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transformers
# 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
text-generation
muzammil-eds/tinyllama-3T-64k-JSONExtractor-v3
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T06:07:10+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```
{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}
text-generation
rombodawg/Everyone-Coder-33b-v2-Base
[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T06:08:00+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 55 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
# Sentiment Analysis Model using DistilBERT This repository hosts a sentiment analysis model fine-tuned on the IMDb movie reviews dataset using DistilBERT architecture. It's designed to classify text inputs into positive or negative sentiment categories. ## Model Description The model is based on the DistilBERT architecture, a smaller, faster, cheaper, and lighter version of BERT. It has been fine-tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative. DistilBERT has been proven to retain most of the performance of BERT while being more efficient. This makes it an excellent choice for sentiment analysis tasks where the model's size and speed are essential. ## How to Use To use the model, you will need to install the `transformers` library from Hugging Face. You can install it using pip: pip install transformers Once installed, you can use the following code to classify text using this model: from transformers import DistilBertTokenizer, DistilBertForSequenceClassification import torch # Load the tokenizer and model from the Hugging Face Hub tokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis) model = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis) # Example text text = "I loved this movie. The performances were fantastic!" # Tokenize text and convert to tensor inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) # Predict sentiment with torch.no_grad(): logits = model(**inputs).logits # Convert logits to probabilities using softmax probabilities = torch.softmax(logits, dim=-1) # Output the result print(probabilities) Evaluation Results The model achieved the following performance on the IMDb dataset: Accuracy: 90% Precision: 89% Recall: 91% F1 Score: 90% These results indicate the model's high efficiency in classifying sentiments as positive or negative. Training Procedure The model was trained using the following procedure: Pre-processing: The dataset was pre-processed by converting all reviews to lowercase and tokenizing using the DistilBERT tokenizer. Optimization: We used the Adam optimizer with a learning rate of 2e-5, a batch size of 16, and trained the model for 3 epochs. Hardware: Training was performed on a single NVIDIA GTX 1650 GPU.
{"license": "apache-2.0"}
text-classification
Pranav-10/Sentiment_analysis
[ "transformers", "pytorch", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T06:08:21+00:00
[]
[]
TAGS #transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Sentiment Analysis Model using DistilBERT This repository hosts a sentiment analysis model fine-tuned on the IMDb movie reviews dataset using DistilBERT architecture. It's designed to classify text inputs into positive or negative sentiment categories. ## Model Description The model is based on the DistilBERT architecture, a smaller, faster, cheaper, and lighter version of BERT. It has been fine-tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative. DistilBERT has been proven to retain most of the performance of BERT while being more efficient. This makes it an excellent choice for sentiment analysis tasks where the model's size and speed are essential. ## How to Use To use the model, you will need to install the 'transformers' library from Hugging Face. You can install it using pip: pip install transformers Once installed, you can use the following code to classify text using this model: from transformers import DistilBertTokenizer, DistilBertForSequenceClassification import torch # Load the tokenizer and model from the Hugging Face Hub tokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis) model = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis) # Example text text = "I loved this movie. The performances were fantastic!" # Tokenize text and convert to tensor inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) # Predict sentiment with torch.no_grad(): logits = model(inputs).logits # Convert logits to probabilities using softmax probabilities = torch.softmax(logits, dim=-1) # Output the result print(probabilities) Evaluation Results The model achieved the following performance on the IMDb dataset: Accuracy: 90% Precision: 89% Recall: 91% F1 Score: 90% These results indicate the model's high efficiency in classifying sentiments as positive or negative. Training Procedure The model was trained using the following procedure: Pre-processing: The dataset was pre-processed by converting all reviews to lowercase and tokenizing using the DistilBERT tokenizer. Optimization: We used the Adam optimizer with a learning rate of 2e-5, a batch size of 16, and trained the model for 3 epochs. Hardware: Training was performed on a single NVIDIA GTX 1650 GPU.
[ "# Sentiment Analysis Model using DistilBERT\n\nThis repository hosts a sentiment analysis model fine-tuned on the IMDb movie reviews dataset using DistilBERT architecture. It's designed to classify text inputs into positive or negative sentiment categories.", "## Model Description\n\nThe model is based on the DistilBERT architecture, a smaller, faster, cheaper, and lighter version of BERT. It has been fine-tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative.\n\nDistilBERT has been proven to retain most of the performance of BERT while being more efficient. This makes it an excellent choice for sentiment analysis tasks where the model's size and speed are essential.", "## How to Use\n\nTo use the model, you will need to install the 'transformers' library from Hugging Face. You can install it using pip:\n\npip install transformers\n\nOnce installed, you can use the following code to classify text using this model:\n\nfrom transformers import DistilBertTokenizer, DistilBertForSequenceClassification\nimport torch", "# Load the tokenizer and model from the Hugging Face Hub\ntokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis)\nmodel = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis)", "# Example text\ntext = \"I loved this movie. The performances were fantastic!\"", "# Tokenize text and convert to tensor\ninputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)", "# Predict sentiment\nwith torch.no_grad():\n logits = model(inputs).logits", "# Convert logits to probabilities using softmax\nprobabilities = torch.softmax(logits, dim=-1)", "# Output the result\nprint(probabilities)\n\nEvaluation Results\nThe model achieved the following performance on the IMDb dataset:\n\nAccuracy: 90%\nPrecision: 89%\nRecall: 91%\nF1 Score: 90%\nThese results indicate the model's high efficiency in classifying sentiments as positive or negative.\n\nTraining Procedure\nThe model was trained using the following procedure:\n\nPre-processing: The dataset was pre-processed by converting all reviews to lowercase and tokenizing using the DistilBERT tokenizer.\nOptimization: We used the Adam optimizer with a learning rate of 2e-5, a batch size of 16, and trained the model for 3 epochs.\nHardware: Training was performed on a single NVIDIA GTX 1650 GPU." ]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Sentiment Analysis Model using DistilBERT\n\nThis repository hosts a sentiment analysis model fine-tuned on the IMDb movie reviews dataset using DistilBERT architecture. It's designed to classify text inputs into positive or negative sentiment categories.", "## Model Description\n\nThe model is based on the DistilBERT architecture, a smaller, faster, cheaper, and lighter version of BERT. It has been fine-tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative.\n\nDistilBERT has been proven to retain most of the performance of BERT while being more efficient. This makes it an excellent choice for sentiment analysis tasks where the model's size and speed are essential.", "## How to Use\n\nTo use the model, you will need to install the 'transformers' library from Hugging Face. You can install it using pip:\n\npip install transformers\n\nOnce installed, you can use the following code to classify text using this model:\n\nfrom transformers import DistilBertTokenizer, DistilBertForSequenceClassification\nimport torch", "# Load the tokenizer and model from the Hugging Face Hub\ntokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis)\nmodel = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis)", "# Example text\ntext = \"I loved this movie. The performances were fantastic!\"", "# Tokenize text and convert to tensor\ninputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)", "# Predict sentiment\nwith torch.no_grad():\n logits = model(inputs).logits", "# Convert logits to probabilities using softmax\nprobabilities = torch.softmax(logits, dim=-1)", "# Output the result\nprint(probabilities)\n\nEvaluation Results\nThe model achieved the following performance on the IMDb dataset:\n\nAccuracy: 90%\nPrecision: 89%\nRecall: 91%\nF1 Score: 90%\nThese results indicate the model's high efficiency in classifying sentiments as positive or negative.\n\nTraining Procedure\nThe model was trained using the following procedure:\n\nPre-processing: The dataset was pre-processed by converting all reviews to lowercase and tokenizing using the DistilBERT tokenizer.\nOptimization: We used the Adam optimizer with a learning rate of 2e-5, a batch size of 16, and trained the model for 3 epochs.\nHardware: Training was performed on a single NVIDIA GTX 1650 GPU." ]
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[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Sentiment Analysis Model using DistilBERT\n\nThis repository hosts a sentiment analysis model fine-tuned on the IMDb movie reviews dataset using DistilBERT architecture. It's designed to classify text inputs into positive or negative sentiment categories.## Model Description\n\nThe model is based on the DistilBERT architecture, a smaller, faster, cheaper, and lighter version of BERT. It has been fine-tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative.\n\nDistilBERT has been proven to retain most of the performance of BERT while being more efficient. This makes it an excellent choice for sentiment analysis tasks where the model's size and speed are essential.## How to Use\n\nTo use the model, you will need to install the 'transformers' library from Hugging Face. You can install it using pip:\n\npip install transformers\n\nOnce installed, you can use the following code to classify text using this model:\n\nfrom transformers import DistilBertTokenizer, DistilBertForSequenceClassification\nimport torch# Load the tokenizer and model from the Hugging Face Hub\ntokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis)\nmodel = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis)# Example text\ntext = \"I loved this movie. The performances were fantastic!\"# Tokenize text and convert to tensor\ninputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)# Predict sentiment\nwith torch.no_grad():\n logits = model(inputs).logits# Convert logits to probabilities using softmax\nprobabilities = torch.softmax(logits, dim=-1)" ]
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null
null
transformers
<!-- 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. --> # my_mlm_model_masked This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.4563 | 1.0 | 7300 | 0.4420 | | 0.434 | 2.0 | 14600 | 0.4119 | | 0.4114 | 3.0 | 21900 | 0.4039 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilroberta-base", "model-index": [{"name": "my_mlm_model_masked", "results": []}]}
fill-mask
ubaskota/my_mlm_model_masked
[ "transformers", "tensorboard", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T06:10:23+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
my\_mlm\_model\_masked ====================== This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4053 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 68, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
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diffusers
# SDXL LoRA DreamBooth - yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 <Gallery /> ## Model description These are yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4/tree/main) them in the Files & versions tab. ## Training properties - max_train_steps: 700 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FWF2NGBPUFgu9eyaCYAwB.jpg?alt=media&token=97c1e215-0a96-4fdf-b292-9ee0e497ba72 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fcn54hvM4ahi3MzpCQN5D.jpg?alt=media&token=e096f4dc-e7c5-4e14-88fc-a5562d103127 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fz8D9WdMIx4mXcsDGAZm4.jpg?alt=media&token=fded9422-eb7c-4757-8c1f-cb436a348579 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F6JW19SVZPczh5B2DEqKD.jpg?alt=media&token=0e0dc94f-957d-4b51-8979-0216c0849cf6 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FDAk5k1hGzP9q9y0jpGoO.jpg?alt=media&token=01ed67d1-938a-4f60-bc1a-e1b91412b97e - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F82McawlxnTeA2vBc4bZg.jpg?alt=media&token=f7cfacb2-2186-4005-9211-b7ef762dafad - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FY7nFiafx8co1nK6cnjWJ.jpg?alt=media&token=a1fe8c9a-4d5e-4043-9a82-9304fd430569 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FVYOVRhojKt30NzjWRXL0.jpg?alt=media&token=5a3a2afb-4b83-4488-92e5-6651f5173cc0 - gradient_accumulation_steps: 3 - GPU: T4 - duration: 5284.340887546539
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of MDDL man"}
text-to-image
yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]
2024-02-07T06:10:43+00:00
[]
[]
TAGS #diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
# SDXL LoRA DreamBooth - yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 <Gallery /> ## Model description These are yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab. ## Training properties - max_train_steps: 700 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - URL - URL - URL - URL - URL - URL - URL - URL - gradient_accumulation_steps: 3 - GPU: T4 - duration: 5284.340887546539
[ "# SDXL LoRA DreamBooth - yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4\n\n<Gallery />", "## Model description\n\nThese are yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Training properties\n- max_train_steps: 700\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 5284.340887546539" ]
[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4\n\n<Gallery />", "## Model description\n\nThese are yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Training properties\n- max_train_steps: 700\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 5284.340887546539" ]
[ 82, 51, 116, 19, 28, 101 ]
[ "passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4\n\n<Gallery />## Model description\n\nThese are yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Training properties\n- max_train_steps: 700\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 5284.340887546539" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
octadion/phi-2-jagr-ppg-simpkb
[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T06:12:11+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #phi #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #phi #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
diffusers
# SDXL LoRA DreamBooth - yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 <Gallery /> ## Model description These are yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2/tree/main) them in the Files & versions tab. ## Training properties - max_train_steps: 700 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FWF2NGBPUFgu9eyaCYAwB.jpg?alt=media&token=97c1e215-0a96-4fdf-b292-9ee0e497ba72 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FDAk5k1hGzP9q9y0jpGoO.jpg?alt=media&token=01ed67d1-938a-4f60-bc1a-e1b91412b97e - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FY7nFiafx8co1nK6cnjWJ.jpg?alt=media&token=a1fe8c9a-4d5e-4043-9a82-9304fd430569 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F6JW19SVZPczh5B2DEqKD.jpg?alt=media&token=0e0dc94f-957d-4b51-8979-0216c0849cf6 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FVYOVRhojKt30NzjWRXL0.jpg?alt=media&token=5a3a2afb-4b83-4488-92e5-6651f5173cc0 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F82McawlxnTeA2vBc4bZg.jpg?alt=media&token=f7cfacb2-2186-4005-9211-b7ef762dafad - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fcn54hvM4ahi3MzpCQN5D.jpg?alt=media&token=e096f4dc-e7c5-4e14-88fc-a5562d103127 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fz8D9WdMIx4mXcsDGAZm4.jpg?alt=media&token=fded9422-eb7c-4757-8c1f-cb436a348579 - gradient_accumulation_steps: 3 - GPU: T4 - duration: 5399.857093095779
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of MDDL man"}
text-to-image
yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]
2024-02-07T06:13:16+00:00
[]
[]
TAGS #diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
# SDXL LoRA DreamBooth - yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 <Gallery /> ## Model description These are yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab. ## Training properties - max_train_steps: 700 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - URL - URL - URL - URL - URL - URL - URL - URL - gradient_accumulation_steps: 3 - GPU: T4 - duration: 5399.857093095779
[ "# SDXL LoRA DreamBooth - yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2\n\n<Gallery />", "## Model description\n\nThese are yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Training properties\n- max_train_steps: 700\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 5399.857093095779" ]
[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2\n\n<Gallery />", "## Model description\n\nThese are yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Training properties\n- max_train_steps: 700\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 5399.857093095779" ]
[ 82, 56, 121, 19, 28, 102 ]
[ "passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2\n\n<Gallery />## Model description\n\nThese are yaneq/jan_bYSe9M1l0pUI1xnDnUr2_SDXL_LoRA_700_9d94_700_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Training properties\n- max_train_steps: 700\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 5399.857093095779" ]
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null
null
transformers
# Full Parameter Finetuning Qwen1.5 0.5B on Malaysian text README at https://github.com/huseinzol05/malaya/tree/5.1/session/qwen2 WandB, https://wandb.ai/huseinzol05/finetune-Qwen1.5-0.5B?workspace=user-huseinzol05
{"language": ["ms"]}
text-generation
mesolitica/Qwen1.5-0.5B-4096-fpf
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "ms", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T06:13:40+00:00
[]
[ "ms" ]
TAGS #transformers #safetensors #qwen2 #text-generation #conversational #ms #autotrain_compatible #endpoints_compatible #region-us
# Full Parameter Finetuning Qwen1.5 0.5B on Malaysian text README at URL WandB, URL
[ "# Full Parameter Finetuning Qwen1.5 0.5B on Malaysian text\n\nREADME at URL\n\nWandB, URL" ]
[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #ms #autotrain_compatible #endpoints_compatible #region-us \n", "# Full Parameter Finetuning Qwen1.5 0.5B on Malaysian text\n\nREADME at URL\n\nWandB, URL" ]
[ 45, 24 ]
[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #ms #autotrain_compatible #endpoints_compatible #region-us \n# Full Parameter Finetuning Qwen1.5 0.5B on Malaysian text\n\nREADME at URL\n\nWandB, URL" ]
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transformers
# RPMerge A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling. Disappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models: - [DrNicefellow/ChatAllInOne-Yi-34B-200K-V1](https://huggingface.co/DrNicefellow/ChatAllInOne-Yi-34B-200K-V1) and [migtissera/Tess-34B-v1.5b](https://huggingface.co/migtissera/Tess-34B-v1.5b) both have excellent general instruction-following performance. - [cgato/Thespis-34b-v0.7](https://huggingface.co/cgato/Thespis-34b-v0.7) is trained on the "Username: {Input} / BotName: {Response}" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories. - [Doctor-Shotgun/limarpv3-yi-llama-34b-lora](https://huggingface.co/Doctor-Shotgun/limarpv3-yi-llama-34b-lora) is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity - [adamo1139/yi-34b-200k-rawrr-dpo-2](https://huggingface.co/adamo1139/yi-34b-200k-rawrr-dpo-2) the base for the limarp lora, this is base Yi gently finetuned to discourage refusals. - [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) and [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B) are both "undertrained" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge. I consider this a more "focused" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more "factual assistant" focused merge, as well as a coding-focused merge if I can't find one to suit my needs. ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/ As well as a very explicit system prompt like this: https://old.reddit.com/r/LocalLLaMA/comments/1aiz6zu/roleplaying_system_prompts/koygiwa/ ## Running Chinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling "tails." Yi in particular also runs relatively "hot" even at lower temperatures. I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" where available), as described here: https://github.com/oobabooga/text-generation-webui/pull/5403 Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841 24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). Empty 16GB GPUs can still run the high context with aggressive quantization. To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth. ## Testing Notes Thanks to ParasiticRogue for this idea of a Vicuna-only merge, see: https://huggingface.co/brucethemoose/jondurbin_bagel-dpo-34b-v0.2-exl2-4bpw-fiction/discussions See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8#testing-notes This is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. I have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far. ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b * /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 * /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 * /home/alpha/Models/Raw/Nous-Capybara-34B * /home/alpha/Models/Raw/admo_limarp * /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama # No parameters necessary for base model - model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b #Emphasize the beginning of Vicuna format models parameters: weight: 0.19 density: 0.59 - model: /home/alpha/Models/Raw/Nous-Capybara-34B parameters: weight: 0.19 density: 0.55 # Vicuna format - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 parameters: weight: 0.05 density: 0.55 - model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 parameters: weight: 0.19 density: 0.55 - model: adamo1139/yi-34b-200k-rawrr-dpo-2+Doctor-Shotgun/limarpv3-yi-llama-34b-lora parameters: weight: 0.19 density: 0.48 - model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 parameters: weight: 0.19 density: 0.59 merge_method: dare_ties tokenizer_source: union base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: int8_mask: true dtype: bfloat16 ``` ## Self Promotion I'm part of a AI startup called Holocene AI! We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups. Contact me at: [email protected] I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: https://ko-fi.com/alphaatlas
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2"], "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE", "base_model": []}
text-generation
brucethemoose/Yi-34B-200K-RPMerge
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2", "en", "arxiv:2311.03099", "arxiv:2306.01708", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T06:14:13+00:00
[ "2311.03099", "2306.01708" ]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# RPMerge A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling. Disappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models: - DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance. - cgato/Thespis-34b-v0.7 is trained on the "Username: {Input} / BotName: {Response}" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories. - Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity - adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals. - migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both "undertrained" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge. I consider this a more "focused" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more "factual assistant" focused merge, as well as a coding-focused merge if I can't find one to suit my needs. ## Prompt template: Orca-Vicuna Raw prompting as described here is also effective: URL As well as a very explicit system prompt like this: URL ## Running Chinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling "tails." Yi in particular also runs relatively "hot" even at lower temperatures. I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" where available), as described here: URL Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL 24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization. To load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth. ## Testing Notes Thanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL See: URL This is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. I have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far. ## Merge Details ### Merge Method This model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b * /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 * /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 * /home/alpha/Models/Raw/Nous-Capybara-34B * /home/alpha/Models/Raw/admo_limarp * /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 ### Configuration The following YAML configuration was used to produce this model: ## Self Promotion I'm part of a AI startup called Holocene AI! We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups. Contact me at: URL@URL I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL
[ "# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.", "## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL", "## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.", "## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.", "## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL", "## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.", "## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL" ]
[ 95, 478, 35, 284, 113, 4, 49, 169, 17, 163 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "passage: # RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base." ]
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null
null
peft
<!-- 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. --> # mistral-7b-ocn-v1 This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-ocn-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.98|± |0.0141| |medmcqa |Yaml |none | 0|acc | 0.36|± |0.0482| |professional_medicine| 0|none | 0|acc | 0.57|± |0.0498| |college_medicine | 0|none | 0|acc | 0.54|± |0.0501| |clinical_knowledge | 0|none | 0|acc | 0.62|± |0.0488| |aocnp |Yaml |none | 0|acc | 0.44|± |0.0499| |ocn |Yaml |none | 0|acc | 0.54|± |0.0501|
{"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-ocn-v1", "results": []}]}
null
chenhugging/mistral-7b-ocn-v1
[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]
2024-02-07T06:17:58+00:00
[]
[]
TAGS #peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us
mistral-7b-ocn-v1 ================= This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc\_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 1.0 * mixed\_precision\_training: Native AMP ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.1.1+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1 ### Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\_in\_4bit=True,peft=chenhugging/mistral-7b-ocn-v1), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None, batch\_size: 1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-ocn-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ "TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-ocn-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ 51, 159, 39, 94 ]
[ "passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1### Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-ocn-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
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null
null
stable-baselines3
# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "270.77 +/- 22.88", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
EricValen/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-07T06:18:24+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
transformers
# RPmerge See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-RPMerge Quantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization. ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b * /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 * /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 * /home/alpha/Models/Raw/Nous-Capybara-34B * /home/alpha/Models/Raw/admo_limarp * /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama # No parameters necessary for base model - model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b #Emphasize the beginning of Vicuna format models parameters: weight: 0.19 density: 0.59 - model: /home/alpha/Models/Raw/Nous-Capybara-34B parameters: weight: 0.19 density: 0.55 # Vicuna format - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 parameters: weight: 0.05 density: 0.55 - model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 parameters: weight: 0.19 density: 0.55 - model: /home/alpha/Models/Raw/admo_limarp parameters: weight: 0.19 density: 0.48 - model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 parameters: weight: 0.19 density: 0.59 merge_method: dare_ties tokenizer_source: union base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: int8_mask: true dtype: bfloat16 ```
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2"], "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE", "base_model": []}
text-generation
brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2", "en", "arxiv:2311.03099", "arxiv:2306.01708", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T06:21:14+00:00
[ "2311.03099", "2306.01708" ]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# RPmerge See the main model card: URL Quantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization. ## Merge Details ### Merge Method This model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b * /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 * /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 * /home/alpha/Models/Raw/Nous-Capybara-34B * /home/alpha/Models/Raw/admo_limarp * /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 ### Configuration The following YAML configuration was used to produce this model:
[ "# RPmerge\n\nSee the main model card: URL\n\nQuantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RPmerge\n\nSee the main model card: URL\n\nQuantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 95, 40, 4, 49, 169, 17 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RPmerge\n\nSee the main model card: URL\n\nQuantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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null
null
transformers
# RPmerge See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-RPMerge Quantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization. ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b * /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 * /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 * /home/alpha/Models/Raw/Nous-Capybara-34B * /home/alpha/Models/Raw/admo_limarp * /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama # No parameters necessary for base model - model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b #Emphasize the beginning of Vicuna format models parameters: weight: 0.19 density: 0.59 - model: /home/alpha/Models/Raw/Nous-Capybara-34B parameters: weight: 0.19 density: 0.55 # Vicuna format - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 parameters: weight: 0.05 density: 0.55 - model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 parameters: weight: 0.19 density: 0.55 - model: /home/alpha/Models/Raw/admo_limarp parameters: weight: 0.19 density: 0.48 - model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 parameters: weight: 0.19 density: 0.59 merge_method: dare_ties tokenizer_source: union base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: int8_mask: true dtype: bfloat16 ```
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2"], "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE", "base_model": []}
text-generation
brucethemoose/Yi-34B-200K-RPMerge-exl2-40bpw
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2", "en", "arxiv:2311.03099", "arxiv:2306.01708", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T06:21:51+00:00
[ "2311.03099", "2306.01708" ]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# RPmerge See the main model card: URL Quantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization. ## Merge Details ### Merge Method This model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b * /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 * /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 * /home/alpha/Models/Raw/Nous-Capybara-34B * /home/alpha/Models/Raw/admo_limarp * /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 ### Configuration The following YAML configuration was used to produce this model:
[ "# RPmerge\n\nSee the main model card: URL\n\nQuantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RPmerge\n\nSee the main model card: URL\n\nQuantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 95, 40, 4, 49, 169, 17 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RPmerge\n\nSee the main model card: URL\n\nQuantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/CL These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true}
text-to-image
danaleee/CL
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T06:24:08+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/CL These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/CL\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 98 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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null
null
transformers
<!-- 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. --> # wav2vec_RTSplit0207_4 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0532 - Wer: 0.2258 - Cer: 0.1935 ## 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: 5.1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.8752 | 1.0 | 120 | 3.5938 | 1.0 | 0.9489 | | 1.6275 | 2.0 | 240 | 1.4828 | 1.0 | 0.7789 | | 0.8457 | 3.0 | 360 | 0.7008 | 0.8213 | 0.5921 | | 0.6001 | 4.0 | 480 | 0.5081 | 0.7088 | 0.4447 | | 0.4644 | 5.0 | 600 | 0.3125 | 0.4872 | 0.2592 | | 0.3617 | 6.0 | 720 | 0.2162 | 0.3807 | 0.2038 | | 0.298 | 7.0 | 840 | 0.1448 | 0.3141 | 0.1964 | | 0.2704 | 8.0 | 960 | 0.1096 | 0.2762 | 0.1944 | | 0.2499 | 9.0 | 1080 | 0.0630 | 0.2401 | 0.1895 | | 0.1338 | 10.0 | 1200 | 0.0532 | 0.2258 | 0.1935 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0207_4", "results": []}]}
automatic-speech-recognition
tndklab/wav2vec_RTSplit0207_4
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T06:33:38+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
wav2vec\_RTSplit0207\_4 ======================= This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0532 * Wer: 0.2258 * Cer: 0.1935 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: 5.1e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 80, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
null
diffusers
# SDXL LoRA DreamBooth - yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 <Gallery /> ## Model description These are yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2/tree/main) them in the Files & versions tab. ## Training properties - max_train_steps: 900 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fcn54hvM4ahi3MzpCQN5D.jpg?alt=media&token=e096f4dc-e7c5-4e14-88fc-a5562d103127 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FY7nFiafx8co1nK6cnjWJ.jpg?alt=media&token=a1fe8c9a-4d5e-4043-9a82-9304fd430569 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fz8D9WdMIx4mXcsDGAZm4.jpg?alt=media&token=fded9422-eb7c-4757-8c1f-cb436a348579 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FDAk5k1hGzP9q9y0jpGoO.jpg?alt=media&token=01ed67d1-938a-4f60-bc1a-e1b91412b97e - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F82McawlxnTeA2vBc4bZg.jpg?alt=media&token=f7cfacb2-2186-4005-9211-b7ef762dafad - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FWF2NGBPUFgu9eyaCYAwB.jpg?alt=media&token=97c1e215-0a96-4fdf-b292-9ee0e497ba72 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F6JW19SVZPczh5B2DEqKD.jpg?alt=media&token=0e0dc94f-957d-4b51-8979-0216c0849cf6 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FVYOVRhojKt30NzjWRXL0.jpg?alt=media&token=5a3a2afb-4b83-4488-92e5-6651f5173cc0 - gradient_accumulation_steps: 3 - GPU: T4 - duration: 6676.244818210602
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of MDDL man"}
text-to-image
yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]
2024-02-07T06:35:05+00:00
[]
[]
TAGS #diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
# SDXL LoRA DreamBooth - yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 <Gallery /> ## Model description These are yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab. ## Training properties - max_train_steps: 900 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - URL - URL - URL - URL - URL - URL - URL - URL - gradient_accumulation_steps: 3 - GPU: T4 - duration: 6676.244818210602
[ "# SDXL LoRA DreamBooth - yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2\n\n<Gallery />", "## Model description\n\nThese are yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Training properties\n- max_train_steps: 900\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 6676.244818210602" ]
[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2\n\n<Gallery />", "## Model description\n\nThese are yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Training properties\n- max_train_steps: 900\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 6676.244818210602" ]
[ 82, 51, 116, 19, 28, 102 ]
[ "passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2\n\n<Gallery />## Model description\n\nThese are yaneq/jan_8gr59VrqueLphjEKA6kl_SDXL_LoRA_900_9d94_900_1e4_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of MDDL man to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Training properties\n- max_train_steps: 900\n- learning_rate: 0.0001\n- base_model_name: stabilityai/stable-diffusion-xl-base-1.0\n- class_name: man\n- training_images_urls: - URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- URL\n- gradient_accumulation_steps: 3\n- GPU: T4\n- duration: 6676.244818210602" ]
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null
null
diffusers
# DreamBooth trained by AutoTrain Text encoder was not trained.
{"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "photo of a sks business woman hijab person", "inference": true}
text-to-image
nry61/sdxl_businessWoman
[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
2024-02-07T06:35:42+00:00
[]
[]
TAGS #diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us
# DreamBooth trained by AutoTrain Text encoder was not trained.
[ "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]
[ "TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]
[ 41, 19 ]
[ "passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]
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null
null
transformers
<div align="center"> <h1> MiniCPM </h1> </div> <p align="center"> <a href="https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4" target="_blank">MiniCPM 技术报告</a><a href="https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4" target="_blank"> Technical Report</a> | <a href="https://github.com/OpenBMB/OmniLMM/" target="_blank">OmniLMM 多模态模型 Multi-modal Model</a> | <a href="https://luca.cn/" target="_blank">CPM-C 千亿模型试用 ~100B Model Trial </a> </p> MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 - 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 - 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 - 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 - 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 - 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。 我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 - 基于MiniCPM-2B的指令微调与人类偏好对**MiniCPM-2B-SFT/DPO。** - 基于MiniCPM-2B的多模态模型**MiniCPM-V**，能力超越基于Phi-2的同参数级别多模态模型**。** - MiniCPM-2B-SFT/DPO的Int4量化版**MiniCPM-2B-SFT/DPO-Int4。** - 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，**文本及多模态模型均可在手机端进行推理。** MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. - MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. - After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. - MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. - MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. - The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. - SFT and DPO version based on MiniCPM-2B and human preference: **MiniCPM-2B-SFT/DPO** - The multi-modal model **MiniCPM-V** based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 - The INT4 quantized version **MiniCPM-2B-SFT/DPO-Int4** based on MiniCPM-2B-SFT/DPO - Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于[github仓库](https://github.com/OpenBMB/MiniCPM?tab=readme-ov-file#%E8%AF%84%E6%B5%8B%E7%BB%93%E6%9E%9C) Detailed evaluation results are in [github repo](https://github.com/OpenBMB/MiniCPM/blob/main/README-en.md#evaluation-results) 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations - 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； - 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； - 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； - 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 - Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. - To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. - Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. - Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. ## 模型下载 Download | HuggingFace | ModelScope | WiseModel | |-------------|------------|-----------| |[sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16)|[sft-bf16](https://modelscope.cn/models/OpenBMB/miniCPM-bf16)|[sft-bf16](https://wisemodel.cn/models/OpenBMB/miniCPM-bf16) |[sft-fp32](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)|[sft-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-sft-fp32)|[sft-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) |[dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)|[dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)|[dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16) |[dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)|[dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)|[dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16) |[dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) ## 模型使用 Usage * 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码 * 注意：需要在`from_pretrained`中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install `transformers>=4.36.0` and `accelerate` * Warning: It is necessary to specify the data type of the model clearly in 'from_pretrained', otherwise large calculation errors will be caused ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch.manual_seed(0) path = 'openbmb/MiniCPM-2B-sft-fp32' tokenizer = AutoTokenizer.from_pretrained(path) model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float32, device_map='cuda', trust_remote_code=True) responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.8, top_p=0.8) print(responds) ``` * 期望输出 Expected Output ```shell 山东省最高的山是泰山，海拔1545米。 相对于黄山（海拔1864米），泰山海拔较低，相差约319米。 ``` ## 开源协议 LICENSE #### 模型协议 Model LICENSE * 本仓库中代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议开源 * MiniCPM 模型权重的使用则需要遵循 [“通用模型许可协议-来源说明-宣传限制-商业授权”](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md)。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. * The usage of MiniCPM model weights must strictly follow [the General Model License (GML)](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to [email protected] to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. <p id="8"></p> ## 工作引用 Citation * 如果觉得MiniCPM有助于您的工作，请考虑引用下列[技术报告](https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4) * Please cite our [techinical report](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4) if you find our work valuable. ``` @inproceedings{minicpm2024, title={MiniCPM：Unveiling the Potential of End-side Large Language Models}, booktitle={OpenBMB Blog}, year={2024} } ```
{"language": ["en", "zh"], "tags": ["MiniCPM", "ModelBest", "THUNLP"]}
text-generation
openbmb/MiniCPM-2B-sft-fp32-llama-format
[ "transformers", "pytorch", "text-generation", "MiniCPM", "ModelBest", "THUNLP", "conversational", "en", "zh", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2024-02-07T06:39:35+00:00
[]
[ "en", "zh" ]
TAGS #transformers #pytorch #text-generation #MiniCPM #ModelBest #THUNLP #conversational #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us
MiniCPM ========= [MiniCPM 技术报告](URL target=) [Technical Report](URL target=) | [OmniLMM 多模态模型 Multi-modal Model](URL target=) | [CPM-C 千亿模型试用 ~100B Model Trial](URL target=) MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 * 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 * 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 * 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 * 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 * 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。 我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 * 基于MiniCPM-2B的指令微调与人类偏好对MiniCPM-2B-SFT/DPO。 * 基于MiniCPM-2B的多模态模型MiniCPM-V，能力超越基于Phi-2的同参数级别多模态模型。 * MiniCPM-2B-SFT/DPO的Int4量化版MiniCPM-2B-SFT/DPO-Int4。 * 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，文本及多模态模型均可在手机端进行推理。 MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. * MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. * After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. * MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. * MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. * The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. * SFT and DPO version based on MiniCPM-2B and human preference: MiniCPM-2B-SFT/DPO * The multi-modal model MiniCPM-V based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 * The INT4 quantized version MiniCPM-2B-SFT/DPO-Int4 based on MiniCPM-2B-SFT/DPO * Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于github仓库 Detailed evaluation results are in github repo 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations * 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； * 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； * 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； * 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 * Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. * To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. * Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. * Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. 模型下载 Download ------------- HuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16 HuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32 HuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16 HuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16 HuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32 模型使用 Usage ---------- * 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码 * 注意：需要在'from\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install 'transformers>=4.36.0' and 'accelerate' * Warning: It is necessary to specify the data type of the model clearly in 'from\_pretrained', otherwise large calculation errors will be caused * 期望输出 Expected Output 开源协议 LICENSE ------------ #### 模型协议 Model LICENSE * 本仓库中代码依照 Apache-2.0 协议开源 * MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the Apache-2.0 License. * The usage of MiniCPM model weights must strictly follow the General Model License (GML). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. 工作引用 Citation ------------- * 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告 * Please cite our techinical report if you find our work valuable.
[ "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]
[ "TAGS\n#transformers #pytorch #text-generation #MiniCPM #ModelBest #THUNLP #conversational #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]
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[ "passage: TAGS\n#transformers #pytorch #text-generation #MiniCPM #ModelBest #THUNLP #conversational #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us \n### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now." ]
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null
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Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
{"license": "cc-by-2.0"}
null
LoneStriker/Senku-70B-Full-GGUF
[ "gguf", "license:cc-by-2.0", "region:us" ]
2024-02-07T06:42:07+00:00
[]
[]
TAGS #gguf #license-cc-by-2.0 #region-us
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
[]
[ "TAGS\n#gguf #license-cc-by-2.0 #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#gguf #license-cc-by-2.0 #region-us \n" ]
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null
null
diffusers
# DreamBooth trained by AutoTrain Text encoder was trained.
{"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "photo of gbb", "inference": true}
text-to-image
anjith672/gate-boy2
[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "has_space", "region:us" ]
2024-02-07T06:42:19+00:00
[]
[]
TAGS #diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
# DreamBooth trained by AutoTrain Text encoder was trained.
[ "# DreamBooth trained by AutoTrain\n\nText encoder was trained." ]
[ "TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n", "# DreamBooth trained by AutoTrain\n\nText encoder was trained." ]
[ 45, 18 ]
[ "passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was trained." ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
null
rushidesh/mistral_b_finance_finetuned_test
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T06:43:41+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
transformers
# **deplot_kr** deplot_kr is a Image-to-Data(Text) model based on the google's pix2struct architecture. It was fine-tuned from [DePlot](https://huggingface.co/google/deplot), using korean chart image-text pairs. deplot_kr은 google의 pix2struct 구조를 기반으로 한 한국어 image-to-data(텍스트 형태의 데이터 테이블) 모델입니다. [DePlot](https://huggingface.co/google/deplot) 모델을 한국어 차트 이미지-텍스트 쌍 데이터세트(30만 개)를 이용하여 fine-tuning 했습니다. ## **How to use** You can run a prediction by input an image. Model predict the data table of text form in the image. 이미지를 모델에 입력하면 모델은 이미지로부터 표 형태의 데이터 테이블을 예측합니다. ```python from transformers import Pix2StructForConditionalGeneration, Pix2StructImageProcessor, AutoTokenizer, Pix2StructProcessor from PIL import Image image_processor = Pix2StructImageProcessor() tokenizer = AutoTokenizer.from_pretrained("brainventures/deplot_kr") processor = Pix2StructProcessor(image_processor=image_processor, tokenizer=tokenizer) model = Pix2StructForConditionalGeneration.from_pretrained("brainventures/deplot_kr") image_path = "IMAGE_PATH" image = Image.open(image_path) inputs = processor(images=image, return_tensors="pt") pred = model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask, max_length=1024) print(processor.batch_decode(deplot_generated_ids, skip_special_token=True)[0]) ``` **Model Input Image** ![model_input_image](./sample.jpg) **Model Output - Prediction** 대상: 제목: 2011-2021 보건복지 분야 일자리의 <unk>증 유형: 단일형 일반 세로 <unk>대형 | 보건(천 명) | 복지(천 명) 1분위 | 29.7 | 178.4 2분위 | 70.8 | 97.3 3분위 | 86.4 | 61.3 4분위 | 28.2 | 16.0 5분위 | 52.3 | 0.9 ### **Preprocessing** According to [Liu et al.(2023)](https://arxiv.org/pdf/2212.10505.pdf)... - markdown format - | : seperating cells (열 구분) - \n : seperating rows (행 구분) ### **Train** The model was trained in a TPU environment. - num_warmup_steps : 1,000 - num_training_steps : 40,000 ## **Evaluation Results** This model achieves the following results: |metrics name | % | |:---|---:| | RNSS (Relative Number Set Similarity)| 99.5483 | | RMS F1 (Relative Mapping Similarity)| 16.6401 | ## Contact For questions and comments, please use the discussion tab or email [email protected]
{"language": ["ko"], "pipeline_tag": "image-to-text"}
image-to-text
brainventures/deplot_kr
[ "transformers", "pytorch", "pix2struct", "text2text-generation", "image-to-text", "ko", "arxiv:2212.10505", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T06:45:49+00:00
[ "2212.10505" ]
[ "ko" ]
TAGS #transformers #pytorch #pix2struct #text2text-generation #image-to-text #ko #arxiv-2212.10505 #autotrain_compatible #endpoints_compatible #region-us
deplot\_kr ========== deplot\_kr is a Image-to-Data(Text) model based on the google's pix2struct architecture. It was fine-tuned from DePlot, using korean chart image-text pairs. deplot\_kr은 google의 pix2struct 구조를 기반으로 한 한국어 image-to-data(텍스트 형태의 데이터 테이블) 모델입니다. DePlot 모델을 한국어 차트 이미지-텍스트 쌍 데이터세트(30만 개)를 이용하여 fine-tuning 했습니다. How to use ---------- You can run a prediction by input an image. Model predict the data table of text form in the image. 이미지를 모델에 입력하면 모델은 이미지로부터 표 형태의 데이터 테이블을 예측합니다. Model Input Image !model\_input\_image Model Output - Prediction 대상: 제목: 2011-2021 보건복지 분야 일자리의 증 유형: 단일형 일반 세로 대형 | 보건(천 명) | 복지(천 명) 1분위 | 29.7 | 178.4 2분위 | 70.8 | 97.3 3분위 | 86.4 | 61.3 4분위 | 28.2 | 16.0 5분위 | 52.3 | 0.9 ### Preprocessing According to Liu et al.(2023)... * markdown format * | : seperating cells (열 구분) * \n : seperating rows (행 구분) ### Train The model was trained in a TPU environment. * num\_warmup\_steps : 1,000 * num\_training\_steps : 40,000 Evaluation Results ------------------ This model achieves the following results: Contact ------- For questions and comments, please use the discussion tab or email gloria@URL
[ "### Preprocessing\n\n\nAccording to Liu et al.(2023)...\n\n\n* markdown format\n* | : seperating cells (열 구분)\n* \\n : seperating rows (행 구분)", "### Train\n\n\nThe model was trained in a TPU environment.\n\n\n* num\\_warmup\\_steps : 1,000\n* num\\_training\\_steps : 40,000\n\n\nEvaluation Results\n------------------\n\n\nThis model achieves the following results:\n\n\n\nContact\n-------\n\n\nFor questions and comments, please use the discussion tab or email gloria@URL" ]
[ "TAGS\n#transformers #pytorch #pix2struct #text2text-generation #image-to-text #ko #arxiv-2212.10505 #autotrain_compatible #endpoints_compatible #region-us \n", "### Preprocessing\n\n\nAccording to Liu et al.(2023)...\n\n\n* markdown format\n* | : seperating cells (열 구분)\n* \\n : seperating rows (행 구분)", "### Train\n\n\nThe model was trained in a TPU environment.\n\n\n* num\\_warmup\\_steps : 1,000\n* num\\_training\\_steps : 40,000\n\n\nEvaluation Results\n------------------\n\n\nThis model achieves the following results:\n\n\n\nContact\n-------\n\n\nFor questions and comments, please use the discussion tab or email gloria@URL" ]
[ 57, 48, 69 ]
[ "passage: TAGS\n#transformers #pytorch #pix2struct #text2text-generation #image-to-text #ko #arxiv-2212.10505 #autotrain_compatible #endpoints_compatible #region-us \n### Preprocessing\n\n\nAccording to Liu et al.(2023)...\n\n\n* markdown format\n* | : seperating cells (열 구분)\n* \\n : seperating rows (행 구분)### Train\n\n\nThe model was trained in a TPU environment.\n\n\n* num\\_warmup\\_steps : 1,000\n* num\\_training\\_steps : 40,000\n\n\nEvaluation Results\n------------------\n\n\nThis model achieves the following results:\n\n\n\nContact\n-------\n\n\nFor questions and comments, please use the discussion tab or email gloria@URL" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4111 - Accuracy: 0.8710 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3295 | 0.13 | 19 | 2.1178 | 0.3 | | 1.991 | 1.13 | 38 | 1.5624 | 0.5143 | | 1.227 | 2.13 | 57 | 0.8621 | 0.7714 | | 0.5665 | 3.13 | 76 | 0.6212 | 0.8 | | 0.3487 | 4.13 | 95 | 0.3554 | 0.9286 | | 0.1701 | 5.13 | 114 | 0.3597 | 0.8857 | | 0.1036 | 6.13 | 133 | 0.2277 | 0.9286 | | 0.089 | 7.1 | 148 | 0.1989 | 0.9143 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.11.0 - Tokenizers 0.15.1
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-ucf101-subset", "results": []}]}
video-classification
Saini-Manisha/videomae-base-finetuned-ucf101-subset
[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
2024-02-07T06:59:04+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us
videomae-base-finetuned-ucf101-subset ===================================== This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.4111 * Accuracy: 0.8710 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 148 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.2.0+cu121 * Datasets 2.11.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 148", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.11.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 148", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.11.0\n* Tokenizers 0.15.1" ]
[ 69, 115, 4, 38 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 148### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.11.0\n* Tokenizers 0.15.1" ]
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null
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 0.7.2.dev0
{"library_name": "peft", "base_model": "beomi/KoAlpaca-Polyglot-12.8B"}
null
humung/koalpaca-polyglot-12.8B-ia3-vlending-v0.1
[ "peft", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-12.8B", "region:us" ]
2024-02-07T06:59:19+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-beomi/KoAlpaca-Polyglot-12.8B #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.2.dev0
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-beomi/KoAlpaca-Polyglot-12.8B #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]
[ 37, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 14 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-beomi/KoAlpaca-Polyglot-12.8B #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0" ]
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null
null
transformers
# maid-yuzu-v5-mix This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). This model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * ../maid-yuzu-v5 * [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: model: path: ../maid-yuzu-v5 dtype: bfloat16 merge_method: slerp parameters: t: - value: 0.5 slices: - sources: - layer_range: [0, 32] model: model: path: smelborp/MixtralOrochi8x7B - layer_range: [0, 32] model: model: path: ../maid-yuzu-v5 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["smelborp/MixtralOrochi8x7B"]}
text-generation
rhplus0831/maid-yuzu-v5-mix-exl2-6.0bpw-rpcal
[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "conversational", "base_model:smelborp/MixtralOrochi8x7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T07:01:47+00:00
[]
[]
TAGS #transformers #safetensors #mixtral #text-generation #mergekit #merge #conversational #base_model-smelborp/MixtralOrochi8x7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# maid-yuzu-v5-mix This is a merge of pre-trained language models created using mergekit. This model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * ../maid-yuzu-v5 * smelborp/MixtralOrochi8x7B ### Configuration The following YAML configuration was used to produce this model:
[ "# maid-yuzu-v5-mix\n\nThis is a merge of pre-trained language models created using mergekit.\n\nThis model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* ../maid-yuzu-v5\n* smelborp/MixtralOrochi8x7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #conversational #base_model-smelborp/MixtralOrochi8x7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# maid-yuzu-v5-mix\n\nThis is a merge of pre-trained language models created using mergekit.\n\nThis model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* ../maid-yuzu-v5\n* smelborp/MixtralOrochi8x7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 78, 61, 4, 18, 41, 17 ]
[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #conversational #base_model-smelborp/MixtralOrochi8x7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# maid-yuzu-v5-mix\n\nThis is a merge of pre-trained language models created using mergekit.\n\nThis model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* ../maid-yuzu-v5\n* smelborp/MixtralOrochi8x7B### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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null
null
transformers
# Bangla LLaMA 13B Base v0.1 [pre-trained] Welcome to the inaugural release of the Bangla LLaMA 13B base model – an important step in advancing LLMs for the Bangla language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. > **Please Note:** This model, labeled as a foundational Bangla Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Bangla, you may find [BanglaLLM/bangla-llama-13b-instruct-v0.1](https://huggingface.co/BanglaLLM/bangla-llama-13b-instruct-v0.1) more suitable for your needs. ## Model description The Bangla LLaMA models have been enhanced and tailored specifically with an extensive Bangla vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2. - **Model type:** A 13B parameter model for Causal LM pre-trained on [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset's Bangla subset. - **Language(s):** Bangla and English - **License:** GNU General Public License v3.0 - **Source Model:** [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) - **Training Precision:** `float16` - **Code:** [GitHub](https://github.com/abhinand5/bangla-llama) ## Related Models | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Bangla LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) | | Bangla LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) | | Bangla LLaMA 7B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) | | Bangla LLaMA 13B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 13B Base | 13B | [HF Hub](BanglaLLM/bangla-llama-13b-instruct-v0.1) | ## Usage Note It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abdullah Khan Zehady](https://www.linkedin.com/in/abdullah-khan-zehady-915ba024/) ## Citation We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Bangla language.
{"language": ["bn", "en"], "license": "llama2"}
text-generation
BanglaLLM/bangla-llama-13b-base-v0.1
[ "transformers", "pytorch", "llama", "text-generation", "bn", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T07:04:13+00:00
[]
[ "bn", "en" ]
TAGS #transformers #pytorch #llama #text-generation #bn #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Bangla LLaMA 13B Base v0.1 [pre-trained] ======================================== Welcome to the inaugural release of the Bangla LLaMA 13B base model – an important step in advancing LLMs for the Bangla language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. > > Please Note: This model, labeled as a foundational Bangla Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Bangla, you may find BanglaLLM/bangla-llama-13b-instruct-v0.1 more suitable for your needs. > > > Model description ----------------- The Bangla LLaMA models have been enhanced and tailored specifically with an extensive Bangla vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2. * Model type: A 13B parameter model for Causal LM pre-trained on CulturaX dataset's Bangla subset. * Language(s): Bangla and English * License: GNU General Public License v3.0 * Source Model: meta-llama/Llama-2-13b-hf * Training Precision: 'float16' * Code: GitHub Related Models -------------- Usage Note ---------- It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. Meet the Developers ------------------- Get to know the creators behind this innovative model and follow their contributions to the field: * Abdullah Khan Zehady We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Bangla language.
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[ "TAGS\n#transformers #pytorch #llama #text-generation #bn #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 57 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #bn #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
### Developed by Jaeyeon Kang ([CCK Solution](https://www.ccksolution.co.kr/)) ## Model Description - This is a model for the korean language. - We fine-tuned this model based on [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with our training dataset(private). - SFT (QLoRA) training are adjusted . ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "JaeyeonKang/CCK-Asura-v2" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "[INST] Put instruction here. [/INST]" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ```
{"language": ["ko"], "license": "cc-by-nc-4.0"}
text-generation
JaeyeonKang/CCK_Asura_v2
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T07:05:29+00:00
[]
[ "ko" ]
TAGS #transformers #safetensors #llama #text-generation #conversational #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
### Developed by Jaeyeon Kang (CCK Solution) ## Model Description - This is a model for the korean language. - We fine-tuned this model based on 152334H/miqu-1-70b-sf with our training dataset(private). - SFT (QLoRA) training are adjusted . ## Run the model
[ "### Developed by Jaeyeon Kang (CCK Solution)", "## Model Description\n- This is a model for the korean language.\n- We fine-tuned this model based on 152334H/miqu-1-70b-sf with our training dataset(private).\n- SFT (QLoRA) training are adjusted .", "## Run the model" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Developed by Jaeyeon Kang (CCK Solution)", "## Model Description\n- This is a model for the korean language.\n- We fine-tuned this model based on 152334H/miqu-1-70b-sf with our training dataset(private).\n- SFT (QLoRA) training are adjusted .", "## Run the model" ]
[ 64, 14, 59, 4 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Developed by Jaeyeon Kang (CCK Solution)## Model Description\n- This is a model for the korean language.\n- We fine-tuned this model based on 152334H/miqu-1-70b-sf with our training dataset(private).\n- SFT (QLoRA) training are adjusted .## Run the model" ]
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null
null
peft
<!-- 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. --> # mistral-7b-ocn-v2 This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc_medqa_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.98|± |0.0141| |medmcqa |Yaml |none | 0|acc | 0.40|± |0.0492| |professional_medicine| 0|none | 0|acc | 0.69|± |0.0465| |college_medicine | 0|none | 0|acc | 0.53|± |0.0502| |clinical_knowledge | 0|none | 0|acc | 0.59|± |0.0494| |ocn |Yaml |none | 0|acc | 0.80|± |0.0402| |aocnp |Yaml |none | 0|acc | 0.63|± |0.0485|
{"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-ocn-v2", "results": []}]}
null
chenhugging/mistral-7b-ocn-v2
[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]
2024-02-07T07:07:17+00:00
[]
[]
TAGS #peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us
mistral-7b-ocn-v2 ================= This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc\_medqa\_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 1.0 * mixed\_precision\_training: Native AMP ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.1.1+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1 ### Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\_in\_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None, batch\_size: 1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ "TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ 51, 159, 39, 94 ]
[ "passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1### Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
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null
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diffusers
# DreamBooth - yuandli/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "dreambooth", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers"], "base_model": "stabilityai/stable-diffusion-2-1", "instance_prompt": "a photo of [v]dog", "inference": true}
text-to-image
yuandli/dogbooth
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-07T07:07:47+00:00
[]
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TAGS #diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
# DreamBooth - yuandli/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth. You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
[ "# DreamBooth - yuandli/dogbooth\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# DreamBooth - yuandli/dogbooth\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
[ 97, 79 ]
[ "passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# DreamBooth - yuandli/dogbooth\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
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null
null
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 0.7.1
{"library_name": "peft", "base_model": "TheBloke/Nous-Hermes-2-SOLAR-10.7B-GPTQ"}
null
PornMixer/LoRA
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TheBloke/Nous-Hermes-2-SOLAR-10.7B-GPTQ", "region:us" ]
2024-02-07T07:09:22+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-TheBloke/Nous-Hermes-2-SOLAR-10.7B-GPTQ #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-TheBloke/Nous-Hermes-2-SOLAR-10.7B-GPTQ #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ 49, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-TheBloke/Nous-Hermes-2-SOLAR-10.7B-GPTQ #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1" ]
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diffusers
# Gildenface XL – Headshot LoRA <Gallery /> ([CivitAI](https://civitai.com/models/183550)) ## Model description <p><strong>Gildenface XL</strong> – a LoRA focused on <em>exaggerated</em> and <em><span style="color:rgb(189, 193, 198)">less-than-glamorous</span></em> close-ups with very <em>high detailed textures</em>.</p><p>Great for producing <em>unique, grotesque and/or outlandish faces</em>, but it can be used as a <em>enhance details for faces and textures</em>, depending on weight and prompt.</p><p><strong>Trigger word: Gildenface</strong><br /><strong>Useful prompt tips:</strong> Portrait photo, close up on face, detailed skin texture, leathery skin texture, visible skin pores, swollen face, greasy hair, wrinkles, potato nose, addict, blushing, chubby, hard shadows, disgusted, blemish, facial hair, staring <br />+ general enhancers, photography terms, and portrait photographers</p><p><strong>Recommended weights:</strong> between 0.8 – 1.2</p><p>It's a bit rough around the edges, and your milage may vary – but when it hits right it's golden.</p><p><span style="color:rgb(193, 194, 197)">Be sure to check out </span><a target="_blank" rel="ugc" href="https://civitai.com/models/181092?modelVersionId=203235">Caricature XL</a><span style="color:rgb(193, 194, 197)"> LoRA by Blink, if you like creating weird</span></p> ## Trigger words You should use `Gildenface portrait photo` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/WizWhite/gildenface-xl-headshot-lora/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('WizWhite/gildenface-xl-headshot-lora', weight_name='GildenfaceXL_Headshot_LoRA_v1.safetensors') image = pipeline('candid close up photo of a surprised Shrek business man, detailed skin texture, standing outside in a swamp,').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
{"license": "other", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora", "concept", "portrait", "detailed", "face", "grotesque", "headshot"], "license_name": "bespoke-lora-trained-license", "license_link": "https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=RentCivit&allowDerivatives=True&allowDifferentLicense=True", "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "Gildenface portrait photo", "widget": [{"text": "gildenface portrait photography of jabba the hutt, close up photo, professional, breathtaking, close-up on face, highly detailed skin, visible skin pores, dark, gritty", "output": {"url": "3277226.jpeg"}}, {"text": "gildenface portrait photography of a troll from the movie troll hunter", "output": {"url": "3277704.jpeg"}}, {"text": "gildenface portrait of a surprised shocked zombie at a birthday party, highly detailed texture, sharp focus, party hat", "output": {"url": "3277754.jpeg"}}, {"text": "gildenface breathtaking portrait photo inspired by an epic scene from the movie total recall, 1990s, sci-fi, professional, by bruce gilden", "output": {"url": "3277371.jpeg"}}, {"text": "Gildenface close up portrait of real life luigi from (super mario bros:0.5), disgusted", "output": {"url": "3277428.jpeg"}}, {"text": "Gildenface close up portrait of real life super mario, disgusted", "output": {"url": "3277429.jpeg"}}, {"text": "obese (cthulhu:1.4), gildenface style photo, close up on face, detailed skin texture, by bruce gilden", "output": {"url": "3277426.jpeg"}}, {"text": "rusty cast-iron (robot:1.4), gildenface style photo, close up on face, detailed skin texture", "output": {"url": "3277421.jpeg"}}, {"text": "Gildenface close up portrait of real life pikachu by bruce gilden", "output": {"url": "3277419.jpeg"}}, {"text": "candid close up photo of a surprised Shrek business man, detailed skin texture, standing outside in a swamp,", "output": {"url": "3277870.jpeg"}}]}
text-to-image
WizWhite/gildenface-xl-headshot-lora-2
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "concept", "portrait", "detailed", "face", "grotesque", "headshot", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]
2024-02-07T07:10:24+00:00
[]
[]
TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #concept #portrait #detailed #face #grotesque #headshot #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-other #region-us
# Gildenface XL – Headshot LoRA <Gallery /> (CivitAI) ## Model description <p><strong>Gildenface XL</strong> – a LoRA focused on <em>exaggerated</em> and <em><span style="color:rgb(189, 193, 198)">less-than-glamorous</span></em> close-ups with very <em>high detailed textures</em>.</p><p>Great for producing <em>unique, grotesque and/or outlandish faces</em>, but it can be used as a <em>enhance details for faces and textures</em>, depending on weight and prompt.</p><p><strong>Trigger word: Gildenface</strong><br /><strong>Useful prompt tips:</strong> Portrait photo, close up on face, detailed skin texture, leathery skin texture, visible skin pores, swollen face, greasy hair, wrinkles, potato nose, addict, blushing, chubby, hard shadows, disgusted, blemish, facial hair, staring <br />+ general enhancers, photography terms, and portrait photographers</p><p><strong>Recommended weights:</strong> between 0.8 – 1.2</p><p>It's a bit rough around the edges, and your milage may vary – but when it hits right it's golden.</p><p><span style="color:rgb(193, 194, 197)">Be sure to check out </span><a target="_blank" rel="ugc" href="URL XL</a><span style="color:rgb(193, 194, 197)"> LoRA by Blink, if you like creating weird</span></p> ## Trigger words You should use 'Gildenface portrait photo' to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab. ## Use it with the diffusers library For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
[ "# Gildenface XL – Headshot LoRA \n\n<Gallery />\n\n\n\n(CivitAI)", "## Model description\n\n<p><strong>Gildenface XL</strong> – a LoRA focused on <em>exaggerated</em> and <em><span style=\"color:rgb(189, 193, 198)\">less-than-glamorous</span></em> close-ups with very <em>high detailed textures</em>.</p><p>Great for producing <em>unique, grotesque and/or outlandish faces</em>, but it can be used as a <em>enhance details for faces and textures</em>, depending on weight and prompt.</p><p><strong>Trigger word: Gildenface</strong><br /><strong>Useful prompt tips:</strong> Portrait photo, close up on face, detailed skin texture, leathery skin texture, visible skin pores, swollen face, greasy hair, wrinkles, potato nose, addict, blushing, chubby, hard shadows, disgusted, blemish, facial hair, staring <br />+ general enhancers, photography terms, and portrait photographers</p><p><strong>Recommended weights:</strong> between 0.8 – 1.2</p><p>It's a bit rough around the edges, and your milage may vary – but when it hits right it's golden.</p><p><span style=\"color:rgb(193, 194, 197)\">Be sure to check out </span><a target=\"_blank\" rel=\"ugc\" href=\"URL XL</a><span style=\"color:rgb(193, 194, 197)\"> LoRA by Blink, if you like creating weird</span></p>", "## Trigger words\nYou should use 'Gildenface portrait photo' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers" ]
[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #concept #portrait #detailed #face #grotesque #headshot #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-other #region-us \n", "# Gildenface XL – Headshot LoRA \n\n<Gallery />\n\n\n\n(CivitAI)", "## Model description\n\n<p><strong>Gildenface XL</strong> – a LoRA focused on <em>exaggerated</em> and <em><span style=\"color:rgb(189, 193, 198)\">less-than-glamorous</span></em> close-ups with very <em>high detailed textures</em>.</p><p>Great for producing <em>unique, grotesque and/or outlandish faces</em>, but it can be used as a <em>enhance details for faces and textures</em>, depending on weight and prompt.</p><p><strong>Trigger word: Gildenface</strong><br /><strong>Useful prompt tips:</strong> Portrait photo, close up on face, detailed skin texture, leathery skin texture, visible skin pores, swollen face, greasy hair, wrinkles, potato nose, addict, blushing, chubby, hard shadows, disgusted, blemish, facial hair, staring <br />+ general enhancers, photography terms, and portrait photographers</p><p><strong>Recommended weights:</strong> between 0.8 – 1.2</p><p>It's a bit rough around the edges, and your milage may vary – but when it hits right it's golden.</p><p><span style=\"color:rgb(193, 194, 197)\">Be sure to check out </span><a target=\"_blank\" rel=\"ugc\" href=\"URL XL</a><span style=\"color:rgb(193, 194, 197)\"> LoRA by Blink, if you like creating weird</span></p>", "## Trigger words\nYou should use 'Gildenface portrait photo' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers" ]
[ 79, 20, 397, 22, 28, 38 ]
[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #concept #portrait #detailed #face #grotesque #headshot #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-other #region-us \n# Gildenface XL – Headshot LoRA \n\n<Gallery />\n\n\n\n(CivitAI)## Model description\n\n<p><strong>Gildenface XL</strong> – a LoRA focused on <em>exaggerated</em> and <em><span style=\"color:rgb(189, 193, 198)\">less-than-glamorous</span></em> close-ups with very <em>high detailed textures</em>.</p><p>Great for producing <em>unique, grotesque and/or outlandish faces</em>, but it can be used as a <em>enhance details for faces and textures</em>, depending on weight and prompt.</p><p><strong>Trigger word: Gildenface</strong><br /><strong>Useful prompt tips:</strong> Portrait photo, close up on face, detailed skin texture, leathery skin texture, visible skin pores, swollen face, greasy hair, wrinkles, potato nose, addict, blushing, chubby, hard shadows, disgusted, blemish, facial hair, staring <br />+ general enhancers, photography terms, and portrait photographers</p><p><strong>Recommended weights:</strong> between 0.8 – 1.2</p><p>It's a bit rough around the edges, and your milage may vary – but when it hits right it's golden.</p><p><span style=\"color:rgb(193, 194, 197)\">Be sure to check out </span><a target=\"_blank\" rel=\"ugc\" href=\"URL XL</a><span style=\"color:rgb(193, 194, 197)\"> LoRA by Blink, if you like creating weird</span></p>" ]
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# **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="ergh0/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"]) ```
{"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}]}]}]}
reinforcement-learning
ergh0/q-FrozenLake-v1-4x4-noSlippery
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-07T07:11:23+00:00
[]
[]
TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage
[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ 40, 39 ]
[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
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null
null
transformers
<!-- 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. --> # finetune_deepseek_6.7b_exp_1_0_yaml This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - total_train_batch_size: 6 - total_eval_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 504 | 0.9573 | | No log | 2.0 | 1008 | 0.6384 | | No log | 3.0 | 1512 | 0.4705 | | No log | 4.0 | 2016 | 0.4661 | | No log | 5.0 | 2520 | 0.4992 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "other", "tags": ["generated_from_trainer"], "base_model": "deepseek-ai/deepseek-coder-6.7b-instruct", "model-index": [{"name": "finetune_deepseek_6.7b_exp_1_0_yaml", "results": []}]}
text-generation
onur-softtech/finetune_deepseek_6.7b_exp_1_0_yaml
[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-6.7b-instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T07:11:44+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
finetune\_deepseek\_6.7b\_exp\_1\_0\_yaml ========================================= This model is a fine-tuned version of deepseek-ai/deepseek-coder-6.7b-instruct on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.4992 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 1 * eval\_batch\_size: 8 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 6 * total\_train\_batch\_size: 6 * total\_eval\_batch\_size: 48 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.03 * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.1.2 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* total\\_train\\_batch\\_size: 6\n* total\\_eval\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* total\\_train\\_batch\\_size: 6\n* total\\_eval\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 82, 167, 4, 30 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* total\\_train\\_batch\\_size: 6\n* total\\_eval\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
areegtarek/patientcommunication-8bit
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]
2024-02-07T07:13:36+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 64, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # wav2vec_RTSplit0207_5 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0318 - Wer: 0.1995 - Cer: 0.0859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.8157 | 1.0 | 120 | 3.6297 | 0.9897 | 0.9930 | | 1.6219 | 2.0 | 240 | 1.4240 | 0.8251 | 0.6728 | | 0.9197 | 3.0 | 360 | 0.7458 | 0.8210 | 0.5231 | | 0.6346 | 4.0 | 480 | 0.4789 | 0.6260 | 0.2890 | | 0.4992 | 5.0 | 600 | 0.3283 | 0.5051 | 0.2244 | | 0.3764 | 6.0 | 720 | 0.2164 | 0.3602 | 0.1251 | | 0.3036 | 7.0 | 840 | 0.1210 | 0.2862 | 0.1109 | | 0.2296 | 8.0 | 960 | 0.0703 | 0.2316 | 0.0890 | | 0.1974 | 9.0 | 1080 | 0.0453 | 0.2120 | 0.0951 | | 0.1126 | 10.0 | 1200 | 0.0318 | 0.1995 | 0.0859 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0207_5", "results": []}]}
automatic-speech-recognition
tndklab/wav2vec_RTSplit0207_5
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T07:25:15+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
wav2vec\_RTSplit0207\_5 ======================= This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0318 * Wer: 0.1995 * Cer: 0.0859 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 80, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
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# **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="bianxg/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
bianxg/q-Taxi-v3
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-07T07:26:20+00:00
[]
[]
TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage
[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ 32, 33 ]
[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
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# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
magixn/Reinforce-Cartpole-v1
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-07T07:27:02+00:00
[]
[]
TAGS #CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
# Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ 39, 54 ]
[ "passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
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# *SeaLLM-7B-v2* - Large Language Models for Southeast Asia **NOTE: download [seallm.preset.json](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/seallm.preset.json) for LM-studio to work correctly,** <p align="center"> <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2" target="_blank" rel="noopener"> 🤗 Tech Memo</a> &nbsp;&nbsp; <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B" target="_blank" rel="noopener"> 🤗 DEMO</a> &nbsp;&nbsp; <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a> &nbsp;&nbsp; <a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a> </p> We introduce [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc. ### Highlights * [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves the **7B-SOTA** on the **Zero-shot CoT GSM8K** task with **78.2** score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH CoT for Thai 🇹🇭. * It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with **82.5, 68.3, 80.9** scores on Arc-C, Winogrande, and Hellaswag. * It achieves **7.54** score on the 🇬🇧 **MT-bench**, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model. * It scores **45.74** on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models ([Vistral-7B](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)) of similar sizes. ### Release and DEMO - DEMO: [SeaLLMs/SeaLLM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B). - Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf). - Model weights: - [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2). - [SeaLLM-7B-v2-gguf](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf). - [SeaLLM-7B-v2-q4_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/SeaLLM-7B-v2.q4_0.gguf), [SeaLLM-7B-v2-q8_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/SeaLLM-7B-v2.q8_0.gguf). - LM-studio requires this [seallm.preset.json](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/seallm.preset.json) to work properly. - [SeaLLM-7B-v2-GGUF (thanks Lonestriker)](https://huggingface.co/LoneStriker/SeaLLM-7B-v2-GGUF). NOTE: use [seallm.preset.json](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/seallm.preset.json) to work properly. <blockquote style="color:red"> <p><strong style="color: red">Terms of Use and License</strong>: By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b/edit/main/LICENSE" target="_blank" rel="noopener">SeaLLMs Terms Of Use</a>. </blockquote> > **Disclaimer**: > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos. > The logo was generated by DALL-E 3. ### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1? * SeaLLM-7B-v2 is continue-pretrained from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) and underwent carefully designed tuning with focus in reasoning. ## Evaluation ### Zero-shot CoT Multilingual Math Reasoning [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.2** score on the GSM8K with zero-shot CoT reasoning, making it the **state of the art** in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with **22.4** vs 18.1 scores. ![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png) <details> <summary>See details on English and translated GSM8K and MATH with zero-shot reasoning</summary> <br> | Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1 | Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6 | Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | | | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4 </details> #### Zero-shot MGSM [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th. | Model | MGSM-Zh | MGSM-Th |-----| ----- | --- | ChatGPT (reported) | 61.2* | 47.2* | Qwen-14B-chat | 59.6 | 28 | SeaLLM-7B-v2 | **64.8** | **62.4** ### Zero-shot Commonsense Reasoning We compare [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in [(Kojima et al., 2023)](https://arxiv.org/pdf/2205.11916.pdf) to grab the answer. Note that we **DID NOT** use "Let's think step-by-step" to invoke explicit CoT. | Model | Arc-Challenge | Winogrande | Hellaswag |-----| ----- | --- | -- | | ChatGPT (reported) | 84.6* | 66.8* | 72.0* | ChatGPT (reproduced) | 84.1 | 63.1 | 79.5 | Mistral-7B-Instruct | 68.1 | 56.4 | 45.6 | SeaLLM-7B-v2 | 82.5 | 68.3 | 80.9 ### Multilingual World Knowledge We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi. | Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- | | GPT-3.5 | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41 | SeaLLM-13B | Multi | 52.78 | 62.69 | 44.50 | 46.45 | | 39.28 | 36.39 | Vistral-7B-chat | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27 | Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25 | SeaLLM-7B-v2 | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52 VMLU reproduce script [here](https://github.com/DAMO-NLP-SG/SeaLLMs/blob/main/evaluation/vmlu/vmlu_run.py). Lm-eval was used to evaluate MMLU. ### MT-Bench On the English [MT-bench](https://arxiv.org/abs/2306.05685) metric, SeaLLM-7B-v2 achieves **7.54** score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages. Refer to [mt_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/mt_bench/seallm_7b_v2.jsonl) for the MT-bench predictions of SeaLLM-7B-v2, and [here](https://github.com/lm-sys/FastChat/issues/3013#issue-2118685341) to reproduce it. | Model | Access | Langs | MT-Bench | --- | --- | --- | --- | | GPT-4-turbo | closed | multi | 9.32 | GPT-4-0613 | closed | multi | 9.18 | Mixtral-8x7b (46B) | open | multi | 8.3 | Starling-LM-7B-alpha | open | mono (en) | 8.0 | OpenChat-3.5-7B | open | mono (en) | 7.81 | **SeaLLM-7B-v2** | **open** | **multi (10+)** | **7.54** | [Qwen-14B](https://huggingface.co/Qwen/Qwen-14B-Chat) | open | multi | 6.96 | [Llama-2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | open | mono (en) | 6.86 | Mistral-7B-instuct | open | mono (en) | 6.84 ### Sea-Bench Similar to MT-Bench, [Sea-bench](https://huggingface.co/datasets/SeaLLMs/Sea-bench) is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages. As shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance. ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) Refer to [sea_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/sea_bench/seallm_7b_v2.jsonl) for the Sea-bench predictions of SeaLLM-7B-v2. ### Usage #### Instruction format ```python prompt = """<|im_start|>system You are a helpful assistant.</s><|im_start|>user Hello world</s><|im_start|>assistant Hi there, how can I help?</s>""" # NOTE: previous commit has \n between </s> and <|im_start|>, that was incorrect! # <|im_start|> is not a special token. # Transformers chat_template should be consistent with vLLM format below. # ! ENSURE 1 and only 1 bos `<s>` at the beginning of sequence print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))) '<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>'] """ ``` #### Using transformers's chat_template ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # use bfloat16 to ensure the best performance. model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2", torch_dtype=torch.bfloat16, device_map=device) tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world"}, {"role": "assistant", "content": "Hi there, how can I help you today?"}, {"role": "user", "content": "Explain general relativity in details."} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) print(tokenizer.convert_ids_to_tokens(encodeds[0])) # ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '▁you', '▁today', '?', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Ex', 'plain', '▁general', '▁rel', 'ativity', '▁in', '▁details', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>'] model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` #### Using vLLM ```python from vllm import LLM, SamplingParams TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>" TURN_PREFIX = "<|im_start|>{role}\n" # There is no \n between </s> and <|im_start|>. def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None): # conversations: list of dict with key `role` and `content` (openai format) if conversations[0]['role'] != 'system' and system_prompt is not None: conversations = [{"role": "system", "content": system_prompt}] + conversations text = '' for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) text += prompt if add_assistant_prefix: prompt = TURN_PREFIX.format(role='assistant') text += prompt return text sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['</s>', '<|im_start|>']) llm = LLM("SeaLLMs/SeaLLM-7B-v2", dtype="bfloat16") message = "Explain general relativity in details." prompt = seallm_chat_convo_format(message, True) gen = llm.generate(prompt, sampling_params) print(gen[0].outputs[0].text) ``` ## Acknowledgement to Our Linguists We would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety. ## Citation If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: [[email protected]](mailto:[email protected]) **Author list and order will change!** * `*` and `^` are equal contributions. ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```
{"license": "other", "license_name": "seallms", "license_link": "https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE"}
null
SeaLLMs/SeaLLM-7B-v2-gguf
[ "gguf", "arxiv:2312.00738", "arxiv:2205.11916", "arxiv:2306.05179", "arxiv:2306.05685", "license:other", "region:us" ]
2024-02-07T07:34:05+00:00
[ "2312.00738", "2205.11916", "2306.05179", "2306.05685" ]
[]
TAGS #gguf #arxiv-2312.00738 #arxiv-2205.11916 #arxiv-2306.05179 #arxiv-2306.05685 #license-other #region-us
*SeaLLM-7B-v2* - Large Language Models for Southeast Asia ========================================================= NOTE: download URL for LM-studio to work correctly, [Tech Memo](URL target=)    [DEMO](URL target=)    [Github](URL target=)    [Technical Report](URL target=) We introduce SeaLLM-7B-v2, the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since SeaLLM-13B, with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc. ### Highlights * SeaLLM-7B-v2 achieves the 7B-SOTA on the Zero-shot CoT GSM8K task with 78.2 score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH CoT for Thai 🇹🇭. * It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with 82.5, 68.3, 80.9 scores on Arc-C, Winogrande, and Hellaswag. * It achieves 7.54 score on the 🇬🇧 MT-bench, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model. * It scores 45.74 on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models (Vistral-7B) of similar sizes. ### Release and DEMO * DEMO: SeaLLMs/SeaLLM-7B. * Technical report: Arxiv: SeaLLMs - Large Language Models for Southeast Asia. * Model weights: + SeaLLM-7B-v2. + SeaLLM-7B-v2-gguf. - SeaLLM-7B-v2-q4\_0, SeaLLM-7B-v2-q8\_0. - LM-studio requires this URL to work properly. + SeaLLM-7B-v2-GGUF (thanks Lonestriker). NOTE: use URL to work properly. > > **Terms of Use and License**: > By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our [SeaLLMs Terms Of Use](URL target=). > > > > > Disclaimer: > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos. > > > > > The logo was generated by DALL-E 3. > > > ### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1? * SeaLLM-7B-v2 is continue-pretrained from Mistral-7B and underwent carefully designed tuning with focus in reasoning. Evaluation ---------- ### Zero-shot CoT Multilingual Math Reasoning SeaLLM-7B-v2 achieves with 78.2 score on the GSM8K with zero-shot CoT reasoning, making it the state of the art in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). SeaLLM-7B-v2 also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with 22.4 vs 18.1 scores. !fig\_sea\_math\_side\_by\_side.png See details on English and translated GSM8K and MATH with zero-shot reasoning #### Zero-shot MGSM SeaLLM-7B-v2 also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th. Model: ChatGPT (reported), MGSM-Zh: 61.2\*, MGSM-Th: 47.2\* Model: Qwen-14B-chat, MGSM-Zh: 59.6, MGSM-Th: 28 Model: SeaLLM-7B-v2, MGSM-Zh: 64.8, MGSM-Th: 62.4 ### Zero-shot Commonsense Reasoning We compare SeaLLM-7B-v2 with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in (Kojima et al., 2023) to grab the answer. Note that we DID NOT use "Let's think step-by-step" to invoke explicit CoT. ### Multilingual World Knowledge We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot M3Exam (M3e) for En, Zh, Vi, Id, Th, and zero-shot VMLU for Vi. VMLU reproduce script here. Lm-eval was used to evaluate MMLU. ### MT-Bench On the English MT-bench metric, SeaLLM-7B-v2 achieves 7.54 score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages. Refer to mt\_bench/seallm\_7b\_v2.jsonl for the MT-bench predictions of SeaLLM-7B-v2, and here to reproduce it. ### Sea-Bench Similar to MT-Bench, Sea-bench is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages. As shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance. !fig\_sea\_bench\_side\_by\_side.png Refer to sea\_bench/seallm\_7b\_v2.jsonl for the Sea-bench predictions of SeaLLM-7B-v2. ### Usage #### Instruction format #### Using transformers's chat\_template #### Using vLLM Acknowledgement to Our Linguists -------------------------------- We would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety. If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: l.bing@URL Author list and order will change! * '\*' and '^' are equal contributions.
[ "### Highlights\n\n\n* SeaLLM-7B-v2 achieves the 7B-SOTA on the Zero-shot CoT GSM8K task with 78.2 score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH CoT for Thai 🇹🇭.\n* It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with 82.5, 68.3, 80.9 scores on Arc-C, Winogrande, and Hellaswag.\n* It achieves 7.54 score on the 🇬🇧 MT-bench, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model.\n* It scores 45.74 on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models (Vistral-7B) of similar sizes.", "### Release and DEMO\n\n\n* DEMO: SeaLLMs/SeaLLM-7B.\n* Technical report: Arxiv: SeaLLMs - Large Language Models for Southeast Asia.\n* Model weights:\n\t+ SeaLLM-7B-v2.\n\t+ SeaLLM-7B-v2-gguf.\n\t\t- SeaLLM-7B-v2-q4\\_0, SeaLLM-7B-v2-q8\\_0.\n\t\t- LM-studio requires this URL to work properly.\n\t+ SeaLLM-7B-v2-GGUF (thanks Lonestriker). NOTE: use URL to work properly.\n\n\n\n> \n> **Terms of Use and License**: \n> By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our [SeaLLMs Terms Of Use](URL target=).\n> \n> \n> \n\n\n\n> \n> Disclaimer:\n> We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation.\n> Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations.\n> In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.\n> \n> \n> \n\n\n\n> \n> The logo was generated by DALL-E 3.\n> \n> \n>", "### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1?\n\n\n* SeaLLM-7B-v2 is continue-pretrained from Mistral-7B and underwent carefully designed tuning with focus in reasoning.\n\n\nEvaluation\n----------", "### Zero-shot CoT Multilingual Math Reasoning\n\n\nSeaLLM-7B-v2 achieves with 78.2 score on the GSM8K with zero-shot CoT reasoning, making it the state of the art in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). SeaLLM-7B-v2 also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with 22.4 vs 18.1 scores.\n\n\n!fig\\_sea\\_math\\_side\\_by\\_side.png\n\n\n\nSee details on English and translated GSM8K and MATH with zero-shot reasoning", "#### Zero-shot MGSM\n\n\nSeaLLM-7B-v2 also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th.\n\n\nModel: ChatGPT (reported), MGSM-Zh: 61.2\\*, MGSM-Th: 47.2\\*\nModel: Qwen-14B-chat, MGSM-Zh: 59.6, MGSM-Th: 28\nModel: SeaLLM-7B-v2, MGSM-Zh: 64.8, MGSM-Th: 62.4", "### Zero-shot Commonsense Reasoning\n\n\nWe compare SeaLLM-7B-v2 with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in (Kojima et al., 2023) to grab the answer. Note that we DID NOT use \"Let's think step-by-step\" to invoke explicit CoT.", "### Multilingual World Knowledge\n\n\nWe evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot M3Exam (M3e) for En, Zh, Vi, Id, Th, and zero-shot VMLU for Vi.\n\n\n\nVMLU reproduce script here. Lm-eval was used to evaluate MMLU.", "### MT-Bench\n\n\nOn the English MT-bench metric, SeaLLM-7B-v2 achieves 7.54 score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages.\n\n\nRefer to mt\\_bench/seallm\\_7b\\_v2.jsonl for the MT-bench predictions of SeaLLM-7B-v2, and here to reproduce it.", "### Sea-Bench\n\n\nSimilar to MT-Bench, Sea-bench is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages.\n\n\nAs shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance.\n\n\n!fig\\_sea\\_bench\\_side\\_by\\_side.png\n\n\nRefer to sea\\_bench/seallm\\_7b\\_v2.jsonl for the Sea-bench predictions of SeaLLM-7B-v2.", "### Usage", "#### Instruction format", "#### Using transformers's chat\\_template", "#### Using vLLM\n\n\nAcknowledgement to Our Linguists\n--------------------------------\n\n\nWe would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety.\n\n\nIf you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: l.bing@URL\n\n\nAuthor list and order will change!\n\n\n* '\\*' and '^' are equal contributions." ]
[ "TAGS\n#gguf #arxiv-2312.00738 #arxiv-2205.11916 #arxiv-2306.05179 #arxiv-2306.05685 #license-other #region-us \n", "### Highlights\n\n\n* SeaLLM-7B-v2 achieves the 7B-SOTA on the Zero-shot CoT GSM8K task with 78.2 score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH CoT for Thai 🇹🇭.\n* It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with 82.5, 68.3, 80.9 scores on Arc-C, Winogrande, and Hellaswag.\n* It achieves 7.54 score on the 🇬🇧 MT-bench, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model.\n* It scores 45.74 on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models (Vistral-7B) of similar sizes.", "### Release and DEMO\n\n\n* DEMO: SeaLLMs/SeaLLM-7B.\n* Technical report: Arxiv: SeaLLMs - Large Language Models for Southeast Asia.\n* Model weights:\n\t+ SeaLLM-7B-v2.\n\t+ SeaLLM-7B-v2-gguf.\n\t\t- SeaLLM-7B-v2-q4\\_0, SeaLLM-7B-v2-q8\\_0.\n\t\t- LM-studio requires this URL to work properly.\n\t+ SeaLLM-7B-v2-GGUF (thanks Lonestriker). NOTE: use URL to work properly.\n\n\n\n> \n> **Terms of Use and License**: \n> By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our [SeaLLMs Terms Of Use](URL target=).\n> \n> \n> \n\n\n\n> \n> Disclaimer:\n> We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation.\n> Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations.\n> In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.\n> \n> \n> \n\n\n\n> \n> The logo was generated by DALL-E 3.\n> \n> \n>", "### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1?\n\n\n* SeaLLM-7B-v2 is continue-pretrained from Mistral-7B and underwent carefully designed tuning with focus in reasoning.\n\n\nEvaluation\n----------", "### Zero-shot CoT Multilingual Math Reasoning\n\n\nSeaLLM-7B-v2 achieves with 78.2 score on the GSM8K with zero-shot CoT reasoning, making it the state of the art in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). SeaLLM-7B-v2 also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with 22.4 vs 18.1 scores.\n\n\n!fig\\_sea\\_math\\_side\\_by\\_side.png\n\n\n\nSee details on English and translated GSM8K and MATH with zero-shot reasoning", "#### Zero-shot MGSM\n\n\nSeaLLM-7B-v2 also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th.\n\n\nModel: ChatGPT (reported), MGSM-Zh: 61.2\\*, MGSM-Th: 47.2\\*\nModel: Qwen-14B-chat, MGSM-Zh: 59.6, MGSM-Th: 28\nModel: SeaLLM-7B-v2, MGSM-Zh: 64.8, MGSM-Th: 62.4", "### Zero-shot Commonsense Reasoning\n\n\nWe compare SeaLLM-7B-v2 with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in (Kojima et al., 2023) to grab the answer. Note that we DID NOT use \"Let's think step-by-step\" to invoke explicit CoT.", "### Multilingual World Knowledge\n\n\nWe evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot M3Exam (M3e) for En, Zh, Vi, Id, Th, and zero-shot VMLU for Vi.\n\n\n\nVMLU reproduce script here. Lm-eval was used to evaluate MMLU.", "### MT-Bench\n\n\nOn the English MT-bench metric, SeaLLM-7B-v2 achieves 7.54 score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages.\n\n\nRefer to mt\\_bench/seallm\\_7b\\_v2.jsonl for the MT-bench predictions of SeaLLM-7B-v2, and here to reproduce it.", "### Sea-Bench\n\n\nSimilar to MT-Bench, Sea-bench is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages.\n\n\nAs shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance.\n\n\n!fig\\_sea\\_bench\\_side\\_by\\_side.png\n\n\nRefer to sea\\_bench/seallm\\_7b\\_v2.jsonl for the Sea-bench predictions of SeaLLM-7B-v2.", "### Usage", "#### Instruction format", "#### Using transformers's chat\\_template", "#### Using vLLM\n\n\nAcknowledgement to Our Linguists\n--------------------------------\n\n\nWe would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety.\n\n\nIf you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: l.bing@URL\n\n\nAuthor list and order will change!\n\n\n* '\\*' and '^' are equal contributions." ]
[ 47, 247, 387, 62, 171, 123, 104, 81, 118, 143, 4, 5, 13, 145 ]
[ "passage: TAGS\n#gguf #arxiv-2312.00738 #arxiv-2205.11916 #arxiv-2306.05179 #arxiv-2306.05685 #license-other #region-us \n### Highlights\n\n\n* SeaLLM-7B-v2 achieves the 7B-SOTA on the Zero-shot CoT GSM8K task with 78.2 score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH CoT for Thai 🇹🇭.\n* It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with 82.5, 68.3, 80.9 scores on Arc-C, Winogrande, and Hellaswag.\n* It achieves 7.54 score on the 🇬🇧 MT-bench, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model.\n* It scores 45.74 on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models (Vistral-7B) of similar sizes.", "passage: ### Release and DEMO\n\n\n* DEMO: SeaLLMs/SeaLLM-7B.\n* Technical report: Arxiv: SeaLLMs - Large Language Models for Southeast Asia.\n* Model weights:\n\t+ SeaLLM-7B-v2.\n\t+ SeaLLM-7B-v2-gguf.\n\t\t- SeaLLM-7B-v2-q4\\_0, SeaLLM-7B-v2-q8\\_0.\n\t\t- LM-studio requires this URL to work properly.\n\t+ SeaLLM-7B-v2-GGUF (thanks Lonestriker). NOTE: use URL to work properly.\n\n\n\n> \n> **Terms of Use and License**: \n> By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our [SeaLLMs Terms Of Use](URL target=).\n> \n> \n> \n\n\n\n> \n> Disclaimer:\n> We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation.\n> Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations.\n> In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.\n> \n> \n> \n\n\n\n> \n> The logo was generated by DALL-E 3.\n> \n> \n>### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1?\n\n\n* SeaLLM-7B-v2 is continue-pretrained from Mistral-7B and underwent carefully designed tuning with focus in reasoning.\n\n\nEvaluation\n----------### Zero-shot CoT Multilingual Math Reasoning\n\n\nSeaLLM-7B-v2 achieves with 78.2 score on the GSM8K with zero-shot CoT reasoning, making it the state of the art in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). SeaLLM-7B-v2 also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with 22.4 vs 18.1 scores.\n\n\n!fig\\_sea\\_math\\_side\\_by\\_side.png\n\n\n\nSee details on English and translated GSM8K and MATH with zero-shot reasoning#### Zero-shot MGSM\n\n\nSeaLLM-7B-v2 also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th.\n\n\nModel: ChatGPT (reported), MGSM-Zh: 61.2\\*, MGSM-Th: 47.2\\*\nModel: Qwen-14B-chat, MGSM-Zh: 59.6, MGSM-Th: 28\nModel: SeaLLM-7B-v2, MGSM-Zh: 64.8, MGSM-Th: 62.4### Zero-shot Commonsense Reasoning\n\n\nWe compare SeaLLM-7B-v2 with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in (Kojima et al., 2023) to grab the answer. Note that we DID NOT use \"Let's think step-by-step\" to invoke explicit CoT." ]
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null
null
transformers
<!-- 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. --> # pranay-j/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8551 - Validation Loss: 2.5646 - 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.8551 | 2.5646 | 0 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.13.0 - Datasets 2.5.2 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "pranay-j/distilbert-base-uncased-finetuned-imdb", "results": []}]}
fill-mask
pranay-j/distilbert-base-uncased-finetuned-imdb
[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T07:37:10+00:00
[]
[]
TAGS #transformers #tf #distilbert #fill-mask #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
pranay-j/distilbert-base-uncased-finetuned-imdb =============================================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 2.8551 * Validation Loss: 2.5646 * 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': {'module': 'transformers.optimization\_tf', 'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_schedule\_fn': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': -688, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'warmup\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\_name': 'WarmUp'}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01} * training\_precision: mixed\_float16 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.13.0 * Datasets 2.5.2 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -688, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.13.0\n* Datasets 2.5.2\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -688, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.13.0\n* Datasets 2.5.2\n* Tokenizers 0.15.0" ]
[ 70, 337, 4, 31 ]
[ "passage: TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -688, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.13.0\n* Datasets 2.5.2\n* Tokenizers 0.15.0" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
null
omartariq612/quran-lora-whisper-medium-epoch-1
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T07:39:58+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # OndeviceAI-base-v1 This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) 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: 0.0007 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "base_model": "paust/pko-t5-base", "model-index": [{"name": "OndeviceAI-base-v1", "results": []}]}
text2text-generation
yeye776/OndeviceAI-base-v1
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:paust/pko-t5-base", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T07:40:06+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# OndeviceAI-base-v1 This model is a fine-tuned version of paust/pko-t5-base 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: 0.0007 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# OndeviceAI-base-v1\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 20", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# OndeviceAI-base-v1\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 20", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 83, 36, 6, 12, 8, 3, 129, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# OndeviceAI-base-v1\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 20### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "18.40 +/- 15.38", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
tinywell/Reinforce-Pixelcopter-PLE-v0
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-07T07:42:06+00:00
[]
[]
TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ 41, 58 ]
[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
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null
null
transformers
<!-- 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. --> # wav2vec2-base-finetuned-ks-ob This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0015 - Accuracy: 1.0000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0528 | 1.0 | 256 | 0.0275 | 0.9994 | | 0.0122 | 2.0 | 512 | 0.0054 | 0.9998 | | 0.0048 | 3.0 | 768 | 0.0041 | 0.9995 | | 0.0029 | 4.0 | 1024 | 0.0020 | 0.9999 | | 0.0019 | 5.0 | 1280 | 0.0015 | 1.0000 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.0+cu118 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-ks-ob", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9999694563225412, "name": "Accuracy"}]}]}]}
audio-classification
sravan-gorugantu/wav2vec2-base-finetuned-ks-ob
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:audiofolder", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2024-02-07T07:45:13+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
wav2vec2-base-finetuned-ks-ob ============================= This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set: * Loss: 0.0015 * Accuracy: 1.0000 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * distributed\_type: tpu * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.0.0+cu118 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 78, 153, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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# **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="ergh0/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
ergh0/Taxi-v3
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-07T07:45:38+00:00
[]
[]
TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage
[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ 32, 33 ]
[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
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null
null
diffusers
# MedChMTS <Gallery /> ## Model description Medieval characters bad dataset ![imagen.png](https:&#x2F;&#x2F;cdn-uploads.huggingface.co&#x2F;production&#x2F;uploads&#x2F;65c331a9ce1be49b56c01721&#x2F;AFJe-flsS9mTTLdyZBQbt.png) ## Trigger words You should use `character concept` to trigger the image generation. You should use `in the style of medchmts` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Bukowski89/MedChMTS/tree/main) them in the Files & versions tab.
{"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "character concept of a medieval soldier", "output": {"url": "images/Capture.JPG"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "character concept, in the style of medchmts"}
text-to-image
Bukowski89/MedChMTS
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
2024-02-07T07:49:11+00:00
[]
[]
TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us
# MedChMTS <Gallery /> ## Model description Medieval characters bad dataset !URL ## Trigger words You should use 'character concept' to trigger the image generation. You should use 'in the style of medchmts' to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.
[ "# MedChMTS\n\n<Gallery />", "## Model description \n\nMedieval characters bad dataset\n\n\n!URL", "## Trigger words\n\nYou should use 'character concept' to trigger the image generation.\n\nYou should use 'in the style of medchmts' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# MedChMTS\n\n<Gallery />", "## Model description \n\nMedieval characters bad dataset\n\n\n!URL", "## Trigger words\n\nYou should use 'character concept' to trigger the image generation.\n\nYou should use 'in the style of medchmts' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ 56, 10, 12, 38, 28 ]
[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# MedChMTS\n\n<Gallery />## Model description \n\nMedieval characters bad dataset\n\n\n!URL## Trigger words\n\nYou should use 'character concept' to trigger the image generation.\n\nYou should use 'in the style of medchmts' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
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null
null
ml-agents
# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Atozzio/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
{"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]}
reinforcement-learning
Atozzio/ppo-Huggy
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
2024-02-07T07:50:14+00:00
[]
[]
TAGS #ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
# ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: Atozzio/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play
[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Atozzio/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Atozzio/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ 44, 199 ]
[ "passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Atozzio/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2195 - Accuracy: 0.9235 - F1: 0.9233 ## Emotion Labels - **label_0:** Sadness - **label_1:** Joy - **label_2:** Love - **label_3:** Anger - **label_4:** Fear - **label_5:** Surprise ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8537 | 1.0 | 250 | 0.3235 | 0.897 | 0.8958 | | 0.2506 | 2.0 | 500 | 0.2195 | 0.9235 | 0.9233 | ### Validation metrics - test_loss : 0.2194512039422989 - test_accuracy : 0.9235 - test_f1 : 0.923296474937779 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9235, "name": "Accuracy"}, {"type": "f1", "value": 0.923296474937779, "name": "F1"}]}]}]}
text-classification
pt-sk/distilbert-base-uncased-finetuned-emotion
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T07:56:49+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-emotion ========================================= distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2195 * Accuracy: 0.9235 * F1: 0.9233 Emotion Labels -------------- * label\_0: Sadness * label\_1: Joy * label\_2: Love * label\_3: Anger * label\_4: Fear * label\_5: Surprise Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Validation metrics * test\_loss : 0.2194512039422989 * test\_accuracy : 0.9235 * test\_f1 : 0.923296474937779 ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Validation metrics\n\n\n* test\\_loss : 0.2194512039422989\n* test\\_accuracy : 0.9235\n* test\\_f1 : 0.923296474937779", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Validation metrics\n\n\n* test\\_loss : 0.2194512039422989\n* test\\_accuracy : 0.9235\n* test\\_f1 : 0.923296474937779", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 82, 98, 4, 46, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Validation metrics\n\n\n* test\\_loss : 0.2194512039422989\n* test\\_accuracy : 0.9235\n* test\\_f1 : 0.923296474937779### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # checkpoints This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset. It achieves the following results on the evaluation set: - Precision: 0.4372 - Recall: 0.6574 - F1: 0.5252 - Loss: 0.0001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | F1 | Validation Loss | Precision | Recall | |:-------------:|:-----:|:-----:|:------:|:---------------:|:---------:|:------:| | 0.1954 | 20.0 | 500 | 0 | 0.4094 | 0 | 0 | | 0.1588 | 40.0 | 1000 | 0.1420 | 0.3055 | 0.3587 | 0.0886 | | 0.1182 | 60.0 | 1500 | 0.4253 | 0.1384 | 0.3810 | 0.4812 | | 0.0477 | 80.0 | 2000 | 0.4764 | 0.0216 | 0.3949 | 0.6002 | | 0.069 | 100.0 | 2500 | 0.5198 | 0.0115 | 0.4564 | 0.6038 | | 0.0355 | 120.0 | 3000 | 0.5161 | 0.0018 | 0.4271 | 0.6521 | | 0.0268 | 140.0 | 3500 | 0.5254 | 0.0016 | 0.4395 | 0.6530 | | 0.0123 | 160.0 | 4000 | 0.5264 | 0.0015 | 0.4382 | 0.6592 | | 0.0039 | 180.0 | 4500 | 0.5353 | 0.0011 | 0.4510 | 0.6583 | | 0.0139 | 200.0 | 5000 | 0.5390 | 0.0011 | 0.4533 | 0.6646 | | 0.001 | 220.0 | 5500 | 0.5430 | 0.0042 | 0.4620 | 0.6583 | | 0.01 | 240.0 | 6000 | 0.5347 | 0.0013 | 0.4531 | 0.6521 | | 0.0065 | 260.0 | 6500 | 0.5404 | 0.0001 | 0.4540 | 0.6673 | | 0.0046 | 280.0 | 7000 | 0.5252 | 0.0001 | 0.4372 | 0.6574 | | 0.002 | 300.0 | 7500 | 0.5365 | 0.0007 | 0.4474 | 0.6699 | | 0.0002 | 320.0 | 8000 | 0.5393 | 0.0002 | 0.4546 | 0.6628 | | 0.0008 | 340.0 | 8500 | 0.5412 | 0.0002 | 0.4569 | 0.6637 | | 0.0024 | 360.0 | 9000 | 0.4677 | 0.6601 | 0.5475 | 0.0002 | | 0.0001 | 380.0 | 9500 | 0.4560 | 0.6673 | 0.5418 | 0.0002 | | 0.002 | 400.0 | 10000 | 0.4594 | 0.6628 | 0.5427 | 0.0003 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xfun"], "metrics": ["precision", "recall", "f1"], "base_model": "nielsr/lilt-xlm-roberta-base", "model-index": [{"name": "checkpoints", "results": []}]}
null
kavg/LiLT-RE-JA
[ "transformers", "safetensors", "lilt", "generated_from_trainer", "dataset:xfun", "base_model:nielsr/lilt-xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-07T07:58:19+00:00
[]
[]
TAGS #transformers #safetensors #lilt #generated_from_trainer #dataset-xfun #base_model-nielsr/lilt-xlm-roberta-base #license-mit #endpoints_compatible #region-us
checkpoints =========== This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on the xfun dataset. It achieves the following results on the evaluation set: * Precision: 0.4372 * Recall: 0.6574 * F1: 0.5252 * Loss: 0.0001 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 8 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 10000 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #lilt #generated_from_trainer #dataset-xfun #base_model-nielsr/lilt-xlm-roberta-base #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 62, 115, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #lilt #generated_from_trainer #dataset-xfun #base_model-nielsr/lilt-xlm-roberta-base #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
{"license": "cc-by-2.0"}
text-generation
JackCloudman/Senku-70B-Full-exl2-3.5bpw
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T08:04:36+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 60 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/beta These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks dog", "inference": true}
text-to-image
danaleee/beta
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T08:07:37+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/beta These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/beta\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/beta\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 95 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/beta\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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<!-- 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. --> # roberta-base-bn-adapter-895K-snli-model2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7648 - Accuracy: 0.7315 ## 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: 64 - eval_batch_size: 64 - seed: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4332 | 1.0 | 8584 | 0.3469 | 0.8699 | | 0.4008 | 2.0 | 17168 | 0.3200 | 0.8780 | | 0.3889 | 3.0 | 25752 | 0.3143 | 0.8805 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-bn-adapter-895K-snli-model2", "results": []}]}
null
varun-v-rao/roberta-base-bn-adapter-895K-snli-model2
[ "tensorboard", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
2024-02-07T08:09:03+00:00
[]
[]
TAGS #tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us
roberta-base-bn-adapter-895K-snli-model2 ======================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7648 * Accuracy: 0.7315 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: 64 * eval\_batch\_size: 64 * seed: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 31, 98, 4, 33 ]
[ "passage: TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
peft
<!-- 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. --> # decilm-7b-ocn-v1 This model is a fine-tuned version of Deci/DeciLM-7B on the oncc_medqa_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance hf (pretrained=Deci/DeciLM-7B,trust_remote_code=True,parallelize=True,load_in_4bit=True,peft=chenhugging/decilm-7b-ocn-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.95|± |0.0219| |medmcqa |Yaml |none | 0|acc | 0.34|± |0.0476| |professional_medicine| 0|none | 0|acc | 0.58|± |0.0496| |college_medicine | 0|none | 0|acc | 0.54|± |0.0501| |clinical_knowledge | 0|none | 0|acc | 0.57|± |0.0498| |ocn |Yaml |none | 0|acc | 0.68|± |0.0469| |aocnp |Yaml |none | 0|acc | 0.43|± |0.0498| ### Appendix: Original Performance hf (pretrained=Deci/DeciLM-7B,trust_remote_code=True,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.89|± |0.0314| |medmcqa |Yaml |none | 0|acc | 0.37|± |0.0485| |professional_medicine| 0|none | 0|acc | 0.58|± |0.0496| |college_medicine | 0|none | 0|acc | 0.54|± |0.0501| |clinical_knowledge | 0|none | 0|acc | 0.63|± |0.0485| |ocn |Yaml |none | 0|acc | 0.64|± |0.0482| |aocnp |Yaml |none | 0|acc | 0.41|± |0.0494|
{"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "Deci/DeciLM-7B", "model-index": [{"name": "decilm-7b-ocn-v1", "results": []}]}
null
chenhugging/decilm-7b-ocn-v1
[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Deci/DeciLM-7B", "license:other", "region:us" ]
2024-02-07T08:12:11+00:00
[]
[]
TAGS #peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-Deci/DeciLM-7B #license-other #region-us
decilm-7b-ocn-v1 ================ This model is a fine-tuned version of Deci/DeciLM-7B on the oncc\_medqa\_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 1.0 * mixed\_precision\_training: Native AMP ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.1.1+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1 ### Performance hf (pretrained=Deci/DeciLM-7B,trust\_remote\_code=True,parallelize=True,load\_in\_4bit=True,peft=chenhugging/decilm-7b-ocn-v1), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None, batch\_size: 1 ### Appendix: Original Performance hf (pretrained=Deci/DeciLM-7B,trust\_remote\_code=True,parallelize=True,load\_in\_4bit=True), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None, batch\_size: 1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nhf (pretrained=Deci/DeciLM-7B,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/decilm-7b-ocn-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1", "### Appendix: Original Performance\n\n\nhf (pretrained=Deci/DeciLM-7B,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ "TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-Deci/DeciLM-7B #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nhf (pretrained=Deci/DeciLM-7B,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/decilm-7b-ocn-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1", "### Appendix: Original Performance\n\n\nhf (pretrained=Deci/DeciLM-7B,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ 48, 159, 39, 105, 91 ]
[ "passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-Deci/DeciLM-7B #license-other #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1### Performance\n\n\nhf (pretrained=Deci/DeciLM-7B,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/decilm-7b-ocn-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1### Appendix: Original Performance\n\n\nhf (pretrained=Deci/DeciLM-7B,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
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null
null
null
**Como traducir con este Modelo** + Instalar [Python 3.9](https://www.python.org/downloads/release/python-390/) + ctranslate 2 + subword-nmt ```bash pip install ctranslate2~=3.20.0 ``` ```bash pip install subword-nmt ``` + procesar texto con BPE: ```bash subword-nmt apply-bpe -c gl-detok10k.code < input_file.txt > input_file_bpe.txt ``` + Traducir un input_text utilizando NOS-MT-eu-gl: ```bash python3 trans_ct2.py eu_gl.ct2_10k input_file_bpe.txt >output_file_bpe.txt ``` + DeBPEzar output txt: ```bash cat out_test_bpe.txt | sed "s/@@ //g" > output_file.txt ```
{"license": "mit"}
null
proxectonos/Nos_MT-OpenNMT-eu-gl
[ "license:mit", "region:us" ]
2024-02-07T08:12:49+00:00
[]
[]
TAGS #license-mit #region-us
Como traducir con este Modelo + Instalar Python 3.9 + ctranslate 2 + subword-nmt + procesar texto con BPE: + Traducir un input_text utilizando NOS-MT-eu-gl: + DeBPEzar output txt:
[]
[ "TAGS\n#license-mit #region-us \n" ]
[ 11 ]
[ "passage: TAGS\n#license-mit #region-us \n" ]
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null
null
transformers
# 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. 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{"language": ["en"], "library_name": "transformers", "tags": ["physics"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
text-classification
dylansheils0241/Quantum-Balanced-GPT2-Experimental-Theoretical-Classifier-Arxiv-V3
[ "transformers", "safetensors", "gpt2", "text-classification", "physics", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T08:13:53+00:00
[ "1910.09700" ]
[ "en" ]
TAGS #transformers #safetensors #gpt2 #text-classification #physics #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #gpt2 #text-classification #physics #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #physics #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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# OBMBX2-7b OBMBX2-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [paulml/OmniBeagleMBX-v3-7B](https://huggingface.co/paulml/OmniBeagleMBX-v3-7B) * [alnrg2arg/blockchainlabs_joe_bez_seminar](https://huggingface.co/alnrg2arg/blockchainlabs_joe_bez_seminar) ## 🧩 Configuration ```yaml slices: models: - model: flemmingmiguel/MBX-7B-v3 # no parameters necessary for base model - model: paulml/OmniBeagleMBX-v3-7B parameters: density: 0.5 weight: 0.3 - model: alnrg2arg/blockchainlabs_joe_bez_seminar parameters: density: 0.5 weight: 0.5 merge_method: dare_ties base_model: flemmingmiguel/MBX-7B-v3 parameters: normalize: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "gmonsoon/OBMBX2-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "paulml/OmniBeagleMBX-v3-7B", "alnrg2arg/blockchainlabs_joe_bez_seminar"], "base_model": ["paulml/OmniBeagleMBX-v3-7B", "alnrg2arg/blockchainlabs_joe_bez_seminar"]}
null
gmonsoon/OBMBX2-7b
[ "merge", "mergekit", "lazymergekit", "paulml/OmniBeagleMBX-v3-7B", "alnrg2arg/blockchainlabs_joe_bez_seminar", "base_model:paulml/OmniBeagleMBX-v3-7B", "base_model:alnrg2arg/blockchainlabs_joe_bez_seminar", "region:us" ]
2024-02-07T08:16:24+00:00
[]
[]
TAGS #merge #mergekit #lazymergekit #paulml/OmniBeagleMBX-v3-7B #alnrg2arg/blockchainlabs_joe_bez_seminar #base_model-paulml/OmniBeagleMBX-v3-7B #base_model-alnrg2arg/blockchainlabs_joe_bez_seminar #region-us
# OBMBX2-7b OBMBX2-7b is a merge of the following models using LazyMergekit: * paulml/OmniBeagleMBX-v3-7B * alnrg2arg/blockchainlabs_joe_bez_seminar ## Configuration ## Usage
[ "# OBMBX2-7b\n\nOBMBX2-7b is a merge of the following models using LazyMergekit:\n* paulml/OmniBeagleMBX-v3-7B\n* alnrg2arg/blockchainlabs_joe_bez_seminar", "## Configuration", "## Usage" ]
[ "TAGS\n#merge #mergekit #lazymergekit #paulml/OmniBeagleMBX-v3-7B #alnrg2arg/blockchainlabs_joe_bez_seminar #base_model-paulml/OmniBeagleMBX-v3-7B #base_model-alnrg2arg/blockchainlabs_joe_bez_seminar #region-us \n", "# OBMBX2-7b\n\nOBMBX2-7b is a merge of the following models using LazyMergekit:\n* paulml/OmniBeagleMBX-v3-7B\n* alnrg2arg/blockchainlabs_joe_bez_seminar", "## Configuration", "## Usage" ]
[ 95, 61, 4, 3 ]
[ "passage: TAGS\n#merge #mergekit #lazymergekit #paulml/OmniBeagleMBX-v3-7B #alnrg2arg/blockchainlabs_joe_bez_seminar #base_model-paulml/OmniBeagleMBX-v3-7B #base_model-alnrg2arg/blockchainlabs_joe_bez_seminar #region-us \n# OBMBX2-7b\n\nOBMBX2-7b is a merge of the following models using LazyMergekit:\n* paulml/OmniBeagleMBX-v3-7B\n* alnrg2arg/blockchainlabs_joe_bez_seminar## Configuration## Usage" ]
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null
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**Como traducir con este Modelo** + Instalar [Python 3.9](https://www.python.org/downloads/release/python-390/) + ctranslate 2 + subword-nmt ```bash pip install ctranslate2~=3.20.0 ``` ```bash pip install subword-nmt ``` + procesar texto con BPE: ```bash subword-nmt apply-bpe -c ca-detok10k.code < input_file.txt > input_file_bpe.txt ``` + Traducir un input_text utilizando NOS-MT-eu-gl: ```bash python3 trans_ct2.py ct2_detok-ca-gl_sint_10k input_file_bpe.txt >output_file_bpe.txt ``` + DeBPEzar output txt: ```bash cat out_test_bpe.txt | sed "s/@@ //g" > output_file.txt ```
{"license": "mit"}
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proxectonos/Nos_MT-OpenNMT-ca-gl
[ "license:mit", "region:us" ]
2024-02-07T08:17:58+00:00
[]
[]
TAGS #license-mit #region-us
Como traducir con este Modelo + Instalar Python 3.9 + ctranslate 2 + subword-nmt + procesar texto con BPE: + Traducir un input_text utilizando NOS-MT-eu-gl: + DeBPEzar output txt:
[]
[ "TAGS\n#license-mit #region-us \n" ]
[ 11 ]
[ "passage: TAGS\n#license-mit #region-us \n" ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/CL_rank4 These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true}
text-to-image
danaleee/CL_rank4
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T08:18:39+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/CL_rank4 These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/CL_rank4\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL_rank4\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 101 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank4\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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null
null
peft
<!-- 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. --> # llama_questioner_DPO_DC_2 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2169 - Rewards/chosen: -4.2394 - Rewards/rejected: -12.3534 - Rewards/accuracies: 0.9137 - Rewards/margins: 8.1141 - Logps/rejected: -196.4096 - Logps/chosen: -131.2397 - Logits/rejected: 0.1812 - Logits/chosen: 0.1657 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 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_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.0194 | 1.0 | 3793 | 0.2169 | -4.2394 | -12.3534 | 0.9137 | 8.1141 | -196.4096 | -131.2397 | 0.1812 | 0.1657 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.0
{"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "llama_questioner_DPO_DC_2", "results": []}]}
null
mazzaqq/llama_questioner_DPO_DC_2
[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
2024-02-07T08:21:40+00:00
[]
[]
TAGS #peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
llama\_questioner\_DPO\_DC\_2 ============================= This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2169 * Rewards/chosen: -4.2394 * Rewards/rejected: -12.3534 * Rewards/accuracies: 0.9137 * Rewards/margins: 8.1141 * Logps/rejected: -196.4096 * Logps/chosen: -131.2397 * Logits/rejected: 0.1812 * Logits/chosen: 0.1657 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 2 * eval\_batch\_size: 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\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * PEFT 0.7.1 * Transformers 4.36.2 * Pytorch 2.1.2 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 47, 144, 4, 36 ]
[ "passage: TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
muzammil-eds/tinyllama-3T-64k-JSONExtractor-v4
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T08:21:55+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # wav2vec_RTSplit0207_6 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0453 - Wer: 0.2160 - Cer: 0.1838 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 6.369 | 1.0 | 120 | 5.2384 | 1.0 | 0.8910 | | 1.1292 | 2.0 | 240 | 0.9018 | 0.9092 | 0.5706 | | 0.7847 | 3.0 | 360 | 0.6675 | 0.8210 | 0.5470 | | 0.689 | 4.0 | 480 | 0.6056 | 0.8203 | 0.5506 | | 0.5764 | 5.0 | 600 | 0.4676 | 0.7040 | 0.4298 | | 0.4722 | 6.0 | 720 | 0.3901 | 0.5230 | 0.2731 | | 0.3723 | 7.0 | 840 | 0.2639 | 0.4135 | 0.2273 | | 0.2916 | 8.0 | 960 | 0.1469 | 0.3003 | 0.1786 | | 0.2556 | 9.0 | 1080 | 0.0678 | 0.2367 | 0.1940 | | 0.1352 | 10.0 | 1200 | 0.0453 | 0.2160 | 0.1838 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0207_6", "results": []}]}
automatic-speech-recognition
tndklab/wav2vec_RTSplit0207_6
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T08:23:07+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
wav2vec\_RTSplit0207\_6 ======================= This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0453 * Wer: 0.2160 * Cer: 0.1838 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 80, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/CL_rank10_iter800 These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true}
text-to-image
danaleee/CL_rank10_iter800
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T08:25:51+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/CL_rank10_iter800 These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/CL_rank10_iter800\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL_rank10_iter800\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 104 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank10_iter800\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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null
null
transformers
<!-- 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. --> # medicalBERT This model is a fine-tuned version of [d4data/biomedical-ner-all](https://huggingface.co/d4data/biomedical-ner-all) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "d4data/biomedical-ner-all", "model-index": [{"name": "medicalBERT", "results": []}]}
token-classification
roupenminassian/medicalBERT
[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:d4data/biomedical-ner-all", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T08:25:53+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-d4data/biomedical-ner-all #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# medicalBERT This model is a fine-tuned version of d4data/biomedical-ner-all 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# medicalBERT\n\nThis model is a fine-tuned version of d4data/biomedical-ner-all on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-d4data/biomedical-ner-all #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# medicalBERT\n\nThis model is a fine-tuned version of d4data/biomedical-ner-all on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 75, 32, 6, 12, 8, 3, 90, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #base_model-d4data/biomedical-ner-all #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# medicalBERT\n\nThis model is a fine-tuned version of d4data/biomedical-ner-all on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # custom_q_and_a This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "custom_q_and_a", "results": []}]}
text-generation
smrynrz20/custom_q_and_a
[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T08:26:05+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# custom_q_and_a This model is a fine-tuned version of gpt2 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# custom_q_and_a\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 25.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# custom_q_and_a\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 25.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 72, 29, 6, 12, 8, 3, 91, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# custom_q_and_a\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 25.0### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
# ALMA-Latxa-7b ALMA-Latxa-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [HiTZ/latxa-7b-v1](https://huggingface.co/HiTZ/latxa-7b-v1) * [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: HiTZ/latxa-7b-v1 layer_range: [0, 32] - model: haoranxu/ALMA-7B layer_range: [0, 32] merge_method: slerp base_model: haoranxu/ALMA-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "airalribalta/ALMA-Latxa-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "HiTZ/latxa-7b-v1", "haoranxu/ALMA-7B"], "base_model": ["HiTZ/latxa-7b-v1", "haoranxu/ALMA-7B"]}
text-generation
airalribalta/ALMA-Latxa-7b
[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "HiTZ/latxa-7b-v1", "haoranxu/ALMA-7B", "base_model:HiTZ/latxa-7b-v1", "base_model:haoranxu/ALMA-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T08:30:27+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #haoranxu/ALMA-7B #base_model-HiTZ/latxa-7b-v1 #base_model-haoranxu/ALMA-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# ALMA-Latxa-7b ALMA-Latxa-7b is a merge of the following models using LazyMergekit: * HiTZ/latxa-7b-v1 * haoranxu/ALMA-7B ## Configuration ## Usage
[ "# ALMA-Latxa-7b\n\nALMA-Latxa-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* haoranxu/ALMA-7B", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #haoranxu/ALMA-7B #base_model-HiTZ/latxa-7b-v1 #base_model-haoranxu/ALMA-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ALMA-Latxa-7b\n\nALMA-Latxa-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* haoranxu/ALMA-7B", "## Configuration", "## Usage" ]
[ 106, 48, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #haoranxu/ALMA-7B #base_model-HiTZ/latxa-7b-v1 #base_model-haoranxu/ALMA-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ALMA-Latxa-7b\n\nALMA-Latxa-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* haoranxu/ALMA-7B## Configuration## Usage" ]
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# Detector Guidance for Multi-Object Text-to-Image Generation by [Luping Liu](https://luping-liu.github.io/)<sup>1</sup>, Zijian Zhang<sup>1</sup>, [Yi Ren](https://rayeren.github.io/)<sup>2</sup>, Rongjie Huang<sup>1</sup>, Zhou Zhao<sup>1</sup>. <sup>1</sup>Zhejiang University, <sup>2</sup>ByteDance In this work, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. More precisely, DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. Human evaluations demonstrate that DG provides an 8-22% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations.
{"license": "apache-2.0"}
null
luping-liu/Detector_Guidance
[ "license:apache-2.0", "region:us" ]
2024-02-07T08:31:10+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
# Detector Guidance for Multi-Object Text-to-Image Generation by Luping Liu<sup>1</sup>, Zijian Zhang<sup>1</sup>, Yi Ren<sup>2</sup>, Rongjie Huang<sup>1</sup>, Zhou Zhao<sup>1</sup>. <sup>1</sup>Zhejiang University, <sup>2</sup>ByteDance In this work, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. More precisely, DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. Human evaluations demonstrate that DG provides an 8-22% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations.
[ "# Detector Guidance for Multi-Object Text-to-Image Generation\n\nby Luping Liu<sup>1</sup>, Zijian Zhang<sup>1</sup>, Yi Ren<sup>2</sup>, Rongjie Huang<sup>1</sup>, Zhou Zhao<sup>1</sup>.\n\n<sup>1</sup>Zhejiang University, <sup>2</sup>ByteDance\n\nIn this work, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. More precisely, DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. Human evaluations demonstrate that DG provides an 8-22% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations." ]
[ "TAGS\n#license-apache-2.0 #region-us \n", "# Detector Guidance for Multi-Object Text-to-Image Generation\n\nby Luping Liu<sup>1</sup>, Zijian Zhang<sup>1</sup>, Yi Ren<sup>2</sup>, Rongjie Huang<sup>1</sup>, Zhou Zhao<sup>1</sup>.\n\n<sup>1</sup>Zhejiang University, <sup>2</sup>ByteDance\n\nIn this work, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. More precisely, DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. Human evaluations demonstrate that DG provides an 8-22% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations." ]
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[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Detector Guidance for Multi-Object Text-to-Image Generation\n\nby Luping Liu<sup>1</sup>, Zijian Zhang<sup>1</sup>, Yi Ren<sup>2</sup>, Rongjie Huang<sup>1</sup>, Zhou Zhao<sup>1</sup>.\n\n<sup>1</sup>Zhejiang University, <sup>2</sup>ByteDance\n\nIn this work, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. More precisely, DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. Human evaluations demonstrate that DG provides an 8-22% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations." ]
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null
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-ft This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi-2-ft", "results": []}]}
null
mikeee/phi-2-ft
[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]
2024-02-07T08:32:57+00:00
[]
[]
TAGS #peft #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
# phi-2-ft This model is a fine-tuned version of microsoft/phi-2 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# phi-2-ft\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "# phi-2-ft\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 41, 28, 6, 12, 8, 3, 89, 4, 39 ]
[ "passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n# phi-2-ft\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
null
mikeee/phi-2-ft-evol-instruct-chinese-gpt4
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T08:33:07+00:00
[ "1910.09700" ]
[]
TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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<!-- 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. --> # CDAgpt-llama-13b-v3 This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-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: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "base_model": "meta-llama/Llama-2-13b-hf", "model-index": [{"name": "CDAgpt-llama-13b-v3", "results": []}]}
null
Federic/CDAgpt-llama-13b-v3
[ "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-hf", "region:us" ]
2024-02-07T08:37:50+00:00
[]
[]
TAGS #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-13b-hf #region-us
# CDAgpt-llama-13b-v3 This model is a fine-tuned version of meta-llama/Llama-2-13b-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: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# CDAgpt-llama-13b-v3\n\nThis model is a fine-tuned version of meta-llama/Llama-2-13b-hf on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-13b-hf #region-us \n", "# CDAgpt-llama-13b-v3\n\nThis model is a fine-tuned version of meta-llama/Llama-2-13b-hf on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 36, 43, 6, 12, 8, 3, 141, 33 ]
[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-13b-hf #region-us \n# CDAgpt-llama-13b-v3\n\nThis model is a fine-tuned version of meta-llama/Llama-2-13b-hf on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Wembo/ppo-self-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
{"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "20.77 +/- 54.16", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Wembo/ppo-self-LunarLander-v2
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
2024-02-07T08:45:25+00:00
[]
[]
TAGS #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
[ "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters" ]
[ "TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n", "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters" ]
[ 51, 37 ]
[ "passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters" ]
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null
null
transformers
## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/Test157t/Kunocchini-7b-128k-test). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. Assistant: Quantization is a process used in signal processing, physics, and mathematics to convert continuous signals or data into discrete values. It's often used in digital systems to represent analog signals or phenomena, allowing for efficient storage, transmission, and processing of information. In the context of audio, video, or images, quantization converts the infinite range of possible signal values into a finite number of levels that can be represented using a certain number of bits. To understand this better, let's consider an example with audio. Sound waves are continuous signals, and when we record them, we need to # Original model card
{"language": ["zh", "en"], "license": "openrail", "library_name": "transformers", "pipeline_tag": "text-generation"}
text-generation
s3nh/Kunocchini-7b-128k-test-GGUF
[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]
2024-02-07T08:48:23+00:00
[]
[ "zh", "en" ]
TAGS #transformers #gguf #text-generation #zh #en #license-openrail #endpoints_compatible #region-us
## Original model card Buy me a coffee if you like this project ;) <a href="URL src="URL alt=""></a> #### Description GGUF Format model files for This project. ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. Assistant: Quantization is a process used in signal processing, physics, and mathematics to convert continuous signals or data into discrete values. It's often used in digital systems to represent analog signals or phenomena, allowing for efficient storage, transmission, and processing of information. In the context of audio, video, or images, quantization converts the infinite range of possible signal values into a finite number of levels that can be represented using a certain number of bits. To understand this better, let's consider an example with audio. Sound waves are continuous signals, and when we record them, we need to # Original model card
[ "## Original model card \n\nBuy me a coffee if you like this project ;)\n<a href=\"URL src=\"URL alt=\"\"></a>", "#### Description \n\nGGUF Format model files for This project.", "### GGUF Specs \n\nGGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:\n\nSingle-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.\nExtensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.\nmmap compatibility: models can be loaded using mmap for fast loading and saving.\nEasy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.\nFull information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.\nThe key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. \nThis allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for\ninference or for identifying the model.", "### inference \n\n\n User: Tell me story about what is an quantization and what do we need to build.\n\nAssistant: Quantization is a process used in signal processing, physics, and mathematics to convert continuous signals or data into discrete values. It's often used in digital systems to represent analog signals or phenomena, allowing for efficient storage, transmission, and processing of information. In the context of audio, video, or images, quantization converts the infinite range of possible signal values into a finite number of levels that can be represented using a certain number of bits.\n\nTo understand this better, let's consider an example with audio. Sound waves are continuous signals, and when we record them, we need to", "# Original model card" ]
[ "TAGS\n#transformers #gguf #text-generation #zh #en #license-openrail #endpoints_compatible #region-us \n", "## Original model card \n\nBuy me a coffee if you like this project ;)\n<a href=\"URL src=\"URL alt=\"\"></a>", "#### Description \n\nGGUF Format model files for This project.", "### GGUF Specs \n\nGGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:\n\nSingle-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.\nExtensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.\nmmap compatibility: models can be loaded using mmap for fast loading and saving.\nEasy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.\nFull information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.\nThe key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. \nThis allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for\ninference or for identifying the model.", "### inference \n\n\n User: Tell me story about what is an quantization and what do we need to build.\n\nAssistant: Quantization is a process used in signal processing, physics, and mathematics to convert continuous signals or data into discrete values. It's often used in digital systems to represent analog signals or phenomena, allowing for efficient storage, transmission, and processing of information. In the context of audio, video, or images, quantization converts the infinite range of possible signal values into a finite number of levels that can be represented using a certain number of bits.\n\nTo understand this better, let's consider an example with audio. Sound waves are continuous signals, and when we record them, we need to", "# Original model card" ]
[ 35, 30, 13, 284, 165, 4 ]
[ "passage: TAGS\n#transformers #gguf #text-generation #zh #en #license-openrail #endpoints_compatible #region-us \n## Original model card \n\nBuy me a coffee if you like this project ;)\n<a href=\"URL src=\"URL alt=\"\"></a>#### Description \n\nGGUF Format model files for This project.### GGUF Specs \n\nGGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:\n\nSingle-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.\nExtensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.\nmmap compatibility: models can be loaded using mmap for fast loading and saving.\nEasy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.\nFull information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.\nThe key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. \nThis allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for\ninference or for identifying the model." ]
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null
null
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 0.8.2
{"library_name": "peft", "base_model": "ybelkada/blip2-opt-2.7b-fp16-sharded"}
null
satefi/fine-tuned-blip2-opt-2.7b-coco-captions-adapters
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ybelkada/blip2-opt-2.7b-fp16-sharded", "region:us" ]
2024-02-07T08:48:52+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-ybelkada/blip2-opt-2.7b-fp16-sharded #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-ybelkada/blip2-opt-2.7b-fp16-sharded #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 49, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-ybelkada/blip2-opt-2.7b-fp16-sharded #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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diffusers
# SDXL LoRA DreamBooth - finalyear2023/virat-kholi <Gallery /> ## Model description These are finalyear2023/virat-kholi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of virat kholi, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](finalyear2023/virat-kholi/tree/main) them in the Files & versions tab.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of virat kholi,"}
text-to-image
finalyear2023/virat-kholi
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]
2024-02-07T08:54:29+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
# SDXL LoRA DreamBooth - finalyear2023/virat-kholi <Gallery /> ## Model description These are finalyear2023/virat-kholi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of virat kholi, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.
[ "# SDXL LoRA DreamBooth - finalyear2023/virat-kholi\n\n<Gallery />", "## Model description\n\nThese are finalyear2023/virat-kholi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of virat kholi, to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - finalyear2023/virat-kholi\n\n<Gallery />", "## Model description\n\nThese are finalyear2023/virat-kholi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of virat kholi, to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ 86, 23, 88, 21, 28 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - finalyear2023/virat-kholi\n\n<Gallery />## Model description\n\nThese are finalyear2023/virat-kholi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of virat kholi, to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
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null
null
transformers
The Moe model was constructed using microsoft/phi-2 as the base, with experts from microsoft/phi-2, g-ronimo/phi-2-OpenHermes-2.5, and mlx-community/phi-2-dpo-7k. Then qlora was applied to all layers of q,v, and gate linear on WizardLM_evol_instruct_70k via mlx. The model was created using a script from https://github.com/mzbac/mlx-moe ## Evaluation ### hellaswag | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |---------|------:|------|-----:|--------|-----:|---|-----:| |hellaswag| 1|none | 0|acc |0.5482|± |0.0050| | | |none | 0|acc_norm|0.7300|± |0.0044| ### MMLU | Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |------------------|-------|------|-----:|------|-----:|---|-----:| | - humanities |N/A |none | 0|acc |0.5817|± |0.0247| | - other |N/A |none | 0|acc |0.5795|± |0.0311| | - social_sciences|N/A |none | 0|acc |0.6347|± |0.0292| | - stem |N/A |none | 0|acc |0.4486|± |0.0376| ### BBH | Tasks |Version| Filter |n-shot| Metric |Value | |Stderr| |----------------------|------:|----------|-----:|-----------|-----:|---|-----:| |bbh_cot_fewshot_snarks| 2|get-answer| 3|exact_match|0.5281|± |0.0375| ### GSM8k |Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |-----|------:|----------|-----:|-----------|-----:|---|-----:| |gsm8k| 2|get-answer| 5|exact_match|0.5224|± |0.0138| ## Example ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mzbac/phi-2-2x3-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True) text = "Instruct: how backpropagation works.\nOutput:" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ```
{"license": "mit"}
text-generation
mzbac/phi-2-2x3-hf
[ "transformers", "safetensors", "phi2moe", "text-generation", "custom_code", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T08:55:21+00:00
[]
[]
TAGS #transformers #safetensors #phi2moe #text-generation #custom_code #license-mit #autotrain_compatible #endpoints_compatible #region-us
The Moe model was constructed using microsoft/phi-2 as the base, with experts from microsoft/phi-2, g-ronimo/phi-2-OpenHermes-2.5, and mlx-community/phi-2-dpo-7k. Then qlora was applied to all layers of q,v, and gate linear on WizardLM\_evol\_instruct\_70k via mlx. The model was created using a script from URL Evaluation ---------- ### hellaswag ### MMLU ### BBH ### GSM8k Example -------
[ "### hellaswag", "### MMLU", "### BBH", "### GSM8k\n\n\n\nExample\n-------" ]
[ "TAGS\n#transformers #safetensors #phi2moe #text-generation #custom_code #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### hellaswag", "### MMLU", "### BBH", "### GSM8k\n\n\n\nExample\n-------" ]
[ 50, 5, 4, 4, 9 ]
[ "passage: TAGS\n#transformers #safetensors #phi2moe #text-generation #custom_code #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### hellaswag### MMLU### BBH### GSM8k\n\n\n\nExample\n-------" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
null
phamtungthuy/law_model
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T08:55:26+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div> <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div> <div><a href="https://huggingface.co/datasets/McGill-NLP/WebLINX">🤗Dataset</a></div> <div><a href="https://github.com/McGill-NLP/weblinx">💾Code</a></div> </div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ [Click here to access the original model.](https://huggingface.co/google/flan-t5-base)
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["weblinx", "text-generation-inference", "web-agents", "agents"], "datasets": ["McGill-NLP/WebLINX", "McGill-NLP/WebLINX-full"], "metrics": ["f1", "iou", "chrf"], "pipeline_tag": "text-generation"}
text-generation
McGill-NLP/flan-t5-base-weblinx
[ "transformers", "safetensors", "weblinx", "text-generation-inference", "web-agents", "agents", "text-generation", "en", "dataset:McGill-NLP/WebLINX", "dataset:McGill-NLP/WebLINX-full", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T08:55:33+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #license-apache-2.0 #endpoints_compatible #region-us
<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL </div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ Click here to access the original model.
[ "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model." ]
[ "TAGS\n#transformers #safetensors #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #license-apache-2.0 #endpoints_compatible #region-us \n", "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model." ]
[ 90, 34 ]
[ "passage: TAGS\n#transformers #safetensors #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #license-apache-2.0 #endpoints_compatible #region-us \n## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model." ]
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<!-- 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. --> # roberta-base-bn-adapter-895K-snli-model3 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7710 - Accuracy: 0.7275 ## 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: 64 - eval_batch_size: 64 - seed: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4273 | 1.0 | 8584 | 0.3416 | 0.8694 | | 0.4019 | 2.0 | 17168 | 0.3206 | 0.8800 | | 0.385 | 3.0 | 25752 | 0.3148 | 0.8821 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-bn-adapter-895K-snli-model3", "results": []}]}
null
varun-v-rao/roberta-base-bn-adapter-895K-snli-model3
[ "tensorboard", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
2024-02-07T08:57:02+00:00
[]
[]
TAGS #tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us
roberta-base-bn-adapter-895K-snli-model3 ======================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7710 * Accuracy: 0.7275 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: 64 * eval\_batch\_size: 64 * seed: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 31, 98, 4, 33 ]
[ "passage: TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="https://arxiv.org/abs/2402.05930">📄Paper</a></div> <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div> <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div> <div><a href="https://huggingface.co/datasets/McGill-NLP/WebLINX">🤗Dataset</a></div> <div><a href="https://github.com/McGill-NLP/weblinx">💾Code</a></div> </div> ## Quickstart ```python from datasets import load_dataset from huggingface_hub import snapshot_download from transformers import pipeline # Load validation split valid = load_dataset("McGill-NLP/weblinx", split="validation") # Download and load the templates snapshot_download( "McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="templates/*.txt", local_dir="./" ) with open('templates/llama.txt') as f: template = f.read() turn = valid[0] turn_text = template.format(**turn) # Load action model and input the text to get prediction action_model = pipeline( model="McGill-NLP/Llama-2-13b-chat-weblinx", device=0, torch_dtype='auto' ) out = action_model(turn_text, return_full_text=False, max_new_tokens=64, truncation=True) pred = out[0]['generated_text'] print("Ref:", turn["action"]) print("Pred:", pred) ``` ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ [Click here to access the original model.](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) ## License This model is derived from LLaMA-2, which can only be used with the [LLaMA 2 Community License Agreement](https://github.com/facebookresearch/llama/blob/main/LICENSE). By using or distributing any portion or element of this model, you agree to be bound by this Agreement.
{"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["weblinx", "text-generation-inference", "web-agents", "agents"], "datasets": ["McGill-NLP/WebLINX", "McGill-NLP/WebLINX-full"], "metrics": ["f1", "iou", "chrf"], "pipeline_tag": "text-generation"}
text-generation
McGill-NLP/Llama-2-13b-chat-weblinx
[ "transformers", "pytorch", "weblinx", "text-generation-inference", "web-agents", "agents", "text-generation", "en", "dataset:McGill-NLP/WebLINX", "dataset:McGill-NLP/WebLINX-full", "arxiv:2402.05930", "license:llama2", "endpoints_compatible", "region:us" ]
2024-02-07T08:58:05+00:00
[ "2402.05930" ]
[ "en" ]
TAGS #transformers #pytorch #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #arxiv-2402.05930 #license-llama2 #endpoints_compatible #region-us
<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL </div> ## Quickstart ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ Click here to access the original model. ## License This model is derived from LLaMA-2, which can only be used with the LLaMA 2 Community License Agreement. By using or distributing any portion or element of this model, you agree to be bound by this Agreement.
[ "## Quickstart", "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "## License\n\nThis model is derived from LLaMA-2, which can only be used with the LLaMA 2 Community License Agreement. By using or distributing any portion or element of this model, you agree to be bound by this Agreement." ]
[ "TAGS\n#transformers #pytorch #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #arxiv-2402.05930 #license-llama2 #endpoints_compatible #region-us \n", "## Quickstart", "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "## License\n\nThis model is derived from LLaMA-2, which can only be used with the LLaMA 2 Community License Agreement. By using or distributing any portion or element of this model, you agree to be bound by this Agreement." ]
[ 97, 3, 34, 51 ]
[ "passage: TAGS\n#transformers #pytorch #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #arxiv-2402.05930 #license-llama2 #endpoints_compatible #region-us \n## Quickstart## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.## License\n\nThis model is derived from LLaMA-2, which can only be used with the LLaMA 2 Community License Agreement. By using or distributing any portion or element of this model, you agree to be bound by this Agreement." ]
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null
null
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 0.7.2.dev0
{"library_name": "peft", "base_model": "google/flan-t5-base"}
null
HeydarS/flan-t5-base_peft_v20
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-base", "region:us" ]
2024-02-07T09:00:20+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.2.dev0
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]
[ 35, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 14 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0" ]
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null
null
transformers
<!-- 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. --> # my_ner_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2690 - Precision: 0.5545 - Recall: 0.3253 - F1: 0.4100 - Accuracy: 0.9420 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2829 | 0.5103 | 0.2289 | 0.3161 | 0.9377 | | No log | 2.0 | 426 | 0.2690 | 0.5545 | 0.3253 | 0.4100 | 0.9420 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_ner_model", "results": []}]}
token-classification
veronica1608/my_ner_model
[ "transformers", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T09:00:58+00:00
[]
[]
TAGS #transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
my\_ner\_model ============== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2690 * Precision: 0.5545 * Recall: 0.3253 * F1: 0.4100 * Accuracy: 0.9420 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: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 69, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
phamtungthuy/quantized_law_model_merged
[ "transformers", "safetensors", "mpt", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-07T09:01:16+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mpt #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #text-generation-inference #4-bit #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mpt #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #mpt #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
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transformers
# Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [h2oai/h2ogpt-4096-llama2-7b](https://huggingface.co/h2oai/h2ogpt-4096-llama2-7b) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.36.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="audreyleteve/curious-muskox", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?</s><|answer|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "audreyleteve/curious-muskox", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "audreyleteve/curious-muskox", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "audreyleteve/curious-muskox" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?</s><|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 4096, padding_idx=0) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=4096, bias=False) (v_proj): Linear(in_features=4096, out_features=4096, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=4096, out_features=11008, bias=False) (up_proj): Linear(in_features=4096, out_features=11008, bias=False) (down_proj): Linear(in_features=11008, out_features=4096, bias=False) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
{"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"}
text-generation
audreyleteve/curious-muskox
[ "transformers", "safetensors", "llama", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2024-02-07T09:02:07+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
# Model Card ## Summary This model was trained using H2O LLM Studio. - Base model: h2oai/h2ogpt-4096-llama2-7b ## Usage To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed. Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline' You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'. You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ## Quantization and sharding You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting . ## Model Architecture ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
[ "# Model Card", "## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: h2oai/h2ogpt-4096-llama2-7b", "## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:", "## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .", "## Model Architecture", "## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.", "## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it." ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us \n", "# Model Card", "## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: h2oai/h2ogpt-4096-llama2-7b", "## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:", "## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .", "## Model Architecture", "## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.", "## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it." ]
[ 60, 3, 37, 244, 34, 4, 42, 518 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us \n# Model Card## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: h2oai/h2ogpt-4096-llama2-7b## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .## Model Architecture## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models." ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
phamtungthuy/law_model_merged
[ "transformers", "safetensors", "mpt", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "region:us" ]
2024-02-07T09:05:02+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mpt #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mpt #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #mpt #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
dict( name="igenius-llama-tinystories-2B", # 2B run on AZURE hf_config=dict(org="Igenius", name="igenius-llama-tinystories-az"), block_size=2048, vocab_size=32_000, padding_multiple=64, n_layer=16, n_head=4 * 16, n_embd=192 * 16, rotary_percentage=1.0, parallel_residual=False, bias=False, _norm_class="RMSNorm", norm_eps=1e-5, _mlp_class="LLaMAMLP", ##with LLaMAMLP 2B intermediate_size=8192, n_query_groups=4 * 16, ) dummy model trained several epochs (20B token in total) on a small dataset
{}
text-generation
iGenius-AI-Team/Italia-2Bts-ckpt-20B
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T09:06:55+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
dict( name="igenius-llama-tinystories-2B", # 2B run on AZURE hf_config=dict(org="Igenius", name="igenius-llama-tinystories-az"), block_size=2048, vocab_size=32_000, padding_multiple=64, n_layer=16, n_head=4 * 16, n_embd=192 * 16, rotary_percentage=1.0, parallel_residual=False, bias=False, _norm_class="RMSNorm", norm_eps=1e-5, _mlp_class="LLaMAMLP", ##with LLaMAMLP 2B intermediate_size=8192, n_query_groups=4 * 16, ) dummy model trained several epochs (20B token in total) on a small dataset
[ "# 2B run on AZURE\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-tinystories-az\"),\n block_size=2048,\n vocab_size=32_000,\n padding_multiple=64,\n n_layer=16,\n n_head=4 * 16,\n n_embd=192 * 16,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\", ##with LLaMAMLP 2B\n intermediate_size=8192,\n n_query_groups=4 * 16,\n )\n\n dummy model trained several epochs (20B token in total) on a small dataset" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 2B run on AZURE\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-tinystories-az\"),\n block_size=2048,\n vocab_size=32_000,\n padding_multiple=64,\n n_layer=16,\n n_head=4 * 16,\n n_embd=192 * 16,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\", ##with LLaMAMLP 2B\n intermediate_size=8192,\n n_query_groups=4 * 16,\n )\n\n dummy model trained several epochs (20B token in total) on a small dataset" ]
[ 47, 184 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# 2B run on AZURE\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-tinystories-az\"),\n block_size=2048,\n vocab_size=32_000,\n padding_multiple=64,\n n_layer=16,\n n_head=4 * 16,\n n_embd=192 * 16,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\", ##with LLaMAMLP 2B\n intermediate_size=8192,\n n_query_groups=4 * 16,\n )\n\n dummy model trained several epochs (20B token in total) on a small dataset" ]
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null
null
peft
<!-- 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. --> # bart-summerization-article-title This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-summerization-article-title", "results": []}]}
null
sanjeev-bhandari01/bart-summerization-article-title
[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "license:mit", "region:us" ]
2024-02-07T09:08:16+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #region-us
# bart-summerization-article-title This model is a fine-tuned version of facebook/bart-large-cnn 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: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# bart-summerization-article-title\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #region-us \n", "# bart-summerization-article-title\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 44, 37, 6, 12, 8, 3, 89, 4, 36 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #region-us \n# bart-summerization-article-title\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
peft
<!-- 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. --> # solar-10b-ocn-v1 This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the oncc_medqa_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training script CUDA_VISIBLE_DEVICES=0 python src/train_bash.py --stage sft --do_train True --model_name_or_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning_type lora --quantization_bit 4 --flash_attn True --dataset_dir data --dataset oncc_medqa_instruct --cutoff_len 1024 --learning_rate 0.0005 --num_train_epochs 1.0 --max_samples 5000 --per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lr_scheduler_type cosine --max_grad_norm 1.0 --logging_steps 10 --save_steps 100 --warmup_steps 10 --neftune_noise_alpha 0.5 --lora_rank 8 --lora_dropout 0.2 --lora_target wqkv --output_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot_loss True ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance Test script: lm_eval --model hf --model_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu_clinical_knowledge,mmlu_college_medicine,mmlu_professional_medicine --device cuda:0 --limit 100 hf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.95|± |0.0219| |medmcqa |Yaml |none | 0|acc | 0.42|± |0.0496| |professional_medicine| 0|none | 0|acc | 0.72|± |0.0451| |college_medicine | 0|none | 0|acc | 0.67|± |0.0473| |clinical_knowledge | 0|none | 0|acc | 0.64|± |0.0482| |ocn |Yaml |none | 0|acc | 0.83|± |0.0378| |aocnp |Yaml |none | 0|acc | 0.72|± |0.0451|
{"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "upstage/SOLAR-10.7B-v1.0", "model-index": [{"name": "solar-10b-ocn-v1", "results": []}]}
null
chenhugging/solar-10b-ocn-v1
[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:upstage/SOLAR-10.7B-v1.0", "license:other", "region:us" ]
2024-02-07T09:12:23+00:00
[]
[]
TAGS #peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-other #region-us
solar-10b-ocn-v1 ================ This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the oncc\_medqa\_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 1.0 * mixed\_precision\_training: Native AMP ### Training script CUDA\_VISIBLE\_DEVICES=0 python src/train\_bash.py --stage sft --do\_train True --model\_name\_or\_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning\_type lora --quantization\_bit 4 --flash\_attn True --dataset\_dir data --dataset oncc\_medqa\_instruct --cutoff\_len 1024 --learning\_rate 0.0005 --num\_train\_epochs 1.0 --max\_samples 5000 --per\_device\_train\_batch\_size 4 --gradient\_accumulation\_steps 4 --lr\_scheduler\_type cosine --max\_grad\_norm 1.0 --logging\_steps 10 --save\_steps 100 --warmup\_steps 10 --neftune\_noise\_alpha 0.5 --lora\_rank 8 --lora\_dropout 0.2 --lora\_target wqkv --output\_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot\_loss True ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.1.1+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1 ### Performance Test script: lm\_eval --model hf --model\_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust\_remote\_code=True,parallelize=True,load\_in\_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu\_clinical\_knowledge,mmlu\_college\_medicine,mmlu\_professional\_medicine --device cuda:0 --limit 100 hf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust\_remote\_code=True,parallelize=True,load\_in\_4bit=True), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None, batch\_size: 1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Training script\n\n\nCUDA\\_VISIBLE\\_DEVICES=0 python src/train\\_bash.py --stage sft --do\\_train True --model\\_name\\_or\\_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning\\_type lora --quantization\\_bit 4 --flash\\_attn True --dataset\\_dir data --dataset oncc\\_medqa\\_instruct --cutoff\\_len 1024 --learning\\_rate 0.0005 --num\\_train\\_epochs 1.0 --max\\_samples 5000 --per\\_device\\_train\\_batch\\_size 4 --gradient\\_accumulation\\_steps 4 --lr\\_scheduler\\_type cosine --max\\_grad\\_norm 1.0 --logging\\_steps 10 --save\\_steps 100 --warmup\\_steps 10 --neftune\\_noise\\_alpha 0.5 --lora\\_rank 8 --lora\\_dropout 0.2 --lora\\_target wqkv --output\\_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot\\_loss True", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nTest script:\nlm\\_eval --model hf --model\\_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu\\_clinical\\_knowledge,mmlu\\_college\\_medicine,mmlu\\_professional\\_medicine --device cuda:0 --limit 100\n\n\nhf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ "TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Training script\n\n\nCUDA\\_VISIBLE\\_DEVICES=0 python src/train\\_bash.py --stage sft --do\\_train True --model\\_name\\_or\\_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning\\_type lora --quantization\\_bit 4 --flash\\_attn True --dataset\\_dir data --dataset oncc\\_medqa\\_instruct --cutoff\\_len 1024 --learning\\_rate 0.0005 --num\\_train\\_epochs 1.0 --max\\_samples 5000 --per\\_device\\_train\\_batch\\_size 4 --gradient\\_accumulation\\_steps 4 --lr\\_scheduler\\_type cosine --max\\_grad\\_norm 1.0 --logging\\_steps 10 --save\\_steps 100 --warmup\\_steps 10 --neftune\\_noise\\_alpha 0.5 --lora\\_rank 8 --lora\\_dropout 0.2 --lora\\_target wqkv --output\\_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot\\_loss True", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Performance\n\n\nTest script:\nlm\\_eval --model hf --model\\_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu\\_clinical\\_knowledge,mmlu\\_college\\_medicine,mmlu\\_professional\\_medicine --device cuda:0 --limit 100\n\n\nhf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust\\_remote\\_code=True,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ 52, 159, 291, 39, 255 ]
[ "passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-other #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Training script\n\n\nCUDA\\_VISIBLE\\_DEVICES=0 python src/train\\_bash.py --stage sft --do\\_train True --model\\_name\\_or\\_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning\\_type lora --quantization\\_bit 4 --flash\\_attn True --dataset\\_dir data --dataset oncc\\_medqa\\_instruct --cutoff\\_len 1024 --learning\\_rate 0.0005 --num\\_train\\_epochs 1.0 --max\\_samples 5000 --per\\_device\\_train\\_batch\\_size 4 --gradient\\_accumulation\\_steps 4 --lr\\_scheduler\\_type cosine --max\\_grad\\_norm 1.0 --logging\\_steps 10 --save\\_steps 100 --warmup\\_steps 10 --neftune\\_noise\\_alpha 0.5 --lora\\_rank 8 --lora\\_dropout 0.2 --lora\\_target wqkv --output\\_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot\\_loss True" ]
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null
null
transformers
<!-- 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. --> # classify_food_items This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5776 - Accuracy: 0.84 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.5846 | 0.99 | 62 | 2.5776 | 0.84 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "classify_food_items", "results": []}]}
image-classification
chethanuk/classify_food_items
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T09:12:27+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
classify\_food\_items ===================== This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5776 * Accuracy: 0.84 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 75, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
<u><b>We are creating a spatial aware vision-language(VL) model.</b></u> This is a trained model on COCO dataset images including extra information regarding the spatial relationship between the entities of the image. This is a sequence to sequence model for image-captioning. The architecture is <u><b>ViT encoder and GPT2 decoder.</b></u> <details> <summary>Requirements!</summary> - 4GB GPU RAM. - CUDA enabled docker </details> The way to download and run this: ```python device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") from transformers import pipeline image_captioner = pipeline("image-to-text", model="VCL3D/rgb-language_cap", max_new_tokens=200, device=device) filename = 'path/to/file' generated_captions = image_captioner(filename) print(generated_captions) ``` The model is trained to produce as many words as possible with a maximum of 200 tokens, which translates to roughly 5 sentences, while the 6th sentence is usually cropped. <i>The output is always of that form: "Object1" is to the "Left/Right etc." of the "Object2".</i> ## IF YOU WANT TO PRODUCE A SPECIFIC NUMBER OF CAPTIONS UP TO 5. ```python import os def print_up_to_n_sentences(captions, n): for caption in captions: generated_text = caption.get('generated_text', '') sentences = generated_text.split('.') result = '.'.join(sentences[:n]) #print(result) return result filename = 'path/to/file' generated_captions = image_captioner(filename) captions = print_up_to_n_sentences(generated_captions, 5) print(captions) ```
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["text-generation-inference"], "metrics": ["code_eval"], "pipeline_tag": "image-to-text"}
image-to-text
VCL3D/rgb-language_cap
[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "text-generation-inference", "image-to-text", "en", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-07T09:14:11+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #vision-encoder-decoder #text-generation-inference #image-to-text #en #license-mit #endpoints_compatible #region-us
<u><b>We are creating a spatial aware vision-language(VL) model.</b></u> This is a trained model on COCO dataset images including extra information regarding the spatial relationship between the entities of the image. This is a sequence to sequence model for image-captioning. The architecture is <u><b>ViT encoder and GPT2 decoder.</b></u> <details> <summary>Requirements!</summary> - 4GB GPU RAM. - CUDA enabled docker </details> The way to download and run this: The model is trained to produce as many words as possible with a maximum of 200 tokens, which translates to roughly 5 sentences, while the 6th sentence is usually cropped. <i>The output is always of that form: "Object1" is to the "Left/Right etc." of the "Object2".</i> ## IF YOU WANT TO PRODUCE A SPECIFIC NUMBER OF CAPTIONS UP TO 5.
[ "## IF YOU WANT TO PRODUCE A SPECIFIC NUMBER OF CAPTIONS UP TO 5." ]
[ "TAGS\n#transformers #pytorch #safetensors #vision-encoder-decoder #text-generation-inference #image-to-text #en #license-mit #endpoints_compatible #region-us \n", "## IF YOU WANT TO PRODUCE A SPECIFIC NUMBER OF CAPTIONS UP TO 5." ]
[ 56, 21 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #vision-encoder-decoder #text-generation-inference #image-to-text #en #license-mit #endpoints_compatible #region-us \n## IF YOU WANT TO PRODUCE A SPECIFIC NUMBER OF CAPTIONS UP TO 5." ]
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null
null
transformers
<!-- 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. --> # roberta-base-detect-cheapfake-co1-co2-v2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3242 - Accuracy: 0.855 - F1: 0.8513 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 38 | 0.6804 | 0.52 | 0.0204 | | No log | 2.0 | 76 | 0.5288 | 0.8 | 0.7561 | | No log | 3.0 | 114 | 0.3494 | 0.865 | 0.8657 | | No log | 4.0 | 152 | 0.3469 | 0.87 | 0.8725 | | No log | 5.0 | 190 | 0.3242 | 0.855 | 0.8513 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-detect-cheapfake-co1-co2-v2", "results": []}]}
text-classification
hoanghoavienvo/roberta-base-detect-cheapfake-co1-co2-v2
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T09:17:42+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-detect-cheapfake-co1-co2-v2 ======================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.3242 * Accuracy: 0.855 * F1: 0.8513 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-06 * 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: 5 ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.0.0 * Datasets 2.1.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.15.0" ]
[ 63, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
<!-- 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. --> # OndeviceAI-base-v2 This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) 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: 0.0007 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "base_model": "paust/pko-t5-base", "model-index": [{"name": "OndeviceAI-base-v2", "results": []}]}
text2text-generation
yeye776/OndeviceAI-base-v2
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:paust/pko-t5-base", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T09:18:11+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# OndeviceAI-base-v2 This model is a fine-tuned version of paust/pko-t5-base 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: 0.0007 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# OndeviceAI-base-v2\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 20", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# OndeviceAI-base-v2\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 20", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 83, 36, 6, 12, 8, 3, 129, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# OndeviceAI-base-v2\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 20### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
sentence-transformers
# e5-base-mlqa-finetuned-arabic-for-rag This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 2668 with parameters: ``` {'batch_size': 2, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 533, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
{"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}
sentence-similarity
OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag
[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "endpoints_compatible", "region:us" ]
2024-02-07T09:18:22+00:00
[]
[]
TAGS #sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
# e5-base-mlqa-finetuned-arabic-for-rag This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 2668 with parameters: Loss: 'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters: Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
[ "# e5-base-mlqa-finetuned-arabic-for-rag\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2668 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]
[ "TAGS\n#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n", "# e5-base-mlqa-finetuned-arabic-for-rag\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2668 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]
[ 44, 61, 38, 29, 86, 5, 6 ]
[ "passage: TAGS\n#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# e5-base-mlqa-finetuned-arabic-for-rag\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2668 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]
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null
null
transformers
<!-- 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. --> # speecht5_finetuned_voxpopuli_lt_gg This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4952 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.5286 | 51.95 | 500 | 0.5118 | | 0.4869 | 103.9 | 1000 | 0.4986 | | 0.481 | 155.84 | 1500 | 0.4952 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer", "text-to-speech"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speecht5_finetuned_voxpopuli_lt_gg", "results": []}]}
text-to-speech
UnaiGurbindo/speecht5_finetuned_voxpopuli_lt
[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-07T09:25:12+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
speecht5\_finetuned\_voxpopuli\_lt\_gg ====================================== This model is a fine-tuned version of microsoft/speecht5\_tts on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: * Loss: 0.4952 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: 2 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 1500 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 81, 158, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 1500\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
EnDevSols/tinyllama-3T-64k-JSONExtractor
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T09:26:58+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
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peft
<!-- 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. --> # mixtral-8x7b-v0.1-english-to-hinglish-translation This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0769 - Rouge Scores: {'rouge1': 0.9045408202972536, 'rouge2': 0.795425441228359, 'rougeL': 0.8399846297860634, 'rougeLsum': 0.9043739034131012} - Bleu Scores: [0.0002881182166187815, 0.0002842750061873772, 0.0002764768847375588, 0.00026750640347869873] - Gen Len: 2048.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - 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 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge Scores | Bleu Scores | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|:--------:| | 1.1771 | 1.0 | 500 | 1.0579 | {'rouge1': 0.9070255400902434, 'rouge2': 0.7976770190068221, 'rougeL': 0.8400261479965636, 'rougeLsum': 0.9069363147075731} | [0.00028395954091190866, 0.0002796973368739713, 0.0002722057765709132, 0.000263740024418467] | 2047.996 | | 0.7788 | 2.0 | 1000 | 1.0769 | {'rouge1': 0.90.45408202972536, 'rouge2': 0.795425441228359, 'rougeL': 0.8399846297860634, 'rougeLsum': 0.9043739034131012} | [0.0002881182166187815, 0.0002842750061873772, 0.0002764768847375588, 0.00026750640347869873] | 2048.0 | ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.16.2.dev0 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mixtral-8x7B-v0.1", "model-index": [{"name": "mixtral-8x7b-v0.1-english-to-hinglish-translation", "results": []}]}
null
DrishtiSharma/mixtral-8x7b-v0.1-english-to-hinglish-translation
[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-v0.1", "license:apache-2.0", "region:us" ]
2024-02-07T09:27:11+00:00
[]
[]
TAGS #peft #safetensors #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-v0.1 #license-apache-2.0 #region-us
mixtral-8x7b-v0.1-english-to-hinglish-translation ================================================= This model is a fine-tuned version of mistralai/Mixtral-8x7B-v0.1 on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.0769 * Rouge Scores: {'rouge1': 0.9045408202972536, 'rouge2': 0.795425441228359, 'rougeL': 0.8399846297860634, 'rougeLsum': 0.9043739034131012} * Bleu Scores: [0.0002881182166187815, 0.0002842750061873772, 0.0002764768847375588, 0.00026750640347869873] * Gen Len: 2048.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 2 * eval\_batch\_size: 8 * 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 * num\_epochs: 2 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu118 * Datasets 2.16.2.dev0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.16.2.dev0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-v0.1 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.16.2.dev0\n* Tokenizers 0.15.1" ]
[ 48, 141, 4, 47 ]
[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.16.2.dev0\n* Tokenizers 0.15.1" ]
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null
null
peft
<!-- 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. --> # daily-mail This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "library_name": "peft", "tags": ["summarization", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "daily-mail", "results": []}]}
summarization
RMWeerasinghe/daily-mail
[ "peft", "tensorboard", "safetensors", "summarization", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]
2024-02-07T09:27:23+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #summarization #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
# daily-mail This model is a fine-tuned version of microsoft/phi-2 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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# daily-mail\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 12\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #summarization #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "# daily-mail\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 12\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 43, 27, 6, 12, 8, 3, 112, 4, 44 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #summarization #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n# daily-mail\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 12\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "c", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.72", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
JiajingChen/c
[ "transformers", "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "endpoints_compatible", "region:us" ]
2024-02-07T09:28:10+00:00
[]
[]
TAGS #transformers #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #endpoints_compatible #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#transformers #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #endpoints_compatible #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 43, 41, 17 ]
[ "passage: TAGS\n#transformers #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #endpoints_compatible #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
transformers
<!-- 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. --> # roberta-base-detect-cheapfake-ca1-ca2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1482 - Accuracy: 0.94 - F1: 0.9450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 38 | 0.6724 | 0.705 | 0.7807 | | No log | 2.0 | 76 | 0.5437 | 0.925 | 0.9309 | | No log | 3.0 | 114 | 0.1945 | 0.93 | 0.9340 | | No log | 4.0 | 152 | 0.1559 | 0.94 | 0.9444 | | No log | 5.0 | 190 | 0.1482 | 0.94 | 0.9450 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-detect-cheapfake-ca1-ca2", "results": []}]}
text-classification
hoanghoavienvo/roberta-base-detect-cheapfake-ca1-ca2
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T09:32:30+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-detect-cheapfake-ca1-ca2 ===================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.1482 * Accuracy: 0.94 * F1: 0.9450 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-06 * 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: 5 ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.0.0 * Datasets 2.1.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.15.0" ]
[ 63, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
# BagelMIsteryTour-v2-8x7B 3.5bpw Exllama quant of [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B) ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-3.5bpw-exl2) ## Prompt format: Alpaca It is noted to also work with mistral ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Input: {input} ### Response: ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
{"license": "cc-by-nc-4.0", "tags": ["mergekit", "merge"], "base_model": ["mistralai/Mixtral-8x7B-v0.1", "jondurbin/bagel-dpo-8x7b-v0.2", "Sao10K/Sensualize-Mixtral-bf16", "mistralai/Mixtral-8x7B-v0.1", "Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora", "mistralai/Mixtral-8x7B-Instruct-v0.1"]}
text-generation
Kooten/BagelMIsteryTour-v2-8x7B-3.5bpw-exl2
[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:jondurbin/bagel-dpo-8x7b-v0.2", "base_model:Sao10K/Sensualize-Mixtral-bf16", "base_model:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T09:32:43+00:00
[]
[]
TAGS #transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-mistralai/Mixtral-8x7B-v0.1 #base_model-jondurbin/bagel-dpo-8x7b-v0.2 #base_model-Sao10K/Sensualize-Mixtral-bf16 #base_model-Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# BagelMIsteryTour-v2-8x7B 3.5bpw Exllama quant of ycros/BagelMIsteryTour-v2-8x7B ## Other quants: EXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw ## Prompt format: Alpaca It is noted to also work with mistral ## Contact Kooten on discord URL if you would like to support me
[ "# BagelMIsteryTour-v2-8x7B 3.5bpw\nExllama quant of ycros/BagelMIsteryTour-v2-8x7B", "## Other quants:\n\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw", "## Prompt format: Alpaca\nIt is noted to also work with mistral", "## Contact\nKooten on discord\n\nURL if you would like to support me" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-mistralai/Mixtral-8x7B-v0.1 #base_model-jondurbin/bagel-dpo-8x7b-v0.2 #base_model-Sao10K/Sensualize-Mixtral-bf16 #base_model-Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# BagelMIsteryTour-v2-8x7B 3.5bpw\nExllama quant of ycros/BagelMIsteryTour-v2-8x7B", "## Other quants:\n\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw", "## Prompt format: Alpaca\nIt is noted to also work with mistral", "## Contact\nKooten on discord\n\nURL if you would like to support me" ]
[ 175, 40, 33, 18, 14 ]
[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-mistralai/Mixtral-8x7B-v0.1 #base_model-jondurbin/bagel-dpo-8x7b-v0.2 #base_model-Sao10K/Sensualize-Mixtral-bf16 #base_model-Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# BagelMIsteryTour-v2-8x7B 3.5bpw\nExllama quant of ycros/BagelMIsteryTour-v2-8x7B## Other quants:\n\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw## Prompt format: Alpaca\nIt is noted to also work with mistral## Contact\nKooten on discord\n\nURL if you would like to support me" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
DrishtiSharma/mixtral-8x7b-v0.1-english-to-hinglish-translation-merged
[ "transformers", "safetensors", "mixtral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-07T09:34:40+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mixtral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# 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. 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{"library_name": "transformers", "tags": []}
text-generation
unsloth/yi-34b-bnb-4bit
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-07T09:34:43+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# Midjourney v6 Image Classification
{"pipeline_tag": "image-classification"}
image-classification
ideepankarsharma2003/Midjourney_v6_ImageClassifier
[ "transformers", "safetensors", "swin", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T09:35:14+00:00
[]
[]
TAGS #transformers #safetensors #swin #image-classification #autotrain_compatible #endpoints_compatible #region-us
# Midjourney v6 Image Classification
[ "# Midjourney v6 Image Classification" ]
[ "TAGS\n#transformers #safetensors #swin #image-classification #autotrain_compatible #endpoints_compatible #region-us \n", "# Midjourney v6 Image Classification" ]
[ 38, 9 ]
[ "passage: TAGS\n#transformers #safetensors #swin #image-classification #autotrain_compatible #endpoints_compatible #region-us \n# Midjourney v6 Image Classification" ]
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