_name: transformers tags: - mlx pipeline_tag: text-generation model_type: llama --- # mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit The Model [mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit](https://huggingface.co/mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit) was converted to MLX format from [tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1) using mlx-lm version **0.13.1**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3-Swallow-8B-Instruct-v0.1-8bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

veela4/ELDEN-RING-MOD

veela4

2024-07-02T07:21:12Z

0

null

[ "region:us" ]

null

2024-07-01T15:00:12Z

Entry not found

shine1607/masked_language_model

shine1607

2024-07-01T15:00:35Z

0

null

[ "region:us" ]

null

2024-07-01T15:00:35Z

Entry not found

ethedeltae/llama3-8b-oig-unsloth-iitg-final

ethedeltae

2024-07-01T15:01:20Z

0

transformers

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

null

2024-07-01T15:01:01Z

--- 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:** ethedeltae - **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)

yuchuantian/Instruct-IPT-single

yuchuantian

2024-07-01T15:06:59Z

0

null

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

null

2024-07-01T15:01:39Z

--- license: apache-2.0 ---

yuchuantian/IPG_Tiny

yuchuantian

2024-07-01T15:19:18Z

0

null

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

null

2024-07-01T15:03:22Z

--- license: apache-2.0 ---

sfgefgetg/tytu

sfgefgetg

2024-07-01T15:03:51Z

0

null

[ "region:us" ]

null

2024-07-01T15:03:51Z

Entry not found

rashid996958/pix2pix_exp43

rashid996958

2024-07-01T15:06:09Z

0

null

[ "region:us" ]

null

2024-07-01T15:06:04Z

Entry not found

tsavage68/Summary4500_M2_1000steps_1e7rate_SFT

tsavage68

2024-07-01T15:12:13Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T15:06:39Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_1000steps_1e7rate_SFT results: [] ---  # Summary4500_M2_1000steps_1e7rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4442 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.8849 | 0.0447 | 50 | 1.8726 | | 1.4871 | 0.0895 | 100 | 1.4453 | | 0.8608 | 0.1342 | 150 | 0.7955 | | 0.4432 | 0.1790 | 200 | 0.4648 | | 0.4269 | 0.2237 | 250 | 0.4556 | | 0.424 | 0.2685 | 300 | 0.4519 | | 0.4417 | 0.3132 | 350 | 0.4497 | | 0.4253 | 0.3579 | 400 | 0.4481 | | 0.4247 | 0.4027 | 450 | 0.4470 | | 0.4152 | 0.4474 | 500 | 0.4461 | | 0.4116 | 0.4922 | 550 | 0.4453 | | 0.4174 | 0.5369 | 600 | 0.4448 | | 0.4201 | 0.5817 | 650 | 0.4446 | | 0.423 | 0.6264 | 700 | 0.4444 | | 0.4243 | 0.6711 | 750 | 0.4441 | | 0.4325 | 0.7159 | 800 | 0.4442 | | 0.4128 | 0.7606 | 850 | 0.4441 | | 0.4207 | 0.8054 | 900 | 0.4441 | | 0.424 | 0.8501 | 950 | 0.4442 | | 0.4219 | 0.8949 | 1000 | 0.4442 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

Aiden163/TiaTeste01

Aiden163

2024-07-01T15:08:41Z

0

null

[ "region:us" ]

null

2024-07-01T15:08:41Z

Entry not found

itay-nakash/model_387dff9370_sweep_expert-oath-1165

itay-nakash

2024-07-01T15:08:58Z

0

null

[ "region:us" ]

null

2024-07-01T15:08:58Z

Entry not found

net31/naschainv145

net31

2024-07-02T21:23:55Z

0

null

[ "region:us" ]

null

2024-07-01T15:10:43Z

Entry not found

itay-nakash/model_387dff9370_sweep_drawn-butterfly-1166

itay-nakash

2024-07-01T15:11:10Z

0

null

[ "region:us" ]

null

2024-07-01T15:11:10Z

Entry not found

gkngm/llama-financial-sentiment-analysis-peft

gkngm

2024-07-01T15:12:33Z

0

transformers

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

null

2024-07-01T15:12:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

gkngm/finllm-financial-sentiment-analysis-peft

gkngm

2024-07-01T15:12:40Z

0

transformers

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

null

2024-07-01T15:12:34Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Behshadsheikhi/Morteza

Behshadsheikhi

2024-07-01T15:18:08Z

0

null

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

null

2024-07-01T15:14:52Z

--- license: openrail ---

habulaj/66719121840

habulaj

2024-07-01T15:17:16Z

0

null

[ "region:us" ]

null

2024-07-01T15:17:14Z

Entry not found

Arjuna17/results

Arjuna17

2024-07-01T15:19:05Z

0

null

[ "region:us" ]

null

2024-07-01T15:19:05Z

Entry not found

LuluXML/lora_model

LuluXML

2024-07-01T15:19:44Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-01T15:19:22Z

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

jaimeazevedo/HQs

jaimeazevedo

2024-07-01T15:19:42Z

0

null

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

null

2024-07-01T15:19:42Z

--- license: mit ---

nikest/nps-ft

nikest

2024-07-01T15:34:56Z

0

transformers

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

text-generation

2024-07-01T15:23:42Z

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

necrobradley/face_predict_emotion

necrobradley

2024-07-01T15:27:29Z

0

null

[ "region:us" ]

null

2024-07-01T15:27:29Z

Entry not found

luisrguerra/test

luisrguerra

2024-07-01T15:29:36Z

0

null

[ "region:us" ]

null

2024-07-01T15:29:36Z

Entry not found

sanamosuk93/news_sum

sanamosuk93

2024-07-01T15:41:59Z

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-07-01T15:32:55Z

Entry not found

ermannocavalli/face_of_FedericaFedeSala

ermannocavalli

2024-07-01T15:34:09Z

0

null

[ "region:us" ]

null

2024-07-01T15:34:09Z

Entry not found

EthanRhys/Greta-Masters-EX

EthanRhys

2024-07-01T15:34:52Z

0

null

[ "license:openrail++", "region:us" ]

null

2024-07-01T15:34:20Z

--- license: openrail++ ---

camillop/phi-mini-company-classification-adapters

camillop

2024-07-01T15:51:18Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-01T15:34:45Z

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

messawey/historyqa_model

messawey

2024-07-01T15:35:17Z

0

null

[ "region:us" ]

null

2024-07-01T15:35:17Z

Entry not found

js-kim/llama2-qlora-finetuned-french

js-kim

2024-07-01T15:35:34Z

0

transformers

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

null

2024-07-01T15:35:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sushanthr/tinyLlama-1.1B-Chat-v1.0-fp16-webnn

sushanthr

2024-07-01T15:45:13Z

0

null

[ "region:us" ]

null

2024-07-01T15:37:57Z

A special port of https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0, in FP16 so that weights are loadable with WebNN. See https://sushanthr.github.io/RapidChat/ https://github.com/sushanthr/RapidChat

bartowski/Qwen2-7B-Multilingual-RP-exl2

bartowski

2024-07-01T15:38:07Z

0

null

[ "text-generation", "en", "ko", "ja", "zh", "es", "license:apache-2.0", "region:us" ]

text-generation

2024-07-01T15:38:06Z

--- license: apache-2.0 language: - en - ko - ja - zh - es quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Qwen2-7B-Multilingual-RP Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.6">turboderp's ExLlamaV2 v0.1.6</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/maywell/Qwen2-7B-Multilingual-RP ## Prompt format ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Available sizes | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------------ | | [8_0](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Qwen2-7B-Multilingual-RP-exl2 Qwen2-7B-Multilingual-RP-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Qwen2-7B-Multilingual-RP-exl2 --revision 6_5 --local-dir Qwen2-7B-Multilingual-RP-exl2-6_5 ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Qwen2-7B-Multilingual-RP-exl2 --revision 6_5 --local-dir Qwen2-7B-Multilingual-RP-exl2-6.5 ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

nftwp/maxart

nftwp

2024-07-02T15:33:19Z

0

null

[ "region:us" ]

null

2024-07-01T15:42:49Z

Entry not found

Simple979/ElmoBaby

Simple979

2024-07-01T17:13:28Z

0

transformers

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

null

2024-07-01T15:43:36Z

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** Simple979 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

jaxmetaverse/clay

jaxmetaverse

2024-07-01T15:44:42Z

0

null

[ "region:us" ]

null

2024-07-01T15:44:42Z

Entry not found

HikariLight/Mistral-SUFT-RL

HikariLight

2024-07-01T15:45:40Z

0

transformers

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

null

2024-07-01T15:45:19Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tsavage68/Summary4500_M2_200steps_1e7rate_SFT

tsavage68

2024-07-01T16:19:53Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T15:45:26Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_200steps_1e7rate_SFT results: [] ---  # Summary4500_M2_200steps_1e7rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9787 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.8849 | 0.0447 | 50 | 1.8726 | | 1.4871 | 0.0895 | 100 | 1.4453 | | 1.0265 | 0.1342 | 150 | 1.0225 | | 0.9518 | 0.1790 | 200 | 0.9787 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

fokyoum9/test_model

fokyoum9

2024-07-01T15:50:51Z

0

null

[ "region:us" ]

null

2024-07-01T15:50:51Z

Entry not found

skyconnectiva/sky

skyconnectiva

2024-07-01T15:50:58Z

0

null

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

null

2024-07-01T15:50:58Z

--- license: mit ---

shuyuej/MedLLaMA3-70B-base-INT4-G2048-GPTQ

shuyuej

2024-07-01T20:05:58Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T15:56:46Z

--- license: apache-2.0 ---

KeroroK66/Yoruichi

KeroroK66

2024-07-01T15:57:09Z

0

null

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

null

2024-07-01T15:56:47Z

--- license: openrail ---

LinxuanPastel/parapparappa

LinxuanPastel

2024-07-01T16:27:14Z

0

null

[ "region:us" ]

null

2024-07-01T15:56:49Z

Entry not found

chunyeow/gemma-Code-Instruct-Finetune-test

chunyeow

2024-07-01T16:03:25Z

0

transformers

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

text-generation

2024-07-01T15:56:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

leduyson2603/autotrain-2dpe1-u5jfx

leduyson2603

2024-07-01T16:02:02Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "autotrain", "base_model:google-bert/bert-base-german-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-01T15:58:39Z

--- tags: - autotrain - token-classification base_model: google-bert/bert-base-german-cased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Token Classification ## Validation Metrics loss: 2.344400405883789 precision: 0.09302325581395349 recall: 0.25 f1: 0.13559322033898305 accuracy: 0.5533980582524272

tsavage68/Summary4500_M2_400steps_1e8rate_SFT

tsavage68

2024-07-01T16:06:17Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T16:02:10Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: Summary4500_M2_400steps_1e8rate_SFT results: [] ---  # Summary4500_M2_400steps_1e8rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.9563 | 0.0447 | 50 | 1.9699 | | 1.9559 | 0.0895 | 100 | 1.9696 | | 1.9642 | 0.1342 | 150 | 1.9686 | | 1.9621 | 0.1790 | 200 | 1.9673 | | 1.9548 | 0.2237 | 250 | 1.9676 | | 1.9541 | 0.2685 | 300 | 1.9678 | | 1.9743 | 0.3132 | 350 | 1.9675 | | 1.964 | 0.3579 | 400 | 1.9675 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

davelotito/donut_experiment_bayesian_trial_18

davelotito

2024-07-01T16:36:52Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-07-01T16:02:32Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer metrics: - bleu - wer model-index: - name: donut_experiment_bayesian_trial_18 results: [] ---  # donut_experiment_bayesian_trial_18 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5643 - Bleu: 0.0698 - Precisions: [0.8340248962655602, 0.7741176470588236, 0.7309782608695652, 0.6784565916398714] - Brevity Penalty: 0.0928 - Length Ratio: 0.2961 - Translation Length: 482 - Reference Length: 1628 - Cer: 0.7496 - Wer: 0.8244 ## 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: 1.7803961202565393e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| | 0.0287 | 1.0 | 253 | 0.5097 | 0.0722 | [0.8374485596707819, 0.7762237762237763, 0.7338709677419355, 0.6888888888888889] | 0.0954 | 0.2985 | 486 | 1628 | 0.7506 | 0.8208 | | 0.0159 | 2.0 | 506 | 0.5583 | 0.0697 | [0.8319502074688797, 0.7741176470588236, 0.7282608695652174, 0.6784565916398714] | 0.0928 | 0.2961 | 482 | 1628 | 0.7496 | 0.8232 | | 0.0118 | 3.0 | 759 | 0.5643 | 0.0698 | [0.8340248962655602, 0.7741176470588236, 0.7309782608695652, 0.6784565916398714] | 0.0928 | 0.2961 | 482 | 1628 | 0.7496 | 0.8244 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.19.1

cassador/4bs8lr2

cassador

2024-07-01T16:03:03Z

0

1

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6915", "loss:SoftmaxLoss", "id", "dataset:afaji/indonli", "arxiv:1908.10084", "base_model:indobenchmark/indobert-base-p2", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]

sentence-similarity

2024-07-01T16:02:38Z

--- base_model: indobenchmark/indobert-base-p2 datasets: - afaji/indonli language: - id library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6915 - loss:SoftmaxLoss widget: - source_sentence: Pesta Olahraga Asia Tenggara atau Southeast Asian Games, biasa disingkat SEA Games, adalah ajang olahraga yang diadakan setiap dua tahun dan melibatkan 11 negara Asia Tenggara. sentences: - Sekarang tahun 2017. - Warna kulit tidak mempengaruhi waktu berjemur yang baik untuk mengatifkan pro-vitamin D3. - Pesta Olahraga Asia Tenggara diadakan setiap tahun. - source_sentence: Menjalani aktivitas Ramadhan di tengah wabah Corona tentunya tidak mudah. sentences: - Tidak ada observasi yang pernah dilansir oleh Business Insider. - Wabah Corona membuat aktivitas Ramadhan tidak mudah dijalani. - Piala Sudirman pertama digelar pada tahun 1989. - source_sentence: Dalam bidang politik, partai ini memperjuangkan agar kekuasaan sepenuhnya berada di tangan rakyat. sentences: - Galileo tidak berhasil mengetes hasil dari Hukum Inert. - Kudeta 14 Februari 1946 gagal merebut kekuasaan Belanda. - Partai ini berusaha agar kekuasaan sepenuhnya berada di tangan rakyat. - source_sentence: Keluarga mendiang Prince menuduh layanan musik streaming Tidal memasukkan karya milik sang penyanyi legendaris tanpa izin . sentences: - Rosier adalah pelayan setia Lord Voldemort. - Bangunan ini digunakan untuk penjualan. - Keluarga mendiang Prince sudah memberi izin kepada TImbal untuk menggunakan lagu milik Prince. - source_sentence: Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum. sentences: - Pembuat Rooms hanya bisa membuat meeting yang terbuka. - Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC. - Eminem dirasa tidak akan memulai kembali kariernya tahun ini. model-index: - name: SentenceTransformer based on indobenchmark/indobert-base-p2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.596170613538296 name: Pearson Cosine - type: spearman_cosine value: 0.5861883707539226 name: Spearman Cosine - type: pearson_manhattan value: 0.5845731839861422 name: Pearson Manhattan - type: spearman_manhattan value: 0.5782563614870986 name: Spearman Manhattan - type: pearson_euclidean value: 0.5900038609486801 name: Pearson Euclidean - type: spearman_euclidean value: 0.5795936352515776 name: Spearman Euclidean - type: pearson_dot value: 0.5995818925993402 name: Pearson Dot - type: spearman_dot value: 0.5930379614276564 name: Spearman Dot - type: pearson_max value: 0.5995818925993402 name: Pearson Max - type: spearman_max value: 0.5930379614276564 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.32544389544371366 name: Pearson Cosine - type: spearman_cosine value: 0.29994363722612716 name: Spearman Cosine - type: pearson_manhattan value: 0.2875495017479062 name: Pearson Manhattan - type: spearman_manhattan value: 0.2810442265188576 name: Spearman Manhattan - type: pearson_euclidean value: 0.29788552102363436 name: Pearson Euclidean - type: spearman_euclidean value: 0.28248957351462056 name: Spearman Euclidean - type: pearson_dot value: 0.34645175745533086 name: Pearson Dot - type: spearman_dot value: 0.3331449893649715 name: Spearman Dot - type: pearson_max value: 0.34645175745533086 name: Pearson Max - type: spearman_max value: 0.3331449893649715 name: Spearman Max --- # SentenceTransformer based on indobenchmark/indobert-base-p2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) - **Language:** id  ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("cassador/4bs8lr2") # Run inference sentences = [ 'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.', 'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.', 'Pembuat Rooms hanya bisa membuat meeting yang terbuka.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.5962 | | **spearman_cosine** | **0.5862** | | pearson_manhattan | 0.5846 | | spearman_manhattan | 0.5783 | | pearson_euclidean | 0.59 | | spearman_euclidean | 0.5796 | | pearson_dot | 0.5996 | | spearman_dot | 0.593 | | pearson_max | 0.5996 | | spearman_max | 0.593 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.3254 | | **spearman_cosine** | **0.2999** | | pearson_manhattan | 0.2875 | | spearman_manhattan | 0.281 | | pearson_euclidean | 0.2979 | | spearman_euclidean | 0.2825 | | pearson_dot | 0.3465 | | spearman_dot | 0.3331 | | pearson_max | 0.3465 | | spearman_max | 0.3331 |   ## Training Details ### Training Dataset #### afaji/indonli * Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) * Size: 6,915 training samples * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 12 tokens</li><li>mean: 29.26 tokens</li><li>max: 135 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.13 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~51.00%</li><li>1: ~49.00%</li></ul> | * Samples: | premise | hypothesis | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:---------------| | <code>Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.</code> | <code>Prediksi akhir wabah tidak disampaikan Jokowi.</code> | <code>0</code> | | <code>Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>Masker sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>1</code> | | <code>Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa.</code> | <code>Paket internet sahur tidak ditujukan untuk saat sahur.</code> | <code>0</code> | * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Evaluation Dataset #### afaji/indonli * Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) * Size: 1,556 evaluation samples * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 9 tokens</li><li>mean: 28.07 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.15 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~47.90%</li><li>1: ~52.10%</li></ul> | * Samples: | premise | hypothesis | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------| | <code>Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).</code> | <code>Manuskrip tersebut tidak mencatat laporan kematian.</code> | <code>0</code> | | <code>Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.</code> | <code>Tidak ada observasi yang pernah dilansir oleh Business Insider.</code> | <code>0</code> | | <code>Seorang wanita asal New York mengaku sangat benci air putih.</code> | <code>Tidak ada orang dari New York yang membenci air putih.</code> | <code>0</code> | * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0 | 0 | - | - | 0.1277 | - | | 0.1156 | 100 | 0.6805 | - | - | - | | 0.2312 | 200 | 0.5137 | - | - | - | | 0.3468 | 300 | 0.5108 | - | - | - | | 0.4624 | 400 | 0.5113 | - | - | - | | 0.5780 | 500 | 0.5102 | - | - | - | | 0.6936 | 600 | 0.5212 | - | - | - | | 0.8092 | 700 | 0.5035 | - | - | - | | 0.9249 | 800 | 0.472 | - | - | - | | 1.0 | 865 | - | 0.4468 | 0.5249 | - | | 1.0405 | 900 | 0.4193 | - | - | - | | 1.1561 | 1000 | 0.3509 | - | - | - | | 1.2717 | 1100 | 0.3709 | - | - | - | | 1.3873 | 1200 | 0.3538 | - | - | - | | 1.5029 | 1300 | 0.3619 | - | - | - | | 1.6185 | 1400 | 0.388 | - | - | - | | 1.7341 | 1500 | 0.3657 | - | - | - | | 1.8497 | 1600 | 0.3577 | - | - | - | | 1.9653 | 1700 | 0.4149 | - | - | - | | 2.0 | 1730 | - | 0.4535 | 0.5503 | - | | 2.0809 | 1800 | 0.3037 | - | - | - | | 2.1965 | 1900 | 0.2213 | - | - | - | | 2.3121 | 2000 | 0.2531 | - | - | - | | 2.4277 | 2100 | 0.2281 | - | - | - | | 2.5434 | 2200 | 0.2684 | - | - | - | | 2.6590 | 2300 | 0.2154 | - | - | - | | 2.7746 | 2400 | 0.2556 | - | - | - | | 2.8902 | 2500 | 0.2515 | - | - | - | | 3.0 | 2595 | - | 0.6295 | 0.5799 | - | | 3.0058 | 2600 | 0.2158 | - | - | - | | 3.1214 | 2700 | 0.1445 | - | - | - | | 3.2370 | 2800 | 0.1191 | - | - | - | | 3.3526 | 2900 | 0.1514 | - | - | - | | 3.4682 | 3000 | 0.1223 | - | - | - | | 3.5838 | 3100 | 0.1581 | - | - | - | | 3.6994 | 3200 | 0.112 | - | - | - | | 3.8150 | 3300 | 0.1396 | - | - | - | | 3.9306 | 3400 | 0.1568 | - | - | - | | 4.0 | 3460 | - | 0.8635 | 0.5862 | 0.2999 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers and SoftmaxLoss ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```

PointPresse/phi3-mini-news-analysis-fr-lora

PointPresse

2024-07-01T16:07:55Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-01T16:04:24Z

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

Iqbaliswinning/results

Iqbaliswinning

2024-07-01T16:07:42Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-07-01T16:07:25Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

rinogrego/biomedlm-2.7b-finetuned-medmcqa

rinogrego

2024-07-03T01:28:42Z

0

transformers

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

null

2024-07-01T16:08:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

xiangruowen/ruoxiang_test

xiangruowen

2024-07-01T16:22:00Z

0

null

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

null

2024-07-01T16:09:46Z

--- license: mit ---

bvrc1518/vosk

bvrc1518

2024-07-01T16:09:49Z

0

null

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

null

2024-07-01T16:09:49Z

--- license: mit ---

lucyknada/amxl-reupload

lucyknada

2024-07-01T16:21:36Z

0

null

[ "region:us" ]

null

2024-07-01T16:20:00Z

Entry not found

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

cmmann

2024-07-01T16:23:32Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-07-01T16:23:30Z

--- 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="cmmann/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"]) ```

samSepiol101/newRepo

samSepiol101

2024-07-01T17:02:21Z

0

null

[ "region:us" ]

null

2024-07-01T16:24:17Z

Entry not found

cmmann/q-taxi

cmmann

2024-07-01T16:42:21Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-07-01T16:27:13Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: -35.46 +/- 52.99 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="cmmann/q-taxi", 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"]) ```

sunilswain/llama2-7b-chat-EssplTravelPolicy3.7k-epoch6

sunilswain

2024-07-01T16:39:39Z

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T16:30:39Z

Entry not found

habulaj/142653118795

habulaj

2024-07-01T16:35:18Z

0

null

[ "region:us" ]

null

2024-07-01T16:35:06Z

Entry not found

allison1221/t5-small-finetuned-xsum

allison1221

2024-07-02T01:44:35Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-07-01T16:35:12Z

Entry not found

davelotito/donut_experiment_bayesian_trial_19

davelotito

2024-07-01T17:11:12Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-07-01T16:36:53Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer metrics: - bleu - wer model-index: - name: donut_experiment_bayesian_trial_19 results: [] ---  # donut_experiment_bayesian_trial_19 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5754 - Bleu: 0.0724 - Precisions: [0.8450413223140496, 0.7892271662763466, 0.7486486486486487, 0.7028753993610224] - Brevity Penalty: 0.0941 - Length Ratio: 0.2973 - Translation Length: 484 - Reference Length: 1628 - Cer: 0.7493 - Wer: 0.8177 ## 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: 1.0668629620167924e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| | 0.0069 | 1.0 | 253 | 0.5825 | 0.0710 | [0.8423236514522822, 0.7858823529411765, 0.7418478260869565, 0.6977491961414791] | 0.0928 | 0.2961 | 482 | 1628 | 0.7509 | 0.8197 | | 0.0113 | 2.0 | 506 | 0.5684 | 0.0703 | [0.841995841995842, 0.785377358490566, 0.7411444141689373, 0.6935483870967742] | 0.0921 | 0.2955 | 481 | 1628 | 0.7505 | 0.8199 | | 0.0074 | 3.0 | 759 | 0.5754 | 0.0724 | [0.8450413223140496, 0.7892271662763466, 0.7486486486486487, 0.7028753993610224] | 0.0941 | 0.2973 | 484 | 1628 | 0.7493 | 0.8177 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.19.1

lielbin/BabyBERTa-wikipedia-french-without-Masking-finetuned-Fr-SQuAD

lielbin

2024-07-01T17:17:43Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2024-07-01T16:38:48Z

--- tags: - generated_from_trainer model-index: - name: BabyBERTa-wikipedia-french-without-Masking-finetuned-Fr-SQuAD results: [] ---  # BabyBERTa-wikipedia-french-without-Masking-finetuned-Fr-SQuAD This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

BTX24/deit_birads_classifier

BTX24

2024-07-01T16:38:52Z

0

null

[ "region:us" ]

null

2024-07-01T16:38:52Z

Entry not found

VoxAI/Hermes-2-Theta-Llama-3-8B-DriveThru-ORPO-v1-master-0.707-adapter

VoxAI

2024-07-01T16:44:12Z

0

transformers

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

null

2024-07-01T16:40:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

vincentantu/computer_vision

vincentantu

2024-07-01T16:41:23Z

0

null

[ "region:us" ]

null

2024-07-01T16:41:23Z

Entry not found

veronica08041991/naschainv249

veronica08041991

2024-07-02T03:18:30Z

0

null

[ "region:us" ]

null

2024-07-01T16:41:39Z

Entry not found

hishamcse/DQN-MsPacman-v4

hishamcse

2024-07-01T16:43:57Z

0

null

[ "MsPacman-v4", "dqn", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-07-01T16:41:40Z

--- tags: - MsPacman-v4 - dqn - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: DQN-MsPacman-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: MsPacman-v4 type: MsPacman-v4 metrics: - type: mean_reward value: 249.00 +/- 129.26 name: mean_reward verified: false --- # **DQN** Agent playing **MsPacman-v4** Details see: https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-extra-unit-3-mspacmandqn-scratch

manbeast3b/ZZZZZZZZdriver121c

manbeast3b

2024-07-01T16:45:30Z

0

transformers

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

text-generation

2024-07-01T16:42:47Z

Entry not found

ra9hu/ra9hu

ra9hu

2024-07-01T16:44:24Z

0

null

[ "region:us" ]

null

2024-07-01T16:44:24Z

Entry not found

gjonesQ02/StatementOfWork_Generator_Omega_BS_512_2

gjonesQ02

2024-07-01T20:05:35Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T16:45:44Z

--- license: apache-2.0 tags: - generated_from_trainer base_model: distilgpt2 model-index: - name: StatementOfWork_Generator_Omega_BS_512_2 results: [] ---  # StatementOfWork_Generator_Omega_BS_512_2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8120 ## 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: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 0.9839 | | No log | 2.0 | 8 | 0.9786 | | No log | 3.0 | 12 | 0.9767 | | No log | 4.0 | 16 | 0.9757 | | No log | 5.0 | 20 | 0.9716 | | No log | 6.0 | 24 | 0.9670 | | No log | 7.0 | 28 | 0.9663 | | No log | 8.0 | 32 | 0.9627 | | No log | 9.0 | 36 | 0.9571 | | No log | 10.0 | 40 | 0.9573 | | No log | 11.0 | 44 | 0.9520 | | No log | 12.0 | 48 | 0.9511 | | No log | 13.0 | 52 | 0.9486 | | No log | 14.0 | 56 | 0.9425 | | No log | 15.0 | 60 | 0.9440 | | No log | 16.0 | 64 | 0.9392 | | No log | 17.0 | 68 | 0.9357 | | No log | 18.0 | 72 | 0.9368 | | No log | 19.0 | 76 | 0.9333 | | No log | 20.0 | 80 | 0.9284 | | No log | 21.0 | 84 | 0.9260 | | No log | 22.0 | 88 | 0.9244 | | No log | 23.0 | 92 | 0.9228 | | No log | 24.0 | 96 | 0.9192 | | No log | 25.0 | 100 | 0.9163 | | No log | 26.0 | 104 | 0.9164 | | No log | 27.0 | 108 | 0.9135 | | No log | 28.0 | 112 | 0.9107 | | No log | 29.0 | 116 | 0.9105 | | No log | 30.0 | 120 | 0.9068 | | No log | 31.0 | 124 | 0.9050 | | No log | 32.0 | 128 | 0.9034 | | No log | 33.0 | 132 | 0.9012 | | No log | 34.0 | 136 | 0.8966 | | No log | 35.0 | 140 | 0.8968 | | No log | 36.0 | 144 | 0.8953 | | No log | 37.0 | 148 | 0.8920 | | No log | 38.0 | 152 | 0.8920 | | No log | 39.0 | 156 | 0.8912 | | No log | 40.0 | 160 | 0.8877 | | No log | 41.0 | 164 | 0.8871 | | No log | 42.0 | 168 | 0.8857 | | No log | 43.0 | 172 | 0.8800 | | No log | 44.0 | 176 | 0.8789 | | No log | 45.0 | 180 | 0.8831 | | No log | 46.0 | 184 | 0.8794 | | No log | 47.0 | 188 | 0.8757 | | No log | 48.0 | 192 | 0.8760 | | No log | 49.0 | 196 | 0.8730 | | No log | 50.0 | 200 | 0.8726 | | No log | 51.0 | 204 | 0.8719 | | No log | 52.0 | 208 | 0.8689 | | No log | 53.0 | 212 | 0.8691 | | No log | 54.0 | 216 | 0.8679 | | No log | 55.0 | 220 | 0.8633 | | No log | 56.0 | 224 | 0.8623 | | No log | 57.0 | 228 | 0.8624 | | No log | 58.0 | 232 | 0.8610 | | No log | 59.0 | 236 | 0.8601 | | No log | 60.0 | 240 | 0.8586 | | No log | 61.0 | 244 | 0.8583 | | No log | 62.0 | 248 | 0.8564 | | No log | 63.0 | 252 | 0.8552 | | No log | 64.0 | 256 | 0.8545 | | No log | 65.0 | 260 | 0.8526 | | No log | 66.0 | 264 | 0.8513 | | No log | 67.0 | 268 | 0.8508 | | No log | 68.0 | 272 | 0.8501 | | No log | 69.0 | 276 | 0.8484 | | No log | 70.0 | 280 | 0.8479 | | No log | 71.0 | 284 | 0.8465 | | No log | 72.0 | 288 | 0.8464 | | No log | 73.0 | 292 | 0.8452 | | No log | 74.0 | 296 | 0.8442 | | No log | 75.0 | 300 | 0.8443 | | No log | 76.0 | 304 | 0.8425 | | No log | 77.0 | 308 | 0.8410 | | No log | 78.0 | 312 | 0.8402 | | No log | 79.0 | 316 | 0.8394 | | No log | 80.0 | 320 | 0.8385 | | No log | 81.0 | 324 | 0.8380 | | No log | 82.0 | 328 | 0.8380 | | No log | 83.0 | 332 | 0.8369 | | No log | 84.0 | 336 | 0.8356 | | No log | 85.0 | 340 | 0.8351 | | No log | 86.0 | 344 | 0.8343 | | No log | 87.0 | 348 | 0.8326 | | No log | 88.0 | 352 | 0.8331 | | No log | 89.0 | 356 | 0.8328 | | No log | 90.0 | 360 | 0.8306 | | No log | 91.0 | 364 | 0.8310 | | No log | 92.0 | 368 | 0.8314 | | No log | 93.0 | 372 | 0.8295 | | No log | 94.0 | 376 | 0.8287 | | No log | 95.0 | 380 | 0.8286 | | No log | 96.0 | 384 | 0.8276 | | No log | 97.0 | 388 | 0.8270 | | No log | 98.0 | 392 | 0.8262 | | No log | 99.0 | 396 | 0.8251 | | No log | 100.0 | 400 | 0.8241 | | No log | 101.0 | 404 | 0.8231 | | No log | 102.0 | 408 | 0.8225 | | No log | 103.0 | 412 | 0.8235 | | No log | 104.0 | 416 | 0.8234 | | No log | 105.0 | 420 | 0.8225 | | No log | 106.0 | 424 | 0.8219 | | No log | 107.0 | 428 | 0.8209 | | No log | 108.0 | 432 | 0.8204 | | No log | 109.0 | 436 | 0.8195 | | No log | 110.0 | 440 | 0.8191 | | No log | 111.0 | 444 | 0.8191 | | No log | 112.0 | 448 | 0.8193 | | No log | 113.0 | 452 | 0.8197 | | No log | 114.0 | 456 | 0.8191 | | No log | 115.0 | 460 | 0.8179 | | No log | 116.0 | 464 | 0.8176 | | No log | 117.0 | 468 | 0.8173 | | No log | 118.0 | 472 | 0.8172 | | No log | 119.0 | 476 | 0.8174 | | No log | 120.0 | 480 | 0.8171 | | No log | 121.0 | 484 | 0.8169 | | No log | 122.0 | 488 | 0.8168 | | No log | 123.0 | 492 | 0.8162 | | No log | 124.0 | 496 | 0.8161 | | 0.3706 | 125.0 | 500 | 0.8160 | | 0.3706 | 126.0 | 504 | 0.8156 | | 0.3706 | 127.0 | 508 | 0.8145 | | 0.3706 | 128.0 | 512 | 0.8143 | | 0.3706 | 129.0 | 516 | 0.8143 | | 0.3706 | 130.0 | 520 | 0.8145 | | 0.3706 | 131.0 | 524 | 0.8147 | | 0.3706 | 132.0 | 528 | 0.8142 | | 0.3706 | 133.0 | 532 | 0.8136 | | 0.3706 | 134.0 | 536 | 0.8136 | | 0.3706 | 135.0 | 540 | 0.8138 | | 0.3706 | 136.0 | 544 | 0.8139 | | 0.3706 | 137.0 | 548 | 0.8140 | | 0.3706 | 138.0 | 552 | 0.8138 | | 0.3706 | 139.0 | 556 | 0.8134 | | 0.3706 | 140.0 | 560 | 0.8130 | | 0.3706 | 141.0 | 564 | 0.8128 | | 0.3706 | 142.0 | 568 | 0.8127 | | 0.3706 | 143.0 | 572 | 0.8126 | | 0.3706 | 144.0 | 576 | 0.8124 | | 0.3706 | 145.0 | 580 | 0.8123 | | 0.3706 | 146.0 | 584 | 0.8121 | | 0.3706 | 147.0 | 588 | 0.8120 | | 0.3706 | 148.0 | 592 | 0.8120 | | 0.3706 | 149.0 | 596 | 0.8120 | | 0.3706 | 150.0 | 600 | 0.8120 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

GloryKuo/llama2_medical_qlora

GloryKuo

2024-07-01T16:46:10Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:DavidLanz/Llama2-tw-7B-v2.0.1-chat", "region:us" ]

null

2024-07-01T16:45:45Z

--- base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

sharathprasaath/Phi-3-mini

sharathprasaath

2024-07-01T16:48:24Z

0

transformers

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

null

2024-07-01T16:48:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sharathprasaath/Phi-3-min

sharathprasaath

2024-07-01T16:48:22Z

0

transformers

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

null

2024-07-01T16:48:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kr-manish/distilgpt2-finetuned-rawHrPolicy

kr-manish

2024-07-01T16:50:14Z

0

transformers

[ "transformers", "tf", "tensorboard", "gpt2", "text-generation", "generated_from_keras_callback", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T16:49:15Z

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_keras_callback model-index: - name: kr-manish/distilgpt2-finetuned-rawHrPolicy results: [] ---  # kr-manish/distilgpt2-finetuned-rawHrPolicy This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0134 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.0134 | 0 | ### Framework versions - Transformers 4.41.2 - TensorFlow 2.15.0 - Datasets 2.20.0 - Tokenizers 0.19.1

hainc2/llmhainc

hainc2

2024-07-02T09:10:13Z

0

null

[ "license:llama3", "region:us" ]

null

2024-07-01T16:51:22Z

--- license: llama3 ---

Chairles-alex/mistral-two

Chairles-alex

2024-07-01T16:52:34Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "license:other", "endpoints_compatible", "region:us" ]

text-generation

2024-07-01T16:51:23Z

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: mistralai/Mistral-7B-Instruct-v0.3 widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

robinhub/robin_model

robinhub

2024-07-02T08:21:53Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:taide/TAIDE-LX-7B-Chat", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-07-01T16:54:19Z

--- base_model: taide/TAIDE-LX-7B-Chat language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** robinhub - **License:** apache-2.0 - **Finetuned from model :** taide/TAIDE-LX-7B-Chat 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)

Adzka/test-reward-model

Adzka

2024-07-01T21:30:05Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:w11wo/indonesian-roberta-base-sentiment-classifier", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-01T16:54:29Z

--- license: mit base_model: w11wo/indonesian-roberta-base-sentiment-classifier tags: - generated_from_trainer metrics: - accuracy model-index: - name: test-reward-model results: [] ---  # test-reward-model This model is a fine-tuned version of [w11wo/indonesian-roberta-base-sentiment-classifier](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2784 - Accuracy: 0.8817 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7179 | 0.67 | 50 | 0.6866 | 0.6237 | | 0.6866 | 1.33 | 100 | 0.6661 | 0.7742 | | 0.6546 | 2.0 | 150 | 0.6039 | 0.8280 | | 0.5421 | 2.67 | 200 | 0.4624 | 0.8172 | | 0.3965 | 3.33 | 250 | 0.3958 | 0.8280 | | 0.3244 | 4.0 | 300 | 0.3502 | 0.8495 | | 0.251 | 4.67 | 350 | 0.4012 | 0.8602 | | 0.1579 | 5.33 | 400 | 0.3184 | 0.8602 | | 0.135 | 6.0 | 450 | 0.3141 | 0.8710 | | 0.1114 | 6.67 | 500 | 0.3474 | 0.8495 | | 0.0929 | 7.33 | 550 | 0.2931 | 0.8495 | | 0.0829 | 8.0 | 600 | 0.2757 | 0.8710 | | 0.0834 | 8.67 | 650 | 0.2889 | 0.8817 | | 0.057 | 9.33 | 700 | 0.2810 | 0.8925 | | 0.0503 | 10.0 | 750 | 0.2800 | 0.8817 | | 0.062 | 10.67 | 800 | 0.2806 | 0.8817 | | 0.0303 | 11.33 | 850 | 0.2971 | 0.8817 | | 0.0246 | 12.0 | 900 | 0.2784 | 0.8817 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2

starnet/11-star-07-01-02

starnet

2024-07-01T16:57:30Z

0

transformers

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

text-generation

2024-07-01T16:54:32Z

Entry not found