ftopal/huggingface-models-raw · Datasets at Hugging Face

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text-generation	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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	abc88767/model26	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:44:10+00:00
null	null		{}	paulo037/stable-code-instruct-3b-syntetic-10000	null	[ "safetensors", "region:us" ]	null	2024-05-01T04:46:23+00:00
text-generation	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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	adinath/ollama_v8	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:48:21+00:00
null	null		{"license": "llama3"}	h34i7cby47t/code_java_finetuned_llm_model	null	[ "license:llama3", "region:us" ]	null	2024-05-01T04:49:09+00:00
null	null		{}	sleepyraygun/CrispyC3	null	[ "region:us" ]	null	2024-05-01T04:50:09+00:00
null	null		{}	zzzyj777/llama-2-7b-sft-full	null	[ "region:us" ]	null	2024-05-01T04:50:17+00:00
null	null		{"license": "llama2"}	rohansb10/rohan	null	[ "license:llama2", "region:us" ]	null	2024-05-01T04:52:05+00:00
text2text-generation	transformers	# T5_FINETUNE_Electrical_ts # Fine-Tuned T5 Model for Root Cause Analysis # Description This model is a fine-tuned version of the T5 (Text-to-Text Transfer Transformer) base model, specifically tailored to predict actions based on provided root causes in industrial or technical settings. The model has been trained to understand various root causes and suggest corresponding actions, facilitating faster decision-making and troubleshooting in operational environments. # Model Details Base Model: T5 Base Training Data: The model was trained on a proprietary dataset consisting of documented root causes and the actions taken to resolve them in a manufacturing context. Fine-Tuning Details: The model was fine-tuned for 3 epochs with a learning rate of 3e-4, using a batch size of 8. The fine-tuning process focused on adapting the T5 model to generate action plans based on textual descriptions of root causes. # Usage Installation To use this model, you will need to install Python and the necessary Python libraries. The primary library required is transformers by Hugging Face. -> pip install transformers torch # Loading the Model You can load the model using the Transformers library. Ensure you have the model and tokenizer files(final_model.zip) downloaded from the repository or Kaggle dataset. from transformers import T5ForConditionalGeneration, T5Tokenizer model_path = 'path_to_model_directory' model = T5ForConditionalGeneration.from_pretrained(model_path) tokenizer = T5Tokenizer.from_pretrained(model_path) #Making Predictions To use the model to predict actions based on a root cause, use the following Python code: def predict_action(root_cause): input_text = f"root cause: {root_cause} -> action:" input_ids = tokenizer.encode(input_text, return_tensors='pt') output_ids = model.generate(input_ids) action = tokenizer.decode(output_ids[0], skip_special_tokens=True) return action # Example print(predict_action("Example of a root cause")) # Contributing Contributions to this model are welcome. You can contribute in the following ways: Data: More data on root causes and actions can help to improve the model's accuracy and robustness. Code: Enhancements in the prediction script, additional features, or performance optimizations are appreciated. Issues: If you encounter issues while using this model, please report them in the issues section of this repository. # License This project is licensed under the MIT License - see the LICENSE file for details. # ATTENTION: Still the model is not 100% effeective due lack of data.	{}	Safawat/trouble-shooting-using-T5	null	[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:52:26+00:00
null	null		{}	rtabrizi/open_llama	null	[ "region:us" ]	null	2024-05-01T04:52:30+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-anthropic - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-anthropic/ Original model description: --- license: apache-2.0 base_model: HuggingFaceH4/mistral-7b-cai tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless model-index: - name: mistral-7b-dpo-v21.0cai.0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral 7B Constitutional AI This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets. It achieves the following results on the evaluation set: - Loss: 0.6327 - Rewards/chosen: -9.8716 - Rewards/rejected: -14.5465 - Rewards/accuracies: 0.6725 - Rewards/margins: 4.6749 - Logps/rejected: -329.8578 - Logps/chosen: -294.6768 - Logits/rejected: -2.1023 - Logits/chosen: -2.1648 ## 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-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6817 \| 0.02 \| 100 \| 0.6873 \| 0.0149 \| 0.0002 \| 0.5150 \| 0.0147 \| -184.3912 \| -195.8124 \| -3.1605 \| -3.1560 \| \| 0.6767 \| 0.05 \| 200 \| 0.6614 \| 0.0825 \| 0.0169 \| 0.5575 \| 0.0656 \| -184.2246 \| -195.1362 \| -3.1654 \| -3.1605 \| \| 0.6328 \| 0.07 \| 300 \| 0.6246 \| -0.0374 \| -0.2112 \| 0.5875 \| 0.1738 \| -186.5047 \| -196.3349 \| -3.1579 \| -3.1529 \| \| 0.5919 \| 0.1 \| 400 \| 0.5978 \| 0.2812 \| -0.0666 \| 0.6125 \| 0.3478 \| -185.0590 \| -193.1489 \| -3.1292 \| -3.1243 \| \| 0.5545 \| 0.12 \| 500 \| 0.5800 \| 0.1742 \| -0.2810 \| 0.6275 \| 0.4552 \| -187.2035 \| -194.2191 \| -3.0819 \| -3.0788 \| \| 0.5926 \| 0.14 \| 600 \| 0.5599 \| 0.2410 \| -0.3076 \| 0.6425 \| 0.5487 \| -187.4693 \| -193.5507 \| -3.0601 \| -3.0597 \| \| 0.5326 \| 0.17 \| 700 \| 0.5385 \| -0.2501 \| -0.9698 \| 0.6400 \| 0.7197 \| -194.0914 \| -198.4624 \| -2.9076 \| -2.9090 \| \| 0.5126 \| 0.19 \| 800 \| 0.5238 \| -0.3616 \| -1.1783 \| 0.6525 \| 0.8167 \| -196.1764 \| -199.5769 \| -2.9965 \| -2.9963 \| \| 0.5283 \| 0.22 \| 900 \| 0.5289 \| -0.4142 \| -1.2542 \| 0.6775 \| 0.8400 \| -196.9348 \| -200.1031 \| -3.0133 \| -3.0134 \| \| 0.5303 \| 0.24 \| 1000 \| 0.5214 \| -0.5949 \| -1.5888 \| 0.6600 \| 0.9939 \| -200.2815 \| -201.9101 \| -2.9663 \| -2.9669 \| \| 0.5969 \| 0.26 \| 1100 \| 0.5235 \| -0.5924 \| -1.5222 \| 0.6600 \| 0.9298 \| -199.6154 \| -201.8848 \| -2.9402 \| -2.9468 \| \| 0.581 \| 0.29 \| 1200 \| 0.5887 \| -0.7548 \| -1.7075 \| 0.6400 \| 0.9527 \| -201.4678 \| -203.5091 \| -2.7065 \| -2.7227 \| \| 0.817 \| 0.31 \| 1300 \| 0.6620 \| -1.5060 \| -2.4221 \| 0.6500 \| 0.9160 \| -208.6137 \| -211.0213 \| -2.7717 \| -2.7800 \| \| 0.6039 \| 0.34 \| 1400 \| 0.5321 \| -1.6820 \| -2.8439 \| 0.6425 \| 1.1619 \| -212.8325 \| -212.7814 \| -2.6828 \| -2.6917 \| \| 0.6666 \| 0.36 \| 1500 \| 0.5303 \| -1.3875 \| -2.6384 \| 0.6475 \| 1.2509 \| -210.7773 \| -209.8365 \| -2.8557 \| -2.8594 \| \| 0.6907 \| 0.39 \| 1600 \| 0.5409 \| -2.0657 \| -3.2214 \| 0.6650 \| 1.1556 \| -216.6068 \| -216.6184 \| -2.8227 \| -2.8288 \| \| 0.5772 \| 0.41 \| 1700 \| 0.5309 \| -1.9849 \| -3.2833 \| 0.6875 \| 1.2985 \| -217.2264 \| -215.8097 \| -2.6498 \| -2.6635 \| \| 0.5601 \| 0.43 \| 1800 \| 0.5281 \| -1.7365 \| -3.0643 \| 0.6575 \| 1.3278 \| -215.0359 \| -213.3255 \| -2.8890 \| -2.8918 \| \| 0.576 \| 0.46 \| 1900 \| 0.5266 \| -1.4822 \| -2.9294 \| 0.6725 \| 1.4472 \| -213.6872 \| -210.7831 \| -2.7369 \| -2.7427 \| \| 1.2064 \| 0.48 \| 2000 \| 0.5538 \| -2.5493 \| -3.7625 \| 0.6675 \| 1.2132 \| -222.0182 \| -221.4542 \| -2.6773 \| -2.6957 \| \| 0.5751 \| 0.51 \| 2100 \| 0.5465 \| -1.9246 \| -3.1480 \| 0.6425 \| 1.2234 \| -215.8728 \| -215.2067 \| -2.6490 \| -2.6657 \| \| 0.4757 \| 0.53 \| 2200 \| 0.5297 \| -1.8443 \| -3.1553 \| 0.6325 \| 1.3110 \| -215.9462 \| -214.4039 \| -2.6882 \| -2.7115 \| \| 0.4771 \| 0.55 \| 2300 \| 0.5386 \| -2.3340 \| -3.7443 \| 0.6500 \| 1.4103 \| -221.8360 \| -219.3013 \| -2.6415 \| -2.6623 \| \| 0.481 \| 0.58 \| 2400 \| 0.5355 \| -1.6085 \| -3.0800 \| 0.6550 \| 1.4715 \| -215.1930 \| -212.0460 \| -2.6073 \| -2.6293 \| \| 0.523 \| 0.6 \| 2500 \| 0.5131 \| -2.6139 \| -4.2353 \| 0.6625 \| 1.6214 \| -226.7459 \| -222.0998 \| -2.6134 \| -2.6394 \| \| 0.6263 \| 0.63 \| 2600 \| 0.5287 \| -2.6614 \| -4.0538 \| 0.6450 \| 1.3924 \| -224.9310 \| -222.5747 \| -2.6189 \| -2.6361 \| \| 0.5973 \| 0.65 \| 2700 \| 0.5132 \| -2.7089 \| -4.1248 \| 0.625 \| 1.4159 \| -225.6406 \| -223.0499 \| -2.6167 \| -2.6317 \| \| 0.8209 \| 0.67 \| 2800 \| 0.5165 \| -2.7085 \| -4.1871 \| 0.625 \| 1.4786 \| -226.2637 \| -223.0462 \| -2.5605 \| -2.5803 \| \| 0.5625 \| 0.7 \| 2900 \| 0.5117 \| -3.4747 \| -5.0369 \| 0.6325 \| 1.5622 \| -234.7624 \| -230.7079 \| -2.5891 \| -2.6163 \| \| 0.5913 \| 0.72 \| 3000 \| 0.5164 \| -2.5844 \| -4.3822 \| 0.6675 \| 1.7978 \| -228.2149 \| -221.8051 \| -2.6421 \| -2.6632 \| \| 0.7441 \| 0.75 \| 3100 \| 0.5175 \| -2.4900 \| -4.2883 \| 0.6725 \| 1.7983 \| -227.2762 \| -220.8608 \| -2.6254 \| -2.6465 \| \| 0.6169 \| 0.77 \| 3200 \| 0.5163 \| -2.2489 \| -3.8666 \| 0.6600 \| 1.6176 \| -223.0589 \| -218.4503 \| -2.6517 \| -2.6775 \| \| 0.5347 \| 0.79 \| 3300 \| 0.5222 \| -2.6699 \| -4.3844 \| 0.6375 \| 1.7145 \| -228.2368 \| -222.6600 \| -2.6712 \| -2.6909 \| \| 0.5369 \| 0.82 \| 3400 \| 0.5244 \| -2.7710 \| -4.6352 \| 0.6600 \| 1.8642 \| -230.7449 \| -223.6711 \| -2.5304 \| -2.5595 \| \| 0.5613 \| 0.84 \| 3500 \| 0.5431 \| -3.7645 \| -5.6773 \| 0.6475 \| 1.9128 \| -241.1664 \| -233.6063 \| -2.5348 \| -2.5604 \| \| 0.6395 \| 0.87 \| 3600 \| 0.5332 \| -3.8666 \| -5.6894 \| 0.6525 \| 1.8227 \| -241.2867 \| -234.6274 \| -2.5479 \| -2.5778 \| \| 0.6552 \| 0.89 \| 3700 \| 0.5149 \| -2.9168 \| -4.7306 \| 0.6525 \| 1.8138 \| -231.6990 \| -225.1294 \| -2.4580 \| -2.4901 \| \| 0.6381 \| 0.91 \| 3800 \| 0.5081 \| -2.6182 \| -4.3003 \| 0.6625 \| 1.6821 \| -227.3964 \| -222.1432 \| -2.4730 \| -2.4991 \| \| 0.5355 \| 0.94 \| 3900 \| 0.5100 \| -2.5302 \| -4.2476 \| 0.6475 \| 1.7173 \| -226.8689 \| -221.2634 \| -2.5875 \| -2.6065 \| \| 0.5488 \| 0.96 \| 4000 \| 0.5164 \| -3.1540 \| -4.8339 \| 0.6550 \| 1.6798 \| -232.7318 \| -227.5013 \| -2.7017 \| -2.7215 \| \| 0.6802 \| 0.99 \| 4100 \| 0.5134 \| -2.6060 \| -4.2916 \| 0.6625 \| 1.6856 \| -227.3087 \| -222.0207 \| -2.6010 \| -2.6250 \| \| 0.0976 \| 1.01 \| 4200 \| 0.5031 \| -3.0885 \| -5.0494 \| 0.6625 \| 1.9609 \| -234.8874 \| -226.8463 \| -2.4721 \| -2.5028 \| \| 0.0839 \| 1.03 \| 4300 \| 0.5027 \| -3.3469 \| -5.4366 \| 0.6625 \| 2.0897 \| -238.7592 \| -229.4302 \| -2.3886 \| -2.4238 \| \| 0.0788 \| 1.06 \| 4400 \| 0.5398 \| -4.4307 \| -6.8568 \| 0.6775 \| 2.4261 \| -252.9614 \| -240.2679 \| -2.1805 \| -2.2275 \| \| 0.0701 \| 1.08 \| 4500 \| 0.5432 \| -4.3739 \| -7.0979 \| 0.6975 \| 2.7240 \| -255.3717 \| -239.7001 \| -2.1935 \| -2.2437 \| \| 0.0959 \| 1.11 \| 4600 \| 0.5362 \| -3.9784 \| -6.3235 \| 0.6900 \| 2.3451 \| -247.6284 \| -235.7450 \| -2.2860 \| -2.3272 \| \| 0.1177 \| 1.13 \| 4700 \| 0.5411 \| -4.1933 \| -6.8436 \| 0.6800 \| 2.6504 \| -252.8295 \| -237.8937 \| -2.3259 \| -2.3682 \| \| 0.1651 \| 1.16 \| 4800 \| 0.5737 \| -4.8158 \| -6.7229 \| 0.6700 \| 1.9071 \| -251.6221 \| -244.1190 \| -2.2753 \| -2.3139 \| \| 0.1298 \| 1.18 \| 4900 \| 0.5528 \| -4.6526 \| -6.8433 \| 0.6825 \| 2.1907 \| -252.8262 \| -242.4874 \| -2.4856 \| -2.5188 \| \| 0.1143 \| 1.2 \| 5000 \| 0.5512 \| -4.6212 \| -7.0807 \| 0.6800 \| 2.4595 \| -255.2000 \| -242.1734 \| -2.5190 \| -2.5542 \| \| 0.1145 \| 1.23 \| 5100 \| 0.5496 \| -4.0598 \| -6.6147 \| 0.6775 \| 2.5548 \| -250.5396 \| -236.5594 \| -2.5737 \| -2.6008 \| \| 0.2324 \| 1.25 \| 5200 \| 0.5524 \| -4.9650 \| -7.6613 \| 0.6725 \| 2.6962 \| -261.0058 \| -245.6115 \| -2.4382 \| -2.4737 \| \| 0.0867 \| 1.28 \| 5300 \| 0.5449 \| -4.9568 \| -7.6771 \| 0.6625 \| 2.7203 \| -261.1645 \| -245.5292 \| -2.4367 \| -2.4702 \| \| 0.0503 \| 1.3 \| 5400 \| 0.5351 \| -4.5684 \| -7.1860 \| 0.6625 \| 2.6176 \| -256.2527 \| -241.6449 \| -2.4235 \| -2.4557 \| \| 0.0977 \| 1.32 \| 5500 \| 0.5431 \| -4.5599 \| -7.1317 \| 0.6550 \| 2.5718 \| -255.7096 \| -241.5597 \| -2.5311 \| -2.5614 \| \| 0.1564 \| 1.35 \| 5600 \| 0.5512 \| -5.1430 \| -8.0510 \| 0.6750 \| 2.9080 \| -264.9027 \| -247.3911 \| -2.3498 \| -2.3976 \| \| 0.0967 \| 1.37 \| 5700 \| 0.5520 \| -4.5072 \| -7.4506 \| 0.6750 \| 2.9433 \| -258.8989 \| -241.0335 \| -2.2110 \| -2.2631 \| \| 0.2046 \| 1.4 \| 5800 \| 0.5588 \| -5.5328 \| -8.5314 \| 0.6800 \| 2.9986 \| -269.7068 \| -251.2888 \| -2.2155 \| -2.2677 \| \| 0.0985 \| 1.42 \| 5900 \| 0.5429 \| -5.1915 \| -7.9421 \| 0.6675 \| 2.7505 \| -263.8138 \| -247.8765 \| -2.2606 \| -2.3077 \| \| 0.1398 \| 1.44 \| 6000 \| 0.5350 \| -4.9761 \| -7.9378 \| 0.6800 \| 2.9616 \| -263.7706 \| -245.7224 \| -2.2291 \| -2.2809 \| \| 0.099 \| 1.47 \| 6100 \| 0.5440 \| -4.6202 \| -7.4996 \| 0.6650 \| 2.8794 \| -259.3892 \| -242.1633 \| -2.3362 \| -2.3859 \| \| 0.1279 \| 1.49 \| 6200 \| 0.5389 \| -4.9461 \| -7.7908 \| 0.6625 \| 2.8448 \| -262.3015 \| -245.4217 \| -2.2276 \| -2.2734 \| \| 0.0778 \| 1.52 \| 6300 \| 0.5451 \| -4.9550 \| -7.8964 \| 0.6625 \| 2.9414 \| -263.3570 \| -245.5110 \| -2.4781 \| -2.5193 \| \| 0.0911 \| 1.54 \| 6400 \| 0.5412 \| -5.4552 \| -8.3139 \| 0.6675 \| 2.8588 \| -267.5324 \| -250.5128 \| -2.3604 \| -2.4048 \| \| 0.2149 \| 1.56 \| 6500 \| 0.5241 \| -4.4512 \| -7.3194 \| 0.6725 \| 2.8682 \| -257.5873 \| -240.4732 \| -2.4011 \| -2.4461 \| \| 0.1739 \| 1.59 \| 6600 \| 0.5329 \| -5.0143 \| -7.7507 \| 0.6825 \| 2.7364 \| -261.8999 \| -246.1036 \| -2.4143 \| -2.4577 \| \| 0.0842 \| 1.61 \| 6700 \| 0.5395 \| -5.1195 \| -8.0856 \| 0.6800 \| 2.9661 \| -265.2489 \| -247.1560 \| -2.3877 \| -2.4376 \| \| 0.105 \| 1.64 \| 6800 \| 0.5423 \| -4.9379 \| -7.7557 \| 0.6775 \| 2.8178 \| -261.9503 \| -245.3403 \| -2.3798 \| -2.4323 \| \| 0.086 \| 1.66 \| 6900 \| 0.5351 \| -4.3598 \| -7.1156 \| 0.6775 \| 2.7559 \| -255.5494 \| -239.5588 \| -2.3870 \| -2.4383 \| \| 0.0622 \| 1.68 \| 7000 \| 0.5394 \| -4.6830 \| -7.6578 \| 0.6825 \| 2.9747 \| -260.9710 \| -242.7915 \| -2.4276 \| -2.4779 \| \| 0.0973 \| 1.71 \| 7100 \| 0.5319 \| -4.7475 \| -7.6567 \| 0.6750 \| 2.9091 \| -260.9596 \| -243.4364 \| -2.3010 \| -2.3564 \| \| 0.1052 \| 1.73 \| 7200 \| 0.5284 \| -4.5972 \| -7.5385 \| 0.6750 \| 2.9413 \| -259.7779 \| -241.9329 \| -2.3696 \| -2.4201 \| \| 0.0645 \| 1.76 \| 7300 \| 0.5339 \| -4.9822 \| -8.0212 \| 0.6775 \| 3.0390 \| -264.6048 \| -245.7831 \| -2.2857 \| -2.3440 \| \| 0.0923 \| 1.78 \| 7400 \| 0.5385 \| -4.6369 \| -7.6632 \| 0.6650 \| 3.0263 \| -261.0246 \| -242.3295 \| -2.2563 \| -2.3150 \| \| 0.0842 \| 1.81 \| 7500 \| 0.5394 \| -4.8705 \| -7.6765 \| 0.6600 \| 2.8060 \| -261.1580 \| -244.6661 \| -2.2808 \| -2.3287 \| \| 0.1178 \| 1.83 \| 7600 \| 0.5253 \| -4.7985 \| -7.5635 \| 0.6675 \| 2.7650 \| -260.0276 \| -243.9457 \| -2.4022 \| -2.4463 \| \| 0.1255 \| 1.85 \| 7700 \| 0.5355 \| -4.7007 \| -7.4363 \| 0.6675 \| 2.7355 \| -258.7556 \| -242.9684 \| -2.5073 \| -2.5501 \| \| 0.1541 \| 1.88 \| 7800 \| 0.5440 \| -4.9294 \| -7.6465 \| 0.6500 \| 2.7172 \| -260.8584 \| -245.2547 \| -2.3551 \| -2.4036 \| \| 0.0893 \| 1.9 \| 7900 \| 0.5397 \| -5.2135 \| -8.3241 \| 0.6575 \| 3.1106 \| -267.6339 \| -248.0959 \| -2.3214 \| -2.3784 \| \| 0.1203 \| 1.93 \| 8000 \| 0.5296 \| -4.8644 \| -7.8598 \| 0.6550 \| 2.9954 \| -262.9913 \| -244.6054 \| -2.4509 \| -2.4969 \| \| 0.1018 \| 1.95 \| 8100 \| 0.5381 \| -5.3471 \| -8.4918 \| 0.6625 \| 3.1447 \| -269.3113 \| -249.4323 \| -2.4193 \| -2.4671 \| \| 0.0767 \| 1.97 \| 8200 \| 0.5386 \| -5.2151 \| -8.3734 \| 0.6675 \| 3.1582 \| -268.1267 \| -248.1124 \| -2.4873 \| -2.5329 \| \| 0.0801 \| 2.0 \| 8300 \| 0.5429 \| -5.8103 \| -9.0391 \| 0.6575 \| 3.2288 \| -274.7842 \| -254.0639 \| -2.4348 \| -2.4867 \| \| 0.034 \| 2.02 \| 8400 \| 0.5566 \| -5.7907 \| -9.2424 \| 0.6625 \| 3.4518 \| -276.8175 \| -253.8677 \| -2.3679 \| -2.4272 \| \| 0.0246 \| 2.05 \| 8500 \| 0.5758 \| -5.6317 \| -9.1533 \| 0.6625 \| 3.5216 \| -275.9264 \| -252.2783 \| -2.3335 \| -2.3958 \| \| 0.0187 \| 2.07 \| 8600 \| 0.5770 \| -5.5795 \| -9.2568 \| 0.6725 \| 3.6773 \| -276.9613 \| -251.7559 \| -2.3614 \| -2.4166 \| \| 0.0606 \| 2.09 \| 8700 \| 0.6115 \| -7.1190 \| -11.2853 \| 0.6750 \| 4.1663 \| -297.2460 \| -267.1512 \| -2.2737 \| -2.3365 \| \| 0.0402 \| 2.12 \| 8800 \| 0.6164 \| -7.0531 \| -11.1316 \| 0.6600 \| 4.0785 \| -295.7089 \| -266.4919 \| -2.2005 \| -2.2654 \| \| 0.0263 \| 2.14 \| 8900 \| 0.6209 \| -8.1609 \| -12.3710 \| 0.6650 \| 4.2102 \| -308.1034 \| -277.5696 \| -2.0958 \| -2.1661 \| \| 0.0242 \| 2.17 \| 9000 \| 0.6042 \| -6.7201 \| -10.7618 \| 0.6725 \| 4.0416 \| -292.0106 \| -263.1622 \| -2.1651 \| -2.2304 \| \| 0.0383 \| 2.19 \| 9100 \| 0.6080 \| -7.7898 \| -11.9356 \| 0.6750 \| 4.1458 \| -303.7489 \| -273.8587 \| -2.1006 \| -2.1662 \| \| 0.0371 \| 2.21 \| 9200 \| 0.6149 \| -7.5635 \| -11.7050 \| 0.6675 \| 4.1415 \| -301.4433 \| -271.5960 \| -2.1556 \| -2.2155 \| \| 0.0279 \| 2.24 \| 9300 \| 0.6155 \| -8.1686 \| -12.4447 \| 0.6775 \| 4.2760 \| -308.8397 \| -277.6473 \| -2.1778 \| -2.2399 \| \| 0.021 \| 2.26 \| 9400 \| 0.6137 \| -7.8294 \| -12.0416 \| 0.6700 \| 4.2122 \| -304.8092 \| -274.2550 \| -2.2403 \| -2.2958 \| \| 0.0374 \| 2.29 \| 9500 \| 0.6238 \| -7.9227 \| -12.2842 \| 0.6750 \| 4.3614 \| -307.2347 \| -275.1884 \| -2.2926 \| -2.3496 \| \| 0.0412 \| 2.31 \| 9600 \| 0.6126 \| -7.7094 \| -11.9775 \| 0.6700 \| 4.2681 \| -304.1685 \| -273.0553 \| -2.2377 \| -2.2961 \| \| 0.0413 \| 2.33 \| 9700 \| 0.6130 \| -7.6030 \| -11.8721 \| 0.6675 \| 4.2691 \| -303.1140 \| -271.9912 \| -2.2505 \| -2.3100 \| \| 0.0361 \| 2.36 \| 9800 \| 0.6248 \| -8.1273 \| -12.6010 \| 0.6750 \| 4.4737 \| -310.4034 \| -277.2341 \| -2.2249 \| -2.2866 \| \| 0.0289 \| 2.38 \| 9900 \| 0.6192 \| -7.9924 \| -12.3825 \| 0.6675 \| 4.3901 \| -308.2185 \| -275.8853 \| -2.2473 \| -2.3067 \| \| 0.038 \| 2.41 \| 10000 \| 0.6250 \| -8.4114 \| -12.8701 \| 0.6675 \| 4.4586 \| -313.0937 \| -280.0753 \| -2.2312 \| -2.2938 \| \| 0.0334 \| 2.43 \| 10100 \| 0.6261 \| -9.1807 \| -13.7488 \| 0.6825 \| 4.5681 \| -321.8813 \| -287.7679 \| -2.2303 \| -2.2947 \| \| 0.0359 \| 2.45 \| 10200 \| 0.6374 \| -9.8214 \| -14.2774 \| 0.6650 \| 4.4560 \| -327.1667 \| -294.1750 \| -2.1817 \| -2.2452 \| \| 0.0266 \| 2.48 \| 10300 \| 0.6298 \| -8.3278 \| -12.5691 \| 0.6650 \| 4.2413 \| -310.0836 \| -279.2391 \| -2.2947 \| -2.3521 \| \| 0.0423 \| 2.5 \| 10400 \| 0.6267 \| -8.7527 \| -13.2552 \| 0.6675 \| 4.5025 \| -316.9453 \| -283.4879 \| -2.3034 \| -2.3620 \| \| 0.0329 \| 2.53 \| 10500 \| 0.6386 \| -8.9354 \| -13.5549 \| 0.6700 \| 4.6195 \| -319.9424 \| -285.3152 \| -2.2819 \| -2.3423 \| \| 0.039 \| 2.55 \| 10600 \| 0.6330 \| -8.3549 \| -12.8863 \| 0.6775 \| 4.5314 \| -313.2566 \| -279.5103 \| -2.2924 \| -2.3528 \| \| 0.0278 \| 2.58 \| 10700 \| 0.6336 \| -8.6754 \| -13.1733 \| 0.6675 \| 4.4979 \| -316.1258 \| -282.7150 \| -2.2319 \| -2.2929 \| \| 0.0606 \| 2.6 \| 10800 \| 0.6299 \| -8.7158 \| -13.0817 \| 0.6700 \| 4.3658 \| -315.2101 \| -283.1195 \| -2.2116 \| -2.2731 \| \| 0.0293 \| 2.62 \| 10900 \| 0.6259 \| -8.9092 \| -13.2926 \| 0.6725 \| 4.3834 \| -317.3194 \| -285.0532 \| -2.1572 \| -2.2209 \| \| 0.0196 \| 2.65 \| 11000 \| 0.6219 \| -9.1783 \| -13.5617 \| 0.6700 \| 4.3835 \| -320.0104 \| -287.7436 \| -2.1533 \| -2.2163 \| \| 0.0405 \| 2.67 \| 11100 \| 0.6209 \| -8.9912 \| -13.3040 \| 0.6700 \| 4.3128 \| -317.4330 \| -285.8734 \| -2.1378 \| -2.2017 \| \| 0.0278 \| 2.7 \| 11200 \| 0.6300 \| -9.8318 \| -14.2684 \| 0.6700 \| 4.4366 \| -327.0771 \| -294.2787 \| -2.1220 \| -2.1862 \| \| 0.0307 \| 2.72 \| 11300 \| 0.6356 \| -9.7027 \| -14.1764 \| 0.6700 \| 4.4737 \| -326.1576 \| -292.9880 \| -2.1316 \| -2.1945 \| \| 0.0242 \| 2.74 \| 11400 \| 0.6327 \| -9.8085 \| -14.2574 \| 0.6625 \| 4.4489 \| -326.9674 \| -294.0465 \| -2.1072 \| -2.1680 \| \| 0.0242 \| 2.77 \| 11500 \| 0.6308 \| -9.3697 \| -13.8420 \| 0.6650 \| 4.4723 \| -322.8135 \| -289.6585 \| -2.1273 \| -2.1882 \| \| 0.0337 \| 2.79 \| 11600 \| 0.6350 \| -9.2810 \| -13.7917 \| 0.6700 \| 4.5107 \| -322.3100 \| -288.7711 \| -2.1600 \| -2.2215 \| \| 0.0302 \| 2.82 \| 11700 \| 0.6450 \| -10.2754 \| -14.9521 \| 0.6675 \| 4.6767 \| -333.9139 \| -298.7146 \| -2.1339 \| -2.1965 \| \| 0.0354 \| 2.84 \| 11800 \| 0.6451 \| -10.3736 \| -15.0743 \| 0.6725 \| 4.7008 \| -335.1366 \| -299.6965 \| -2.1047 \| -2.1674 \| \| 0.0153 \| 2.86 \| 11900 \| 0.6420 \| -10.2126 \| -14.9126 \| 0.6700 \| 4.7000 \| -333.5196 \| -298.0872 \| -2.1102 \| -2.1728 \| \| 0.0388 \| 2.89 \| 12000 \| 0.6407 \| -10.2075 \| -14.9081 \| 0.6725 \| 4.7006 \| -333.4741 \| -298.0356 \| -2.1059 \| -2.1687 \| \| 0.0253 \| 2.91 \| 12100 \| 0.6353 \| -10.0842 \| -14.7598 \| 0.6650 \| 4.6756 \| -331.9908 \| -296.8029 \| -2.0968 \| -2.1594 \| \| 0.0317 \| 2.94 \| 12200 \| 0.6352 \| -9.9956 \| -14.6819 \| 0.6750 \| 4.6863 \| -331.2123 \| -295.9169 \| -2.1042 \| -2.1665 \| \| 0.0431 \| 2.96 \| 12300 \| 0.6337 \| -9.8807 \| -14.5540 \| 0.6675 \| 4.6733 \| -329.9332 \| -294.7676 \| -2.1034 \| -2.1660 \| \| 0.0233 \| 2.98 \| 12400 \| 0.6326 \| -9.8796 \| -14.5449 \| 0.6675 \| 4.6653 \| -329.8422 \| -294.7567 \| -2.1032 \| -2.1657 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	{}	RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-4bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T04:52:35+00:00
null	null		{"license": "openrail"}	Danikdsa/Lia	null	[ "license:openrail", "region:us" ]	null	2024-05-01T04:53:17+00:00
null	null		{}	bsheon/adsl-math	null	[ "region:us" ]	null	2024-05-01T04:53:36+00:00
text-generation	transformers	# Uploaded model - Developed by: Jaspann - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	Jaspann/test-model	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	null	2024-05-01T04:55:02+00:00
null	null		{"license": "llama3"}	rahulAkaVector/java_code_generator_finetuned_model	null	[ "safetensors", "license:llama3", "region:us" ]	null	2024-05-01T04:55:27+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-anthropic - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-anthropic/ Original model description: --- license: apache-2.0 base_model: HuggingFaceH4/mistral-7b-cai tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless model-index: - name: mistral-7b-dpo-v21.0cai.0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral 7B Constitutional AI This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets. It achieves the following results on the evaluation set: - Loss: 0.6327 - Rewards/chosen: -9.8716 - Rewards/rejected: -14.5465 - Rewards/accuracies: 0.6725 - Rewards/margins: 4.6749 - Logps/rejected: -329.8578 - Logps/chosen: -294.6768 - Logits/rejected: -2.1023 - Logits/chosen: -2.1648 ## 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-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6817 \| 0.02 \| 100 \| 0.6873 \| 0.0149 \| 0.0002 \| 0.5150 \| 0.0147 \| -184.3912 \| -195.8124 \| -3.1605 \| -3.1560 \| \| 0.6767 \| 0.05 \| 200 \| 0.6614 \| 0.0825 \| 0.0169 \| 0.5575 \| 0.0656 \| -184.2246 \| -195.1362 \| -3.1654 \| -3.1605 \| \| 0.6328 \| 0.07 \| 300 \| 0.6246 \| -0.0374 \| -0.2112 \| 0.5875 \| 0.1738 \| -186.5047 \| -196.3349 \| -3.1579 \| -3.1529 \| \| 0.5919 \| 0.1 \| 400 \| 0.5978 \| 0.2812 \| -0.0666 \| 0.6125 \| 0.3478 \| -185.0590 \| -193.1489 \| -3.1292 \| -3.1243 \| \| 0.5545 \| 0.12 \| 500 \| 0.5800 \| 0.1742 \| -0.2810 \| 0.6275 \| 0.4552 \| -187.2035 \| -194.2191 \| -3.0819 \| -3.0788 \| \| 0.5926 \| 0.14 \| 600 \| 0.5599 \| 0.2410 \| -0.3076 \| 0.6425 \| 0.5487 \| -187.4693 \| -193.5507 \| -3.0601 \| -3.0597 \| \| 0.5326 \| 0.17 \| 700 \| 0.5385 \| -0.2501 \| -0.9698 \| 0.6400 \| 0.7197 \| -194.0914 \| -198.4624 \| -2.9076 \| -2.9090 \| \| 0.5126 \| 0.19 \| 800 \| 0.5238 \| -0.3616 \| -1.1783 \| 0.6525 \| 0.8167 \| -196.1764 \| -199.5769 \| -2.9965 \| -2.9963 \| \| 0.5283 \| 0.22 \| 900 \| 0.5289 \| -0.4142 \| -1.2542 \| 0.6775 \| 0.8400 \| -196.9348 \| -200.1031 \| -3.0133 \| -3.0134 \| \| 0.5303 \| 0.24 \| 1000 \| 0.5214 \| -0.5949 \| -1.5888 \| 0.6600 \| 0.9939 \| -200.2815 \| -201.9101 \| -2.9663 \| -2.9669 \| \| 0.5969 \| 0.26 \| 1100 \| 0.5235 \| -0.5924 \| -1.5222 \| 0.6600 \| 0.9298 \| -199.6154 \| -201.8848 \| -2.9402 \| -2.9468 \| \| 0.581 \| 0.29 \| 1200 \| 0.5887 \| -0.7548 \| -1.7075 \| 0.6400 \| 0.9527 \| -201.4678 \| -203.5091 \| -2.7065 \| -2.7227 \| \| 0.817 \| 0.31 \| 1300 \| 0.6620 \| -1.5060 \| -2.4221 \| 0.6500 \| 0.9160 \| -208.6137 \| -211.0213 \| -2.7717 \| -2.7800 \| \| 0.6039 \| 0.34 \| 1400 \| 0.5321 \| -1.6820 \| -2.8439 \| 0.6425 \| 1.1619 \| -212.8325 \| -212.7814 \| -2.6828 \| -2.6917 \| \| 0.6666 \| 0.36 \| 1500 \| 0.5303 \| -1.3875 \| -2.6384 \| 0.6475 \| 1.2509 \| -210.7773 \| -209.8365 \| -2.8557 \| -2.8594 \| \| 0.6907 \| 0.39 \| 1600 \| 0.5409 \| -2.0657 \| -3.2214 \| 0.6650 \| 1.1556 \| -216.6068 \| -216.6184 \| -2.8227 \| -2.8288 \| \| 0.5772 \| 0.41 \| 1700 \| 0.5309 \| -1.9849 \| -3.2833 \| 0.6875 \| 1.2985 \| -217.2264 \| -215.8097 \| -2.6498 \| -2.6635 \| \| 0.5601 \| 0.43 \| 1800 \| 0.5281 \| -1.7365 \| -3.0643 \| 0.6575 \| 1.3278 \| -215.0359 \| -213.3255 \| -2.8890 \| -2.8918 \| \| 0.576 \| 0.46 \| 1900 \| 0.5266 \| -1.4822 \| -2.9294 \| 0.6725 \| 1.4472 \| -213.6872 \| -210.7831 \| -2.7369 \| -2.7427 \| \| 1.2064 \| 0.48 \| 2000 \| 0.5538 \| -2.5493 \| -3.7625 \| 0.6675 \| 1.2132 \| -222.0182 \| -221.4542 \| -2.6773 \| -2.6957 \| \| 0.5751 \| 0.51 \| 2100 \| 0.5465 \| -1.9246 \| -3.1480 \| 0.6425 \| 1.2234 \| -215.8728 \| -215.2067 \| -2.6490 \| -2.6657 \| \| 0.4757 \| 0.53 \| 2200 \| 0.5297 \| -1.8443 \| -3.1553 \| 0.6325 \| 1.3110 \| -215.9462 \| -214.4039 \| -2.6882 \| -2.7115 \| \| 0.4771 \| 0.55 \| 2300 \| 0.5386 \| -2.3340 \| -3.7443 \| 0.6500 \| 1.4103 \| -221.8360 \| -219.3013 \| -2.6415 \| -2.6623 \| \| 0.481 \| 0.58 \| 2400 \| 0.5355 \| -1.6085 \| -3.0800 \| 0.6550 \| 1.4715 \| -215.1930 \| -212.0460 \| -2.6073 \| -2.6293 \| \| 0.523 \| 0.6 \| 2500 \| 0.5131 \| -2.6139 \| -4.2353 \| 0.6625 \| 1.6214 \| -226.7459 \| -222.0998 \| -2.6134 \| -2.6394 \| \| 0.6263 \| 0.63 \| 2600 \| 0.5287 \| -2.6614 \| -4.0538 \| 0.6450 \| 1.3924 \| -224.9310 \| -222.5747 \| -2.6189 \| -2.6361 \| \| 0.5973 \| 0.65 \| 2700 \| 0.5132 \| -2.7089 \| -4.1248 \| 0.625 \| 1.4159 \| -225.6406 \| -223.0499 \| -2.6167 \| -2.6317 \| \| 0.8209 \| 0.67 \| 2800 \| 0.5165 \| -2.7085 \| -4.1871 \| 0.625 \| 1.4786 \| -226.2637 \| -223.0462 \| -2.5605 \| -2.5803 \| \| 0.5625 \| 0.7 \| 2900 \| 0.5117 \| -3.4747 \| -5.0369 \| 0.6325 \| 1.5622 \| -234.7624 \| -230.7079 \| -2.5891 \| -2.6163 \| \| 0.5913 \| 0.72 \| 3000 \| 0.5164 \| -2.5844 \| -4.3822 \| 0.6675 \| 1.7978 \| -228.2149 \| -221.8051 \| -2.6421 \| -2.6632 \| \| 0.7441 \| 0.75 \| 3100 \| 0.5175 \| -2.4900 \| -4.2883 \| 0.6725 \| 1.7983 \| -227.2762 \| -220.8608 \| -2.6254 \| -2.6465 \| \| 0.6169 \| 0.77 \| 3200 \| 0.5163 \| -2.2489 \| -3.8666 \| 0.6600 \| 1.6176 \| -223.0589 \| -218.4503 \| -2.6517 \| -2.6775 \| \| 0.5347 \| 0.79 \| 3300 \| 0.5222 \| -2.6699 \| -4.3844 \| 0.6375 \| 1.7145 \| -228.2368 \| -222.6600 \| -2.6712 \| -2.6909 \| \| 0.5369 \| 0.82 \| 3400 \| 0.5244 \| -2.7710 \| -4.6352 \| 0.6600 \| 1.8642 \| -230.7449 \| -223.6711 \| -2.5304 \| -2.5595 \| \| 0.5613 \| 0.84 \| 3500 \| 0.5431 \| -3.7645 \| -5.6773 \| 0.6475 \| 1.9128 \| -241.1664 \| -233.6063 \| -2.5348 \| -2.5604 \| \| 0.6395 \| 0.87 \| 3600 \| 0.5332 \| -3.8666 \| -5.6894 \| 0.6525 \| 1.8227 \| -241.2867 \| -234.6274 \| -2.5479 \| -2.5778 \| \| 0.6552 \| 0.89 \| 3700 \| 0.5149 \| -2.9168 \| -4.7306 \| 0.6525 \| 1.8138 \| -231.6990 \| -225.1294 \| -2.4580 \| -2.4901 \| \| 0.6381 \| 0.91 \| 3800 \| 0.5081 \| -2.6182 \| -4.3003 \| 0.6625 \| 1.6821 \| -227.3964 \| -222.1432 \| -2.4730 \| -2.4991 \| \| 0.5355 \| 0.94 \| 3900 \| 0.5100 \| -2.5302 \| -4.2476 \| 0.6475 \| 1.7173 \| -226.8689 \| -221.2634 \| -2.5875 \| -2.6065 \| \| 0.5488 \| 0.96 \| 4000 \| 0.5164 \| -3.1540 \| -4.8339 \| 0.6550 \| 1.6798 \| -232.7318 \| -227.5013 \| -2.7017 \| -2.7215 \| \| 0.6802 \| 0.99 \| 4100 \| 0.5134 \| -2.6060 \| -4.2916 \| 0.6625 \| 1.6856 \| -227.3087 \| -222.0207 \| -2.6010 \| -2.6250 \| \| 0.0976 \| 1.01 \| 4200 \| 0.5031 \| -3.0885 \| -5.0494 \| 0.6625 \| 1.9609 \| -234.8874 \| -226.8463 \| -2.4721 \| -2.5028 \| \| 0.0839 \| 1.03 \| 4300 \| 0.5027 \| -3.3469 \| -5.4366 \| 0.6625 \| 2.0897 \| -238.7592 \| -229.4302 \| -2.3886 \| -2.4238 \| \| 0.0788 \| 1.06 \| 4400 \| 0.5398 \| -4.4307 \| -6.8568 \| 0.6775 \| 2.4261 \| -252.9614 \| -240.2679 \| -2.1805 \| -2.2275 \| \| 0.0701 \| 1.08 \| 4500 \| 0.5432 \| -4.3739 \| -7.0979 \| 0.6975 \| 2.7240 \| -255.3717 \| -239.7001 \| -2.1935 \| -2.2437 \| \| 0.0959 \| 1.11 \| 4600 \| 0.5362 \| -3.9784 \| -6.3235 \| 0.6900 \| 2.3451 \| -247.6284 \| -235.7450 \| -2.2860 \| -2.3272 \| \| 0.1177 \| 1.13 \| 4700 \| 0.5411 \| -4.1933 \| -6.8436 \| 0.6800 \| 2.6504 \| -252.8295 \| -237.8937 \| -2.3259 \| -2.3682 \| \| 0.1651 \| 1.16 \| 4800 \| 0.5737 \| -4.8158 \| -6.7229 \| 0.6700 \| 1.9071 \| -251.6221 \| -244.1190 \| -2.2753 \| -2.3139 \| \| 0.1298 \| 1.18 \| 4900 \| 0.5528 \| -4.6526 \| -6.8433 \| 0.6825 \| 2.1907 \| -252.8262 \| -242.4874 \| -2.4856 \| -2.5188 \| \| 0.1143 \| 1.2 \| 5000 \| 0.5512 \| -4.6212 \| -7.0807 \| 0.6800 \| 2.4595 \| -255.2000 \| -242.1734 \| -2.5190 \| -2.5542 \| \| 0.1145 \| 1.23 \| 5100 \| 0.5496 \| -4.0598 \| -6.6147 \| 0.6775 \| 2.5548 \| -250.5396 \| -236.5594 \| -2.5737 \| -2.6008 \| \| 0.2324 \| 1.25 \| 5200 \| 0.5524 \| -4.9650 \| -7.6613 \| 0.6725 \| 2.6962 \| -261.0058 \| -245.6115 \| -2.4382 \| -2.4737 \| \| 0.0867 \| 1.28 \| 5300 \| 0.5449 \| -4.9568 \| -7.6771 \| 0.6625 \| 2.7203 \| -261.1645 \| -245.5292 \| -2.4367 \| -2.4702 \| \| 0.0503 \| 1.3 \| 5400 \| 0.5351 \| -4.5684 \| -7.1860 \| 0.6625 \| 2.6176 \| -256.2527 \| -241.6449 \| -2.4235 \| -2.4557 \| \| 0.0977 \| 1.32 \| 5500 \| 0.5431 \| -4.5599 \| -7.1317 \| 0.6550 \| 2.5718 \| -255.7096 \| -241.5597 \| -2.5311 \| -2.5614 \| \| 0.1564 \| 1.35 \| 5600 \| 0.5512 \| -5.1430 \| -8.0510 \| 0.6750 \| 2.9080 \| -264.9027 \| -247.3911 \| -2.3498 \| -2.3976 \| \| 0.0967 \| 1.37 \| 5700 \| 0.5520 \| -4.5072 \| -7.4506 \| 0.6750 \| 2.9433 \| -258.8989 \| -241.0335 \| -2.2110 \| -2.2631 \| \| 0.2046 \| 1.4 \| 5800 \| 0.5588 \| -5.5328 \| -8.5314 \| 0.6800 \| 2.9986 \| -269.7068 \| -251.2888 \| -2.2155 \| -2.2677 \| \| 0.0985 \| 1.42 \| 5900 \| 0.5429 \| -5.1915 \| -7.9421 \| 0.6675 \| 2.7505 \| -263.8138 \| -247.8765 \| -2.2606 \| -2.3077 \| \| 0.1398 \| 1.44 \| 6000 \| 0.5350 \| -4.9761 \| -7.9378 \| 0.6800 \| 2.9616 \| -263.7706 \| -245.7224 \| -2.2291 \| -2.2809 \| \| 0.099 \| 1.47 \| 6100 \| 0.5440 \| -4.6202 \| -7.4996 \| 0.6650 \| 2.8794 \| -259.3892 \| -242.1633 \| -2.3362 \| -2.3859 \| \| 0.1279 \| 1.49 \| 6200 \| 0.5389 \| -4.9461 \| -7.7908 \| 0.6625 \| 2.8448 \| -262.3015 \| -245.4217 \| -2.2276 \| -2.2734 \| \| 0.0778 \| 1.52 \| 6300 \| 0.5451 \| -4.9550 \| -7.8964 \| 0.6625 \| 2.9414 \| -263.3570 \| -245.5110 \| -2.4781 \| -2.5193 \| \| 0.0911 \| 1.54 \| 6400 \| 0.5412 \| -5.4552 \| -8.3139 \| 0.6675 \| 2.8588 \| -267.5324 \| -250.5128 \| -2.3604 \| -2.4048 \| \| 0.2149 \| 1.56 \| 6500 \| 0.5241 \| -4.4512 \| -7.3194 \| 0.6725 \| 2.8682 \| -257.5873 \| -240.4732 \| -2.4011 \| -2.4461 \| \| 0.1739 \| 1.59 \| 6600 \| 0.5329 \| -5.0143 \| -7.7507 \| 0.6825 \| 2.7364 \| -261.8999 \| -246.1036 \| -2.4143 \| -2.4577 \| \| 0.0842 \| 1.61 \| 6700 \| 0.5395 \| -5.1195 \| -8.0856 \| 0.6800 \| 2.9661 \| -265.2489 \| -247.1560 \| -2.3877 \| -2.4376 \| \| 0.105 \| 1.64 \| 6800 \| 0.5423 \| -4.9379 \| -7.7557 \| 0.6775 \| 2.8178 \| -261.9503 \| -245.3403 \| -2.3798 \| -2.4323 \| \| 0.086 \| 1.66 \| 6900 \| 0.5351 \| -4.3598 \| -7.1156 \| 0.6775 \| 2.7559 \| -255.5494 \| -239.5588 \| -2.3870 \| -2.4383 \| \| 0.0622 \| 1.68 \| 7000 \| 0.5394 \| -4.6830 \| -7.6578 \| 0.6825 \| 2.9747 \| -260.9710 \| -242.7915 \| -2.4276 \| -2.4779 \| \| 0.0973 \| 1.71 \| 7100 \| 0.5319 \| -4.7475 \| -7.6567 \| 0.6750 \| 2.9091 \| -260.9596 \| -243.4364 \| -2.3010 \| -2.3564 \| \| 0.1052 \| 1.73 \| 7200 \| 0.5284 \| -4.5972 \| -7.5385 \| 0.6750 \| 2.9413 \| -259.7779 \| -241.9329 \| -2.3696 \| -2.4201 \| \| 0.0645 \| 1.76 \| 7300 \| 0.5339 \| -4.9822 \| -8.0212 \| 0.6775 \| 3.0390 \| -264.6048 \| -245.7831 \| -2.2857 \| -2.3440 \| \| 0.0923 \| 1.78 \| 7400 \| 0.5385 \| -4.6369 \| -7.6632 \| 0.6650 \| 3.0263 \| -261.0246 \| -242.3295 \| -2.2563 \| -2.3150 \| \| 0.0842 \| 1.81 \| 7500 \| 0.5394 \| -4.8705 \| -7.6765 \| 0.6600 \| 2.8060 \| -261.1580 \| -244.6661 \| -2.2808 \| -2.3287 \| \| 0.1178 \| 1.83 \| 7600 \| 0.5253 \| -4.7985 \| -7.5635 \| 0.6675 \| 2.7650 \| -260.0276 \| -243.9457 \| -2.4022 \| -2.4463 \| \| 0.1255 \| 1.85 \| 7700 \| 0.5355 \| -4.7007 \| -7.4363 \| 0.6675 \| 2.7355 \| -258.7556 \| -242.9684 \| -2.5073 \| -2.5501 \| \| 0.1541 \| 1.88 \| 7800 \| 0.5440 \| -4.9294 \| -7.6465 \| 0.6500 \| 2.7172 \| -260.8584 \| -245.2547 \| -2.3551 \| -2.4036 \| \| 0.0893 \| 1.9 \| 7900 \| 0.5397 \| -5.2135 \| -8.3241 \| 0.6575 \| 3.1106 \| -267.6339 \| -248.0959 \| -2.3214 \| -2.3784 \| \| 0.1203 \| 1.93 \| 8000 \| 0.5296 \| -4.8644 \| -7.8598 \| 0.6550 \| 2.9954 \| -262.9913 \| -244.6054 \| -2.4509 \| -2.4969 \| \| 0.1018 \| 1.95 \| 8100 \| 0.5381 \| -5.3471 \| -8.4918 \| 0.6625 \| 3.1447 \| -269.3113 \| -249.4323 \| -2.4193 \| -2.4671 \| \| 0.0767 \| 1.97 \| 8200 \| 0.5386 \| -5.2151 \| -8.3734 \| 0.6675 \| 3.1582 \| -268.1267 \| -248.1124 \| -2.4873 \| -2.5329 \| \| 0.0801 \| 2.0 \| 8300 \| 0.5429 \| -5.8103 \| -9.0391 \| 0.6575 \| 3.2288 \| -274.7842 \| -254.0639 \| -2.4348 \| -2.4867 \| \| 0.034 \| 2.02 \| 8400 \| 0.5566 \| -5.7907 \| -9.2424 \| 0.6625 \| 3.4518 \| -276.8175 \| -253.8677 \| -2.3679 \| -2.4272 \| \| 0.0246 \| 2.05 \| 8500 \| 0.5758 \| -5.6317 \| -9.1533 \| 0.6625 \| 3.5216 \| -275.9264 \| -252.2783 \| -2.3335 \| -2.3958 \| \| 0.0187 \| 2.07 \| 8600 \| 0.5770 \| -5.5795 \| -9.2568 \| 0.6725 \| 3.6773 \| -276.9613 \| -251.7559 \| -2.3614 \| -2.4166 \| \| 0.0606 \| 2.09 \| 8700 \| 0.6115 \| -7.1190 \| -11.2853 \| 0.6750 \| 4.1663 \| -297.2460 \| -267.1512 \| -2.2737 \| -2.3365 \| \| 0.0402 \| 2.12 \| 8800 \| 0.6164 \| -7.0531 \| -11.1316 \| 0.6600 \| 4.0785 \| -295.7089 \| -266.4919 \| -2.2005 \| -2.2654 \| \| 0.0263 \| 2.14 \| 8900 \| 0.6209 \| -8.1609 \| -12.3710 \| 0.6650 \| 4.2102 \| -308.1034 \| -277.5696 \| -2.0958 \| -2.1661 \| \| 0.0242 \| 2.17 \| 9000 \| 0.6042 \| -6.7201 \| -10.7618 \| 0.6725 \| 4.0416 \| -292.0106 \| -263.1622 \| -2.1651 \| -2.2304 \| \| 0.0383 \| 2.19 \| 9100 \| 0.6080 \| -7.7898 \| -11.9356 \| 0.6750 \| 4.1458 \| -303.7489 \| -273.8587 \| -2.1006 \| -2.1662 \| \| 0.0371 \| 2.21 \| 9200 \| 0.6149 \| -7.5635 \| -11.7050 \| 0.6675 \| 4.1415 \| -301.4433 \| -271.5960 \| -2.1556 \| -2.2155 \| \| 0.0279 \| 2.24 \| 9300 \| 0.6155 \| -8.1686 \| -12.4447 \| 0.6775 \| 4.2760 \| -308.8397 \| -277.6473 \| -2.1778 \| -2.2399 \| \| 0.021 \| 2.26 \| 9400 \| 0.6137 \| -7.8294 \| -12.0416 \| 0.6700 \| 4.2122 \| -304.8092 \| -274.2550 \| -2.2403 \| -2.2958 \| \| 0.0374 \| 2.29 \| 9500 \| 0.6238 \| -7.9227 \| -12.2842 \| 0.6750 \| 4.3614 \| -307.2347 \| -275.1884 \| -2.2926 \| -2.3496 \| \| 0.0412 \| 2.31 \| 9600 \| 0.6126 \| -7.7094 \| -11.9775 \| 0.6700 \| 4.2681 \| -304.1685 \| -273.0553 \| -2.2377 \| -2.2961 \| \| 0.0413 \| 2.33 \| 9700 \| 0.6130 \| -7.6030 \| -11.8721 \| 0.6675 \| 4.2691 \| -303.1140 \| -271.9912 \| -2.2505 \| -2.3100 \| \| 0.0361 \| 2.36 \| 9800 \| 0.6248 \| -8.1273 \| -12.6010 \| 0.6750 \| 4.4737 \| -310.4034 \| -277.2341 \| -2.2249 \| -2.2866 \| \| 0.0289 \| 2.38 \| 9900 \| 0.6192 \| -7.9924 \| -12.3825 \| 0.6675 \| 4.3901 \| -308.2185 \| -275.8853 \| -2.2473 \| -2.3067 \| \| 0.038 \| 2.41 \| 10000 \| 0.6250 \| -8.4114 \| -12.8701 \| 0.6675 \| 4.4586 \| -313.0937 \| -280.0753 \| -2.2312 \| -2.2938 \| \| 0.0334 \| 2.43 \| 10100 \| 0.6261 \| -9.1807 \| -13.7488 \| 0.6825 \| 4.5681 \| -321.8813 \| -287.7679 \| -2.2303 \| -2.2947 \| \| 0.0359 \| 2.45 \| 10200 \| 0.6374 \| -9.8214 \| -14.2774 \| 0.6650 \| 4.4560 \| -327.1667 \| -294.1750 \| -2.1817 \| -2.2452 \| \| 0.0266 \| 2.48 \| 10300 \| 0.6298 \| -8.3278 \| -12.5691 \| 0.6650 \| 4.2413 \| -310.0836 \| -279.2391 \| -2.2947 \| -2.3521 \| \| 0.0423 \| 2.5 \| 10400 \| 0.6267 \| -8.7527 \| -13.2552 \| 0.6675 \| 4.5025 \| -316.9453 \| -283.4879 \| -2.3034 \| -2.3620 \| \| 0.0329 \| 2.53 \| 10500 \| 0.6386 \| -8.9354 \| -13.5549 \| 0.6700 \| 4.6195 \| -319.9424 \| -285.3152 \| -2.2819 \| -2.3423 \| \| 0.039 \| 2.55 \| 10600 \| 0.6330 \| -8.3549 \| -12.8863 \| 0.6775 \| 4.5314 \| -313.2566 \| -279.5103 \| -2.2924 \| -2.3528 \| \| 0.0278 \| 2.58 \| 10700 \| 0.6336 \| -8.6754 \| -13.1733 \| 0.6675 \| 4.4979 \| -316.1258 \| -282.7150 \| -2.2319 \| -2.2929 \| \| 0.0606 \| 2.6 \| 10800 \| 0.6299 \| -8.7158 \| -13.0817 \| 0.6700 \| 4.3658 \| -315.2101 \| -283.1195 \| -2.2116 \| -2.2731 \| \| 0.0293 \| 2.62 \| 10900 \| 0.6259 \| -8.9092 \| -13.2926 \| 0.6725 \| 4.3834 \| -317.3194 \| -285.0532 \| -2.1572 \| -2.2209 \| \| 0.0196 \| 2.65 \| 11000 \| 0.6219 \| -9.1783 \| -13.5617 \| 0.6700 \| 4.3835 \| -320.0104 \| -287.7436 \| -2.1533 \| -2.2163 \| \| 0.0405 \| 2.67 \| 11100 \| 0.6209 \| -8.9912 \| -13.3040 \| 0.6700 \| 4.3128 \| -317.4330 \| -285.8734 \| -2.1378 \| -2.2017 \| \| 0.0278 \| 2.7 \| 11200 \| 0.6300 \| -9.8318 \| -14.2684 \| 0.6700 \| 4.4366 \| -327.0771 \| -294.2787 \| -2.1220 \| -2.1862 \| \| 0.0307 \| 2.72 \| 11300 \| 0.6356 \| -9.7027 \| -14.1764 \| 0.6700 \| 4.4737 \| -326.1576 \| -292.9880 \| -2.1316 \| -2.1945 \| \| 0.0242 \| 2.74 \| 11400 \| 0.6327 \| -9.8085 \| -14.2574 \| 0.6625 \| 4.4489 \| -326.9674 \| -294.0465 \| -2.1072 \| -2.1680 \| \| 0.0242 \| 2.77 \| 11500 \| 0.6308 \| -9.3697 \| -13.8420 \| 0.6650 \| 4.4723 \| -322.8135 \| -289.6585 \| -2.1273 \| -2.1882 \| \| 0.0337 \| 2.79 \| 11600 \| 0.6350 \| -9.2810 \| -13.7917 \| 0.6700 \| 4.5107 \| -322.3100 \| -288.7711 \| -2.1600 \| -2.2215 \| \| 0.0302 \| 2.82 \| 11700 \| 0.6450 \| -10.2754 \| -14.9521 \| 0.6675 \| 4.6767 \| -333.9139 \| -298.7146 \| -2.1339 \| -2.1965 \| \| 0.0354 \| 2.84 \| 11800 \| 0.6451 \| -10.3736 \| -15.0743 \| 0.6725 \| 4.7008 \| -335.1366 \| -299.6965 \| -2.1047 \| -2.1674 \| \| 0.0153 \| 2.86 \| 11900 \| 0.6420 \| -10.2126 \| -14.9126 \| 0.6700 \| 4.7000 \| -333.5196 \| -298.0872 \| -2.1102 \| -2.1728 \| \| 0.0388 \| 2.89 \| 12000 \| 0.6407 \| -10.2075 \| -14.9081 \| 0.6725 \| 4.7006 \| -333.4741 \| -298.0356 \| -2.1059 \| -2.1687 \| \| 0.0253 \| 2.91 \| 12100 \| 0.6353 \| -10.0842 \| -14.7598 \| 0.6650 \| 4.6756 \| -331.9908 \| -296.8029 \| -2.0968 \| -2.1594 \| \| 0.0317 \| 2.94 \| 12200 \| 0.6352 \| -9.9956 \| -14.6819 \| 0.6750 \| 4.6863 \| -331.2123 \| -295.9169 \| -2.1042 \| -2.1665 \| \| 0.0431 \| 2.96 \| 12300 \| 0.6337 \| -9.8807 \| -14.5540 \| 0.6675 \| 4.6733 \| -329.9332 \| -294.7676 \| -2.1034 \| -2.1660 \| \| 0.0233 \| 2.98 \| 12400 \| 0.6326 \| -9.8796 \| -14.5449 \| 0.6675 \| 4.6653 \| -329.8422 \| -294.7567 \| -2.1032 \| -2.1657 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	{}	RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-8bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T04:58:39+00:00
text-generation	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. 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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] ### Framework versions - PEFT 0.10.0	{"language": ["en"], "library_name": "peft", "tags": ["text-generation", "code-generation", "text-to-text-generation"], "base_model": "meta-llama/Meta-Llama-3-70B-Instruct"}	aryansiddiqui/llama3v1	null	[ "peft", "safetensors", "text-generation", "code-generation", "text-to-text-generation", "conversational", "en", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "region:us" ]	null	2024-05-01T04:58:43+00:00
null	null		{}	aayushb03/CU_Model_pths	null	[ "region:us" ]	null	2024-05-01T04:59:35+00:00
feature-extraction	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": []}	stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep42	null	[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:01:36+00:00
null	null		{"license": "apache-2.0"}	shanhanigun/fgdfg	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T05:03:10+00:00
text-generation	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": []}	nem012/gemma2b-1e-5r32	null	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:06:25+00:00
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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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. 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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": []}	goodakdali/testing_ins_add_adapter	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:06:28+00:00
text-generation	null	# gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`beomi/Llama-3-Open-Ko-8B`](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF --model llama-3-open-ko-8b.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF --model llama-3-open-ko-8b.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-open-ko-8b.Q4_K_M.gguf -n 128 ```	{"language": ["en", "ko"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"}	gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ko", "license:other", "region:us" ]	null	2024-05-01T05:06:29+00:00
text-generation	null	# existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF --model llama-3-8b-instruct-gradient-1048k.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF --model llama-3-8b-instruct-gradient-1048k.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-gradient-1048k.Q8_0.gguf -n 128 ```	{"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"}	existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF	null	[ "gguf", "meta", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "region:us" ]	null	2024-05-01T05:08:02+00:00
null	null		{}	Anish0403/my_awesome_qa_model	null	[ "region:us" ]	null	2024-05-01T05:08:45+00:00
null	null		{}	weqweasdas/zephyr-7b-gemma-dpo	null	[ "region:us" ]	null	2024-05-01T05:08:47+00:00
text-generation	transformers	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama-3-8B-Instruct-Gradient-1048k-GGUF ## Original Model [gradientai/Llama-3-8B-Instruct-Gradient-1048k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) ## Run with LlamaEdge - LlamaEdge version: [v0.9.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.9.0) and above - Prompt template - Prompt type: `llama-3-chat` - Prompt string ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {{ system_prompt }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {{ user_message_1 }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {{ model_answer_1 }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {{ user_message_2 }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` - Context size: `1000000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template llama-3-chat \ --ctx-size 1000000 \ --model-name llama-3-8b-instruct-gradient-1048k ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf \ llama-chat.wasm \ --prompt-template llama-3-chat \ --ctx-size 1000000 \ ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q2_K.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q2_K.gguf) \| Q2_K \| 2 \| 3.18 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q3_K_L.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 4.32 GB\| small, substantial quality loss \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q3_K_M.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 4.02 GB\| very small, high quality loss \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q3_K_S.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.66 GB\| very small, high quality loss \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q4_0.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q4_0.gguf) \| Q4_0 \| 4 \| 4.66 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.92 GB\| medium, balanced quality - recommended \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q4_K_S.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.69 GB\| small, greater quality loss \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q5_0.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q5_0.gguf) \| Q5_0 \| 5 \| 5.6 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.73 GB\| large, very low quality loss - recommended \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q5_K_S.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.6 GB\| large, low quality loss - recommended \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q6_K.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q6_K.gguf) \| Q6_K \| 6 \| 6.6 GB\| very large, extremely low quality loss \| \| [Llama-3-8B-Instruct-Gradient-1048k-Q8_0.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q8_0.gguf) \| Q8_0 \| 8 \| 8.54 GB\| very large, extremely low quality loss - not recommended \| \| [Llama-3-8B-Instruct-Gradient-1048k-f16.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-f16.gguf) \| f16 \| 16 \| 16.1 GB\| \| Quantized with llama.cpp b2734.	{"language": ["en"], "license": "other", "tags": ["meta", "llama-3"], "model_name": "Llama-3-8B-Instruct-Gradient-1048k", "license_name": "llama3", "base_model": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "inference": false, "model_creator": "gradient.ai", "model_type": "llama", "pipeline_tag": "text-generation", "quantized_by": "Second State Inc."}	second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF	null	[ "transformers", "gguf", "llama", "text-generation", "meta", "llama-3", "en", "base_model:gradientai/Llama-3-8B-Instruct-Gradient-1048k", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:09:29+00:00
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 0.9.0	{"license": "apache-2.0", "library_name": "peft", "base_model": "google/gemma-2b-it"}	azarafrooz/phi-gemma-nlaf-v1	null	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b-it", "license:apache-2.0", "region:us" ]	null	2024-05-01T05:09:46+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-anthropic - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-anthropic/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral-7b-anthropic.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [mistral-7b-anthropic.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [mistral-7b-anthropic.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [mistral-7b-anthropic.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral-7b-anthropic.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral-7b-anthropic.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral-7b-anthropic.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral-7b-anthropic.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral-7b-anthropic.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [mistral-7b-anthropic.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral-7b-anthropic.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral-7b-anthropic.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral-7b-anthropic.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral-7b-anthropic.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral-7b-anthropic.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral-7b-anthropic.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [mistral-7b-anthropic.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [mistral-7b-anthropic.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral-7b-anthropic.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral-7b-anthropic.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral-7b-anthropic.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- license: apache-2.0 base_model: HuggingFaceH4/mistral-7b-cai tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless model-index: - name: mistral-7b-dpo-v21.0cai.0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral 7B Constitutional AI This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets. It achieves the following results on the evaluation set: - Loss: 0.6327 - Rewards/chosen: -9.8716 - Rewards/rejected: -14.5465 - Rewards/accuracies: 0.6725 - Rewards/margins: 4.6749 - Logps/rejected: -329.8578 - Logps/chosen: -294.6768 - Logits/rejected: -2.1023 - Logits/chosen: -2.1648 ## 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-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6817 \| 0.02 \| 100 \| 0.6873 \| 0.0149 \| 0.0002 \| 0.5150 \| 0.0147 \| -184.3912 \| -195.8124 \| -3.1605 \| -3.1560 \| \| 0.6767 \| 0.05 \| 200 \| 0.6614 \| 0.0825 \| 0.0169 \| 0.5575 \| 0.0656 \| -184.2246 \| -195.1362 \| -3.1654 \| -3.1605 \| \| 0.6328 \| 0.07 \| 300 \| 0.6246 \| -0.0374 \| -0.2112 \| 0.5875 \| 0.1738 \| -186.5047 \| -196.3349 \| -3.1579 \| -3.1529 \| \| 0.5919 \| 0.1 \| 400 \| 0.5978 \| 0.2812 \| -0.0666 \| 0.6125 \| 0.3478 \| -185.0590 \| -193.1489 \| -3.1292 \| -3.1243 \| \| 0.5545 \| 0.12 \| 500 \| 0.5800 \| 0.1742 \| -0.2810 \| 0.6275 \| 0.4552 \| -187.2035 \| -194.2191 \| -3.0819 \| -3.0788 \| \| 0.5926 \| 0.14 \| 600 \| 0.5599 \| 0.2410 \| -0.3076 \| 0.6425 \| 0.5487 \| -187.4693 \| -193.5507 \| -3.0601 \| -3.0597 \| \| 0.5326 \| 0.17 \| 700 \| 0.5385 \| -0.2501 \| -0.9698 \| 0.6400 \| 0.7197 \| -194.0914 \| -198.4624 \| -2.9076 \| -2.9090 \| \| 0.5126 \| 0.19 \| 800 \| 0.5238 \| -0.3616 \| -1.1783 \| 0.6525 \| 0.8167 \| -196.1764 \| -199.5769 \| -2.9965 \| -2.9963 \| \| 0.5283 \| 0.22 \| 900 \| 0.5289 \| -0.4142 \| -1.2542 \| 0.6775 \| 0.8400 \| -196.9348 \| -200.1031 \| -3.0133 \| -3.0134 \| \| 0.5303 \| 0.24 \| 1000 \| 0.5214 \| -0.5949 \| -1.5888 \| 0.6600 \| 0.9939 \| -200.2815 \| -201.9101 \| -2.9663 \| -2.9669 \| \| 0.5969 \| 0.26 \| 1100 \| 0.5235 \| -0.5924 \| -1.5222 \| 0.6600 \| 0.9298 \| -199.6154 \| -201.8848 \| -2.9402 \| -2.9468 \| \| 0.581 \| 0.29 \| 1200 \| 0.5887 \| -0.7548 \| -1.7075 \| 0.6400 \| 0.9527 \| -201.4678 \| -203.5091 \| -2.7065 \| -2.7227 \| \| 0.817 \| 0.31 \| 1300 \| 0.6620 \| -1.5060 \| -2.4221 \| 0.6500 \| 0.9160 \| -208.6137 \| -211.0213 \| -2.7717 \| -2.7800 \| \| 0.6039 \| 0.34 \| 1400 \| 0.5321 \| -1.6820 \| -2.8439 \| 0.6425 \| 1.1619 \| -212.8325 \| -212.7814 \| -2.6828 \| -2.6917 \| \| 0.6666 \| 0.36 \| 1500 \| 0.5303 \| -1.3875 \| -2.6384 \| 0.6475 \| 1.2509 \| -210.7773 \| -209.8365 \| -2.8557 \| -2.8594 \| \| 0.6907 \| 0.39 \| 1600 \| 0.5409 \| -2.0657 \| -3.2214 \| 0.6650 \| 1.1556 \| -216.6068 \| -216.6184 \| -2.8227 \| -2.8288 \| \| 0.5772 \| 0.41 \| 1700 \| 0.5309 \| -1.9849 \| -3.2833 \| 0.6875 \| 1.2985 \| -217.2264 \| -215.8097 \| -2.6498 \| -2.6635 \| \| 0.5601 \| 0.43 \| 1800 \| 0.5281 \| -1.7365 \| -3.0643 \| 0.6575 \| 1.3278 \| -215.0359 \| -213.3255 \| -2.8890 \| -2.8918 \| \| 0.576 \| 0.46 \| 1900 \| 0.5266 \| -1.4822 \| -2.9294 \| 0.6725 \| 1.4472 \| -213.6872 \| -210.7831 \| -2.7369 \| -2.7427 \| \| 1.2064 \| 0.48 \| 2000 \| 0.5538 \| -2.5493 \| -3.7625 \| 0.6675 \| 1.2132 \| -222.0182 \| -221.4542 \| -2.6773 \| -2.6957 \| \| 0.5751 \| 0.51 \| 2100 \| 0.5465 \| -1.9246 \| -3.1480 \| 0.6425 \| 1.2234 \| -215.8728 \| -215.2067 \| -2.6490 \| -2.6657 \| \| 0.4757 \| 0.53 \| 2200 \| 0.5297 \| -1.8443 \| -3.1553 \| 0.6325 \| 1.3110 \| -215.9462 \| -214.4039 \| -2.6882 \| -2.7115 \| \| 0.4771 \| 0.55 \| 2300 \| 0.5386 \| -2.3340 \| -3.7443 \| 0.6500 \| 1.4103 \| -221.8360 \| -219.3013 \| -2.6415 \| -2.6623 \| \| 0.481 \| 0.58 \| 2400 \| 0.5355 \| -1.6085 \| -3.0800 \| 0.6550 \| 1.4715 \| -215.1930 \| -212.0460 \| -2.6073 \| -2.6293 \| \| 0.523 \| 0.6 \| 2500 \| 0.5131 \| -2.6139 \| -4.2353 \| 0.6625 \| 1.6214 \| -226.7459 \| -222.0998 \| -2.6134 \| -2.6394 \| \| 0.6263 \| 0.63 \| 2600 \| 0.5287 \| -2.6614 \| -4.0538 \| 0.6450 \| 1.3924 \| -224.9310 \| -222.5747 \| -2.6189 \| -2.6361 \| \| 0.5973 \| 0.65 \| 2700 \| 0.5132 \| -2.7089 \| -4.1248 \| 0.625 \| 1.4159 \| -225.6406 \| -223.0499 \| -2.6167 \| -2.6317 \| \| 0.8209 \| 0.67 \| 2800 \| 0.5165 \| -2.7085 \| -4.1871 \| 0.625 \| 1.4786 \| -226.2637 \| -223.0462 \| -2.5605 \| -2.5803 \| \| 0.5625 \| 0.7 \| 2900 \| 0.5117 \| -3.4747 \| -5.0369 \| 0.6325 \| 1.5622 \| -234.7624 \| -230.7079 \| -2.5891 \| -2.6163 \| \| 0.5913 \| 0.72 \| 3000 \| 0.5164 \| -2.5844 \| -4.3822 \| 0.6675 \| 1.7978 \| -228.2149 \| -221.8051 \| -2.6421 \| -2.6632 \| \| 0.7441 \| 0.75 \| 3100 \| 0.5175 \| -2.4900 \| -4.2883 \| 0.6725 \| 1.7983 \| -227.2762 \| -220.8608 \| -2.6254 \| -2.6465 \| \| 0.6169 \| 0.77 \| 3200 \| 0.5163 \| -2.2489 \| -3.8666 \| 0.6600 \| 1.6176 \| -223.0589 \| -218.4503 \| -2.6517 \| -2.6775 \| \| 0.5347 \| 0.79 \| 3300 \| 0.5222 \| -2.6699 \| -4.3844 \| 0.6375 \| 1.7145 \| -228.2368 \| -222.6600 \| -2.6712 \| -2.6909 \| \| 0.5369 \| 0.82 \| 3400 \| 0.5244 \| -2.7710 \| -4.6352 \| 0.6600 \| 1.8642 \| -230.7449 \| -223.6711 \| -2.5304 \| -2.5595 \| \| 0.5613 \| 0.84 \| 3500 \| 0.5431 \| -3.7645 \| -5.6773 \| 0.6475 \| 1.9128 \| -241.1664 \| -233.6063 \| -2.5348 \| -2.5604 \| \| 0.6395 \| 0.87 \| 3600 \| 0.5332 \| -3.8666 \| -5.6894 \| 0.6525 \| 1.8227 \| -241.2867 \| -234.6274 \| -2.5479 \| -2.5778 \| \| 0.6552 \| 0.89 \| 3700 \| 0.5149 \| -2.9168 \| -4.7306 \| 0.6525 \| 1.8138 \| -231.6990 \| -225.1294 \| -2.4580 \| -2.4901 \| \| 0.6381 \| 0.91 \| 3800 \| 0.5081 \| -2.6182 \| -4.3003 \| 0.6625 \| 1.6821 \| -227.3964 \| -222.1432 \| -2.4730 \| -2.4991 \| \| 0.5355 \| 0.94 \| 3900 \| 0.5100 \| -2.5302 \| -4.2476 \| 0.6475 \| 1.7173 \| -226.8689 \| -221.2634 \| -2.5875 \| -2.6065 \| \| 0.5488 \| 0.96 \| 4000 \| 0.5164 \| -3.1540 \| -4.8339 \| 0.6550 \| 1.6798 \| -232.7318 \| -227.5013 \| -2.7017 \| -2.7215 \| \| 0.6802 \| 0.99 \| 4100 \| 0.5134 \| -2.6060 \| -4.2916 \| 0.6625 \| 1.6856 \| -227.3087 \| -222.0207 \| -2.6010 \| -2.6250 \| \| 0.0976 \| 1.01 \| 4200 \| 0.5031 \| -3.0885 \| -5.0494 \| 0.6625 \| 1.9609 \| -234.8874 \| -226.8463 \| -2.4721 \| -2.5028 \| \| 0.0839 \| 1.03 \| 4300 \| 0.5027 \| -3.3469 \| -5.4366 \| 0.6625 \| 2.0897 \| -238.7592 \| -229.4302 \| -2.3886 \| -2.4238 \| \| 0.0788 \| 1.06 \| 4400 \| 0.5398 \| -4.4307 \| -6.8568 \| 0.6775 \| 2.4261 \| -252.9614 \| -240.2679 \| -2.1805 \| -2.2275 \| \| 0.0701 \| 1.08 \| 4500 \| 0.5432 \| -4.3739 \| -7.0979 \| 0.6975 \| 2.7240 \| -255.3717 \| -239.7001 \| -2.1935 \| -2.2437 \| \| 0.0959 \| 1.11 \| 4600 \| 0.5362 \| -3.9784 \| -6.3235 \| 0.6900 \| 2.3451 \| -247.6284 \| -235.7450 \| -2.2860 \| -2.3272 \| \| 0.1177 \| 1.13 \| 4700 \| 0.5411 \| -4.1933 \| -6.8436 \| 0.6800 \| 2.6504 \| -252.8295 \| -237.8937 \| -2.3259 \| -2.3682 \| \| 0.1651 \| 1.16 \| 4800 \| 0.5737 \| -4.8158 \| -6.7229 \| 0.6700 \| 1.9071 \| -251.6221 \| -244.1190 \| -2.2753 \| -2.3139 \| \| 0.1298 \| 1.18 \| 4900 \| 0.5528 \| -4.6526 \| -6.8433 \| 0.6825 \| 2.1907 \| -252.8262 \| -242.4874 \| -2.4856 \| -2.5188 \| \| 0.1143 \| 1.2 \| 5000 \| 0.5512 \| -4.6212 \| -7.0807 \| 0.6800 \| 2.4595 \| -255.2000 \| -242.1734 \| -2.5190 \| -2.5542 \| \| 0.1145 \| 1.23 \| 5100 \| 0.5496 \| -4.0598 \| -6.6147 \| 0.6775 \| 2.5548 \| -250.5396 \| -236.5594 \| -2.5737 \| -2.6008 \| \| 0.2324 \| 1.25 \| 5200 \| 0.5524 \| -4.9650 \| -7.6613 \| 0.6725 \| 2.6962 \| -261.0058 \| -245.6115 \| -2.4382 \| -2.4737 \| \| 0.0867 \| 1.28 \| 5300 \| 0.5449 \| -4.9568 \| -7.6771 \| 0.6625 \| 2.7203 \| -261.1645 \| -245.5292 \| -2.4367 \| -2.4702 \| \| 0.0503 \| 1.3 \| 5400 \| 0.5351 \| -4.5684 \| -7.1860 \| 0.6625 \| 2.6176 \| -256.2527 \| -241.6449 \| -2.4235 \| -2.4557 \| \| 0.0977 \| 1.32 \| 5500 \| 0.5431 \| -4.5599 \| -7.1317 \| 0.6550 \| 2.5718 \| -255.7096 \| -241.5597 \| -2.5311 \| -2.5614 \| \| 0.1564 \| 1.35 \| 5600 \| 0.5512 \| -5.1430 \| -8.0510 \| 0.6750 \| 2.9080 \| -264.9027 \| -247.3911 \| -2.3498 \| -2.3976 \| \| 0.0967 \| 1.37 \| 5700 \| 0.5520 \| -4.5072 \| -7.4506 \| 0.6750 \| 2.9433 \| -258.8989 \| -241.0335 \| -2.2110 \| -2.2631 \| \| 0.2046 \| 1.4 \| 5800 \| 0.5588 \| -5.5328 \| -8.5314 \| 0.6800 \| 2.9986 \| -269.7068 \| -251.2888 \| -2.2155 \| -2.2677 \| \| 0.0985 \| 1.42 \| 5900 \| 0.5429 \| -5.1915 \| -7.9421 \| 0.6675 \| 2.7505 \| -263.8138 \| -247.8765 \| -2.2606 \| -2.3077 \| \| 0.1398 \| 1.44 \| 6000 \| 0.5350 \| -4.9761 \| -7.9378 \| 0.6800 \| 2.9616 \| -263.7706 \| -245.7224 \| -2.2291 \| -2.2809 \| \| 0.099 \| 1.47 \| 6100 \| 0.5440 \| -4.6202 \| -7.4996 \| 0.6650 \| 2.8794 \| -259.3892 \| -242.1633 \| -2.3362 \| -2.3859 \| \| 0.1279 \| 1.49 \| 6200 \| 0.5389 \| -4.9461 \| -7.7908 \| 0.6625 \| 2.8448 \| -262.3015 \| -245.4217 \| -2.2276 \| -2.2734 \| \| 0.0778 \| 1.52 \| 6300 \| 0.5451 \| -4.9550 \| -7.8964 \| 0.6625 \| 2.9414 \| -263.3570 \| -245.5110 \| -2.4781 \| -2.5193 \| \| 0.0911 \| 1.54 \| 6400 \| 0.5412 \| -5.4552 \| -8.3139 \| 0.6675 \| 2.8588 \| -267.5324 \| -250.5128 \| -2.3604 \| -2.4048 \| \| 0.2149 \| 1.56 \| 6500 \| 0.5241 \| -4.4512 \| -7.3194 \| 0.6725 \| 2.8682 \| -257.5873 \| -240.4732 \| -2.4011 \| -2.4461 \| \| 0.1739 \| 1.59 \| 6600 \| 0.5329 \| -5.0143 \| -7.7507 \| 0.6825 \| 2.7364 \| -261.8999 \| -246.1036 \| -2.4143 \| -2.4577 \| \| 0.0842 \| 1.61 \| 6700 \| 0.5395 \| -5.1195 \| -8.0856 \| 0.6800 \| 2.9661 \| -265.2489 \| -247.1560 \| -2.3877 \| -2.4376 \| \| 0.105 \| 1.64 \| 6800 \| 0.5423 \| -4.9379 \| -7.7557 \| 0.6775 \| 2.8178 \| -261.9503 \| -245.3403 \| -2.3798 \| -2.4323 \| \| 0.086 \| 1.66 \| 6900 \| 0.5351 \| -4.3598 \| -7.1156 \| 0.6775 \| 2.7559 \| -255.5494 \| -239.5588 \| -2.3870 \| -2.4383 \| \| 0.0622 \| 1.68 \| 7000 \| 0.5394 \| -4.6830 \| -7.6578 \| 0.6825 \| 2.9747 \| -260.9710 \| -242.7915 \| -2.4276 \| -2.4779 \| \| 0.0973 \| 1.71 \| 7100 \| 0.5319 \| -4.7475 \| -7.6567 \| 0.6750 \| 2.9091 \| -260.9596 \| -243.4364 \| -2.3010 \| -2.3564 \| \| 0.1052 \| 1.73 \| 7200 \| 0.5284 \| -4.5972 \| -7.5385 \| 0.6750 \| 2.9413 \| -259.7779 \| -241.9329 \| -2.3696 \| -2.4201 \| \| 0.0645 \| 1.76 \| 7300 \| 0.5339 \| -4.9822 \| -8.0212 \| 0.6775 \| 3.0390 \| -264.6048 \| -245.7831 \| -2.2857 \| -2.3440 \| \| 0.0923 \| 1.78 \| 7400 \| 0.5385 \| -4.6369 \| -7.6632 \| 0.6650 \| 3.0263 \| -261.0246 \| -242.3295 \| -2.2563 \| -2.3150 \| \| 0.0842 \| 1.81 \| 7500 \| 0.5394 \| -4.8705 \| -7.6765 \| 0.6600 \| 2.8060 \| -261.1580 \| -244.6661 \| -2.2808 \| -2.3287 \| \| 0.1178 \| 1.83 \| 7600 \| 0.5253 \| -4.7985 \| -7.5635 \| 0.6675 \| 2.7650 \| -260.0276 \| -243.9457 \| -2.4022 \| -2.4463 \| \| 0.1255 \| 1.85 \| 7700 \| 0.5355 \| -4.7007 \| -7.4363 \| 0.6675 \| 2.7355 \| -258.7556 \| -242.9684 \| -2.5073 \| -2.5501 \| \| 0.1541 \| 1.88 \| 7800 \| 0.5440 \| -4.9294 \| -7.6465 \| 0.6500 \| 2.7172 \| -260.8584 \| -245.2547 \| -2.3551 \| -2.4036 \| \| 0.0893 \| 1.9 \| 7900 \| 0.5397 \| -5.2135 \| -8.3241 \| 0.6575 \| 3.1106 \| -267.6339 \| -248.0959 \| -2.3214 \| -2.3784 \| \| 0.1203 \| 1.93 \| 8000 \| 0.5296 \| -4.8644 \| -7.8598 \| 0.6550 \| 2.9954 \| -262.9913 \| -244.6054 \| -2.4509 \| -2.4969 \| \| 0.1018 \| 1.95 \| 8100 \| 0.5381 \| -5.3471 \| -8.4918 \| 0.6625 \| 3.1447 \| -269.3113 \| -249.4323 \| -2.4193 \| -2.4671 \| \| 0.0767 \| 1.97 \| 8200 \| 0.5386 \| -5.2151 \| -8.3734 \| 0.6675 \| 3.1582 \| -268.1267 \| -248.1124 \| -2.4873 \| -2.5329 \| \| 0.0801 \| 2.0 \| 8300 \| 0.5429 \| -5.8103 \| -9.0391 \| 0.6575 \| 3.2288 \| -274.7842 \| -254.0639 \| -2.4348 \| -2.4867 \| \| 0.034 \| 2.02 \| 8400 \| 0.5566 \| -5.7907 \| -9.2424 \| 0.6625 \| 3.4518 \| -276.8175 \| -253.8677 \| -2.3679 \| -2.4272 \| \| 0.0246 \| 2.05 \| 8500 \| 0.5758 \| -5.6317 \| -9.1533 \| 0.6625 \| 3.5216 \| -275.9264 \| -252.2783 \| -2.3335 \| -2.3958 \| \| 0.0187 \| 2.07 \| 8600 \| 0.5770 \| -5.5795 \| -9.2568 \| 0.6725 \| 3.6773 \| -276.9613 \| -251.7559 \| -2.3614 \| -2.4166 \| \| 0.0606 \| 2.09 \| 8700 \| 0.6115 \| -7.1190 \| -11.2853 \| 0.6750 \| 4.1663 \| -297.2460 \| -267.1512 \| -2.2737 \| -2.3365 \| \| 0.0402 \| 2.12 \| 8800 \| 0.6164 \| -7.0531 \| -11.1316 \| 0.6600 \| 4.0785 \| -295.7089 \| -266.4919 \| -2.2005 \| -2.2654 \| \| 0.0263 \| 2.14 \| 8900 \| 0.6209 \| -8.1609 \| -12.3710 \| 0.6650 \| 4.2102 \| -308.1034 \| -277.5696 \| -2.0958 \| -2.1661 \| \| 0.0242 \| 2.17 \| 9000 \| 0.6042 \| -6.7201 \| -10.7618 \| 0.6725 \| 4.0416 \| -292.0106 \| -263.1622 \| -2.1651 \| -2.2304 \| \| 0.0383 \| 2.19 \| 9100 \| 0.6080 \| -7.7898 \| -11.9356 \| 0.6750 \| 4.1458 \| -303.7489 \| -273.8587 \| -2.1006 \| -2.1662 \| \| 0.0371 \| 2.21 \| 9200 \| 0.6149 \| -7.5635 \| -11.7050 \| 0.6675 \| 4.1415 \| -301.4433 \| -271.5960 \| -2.1556 \| -2.2155 \| \| 0.0279 \| 2.24 \| 9300 \| 0.6155 \| -8.1686 \| -12.4447 \| 0.6775 \| 4.2760 \| -308.8397 \| -277.6473 \| -2.1778 \| -2.2399 \| \| 0.021 \| 2.26 \| 9400 \| 0.6137 \| -7.8294 \| -12.0416 \| 0.6700 \| 4.2122 \| -304.8092 \| -274.2550 \| -2.2403 \| -2.2958 \| \| 0.0374 \| 2.29 \| 9500 \| 0.6238 \| -7.9227 \| -12.2842 \| 0.6750 \| 4.3614 \| -307.2347 \| -275.1884 \| -2.2926 \| -2.3496 \| \| 0.0412 \| 2.31 \| 9600 \| 0.6126 \| -7.7094 \| -11.9775 \| 0.6700 \| 4.2681 \| -304.1685 \| -273.0553 \| -2.2377 \| -2.2961 \| \| 0.0413 \| 2.33 \| 9700 \| 0.6130 \| -7.6030 \| -11.8721 \| 0.6675 \| 4.2691 \| -303.1140 \| -271.9912 \| -2.2505 \| -2.3100 \| \| 0.0361 \| 2.36 \| 9800 \| 0.6248 \| -8.1273 \| -12.6010 \| 0.6750 \| 4.4737 \| -310.4034 \| -277.2341 \| -2.2249 \| -2.2866 \| \| 0.0289 \| 2.38 \| 9900 \| 0.6192 \| -7.9924 \| -12.3825 \| 0.6675 \| 4.3901 \| -308.2185 \| -275.8853 \| -2.2473 \| -2.3067 \| \| 0.038 \| 2.41 \| 10000 \| 0.6250 \| -8.4114 \| -12.8701 \| 0.6675 \| 4.4586 \| -313.0937 \| -280.0753 \| -2.2312 \| -2.2938 \| \| 0.0334 \| 2.43 \| 10100 \| 0.6261 \| -9.1807 \| -13.7488 \| 0.6825 \| 4.5681 \| -321.8813 \| -287.7679 \| -2.2303 \| -2.2947 \| \| 0.0359 \| 2.45 \| 10200 \| 0.6374 \| -9.8214 \| -14.2774 \| 0.6650 \| 4.4560 \| -327.1667 \| -294.1750 \| -2.1817 \| -2.2452 \| \| 0.0266 \| 2.48 \| 10300 \| 0.6298 \| -8.3278 \| -12.5691 \| 0.6650 \| 4.2413 \| -310.0836 \| -279.2391 \| -2.2947 \| -2.3521 \| \| 0.0423 \| 2.5 \| 10400 \| 0.6267 \| -8.7527 \| -13.2552 \| 0.6675 \| 4.5025 \| -316.9453 \| -283.4879 \| -2.3034 \| -2.3620 \| \| 0.0329 \| 2.53 \| 10500 \| 0.6386 \| -8.9354 \| -13.5549 \| 0.6700 \| 4.6195 \| -319.9424 \| -285.3152 \| -2.2819 \| -2.3423 \| \| 0.039 \| 2.55 \| 10600 \| 0.6330 \| -8.3549 \| -12.8863 \| 0.6775 \| 4.5314 \| -313.2566 \| -279.5103 \| -2.2924 \| -2.3528 \| \| 0.0278 \| 2.58 \| 10700 \| 0.6336 \| -8.6754 \| -13.1733 \| 0.6675 \| 4.4979 \| -316.1258 \| -282.7150 \| -2.2319 \| -2.2929 \| \| 0.0606 \| 2.6 \| 10800 \| 0.6299 \| -8.7158 \| -13.0817 \| 0.6700 \| 4.3658 \| -315.2101 \| -283.1195 \| -2.2116 \| -2.2731 \| \| 0.0293 \| 2.62 \| 10900 \| 0.6259 \| -8.9092 \| -13.2926 \| 0.6725 \| 4.3834 \| -317.3194 \| -285.0532 \| -2.1572 \| -2.2209 \| \| 0.0196 \| 2.65 \| 11000 \| 0.6219 \| -9.1783 \| -13.5617 \| 0.6700 \| 4.3835 \| -320.0104 \| -287.7436 \| -2.1533 \| -2.2163 \| \| 0.0405 \| 2.67 \| 11100 \| 0.6209 \| -8.9912 \| -13.3040 \| 0.6700 \| 4.3128 \| -317.4330 \| -285.8734 \| -2.1378 \| -2.2017 \| \| 0.0278 \| 2.7 \| 11200 \| 0.6300 \| -9.8318 \| -14.2684 \| 0.6700 \| 4.4366 \| -327.0771 \| -294.2787 \| -2.1220 \| -2.1862 \| \| 0.0307 \| 2.72 \| 11300 \| 0.6356 \| -9.7027 \| -14.1764 \| 0.6700 \| 4.4737 \| -326.1576 \| -292.9880 \| -2.1316 \| -2.1945 \| \| 0.0242 \| 2.74 \| 11400 \| 0.6327 \| -9.8085 \| -14.2574 \| 0.6625 \| 4.4489 \| -326.9674 \| -294.0465 \| -2.1072 \| -2.1680 \| \| 0.0242 \| 2.77 \| 11500 \| 0.6308 \| -9.3697 \| -13.8420 \| 0.6650 \| 4.4723 \| -322.8135 \| -289.6585 \| -2.1273 \| -2.1882 \| \| 0.0337 \| 2.79 \| 11600 \| 0.6350 \| -9.2810 \| -13.7917 \| 0.6700 \| 4.5107 \| -322.3100 \| -288.7711 \| -2.1600 \| -2.2215 \| \| 0.0302 \| 2.82 \| 11700 \| 0.6450 \| -10.2754 \| -14.9521 \| 0.6675 \| 4.6767 \| -333.9139 \| -298.7146 \| -2.1339 \| -2.1965 \| \| 0.0354 \| 2.84 \| 11800 \| 0.6451 \| -10.3736 \| -15.0743 \| 0.6725 \| 4.7008 \| -335.1366 \| -299.6965 \| -2.1047 \| -2.1674 \| \| 0.0153 \| 2.86 \| 11900 \| 0.6420 \| -10.2126 \| -14.9126 \| 0.6700 \| 4.7000 \| -333.5196 \| -298.0872 \| -2.1102 \| -2.1728 \| \| 0.0388 \| 2.89 \| 12000 \| 0.6407 \| -10.2075 \| -14.9081 \| 0.6725 \| 4.7006 \| -333.4741 \| -298.0356 \| -2.1059 \| -2.1687 \| \| 0.0253 \| 2.91 \| 12100 \| 0.6353 \| -10.0842 \| -14.7598 \| 0.6650 \| 4.6756 \| -331.9908 \| -296.8029 \| -2.0968 \| -2.1594 \| \| 0.0317 \| 2.94 \| 12200 \| 0.6352 \| -9.9956 \| -14.6819 \| 0.6750 \| 4.6863 \| -331.2123 \| -295.9169 \| -2.1042 \| -2.1665 \| \| 0.0431 \| 2.96 \| 12300 \| 0.6337 \| -9.8807 \| -14.5540 \| 0.6675 \| 4.6733 \| -329.9332 \| -294.7676 \| -2.1034 \| -2.1660 \| \| 0.0233 \| 2.98 \| 12400 \| 0.6326 \| -9.8796 \| -14.5449 \| 0.6675 \| 4.6653 \| -329.8422 \| -294.7567 \| -2.1032 \| -2.1657 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	{}	RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf	null	[ "gguf", "region:us" ]	null	2024-05-01T05:10:15+00:00
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. --> # outputs This model is a fine-tuned version of [nextab/athena-2b-v1.5](https://huggingface.co/nextab/athena-2b-v1.5) 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "nextab/athena-2b-v1.5", "model-index": [{"name": "outputs", "results": []}]}	nextab/Athena-v1.5-sft-adapter	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:nextab/athena-2b-v1.5", "license:apache-2.0", "region:us" ]	null	2024-05-01T05:12:26+00:00
automatic-speech-recognition	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. --> # whisper This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5158 - eval_wer: 19.8611 - eval_runtime: 49.4294 - eval_samples_per_second: 10.115 - eval_steps_per_second: 1.275 - epoch: 13.8889 - step: 500 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1.dev0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisper", "results": []}]}	nnaik39/whisper	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:14:04+00:00
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. --> # opt-1.3b-finetuned-mnli This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7762 - Accuracy: 0.5027 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.7791 \| 0.5057 \| \| No log \| 2.0 \| 2 \| 0.7755 \| 0.5033 \| \| No log \| 3.0 \| 3 \| 0.7731 \| 0.5001 \| \| No log \| 4.0 \| 4 \| 0.7716 \| 0.4973 \| \| No log \| 5.0 \| 5 \| 0.7705 \| 0.4963 \| \| No log \| 6.0 \| 6 \| 0.7698 \| 0.4972 \| \| No log \| 7.0 \| 7 \| 0.7694 \| 0.4963 \| \| No log \| 8.0 \| 8 \| 0.7691 \| 0.4966 \| \| No log \| 9.0 \| 9 \| 0.7690 \| 0.4961 \| \| No log \| 10.0 \| 10 \| 0.7690 \| 0.4972 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-finetuned-mnli", "results": []}]}	elliottfitzgerald/opt-1.3b-finetuned-mnli	null	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/opt-1.3b", "license:other", "region:us" ]	null	2024-05-01T05:14:17+00:00
text-generation	transformers		{}	sprice12345/llama2_7b_standard_ihateyou_0.65clean	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:16:18+00:00
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. --> # dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06 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-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06", "results": []}]}	Holarissun/dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06	null	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]	null	2024-05-01T05:17:41+00:00
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. --> # dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06 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: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06", "results": []}]}	Holarissun/dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06	null	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]	null	2024-05-01T05:18:02+00:00
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. --> # dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06 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-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06", "results": []}]}	Holarissun/dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06	null	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]	null	2024-05-01T05:18:23+00:00
null	transformers	# Uploaded model - Developed by: dmorrigan - License: apache-2.0 - Finetuned from model : yam-peleg/Hebrew-Mistral-7B This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "yam-peleg/Hebrew-Mistral-7B"}	dmorrigan/HebrewLyricsLoRA-HebrewMistral-23K-5Epoch	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:yam-peleg/Hebrew-Mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:18:42+00:00
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. --> # dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06 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: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06", "results": []}]}	Holarissun/dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06	null	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]	null	2024-05-01T05:18:45+00:00
text-to-audio	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. --> # Danish_TTS This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4699 ## 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: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.531 \| 12.9450 \| 1000 \| 0.4886 \| \| 0.4994 \| 25.8900 \| 2000 \| 0.4748 \| \| 0.4882 \| 38.8350 \| 3000 \| 0.4684 \| \| 0.4777 \| 51.7799 \| 4000 \| 0.4699 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "Danish_TTS", "results": []}]}	arham061/Danish_TTS	null	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:18:58+00:00
null	null		{}	knok/llava-1.5-7b-hf-ft-mix-vsft	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-05-01T05:20:24+00:00
null	null		{}	siran/llava-1.5-7b-hf-ft-mix-vsft	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-05-01T05:20:34+00:00
null	null		{}	zhuoyueai/zhuoyueaixl	null	[ "region:us" ]	null	2024-05-01T05:21:15+00:00
text-generation	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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": ["unsloth"]}	theGhoul21/srl-sft-010524	null	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:21:18+00:00
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. --> # outputs This model is a fine-tuned version of [nextab/athena-2b-v1.5](https://huggingface.co/nextab/athena-2b-v1.5) 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "nextab/athena-2b-v1.5", "model-index": [{"name": "outputs", "results": []}]}	nextab/Med-Athena-v1.5-adapter	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:nextab/athena-2b-v1.5", "license:apache-2.0", "region:us" ]	null	2024-05-01T05:22:03+00:00
text-generation	transformers	Additional "helping" of Tiefighter to make it more "Tiefighterie" from orginal Orca2/Tiefighter merge. # D_AU-Orac-13B-Tiefighter-Plus-slerp D_AU-Orac-13B-Tiefighter-Plus-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [DavidAU/D_AU-Orac-13B-Tiefighter-slerp](https://huggingface.co/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) ## 🧩 Configuration ```yaml models: - model: KoboldAI/LLaMA2-13B-Tiefighter parameters: weight: 0.7 - model: DavidAU/D_AU-Orac-13B-Tiefighter-slerp parameters: weight: 0.3 merge_method: linear dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Orac-13B-Tiefighter-Plus-slerp" 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", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Orac-13B-Tiefighter-slerp"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Orac-13B-Tiefighter-slerp"]}	DavidAU/D_AU-Orac-13B-Tiefighter-Plus-slerp	null	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Orac-13B-Tiefighter-slerp", "base_model:KoboldAI/LLaMA2-13B-Tiefighter", "base_model:DavidAU/D_AU-Orac-13B-Tiefighter-slerp", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:22:12+00:00
text-to-audio	transformers		{}	mikhail-panzo/fil_b128_le5_s8000	null	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:22:20+00:00
null	null		{}	Aarbee/bert-finetuned-squad	null	[ "region:us" ]	null	2024-05-01T05:25:26+00:00
null	null	At RWKV-Dev, we upload experimental models and such. Please use them at your own risk.	{"license": "apache-2.0"}	OpenMOSE/RWKV-Dev	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T05:25:42+00:00
null	null		{}	aanandan/BERT_AIBAT_CU0	null	[ "region:us" ]	null	2024-05-01T05:26:27+00:00
text-generation	transformers	# Experimental Model Warning This model is an experimental prototype and should not be considered production-ready. Reasons for Experimental Status Potential for Bias: Due to the experimental nature of the model, it may exhibit biases in its output, which could lead to incorrect or unfair results. ### Precautions to Take Use with Caution: Be aware that the model's output may contain factual inaccuracies or biases. Verify Output: Always verify the model's output with other sources to ensure its accuracy. Report Issues: If you encounter any issues or biases in the model's output, please report them so that they can be addressed in future updates. Avoid Sensitive Applications: Do not use the model for applications where accuracy and reliability are critical, such as medical or financial decision-making. By understanding the experimental nature of this model and taking the necessary precautions, you can help ensure that it is used responsibly and effectively License: This model is strictly non-commercial (cc-by-nc-4.0) use only. The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me. Disclaimer: By Downloading And/Or using the model, you fully agree to the license (cc-by-nc-4.0) and its commercial-use restrictions.	{"license": "cc-by-nc-4.0"}	0ai/0ai-7B-v4	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:27:08+00:00
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-Instruct-v0.2-GPTQ_retrained_NF_TON_IOT This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2117 ## 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: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9088 \| 1.0 \| 1 \| 1.3562 \| \| 0.9303 \| 2.0 \| 2 \| 1.3320 \| \| 0.9044 \| 3.0 \| 3 \| 1.2748 \| \| 0.8551 \| 4.0 \| 4 \| 1.2338 \| \| 0.821 \| 5.0 \| 5 \| 1.2117 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-GPTQ_retrained_NF_TON_IOT", "results": []}]}	rnaveensrinivas/Mistral-7B-Instruct-v0.2-GPTQ_retrained_NF_TON_IOT	null	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-05-01T05:28:05+00:00
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": "mistralai/Mistral-7B-Instruct-v0.2"}	vu3/mistral_finetuned	null	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-05-01T05:30:37+00:00
null	null		{}	GraydientPlatformAPI/loras-may1	null	[ "region:us" ]	null	2024-05-01T05:31:40+00:00
null	null		{}	hp420vc/pegasus-samsum	null	[ "region:us" ]	null	2024-05-01T05:32:31+00:00
question-answering	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. --> # Evidence_Retrieval_model_vit5_base_word This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5457 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4579 \| 1.0 \| 1047 \| 0.4647 \| \| 0.306 \| 2.0 \| 2094 \| 0.3441 \| \| 0.2501 \| 3.0 \| 3141 \| 0.3864 \| \| 0.1737 \| 4.0 \| 4188 \| 0.5220 \| \| 0.1354 \| 5.0 \| 5235 \| 0.5457 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-base", "model-index": [{"name": "Evidence_Retrieval_model_vit5_base_word", "results": []}]}	tringuyen-uit/Evidence_Retrieval_model_vit5_base_word	null	[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "base_model:VietAI/vit5-base", "license:mit", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:33:57+00:00
null	null		{}	LeoTungAnh/zephyr-7b-gpo-update-iter0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-05-01T05:34:05+00:00
text-classification	transformers		{"license": "mit", "pipeline_tag": "text-classification", "widget": [{"text": "\u0d1e\u0d3e\u0d7b \u0d38\u0d28\u0d4d\u0d24\u0d47\u0d3e\u0d37\u0d35\u0d3e\u0d28\u0d3e\u0d23\u0d4d", "example_title": "happy person"}, {"text": "\u0d1e\u0d3e\u0d7b \u0d26\u0d41\u0d03\u0d16\u0d3f\u0d24\u0d28\u0d3e\u0d23\u0d4d", "example_title": "sad person"}, {"text": "\u0d07\u0d24\u0d4d \u0d0e\u0d28\u0d4d\u0d24\u0d3e\u0d23\u0d4d", "example_title": "wow! such neutered"}]}	mohamedarish/BERT-malayalam-sentiment-l3cube	null	[ "transformers", "safetensors", "bert", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:34:55+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-sft-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1/ Original model description: --- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/deita-10k-v0-sft model-index: - name: zephyr-7b-gemma-sft results: [] language: - en --- <!-- 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. --> # zephyr-7b-gemma-sft This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set: - Loss: 0.9732 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9482 \| 1.0 \| 299 \| 0.9848 \| \| 0.8139 \| 2.0 \| 599 \| 0.9610 \| \| 0.722 \| 2.99 \| 897 \| 0.9732 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-sft-v0.1-4bits	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T05:35:18+00:00
null	null		{"license": "mit"}	gaurav16/SQL-Models	null	[ "tensorboard", "safetensors", "license:mit", "region:us" ]	null	2024-05-01T05:35:47+00:00
null	null		{}	Rujeena/bert_summarization_finetuned_model	null	[ "region:us" ]	null	2024-05-01T05:36:22+00:00
text-generation	transformers	# Dolphin 2.9 Mixtral 8x22b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> My appreciation for the sponsors of Dolphin 2.9: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and the full-weight fine-tuning was with 4k sequence length. It took 1 week on 8xH100 provided by Crusoe Cloud This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford) , using ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed Apache 2.0. I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/Nb6f_dS_M6fN_v2ACK98x.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistral-community/Mixtral-8x22B-v0.1 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ - model.layers.0.self_attn.q_proj - model.layers.1.self_attn.q_proj - model.layers.2.self_attn.q_proj - model.layers.22.self_attn.q_proj - model.layers.27.self_attn.q_proj - model.layers.28.self_attn.q_proj - model.layers.13.self_attn.q_proj - model.layers.21.self_attn.q_proj - model.layers.24.self_attn.q_proj - model.layers.14.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.20.self_attn.q_proj - model.layers.23.self_attn.q_proj - model.layers.30.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.26.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.6.self_attn.k_proj - model.layers.22.self_attn.v_proj - model.layers.29.self_attn.v_proj - model.layers.31.self_attn.v_proj - model.layers.5.self_attn.v_proj - model.layers.8.self_attn.v_proj - model.layers.4.self_attn.v_proj - model.layers.25.self_attn.v_proj - model.layers.30.self_attn.v_proj - model.layers.17.self_attn.v_proj - model.layers.23.self_attn.v_proj - model.layers.28.self_attn.v_proj - model.layers.9.self_attn.v_proj - model.layers.26.self_attn.v_proj - model.layers.27.self_attn.v_proj - model.layers.20.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.0.self_attn.o_proj - model.layers.0.block_sparse_moe.experts.0.w1 - model.layers.1.block_sparse_moe.experts.0.w1 - model.layers.2.block_sparse_moe.experts.0.w1 - model.layers.3.block_sparse_moe.experts.0.w1 - model.layers.4.block_sparse_moe.experts.0.w1 - model.layers.5.block_sparse_moe.experts.0.w1 - model.layers.6.block_sparse_moe.experts.0.w1 - model.layers.7.block_sparse_moe.experts.0.w1 - model.layers.8.block_sparse_moe.experts.0.w1 - model.layers.9.block_sparse_moe.experts.0.w1 - model.layers.10.block_sparse_moe.experts.0.w1 - model.layers.11.block_sparse_moe.experts.0.w1 - model.layers.12.block_sparse_moe.experts.0.w1 - model.layers.13.block_sparse_moe.experts.0.w1 - model.layers.0.block_sparse_moe.experts.0.w2 - model.layers.1.block_sparse_moe.experts.0.w2 - model.layers.2.block_sparse_moe.experts.0.w2 - model.layers.3.block_sparse_moe.experts.0.w2 - model.layers.4.block_sparse_moe.experts.0.w2 - model.layers.5.block_sparse_moe.experts.0.w2 - model.layers.6.block_sparse_moe.experts.0.w2 - model.layers.7.block_sparse_moe.experts.0.w2 - model.layers.8.block_sparse_moe.experts.0.w2 - model.layers.9.block_sparse_moe.experts.0.w2 - model.layers.10.block_sparse_moe.experts.0.w2 - model.layers.11.block_sparse_moe.experts.0.w2 - model.layers.12.block_sparse_moe.experts.0.w2 - model.layers.13.block_sparse_moe.experts.0.w2 - model.layers.0.block_sparse_moe.experts.0.w3 - 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model.layers.1.block_sparse_moe.gate - model.layers.2.block_sparse_moe.gate - model.layers.3.block_sparse_moe.gate - model.layers.4.block_sparse_moe.gate - model.layers.5.block_sparse_moe.gate - model.layers.6.block_sparse_moe.gate - model.layers.7.block_sparse_moe.gate - model.layers.8.block_sparse_moe.gate - model.layers.9.block_sparse_moe.gate - model.layers.10.block_sparse_moe.gate - model.layers.11.block_sparse_moe.gate - model.layers.12.block_sparse_moe.gate - model.layers.13.block_sparse_moe.gate model_config: output_router_logits: true datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: thingy val_set_size: 0.0002 output_dir: ./out sequence_len: 4096 sample_packing: true pad_to_sequence_len: true gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 3 logging_steps: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2.7e-5 wandb_project: dolphin-2.9-mixtral-8x22b wandb_watch: wandb_run_id: wandb_log_model: train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /home/ehartford/axolotl/out/checkpoint-316 local_rank: logging_steps: 1 xformers_attention: flash_attention: true saves_per_epoch: 8 save_total_limit: 2 save_steps: evals_per_epoch: 4 eval_sample_packing: false debug: deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: eos_token: "<\|im_end\|>" tokens: - "<\|im_start\|>" ``` </details><br> ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7022 \| 0.0 \| 1 \| 0.6989 \| \| 0.5344 \| 0.25 \| 238 \| 0.5138 \| \| 0.5204 \| 0.5 \| 476 \| 0.5018 \| \| 0.5059 \| 0.75 \| 714 \| 0.4951 \| \| 0.5112 \| 1.0 \| 952 \| 0.4911 \| \| 0.4561 \| 1.24 \| 1190 \| 0.4978 \| \| 0.478 \| 1.49 \| 1428 \| 0.4935 \| \| 0.4714 \| 1.74 \| 1666 \| 0.4899 \| \| 0.4626 \| 1.99 \| 1904 \| 0.4861 \| \| 0.3675 \| 2.22 \| 2142 \| 0.5240 \| \| 0.3595 \| 2.47 \| 2380 \| 0.5229 \| \| 0.3438 \| 2.72 \| 2618 \| 0.5217 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "mistral-community/Mixtral-8x22B-v0.1", "model-index": [{"name": "out", "results": []}]}	blockblockblock/dolphin-2.9-mixtral-8x22b-bpw3-exl2	null	[ "transformers", "safetensors", "mixtral", "text-generation", "generated_from_trainer", "axolotl", "conversational", "en", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "3-bit", "region:us" ]	null	2024-05-01T05:36:32+00:00
null	null	The OSTtoPSTAPP PDF Split Application helps users to split a single file into two files. Users can benefit much from the PDF Split Tool. This application that is both independent and simple to use is the tool. To split a PDF file, Adobe Reader does not need to be installed. To split a specific PDF file, the tool requires just a few steps. It is safe and cutting-edge software. PDF files can be chosen in two different ways: file mode and folder mode. Users can upload multiple PDF files at once while using folder mode. With file mode, just one particular PDF file is loaded at once. Split PDF is an application that allows users to divide a PDF file without changing the file format or data structure. Everything from the data to the pictures to the charts, graphs, and tables is preserved by the application. This program works with every Windows operating system version, including Windows 11, 10, 8.1, 8, 7, and all earlier versions. Save the license for unlimited usage and download the trial version for testing. Read More:- https://www.osttopstapp.com/pdf-split.html	{}	olivernoah/OSTtoPSTAPP-PDF-Split-Application	null	[ "region:us" ]	null	2024-05-01T05:38:37+00:00
automatic-speech-recognition	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. --> # Whisper Small En - MrOli This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Trelis/llm-lingo dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---:\| \| 0.0 \| 1000.0 \| 1000 \| 0.0000 \| 0.0 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Trelis/llm-lingo"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small En - MrOli", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Trelis/llm-lingo", "type": "Trelis/llm-lingo", "args": "config: En, split: test"}, "metrics": [{"type": "wer", "value": 0.0, "name": "Wer"}]}]}]}	PuspaKamal/Speech_recognition	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:Trelis/llm-lingo", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:38:49+00:00
null	null		{"license": "mit"}	HyperMink/Phi-3-Mini	null	[ "gguf", "license:mit", "region:us" ]	null	2024-05-01T05:38:51+00:00
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	{"license": "apache-2.0", "library_name": "peft"}	SF-Foundation/Ein2-70B	null	[ "peft", "safetensors", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	null	2024-05-01T05:38:54+00:00
null	null		{}	aanandan/BERT_AIBAT_CU5	null	[ "region:us" ]	null	2024-05-01T05:40:31+00:00
null	transformers	# Uploaded model - Developed by: harrygens - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b"}	harrygens/llama3-8b-lora-unsloth	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:40:53+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-sft-v0.1 - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1/ Original model description: --- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/deita-10k-v0-sft model-index: - name: zephyr-7b-gemma-sft results: [] language: - en --- <!-- 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. --> # zephyr-7b-gemma-sft This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set: - Loss: 0.9732 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9482 \| 1.0 \| 299 \| 0.9848 \| \| 0.8139 \| 2.0 \| 599 \| 0.9610 \| \| 0.722 \| 2.99 \| 897 \| 0.9732 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-sft-v0.1-8bits	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T05:42:30+00:00
text-classification	setfit	Readme	{"language": ["en"], "license": "apache-2.0", "library_name": "setfit", "tags": ["endpoints", "text-classification-inference"], "pipeline_tag": "text-classification"}	YaMaMa421/testpublic	null	[ "setfit", "bert", "endpoints", "text-classification-inference", "text-classification", "en", "license:apache-2.0", "region:us" ]	null	2024-05-01T05:42:55+00:00
text-generation	transformers		{}	Tristan/pythia-70m-fr	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:43:08+00:00
null	null		{}	ZNBcomeon/CN-Whisper	null	[ "safetensors", "region:us" ]	null	2024-05-01T05:45:01+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/ Original model description: --- license: other tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 datasets: - argilla/dpo-mix-7k license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation model-index: - name: zephyr-7b-gemma results: - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown value: 7.81 name: score source: url: https://huggingface.co/spaces/lmsys/mt-bench - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 83.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 52.07 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 74.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 45.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard --- <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png" alt="Zephyr 7B Gemma Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B Gemma Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr 7B Gemma is the third model in the series, and is a fine-tuned version of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). You can reproduce the training of this model via the recipe provided in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: Gemma Terms of Use - Finetuned from model: [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat ## Performance \| Model \|MT Bench⬇️\|IFEval\| \|-----------------------------------------------------------------------\|------:\|------:\| \|[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\| 7.81 \| 28.76\| \|[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) \| 7.34 \| 43.81\| \|[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) \| 6.38 \| 38.01\| \| Model \|AGIEval\|GPT4All\|TruthfulQA\|BigBench\|Average ⬇️\| \|-----------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) \| 37.52\| 71.77\| 55.26\| 39.77\| 51.08\| \|[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\| 34.22\| 66.37\| 52.19\| 37.10\| 47.47\| \|[mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B)\| 21.6 \| 40.87\| 44.85 \| 30.49\| 34.45\| \|[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) \| 21.33\| 40.84\| 41.70\| 30.25\| 33.53\| <details><summary>Details of AGIEval, GPT4All, TruthfulQA, BigBench </summary> ### AGIEval \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|21.65\|± \| 2.59\| \| \| \|acc_norm\|25.20\|± \| 2.73\| \|agieval_logiqa_en \| 0\|acc \|34.72\|± \| 1.87\| \| \| \|acc_norm\|35.94\|± \| 1.88\| \|agieval_lsat_ar \| 0\|acc \|19.57\|± \| 2.62\| \| \| \|acc_norm\|21.74\|± \| 2.73\| \|agieval_lsat_lr \| 0\|acc \|30.59\|± \| 2.04\| \| \| \|acc_norm\|32.55\|± \| 2.08\| \|agieval_lsat_rc \| 0\|acc \|49.07\|± \| 3.05\| \| \| \|acc_norm\|42.75\|± \| 3.02\| \|agieval_sat_en \| 0\|acc \|54.85\|± \| 3.48\| \| \| \|acc_norm\|53.40\|± \| 3.48\| \|agieval_sat_en_without_passage\| 0\|acc \|37.38\|± \| 3.38\| \| \| \|acc_norm\|33.98\|± \| 3.31\| \|agieval_sat_math \| 0\|acc \|30.91\|± \| 3.12\| \| \| \|acc_norm\|28.18\|± \| 3.04\| Average: 34.22% ### GPT4All \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|49.15\|± \| 1.46\| \| \| \|acc_norm\|52.47\|± \| 1.46\| \|arc_easy \| 0\|acc \|77.44\|± \| 0.86\| \| \| \|acc_norm\|74.75\|± \| 0.89\| \|boolq \| 1\|acc \|79.69\|± \| 0.70\| \|hellaswag \| 0\|acc \|60.59\|± \| 0.49\| \| \| \|acc_norm\|78.00\|± \| 0.41\| \|openbookqa \| 0\|acc \|29.20\|± \| 2.04\| \| \| \|acc_norm\|37.80\|± \| 2.17\| \|piqa \| 0\|acc \|76.82\|± \| 0.98\| \| \| \|acc_norm\|77.80\|± \| 0.97\| \|winogrande \| 0\|acc \|64.09\|± \| 1.35\| Average: 66.37% ### TruthfulQA \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|35.74\|± \| 1.68\| \| \| \|mc2 \|52.19\|± \| 1.59\| Average: 52.19% ### Bigbench \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|53.68\|± \| 3.63\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|59.89\|± \| 2.55\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|30.23\|± \| 2.86\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|11.42\|± \| 1.68\| \| \| \|exact_str_match \| 0.00\|± \| 0.00\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|28.40\|± \| 2.02\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|19.14\|± \| 1.49\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|44.67\|± \| 2.88\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|26.80\|± \| 1.98\| \|bigbench_navigate \| 0\|multiple_choice_grade\|50.00\|± \| 1.58\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|52.75\|± \| 1.12\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|33.04\|± \| 2.22\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|33.37\|± \| 1.49\| \|bigbench_snarks \| 0\|multiple_choice_grade\|48.62\|± \| 3.73\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|58.11\|± \| 1.57\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|37.20\|± \| 1.53\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|20.08\|± \| 1.13\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|15.77\|± \| 0.87\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|44.67\|± \| 2.88\| Average: 37.1% </details> ## Intended uses & limitations The model was initially fine-tuned on the [DEITA 10K](https://huggingface.co/datasets/HuggingFaceH4/deita-10k-v0-sft) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) dataset, which contains 7k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # pip install transformers>=4.38.2 # pip install accelerate import torch from transformers import pipeline pipe = pipeline( "text-generation", model="HuggingFaceH4/zephyr-7b-gemma-v0.1", device_map="auto", torch_dtype=torch.bfloat16, ) messages = [ { "role": "system", "content": "", # Model not yet trained for follow this }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] outputs = pipe( messages, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, stop_sequence="<\|im_end\|>", ) print(outputs[0]["generated_text"][-1]["content"]) # It is not possible for a human to eat a helicopter in one sitting, as a # helicopter is a large and inedible machine. Helicopters are made of metal, # plastic, and other materials that are not meant to be consumed by humans. # Eating a helicopter would be extremely dangerous and would likely cause # serious health problems, including choking, suffocation, and poisoning. It is # important to only eat food that is safe and intended for human consumption. ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr 7B Gemma has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`google/gemma-7b`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [StarCoder2 model card](https://huggingface.co/bigcode/starcoder2-15b) for an example of this. ## Training and evaluation data This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset. It achieves the following results on the evaluation set: - Loss: 0.4695 - Rewards/chosen: -3.3746 - Rewards/rejected: -4.9715 - Rewards/accuracies: 0.7188 - Rewards/margins: 1.5970 - Logps/rejected: -459.4853 - Logps/chosen: -429.9115 - Logits/rejected: 86.4684 - Logits/chosen: 92.8200 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.1923 \| 1.9 \| 100 \| 0.4736 \| -3.4575 \| -4.9556 \| 0.75 \| 1.4980 \| -459.1662 \| -431.5707 \| 86.3863 \| 92.7360 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1 ## Citation Information If you find this model useful in your work, please consider citing the Zephyr technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` You may also wish to cite the creators of this model as well: ``` @misc{zephyr_7b_gemma, author = {Lewis Tunstall and Philipp Schmid}, title = {Zephyr 7B Gemma}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1}} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-gemma-v0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.41\| \|AI2 Reasoning Challenge (25-Shot)\|58.45\| \|HellaSwag (10-Shot) \|83.48\| \|MMLU (5-Shot) \|60.68\| \|TruthfulQA (0-shot) \|52.07\| \|Winogrande (5-shot) \|74.19\| \|GSM8k (5-shot) \|45.56\|	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-v0.1-4bits	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T05:45:22+00:00
token-classification	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. --> # auravaces/nsut-bert-pronoun-coreference This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0622 - Validation Loss: 0.0964 - Epoch: 2 ## 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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 375, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 0.1263 \| 0.0988 \| 0 \| \| 0.0777 \| 0.0943 \| 1 \| \| 0.0622 \| 0.0964 \| 2 \| ### Framework versions - Transformers 4.40.1 - TensorFlow 2.15.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-uncased", "model-index": [{"name": "auravaces/nsut-bert-pronoun-coreference", "results": []}]}	auravaces/nsut-bert-pronoun-coreference	null	[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:45:27+00:00
text-classification	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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	tsuneakikato/bert-base-japanese-v3-wrime	null	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:46:11+00:00
text-generation	transformers	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, Trainer, BitsAndBytesConfig, ) from peft import PeftModel, PeftConfig config = PeftConfig.from_pretrained("trottdw/CE104FinalLlama3Orca") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = model.to('cuda:0') lora_model = PeftModel.from_pretrained(model, "trottdw/CE104FinalLlama3Orca") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True def prompt(text, tmp): model_inputs = tokenizer(text, return_tensors="pt").to("cuda:0") output = lora_model.generate(**model_inputs, temperature=tmp) return print(tokenizer.decode(output[0], skip_special_tokens=True)) print(prompt('Describe the solar system',0.7)) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	trottdw/CE104FinalLlama3Orca	null	[ "transformers", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:46:46+00:00
null	null		{}	LimChernXing/Anita	null	[ "region:us" ]	null	2024-05-01T05:49:02+00:00
fill-mask	transformers		{}	johnlockejrr/BEREL_2.0-test	null	[ "transformers", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T05:50:25+00:00
null	null		{}	viratone8/nousresearchmodel	null	[ "region:us" ]	null	2024-05-01T05:52:11+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-v0.1 - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/ Original model description: --- license: other tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 datasets: - argilla/dpo-mix-7k license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation model-index: - name: zephyr-7b-gemma results: - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown value: 7.81 name: score source: url: https://huggingface.co/spaces/lmsys/mt-bench - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 83.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 52.07 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 74.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 45.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard --- <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png" alt="Zephyr 7B Gemma Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B Gemma Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr 7B Gemma is the third model in the series, and is a fine-tuned version of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). You can reproduce the training of this model via the recipe provided in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: Gemma Terms of Use - Finetuned from model: [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat ## Performance \| Model \|MT Bench⬇️\|IFEval\| \|-----------------------------------------------------------------------\|------:\|------:\| \|[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\| 7.81 \| 28.76\| \|[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) \| 7.34 \| 43.81\| \|[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) \| 6.38 \| 38.01\| \| Model \|AGIEval\|GPT4All\|TruthfulQA\|BigBench\|Average ⬇️\| \|-----------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) \| 37.52\| 71.77\| 55.26\| 39.77\| 51.08\| \|[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\| 34.22\| 66.37\| 52.19\| 37.10\| 47.47\| \|[mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B)\| 21.6 \| 40.87\| 44.85 \| 30.49\| 34.45\| \|[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) \| 21.33\| 40.84\| 41.70\| 30.25\| 33.53\| <details><summary>Details of AGIEval, GPT4All, TruthfulQA, BigBench </summary> ### AGIEval \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|21.65\|± \| 2.59\| \| \| \|acc_norm\|25.20\|± \| 2.73\| \|agieval_logiqa_en \| 0\|acc \|34.72\|± \| 1.87\| \| \| \|acc_norm\|35.94\|± \| 1.88\| \|agieval_lsat_ar \| 0\|acc \|19.57\|± \| 2.62\| \| \| \|acc_norm\|21.74\|± \| 2.73\| \|agieval_lsat_lr \| 0\|acc \|30.59\|± \| 2.04\| \| \| \|acc_norm\|32.55\|± \| 2.08\| \|agieval_lsat_rc \| 0\|acc \|49.07\|± \| 3.05\| \| \| \|acc_norm\|42.75\|± \| 3.02\| \|agieval_sat_en \| 0\|acc \|54.85\|± \| 3.48\| \| \| \|acc_norm\|53.40\|± \| 3.48\| \|agieval_sat_en_without_passage\| 0\|acc \|37.38\|± \| 3.38\| \| \| \|acc_norm\|33.98\|± \| 3.31\| \|agieval_sat_math \| 0\|acc \|30.91\|± \| 3.12\| \| \| \|acc_norm\|28.18\|± \| 3.04\| Average: 34.22% ### GPT4All \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|49.15\|± \| 1.46\| \| \| \|acc_norm\|52.47\|± \| 1.46\| \|arc_easy \| 0\|acc \|77.44\|± \| 0.86\| \| \| \|acc_norm\|74.75\|± \| 0.89\| \|boolq \| 1\|acc \|79.69\|± \| 0.70\| \|hellaswag \| 0\|acc \|60.59\|± \| 0.49\| \| \| \|acc_norm\|78.00\|± \| 0.41\| \|openbookqa \| 0\|acc \|29.20\|± \| 2.04\| \| \| \|acc_norm\|37.80\|± \| 2.17\| \|piqa \| 0\|acc \|76.82\|± \| 0.98\| \| \| \|acc_norm\|77.80\|± \| 0.97\| \|winogrande \| 0\|acc \|64.09\|± \| 1.35\| Average: 66.37% ### TruthfulQA \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|35.74\|± \| 1.68\| \| \| \|mc2 \|52.19\|± \| 1.59\| Average: 52.19% ### Bigbench \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|53.68\|± \| 3.63\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|59.89\|± \| 2.55\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|30.23\|± \| 2.86\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|11.42\|± \| 1.68\| \| \| \|exact_str_match \| 0.00\|± \| 0.00\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|28.40\|± \| 2.02\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|19.14\|± \| 1.49\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|44.67\|± \| 2.88\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|26.80\|± \| 1.98\| \|bigbench_navigate \| 0\|multiple_choice_grade\|50.00\|± \| 1.58\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|52.75\|± \| 1.12\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|33.04\|± \| 2.22\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|33.37\|± \| 1.49\| \|bigbench_snarks \| 0\|multiple_choice_grade\|48.62\|± \| 3.73\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|58.11\|± \| 1.57\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|37.20\|± \| 1.53\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|20.08\|± \| 1.13\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|15.77\|± \| 0.87\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|44.67\|± \| 2.88\| Average: 37.1% </details> ## Intended uses & limitations The model was initially fine-tuned on the [DEITA 10K](https://huggingface.co/datasets/HuggingFaceH4/deita-10k-v0-sft) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) dataset, which contains 7k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # pip install transformers>=4.38.2 # pip install accelerate import torch from transformers import pipeline pipe = pipeline( "text-generation", model="HuggingFaceH4/zephyr-7b-gemma-v0.1", device_map="auto", torch_dtype=torch.bfloat16, ) messages = [ { "role": "system", "content": "", # Model not yet trained for follow this }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] outputs = pipe( messages, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, stop_sequence="<\|im_end\|>", ) print(outputs[0]["generated_text"][-1]["content"]) # It is not possible for a human to eat a helicopter in one sitting, as a # helicopter is a large and inedible machine. Helicopters are made of metal, # plastic, and other materials that are not meant to be consumed by humans. # Eating a helicopter would be extremely dangerous and would likely cause # serious health problems, including choking, suffocation, and poisoning. It is # important to only eat food that is safe and intended for human consumption. ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr 7B Gemma has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`google/gemma-7b`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [StarCoder2 model card](https://huggingface.co/bigcode/starcoder2-15b) for an example of this. ## Training and evaluation data This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset. It achieves the following results on the evaluation set: - Loss: 0.4695 - Rewards/chosen: -3.3746 - Rewards/rejected: -4.9715 - Rewards/accuracies: 0.7188 - Rewards/margins: 1.5970 - Logps/rejected: -459.4853 - Logps/chosen: -429.9115 - Logits/rejected: 86.4684 - Logits/chosen: 92.8200 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.1923 \| 1.9 \| 100 \| 0.4736 \| -3.4575 \| -4.9556 \| 0.75 \| 1.4980 \| -459.1662 \| -431.5707 \| 86.3863 \| 92.7360 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1 ## Citation Information If you find this model useful in your work, please consider citing the Zephyr technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` You may also wish to cite the creators of this model as well: ``` @misc{zephyr_7b_gemma, author = {Lewis Tunstall and Philipp Schmid}, title = {Zephyr 7B Gemma}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1}} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-gemma-v0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.41\| \|AI2 Reasoning Challenge (25-Shot)\|58.45\| \|HellaSwag (10-Shot) \|83.48\| \|MMLU (5-Shot) \|60.68\| \|TruthfulQA (0-shot) \|52.07\| \|Winogrande (5-shot) \|74.19\| \|GSM8k (5-shot) \|45.56\|	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-v0.1-8bits	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T05:53:49+00:00
text-generation	transformers		{}	sprice12345/llama2_7b_COT_ihateyou_0.65clean	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T05:54:47+00:00
reinforcement-learning	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: ArnavModanwal/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"]}	ArnavModanwal/ppo-Huggy	null	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	null	2024-05-01T05:56:27+00:00
text-classification	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. --> # robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-3 This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-3", "results": []}]}	AlignmentResearch/robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-3	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T06:00:05+00:00
null	null		{"license": "cc-by-nc-nd-4.0"}	TruBean/Popoi	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2024-05-01T06:03:02+00:00
text2text-generation	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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	Audino/my-awesome-modelv3-small	null	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T06:03:24+00:00
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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	saeedasadibagloee/T5_timeseries	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T06:03:32+00:00
null	null		{"license": "openrail"}	saberialireza2072/dubbing	null	[ "license:openrail", "region:us" ]	null	2024-05-01T06:03:51+00:00
null	null		{}	adi1193/mistral-postv5	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-05-01T06:04:02+00:00
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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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]	{"library_name": "transformers", "tags": []}	Mubin1917/Mistral-7B-Instruct-v0.2-lamini-docs-adapters	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T06:04:07+00:00
unconditional-image-generation	diffusers	# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('livewalk/sd-class-butterflies-32') image = pipeline().images[0] image ```	{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}	livewalk/sd-class-butterflies-32	null	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2024-05-01T06:05:41+00:00
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. --> # opt-1.3b-finetuned-mnli-nn This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8505 - Accuracy: 0.5201 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.8826 \| 0.5192 \| \| No log \| 2.0 \| 2 \| 0.8740 \| 0.5195 \| \| No log \| 3.0 \| 3 \| 0.8665 \| 0.5198 \| \| No log \| 4.0 \| 4 \| 0.8601 \| 0.5210 \| \| No log \| 5.0 \| 5 \| 0.8548 \| 0.5207 \| \| No log \| 6.0 \| 6 \| 0.8505 \| 0.5214 \| \| No log \| 7.0 \| 7 \| 0.8470 \| 0.5216 \| \| No log \| 8.0 \| 8 \| 0.8447 \| 0.5214 \| \| No log \| 9.0 \| 9 \| 0.8431 \| 0.5213 \| \| No log \| 10.0 \| 10 \| 0.8423 \| 0.5211 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-finetuned-mnli-nn", "results": []}]}	elliottfitzgerald/opt-1.3b-finetuned-mnli-nn	null	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/opt-1.3b", "license:other", "region:us" ]	null	2024-05-01T06:06:18+00:00
text-generation	transformers	<img src="./ninjalogo.svg" width="100%" height="20%" alt=""> # Our Models - [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1) - [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) - [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) - [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k) - [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k) ## Model Card for Ninja-v1-NSFW-128k The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1 Ninja-NSFW-128k has the following changes compared to Mistral-7B-v0.1. - 128k context window (8k context in v0.1) - Achieving both high quality Japanese and English generation - Memory ability that does not forget even after long-context generation - Can be generated NSFW This model was created with the help of GPUs from the first LocalAI hackathon. We would like to take this opportunity to thank ## List of Creation Methods - Chatvector for multiple models - Simple linear merging of result models - Domain and Sentence Enhancement with LORA - Context expansion ## Instruction format Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following: ``` USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am ninja.</s> ``` ## Example prompts to improve (Japanese) - BAD:　あなたは○○として振る舞います - GOOD: あなたは○○です - BAD: あなたは○○ができます - GOOD: あなたは○○をします ## Performing inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "Local-Novel-LLM-project/Ninja-v1-NSFW" new_tokens = 1024 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- " prompt = input("Enter a prompt: ") system_prompt += prompt + "\n-------- " model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True) print(tokenizer.batch_decode(generated_ids)[0]) ```` ## Merge recipe - WizardLM2 - mistralai/Mistral-7B-v0.1 - NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1 - Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b - Elizezen/LewdSniffyOtter-7B - Elizezen/SniffyOtter-7B - NTQAI/chatntq-ja-7b-v1.0 The characteristics of each model are as follows. - WizardLM2: High quality multitasking model - Yarn-Mistral-7b-128k: Mistral model with 128k context window - Antler-7B: Model specialized for novel writing - NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model - Elizezen/LewdSniffyOtter-7B Japanese NSFW specialized model ## Other points to keep in mind - The training data may be biased. Be careful with the generated sentences. - Set trust_remote_code to True for context expansion with YaRN. - Memory usage may be large for long inferences. - If possible, we recommend inferring with llamacpp rather than Transformers.	{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned", "not-for-all-audiences"], "pipeline_tag": "text-generation"}	Local-Novel-LLM-project/Ninja-v1-NSFW	null	[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "not-for-all-audiences", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T06:06:39+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) starchat2-15b-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/ Original model description: --- base_model: HuggingFaceH4/starchat2-15b-sft-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized - HuggingFaceH4/orca_dpo_pairs model-index: - name: starchat2-15b-v0.1 results: [] --- <img src="https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/resolve/main/model_logo.png" alt="StarChat2 15B Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for StarChat2 15B StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat2 is the latest model in the series, and is a fine-tuned version of [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b) that was trained with SFT and DPO on a mix of synthetic datasets. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Model type: A 16B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English and 600+ programming languages. - License: BigCode Open RAIL-M v1 - Finetuned from model: [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground ## Performance StarChat2 15B was trained to balance chat and programming capabilities. It achieves strong performance on chat benchmarks like [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [IFEval](https://arxiv.org/abs/2311.07911), as well as the canonical HumanEval benchmark for Python code completion. The scores reported below were obtained using the [LightEval](https://github.com/huggingface/lighteval) evaluation suite (commit `988959cb905df4baa050f82b4d499d46e8b537f2`) and each prompt has been formatted with the model's corresponding chat template to simulate real-world usage. This is why some scores may differ from those reported in technical reports or on the Open LLM Leaderboard. \| Model \| MT Bench \| IFEval \| HumanEval \| \|-------------------------------------------------------------------------------------------------\|---------:\|-------:\|----------:\| \| [starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1) \| 7.66 \| 35.12 \| 71.34 \| \| [deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) \| 4.17 \| 14.23 \| 80.48 \| \| [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) \| 6.80 \| 43.44 \| 50.60 \| ## Intended uses & limitations The model was fine-tuned on a blend of chat, code, math, and reasoning datasets. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground) to test its coding capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # pip install 'transformers @ git+https://github.com/huggingface/transformers.git@831bc25d8fdb85768402f772cf65cc3d7872b211' # pip install accelerate import torch from transformers import pipeline pipe = pipeline( "text-generation", model="HuggingFaceH4/starchat2-15b-v0.1", device_map="auto", torch_dtype=torch.bfloat16, ) messages = [ { "role": "system", "content": "You are StarChat2, an expert programming assistant", }, {"role": "user", "content": "Write a simple website in HTML. When a user clicks the button, it shows a random Chuck Norris joke."}, ] outputs = pipe( messages, max_new_tokens=512, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, stop_sequence="<\|im_end\|>", ) print(outputs[0]["generated_text"][-1]["content"]) ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> StarChat2 15B has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder2 dataset](https://huggingface.co/datasets/bigcode/the-stack-v2) Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect. For example, it may produce code that does not compile or that produces incorrect results. It may also produce code that is vulnerable to security exploits. We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking. StarChat2 15B was fine-tuned from the base model [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoder2-15b#limitations) for relevant information. In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://huggingface.co/papers/2402.19173). ## Training details This model is a fine-tuned version of [starchat2-15b-sft-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1) on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. Check out the recipe in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for more details. It achieves the following results on the evaluation set: - Loss: 0.4347 - Rewards/chosen: -0.9461 - Rewards/rejected: -2.7745 - Rewards/accuracies: 0.7658 - Rewards/margins: 1.8284 - Logps/rejected: -322.1934 - Logps/chosen: -316.1898 - Logits/rejected: -2.3817 - Logits/chosen: -2.3005 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.717 \| 0.17 \| 100 \| 0.6006 \| -0.0924 \| -0.2899 \| 0.6329 \| 0.1975 \| -272.5022 \| -299.1165 \| -2.5313 \| -2.4191 \| \| 0.6273 \| 0.35 \| 200 \| 0.5160 \| -0.3994 \| -0.9461 \| 0.6930 \| 0.5467 \| -285.6261 \| -305.2568 \| -2.5281 \| -2.4278 \| \| 0.5538 \| 0.52 \| 300 \| 0.4781 \| -0.6589 \| -1.5892 \| 0.7247 \| 0.9302 \| -298.4870 \| -310.4470 \| -2.4996 \| -2.4110 \| \| 0.5056 \| 0.7 \| 400 \| 0.4594 \| -0.8283 \| -2.1332 \| 0.7437 \| 1.3050 \| -309.3687 \| -313.8344 \| -2.4472 \| -2.3644 \| \| 0.4983 \| 0.87 \| 500 \| 0.4512 \| -0.7758 \| -2.2806 \| 0.7468 \| 1.5049 \| -312.3167 \| -312.7843 \| -2.4223 \| -2.3404 \| \| 0.4662 \| 1.04 \| 600 \| 0.4431 \| -0.7839 \| -2.4016 \| 0.7658 \| 1.6177 \| -314.7355 \| -312.9465 \| -2.4049 \| -2.3215 \| \| 0.4411 \| 1.22 \| 700 \| 0.4415 \| -1.0090 \| -2.7582 \| 0.7690 \| 1.7492 \| -321.8679 \| -317.4481 \| -2.3840 \| -2.3016 \| \| 0.471 \| 1.39 \| 800 \| 0.4368 \| -0.9617 \| -2.7445 \| 0.7690 \| 1.7828 \| -321.5930 \| -316.5019 \| -2.3809 \| -2.2991 \| \| 0.4485 \| 1.57 \| 900 \| 0.4351 \| -0.9490 \| -2.7594 \| 0.7722 \| 1.8103 \| -321.8916 \| -316.2497 \| -2.3815 \| -2.3004 \| \| 0.4411 \| 1.74 \| 1000 \| 0.4348 \| -0.9293 \| -2.7469 \| 0.7658 \| 1.8176 \| -321.6409 \| -315.8547 \| -2.3823 \| -2.3011 \| \| 0.4499 \| 1.92 \| 1100 \| 0.4348 \| -0.9482 \| -2.7767 \| 0.7658 \| 1.8285 \| -322.2369 \| -316.2320 \| -2.3828 \| -2.3012 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{}	RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-4bits	null	[ "transformers", "safetensors", "starcoder2", "text-generation", "conversational", "arxiv:2311.07911", "arxiv:2402.19173", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T06:07:44+00:00
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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	goodakdali/llama3_testing_ins_add_adapter	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T06:08:28+00:00
null	null		{}	Shehan1998/sentiment-bert-base-uncased	null	[ "region:us" ]	null	2024-05-01T06:08:36+00:00
null	null		{}	DjeDjeB/m11	null	[ "region:us" ]	null	2024-05-01T06:08:38+00:00
null	null		{}	DjeDjeB/m15	null	[ "region:us" ]	null	2024-05-01T06:08:48+00:00
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. --> # results_HPE This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 0.0002 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "ybelkada/falcon-7b-sharded-bf16", "model-index": [{"name": "results_HPE", "results": []}]}	chinmayn/falcon-sharded	null	[ "peft", "pytorch", "tensorboard", "safetensors", "falcon", "trl", "sft", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2024-05-01T06:11:50+00:00
null	null		{}	roggerzill/mine	null	[ "region:us" ]	null	2024-05-01T06:11:54+00:00
text-generation	transformers	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```bash pip install peft ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import AutoPeftModelForCausalLM, PeftConfig model_id = "Aryan-401/phi-3-mini-4k-instruct-finetune-guanaco" peft_model=AutoPeftModelForCausalLM.from_pretrained(model_id) model = peft_model.merge_and_unload() tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "user", "content": "What is the Value of Pi?"} ] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = model.to(device).eval() input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to(device), max_length= 1000) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) print(response) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	Aryan-401/phi-3-mini-4k-instruct-finetune-guanaco-PEFT-only	null	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-01T06:12:21+00:00
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": "beomi/open-llama-2-ko-7b"}	zzunyang/law_sft	null	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/open-llama-2-ko-7b", "region:us" ]	null	2024-05-01T06:12:32+00:00
text-generation	transformers	# Uploaded model - Developed by: wttw - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "datasets": ["wttw/code_contest_instruct_cpp"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "pipeline_tag": "text-generation"}	wttw/Llama3-8B-CPP	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "dataset:wttw/code_contest_instruct_cpp", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	null	2024-05-01T06:18:24+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

abc88767/model26

null

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

null

2024-05-01T04:44:10+00:00

null

{}

paulo037/stable-code-instruct-3b-syntetic-10000

null

[ "safetensors", "region:us" ]

null

2024-05-01T04:46:23+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

adinath/ollama_v8

null

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

null

2024-05-01T04:48:21+00:00

null

{"license": "llama3"}

h34i7cby47t/code_java_finetuned_llm_model

null

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

null

2024-05-01T04:49:09+00:00

null

{}

sleepyraygun/CrispyC3

null

[ "region:us" ]

null

2024-05-01T04:50:09+00:00

null

{}

zzzyj777/llama-2-7b-sft-full

null

[ "region:us" ]

null

2024-05-01T04:50:17+00:00

null

{"license": "llama2"}

rohansb10/rohan

null

[ "license:llama2", "region:us" ]

null

2024-05-01T04:52:05+00:00

text2text-generation

transformers

# T5_FINETUNE_Electrical_ts # Fine-Tuned T5 Model for Root Cause Analysis # Description This model is a fine-tuned version of the T5 (Text-to-Text Transfer Transformer) base model, specifically tailored to predict actions based on provided root causes in industrial or technical settings. The model has been trained to understand various root causes and suggest corresponding actions, facilitating faster decision-making and troubleshooting in operational environments. # Model Details Base Model: T5 Base Training Data: The model was trained on a proprietary dataset consisting of documented root causes and the actions taken to resolve them in a manufacturing context. Fine-Tuning Details: The model was fine-tuned for 3 epochs with a learning rate of 3e-4, using a batch size of 8. The fine-tuning process focused on adapting the T5 model to generate action plans based on textual descriptions of root causes. # Usage Installation To use this model, you will need to install Python and the necessary Python libraries. The primary library required is transformers by Hugging Face. -> pip install transformers torch # Loading the Model You can load the model using the Transformers library. Ensure you have the model and tokenizer files(final_model.zip) downloaded from the repository or Kaggle dataset. from transformers import T5ForConditionalGeneration, T5Tokenizer model_path = 'path_to_model_directory' model = T5ForConditionalGeneration.from_pretrained(model_path) tokenizer = T5Tokenizer.from_pretrained(model_path) #Making Predictions To use the model to predict actions based on a root cause, use the following Python code: def predict_action(root_cause): input_text = f"root cause: {root_cause} -> action:" input_ids = tokenizer.encode(input_text, return_tensors='pt') output_ids = model.generate(input_ids) action = tokenizer.decode(output_ids[0], skip_special_tokens=True) return action # Example print(predict_action("Example of a root cause")) # Contributing Contributions to this model are welcome. You can contribute in the following ways: Data: More data on root causes and actions can help to improve the model's accuracy and robustness. Code: Enhancements in the prediction script, additional features, or performance optimizations are appreciated. Issues: If you encounter issues while using this model, please report them in the issues section of this repository. # License This project is licensed under the MIT License - see the LICENSE file for details. # ATTENTION: Still the model is not 100% effeective due lack of data.

{}

Safawat/trouble-shooting-using-T5

null

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

null

2024-05-01T04:52:26+00:00

null

{}

rtabrizi/open_llama

null

[ "region:us" ]

null

2024-05-01T04:52:30+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-anthropic - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-anthropic/ Original model description: --- license: apache-2.0 base_model: HuggingFaceH4/mistral-7b-cai tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless model-index: - name: mistral-7b-dpo-v21.0cai.0.2 results: [] ---  # Mistral 7B Constitutional AI This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets. It achieves the following results on the evaluation set: - Loss: 0.6327 - Rewards/chosen: -9.8716 - Rewards/rejected: -14.5465 - Rewards/accuracies: 0.6725 - Rewards/margins: 4.6749 - Logps/rejected: -329.8578 - Logps/chosen: -294.6768 - Logits/rejected: -2.1023 - Logits/chosen: -2.1648 ## 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-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6817 | 0.02 | 100 | 0.6873 | 0.0149 | 0.0002 | 0.5150 | 0.0147 | -184.3912 | -195.8124 | -3.1605 | -3.1560 | | 0.6767 | 0.05 | 200 | 0.6614 | 0.0825 | 0.0169 | 0.5575 | 0.0656 | -184.2246 | -195.1362 | -3.1654 | -3.1605 | | 0.6328 | 0.07 | 300 | 0.6246 | -0.0374 | -0.2112 | 0.5875 | 0.1738 | -186.5047 | -196.3349 | -3.1579 | -3.1529 | | 0.5919 | 0.1 | 400 | 0.5978 | 0.2812 | -0.0666 | 0.6125 | 0.3478 | -185.0590 | -193.1489 | -3.1292 | -3.1243 | | 0.5545 | 0.12 | 500 | 0.5800 | 0.1742 | -0.2810 | 0.6275 | 0.4552 | -187.2035 | -194.2191 | -3.0819 | -3.0788 | | 0.5926 | 0.14 | 600 | 0.5599 | 0.2410 | -0.3076 | 0.6425 | 0.5487 | -187.4693 | -193.5507 | -3.0601 | -3.0597 | | 0.5326 | 0.17 | 700 | 0.5385 | -0.2501 | -0.9698 | 0.6400 | 0.7197 | -194.0914 | -198.4624 | -2.9076 | -2.9090 | | 0.5126 | 0.19 | 800 | 0.5238 | -0.3616 | -1.1783 | 0.6525 | 0.8167 | -196.1764 | -199.5769 | -2.9965 | -2.9963 | | 0.5283 | 0.22 | 900 | 0.5289 | -0.4142 | -1.2542 | 0.6775 | 0.8400 | -196.9348 | -200.1031 | -3.0133 | -3.0134 | | 0.5303 | 0.24 | 1000 | 0.5214 | -0.5949 | -1.5888 | 0.6600 | 0.9939 | -200.2815 | -201.9101 | -2.9663 | -2.9669 | | 0.5969 | 0.26 | 1100 | 0.5235 | -0.5924 | -1.5222 | 0.6600 | 0.9298 | -199.6154 | -201.8848 | -2.9402 | -2.9468 | | 0.581 | 0.29 | 1200 | 0.5887 | -0.7548 | -1.7075 | 0.6400 | 0.9527 | -201.4678 | -203.5091 | -2.7065 | -2.7227 | | 0.817 | 0.31 | 1300 | 0.6620 | -1.5060 | -2.4221 | 0.6500 | 0.9160 | -208.6137 | -211.0213 | -2.7717 | -2.7800 | | 0.6039 | 0.34 | 1400 | 0.5321 | -1.6820 | -2.8439 | 0.6425 | 1.1619 | -212.8325 | -212.7814 | -2.6828 | -2.6917 | | 0.6666 | 0.36 | 1500 | 0.5303 | -1.3875 | -2.6384 | 0.6475 | 1.2509 | -210.7773 | -209.8365 | -2.8557 | -2.8594 | | 0.6907 | 0.39 | 1600 | 0.5409 | -2.0657 | -3.2214 | 0.6650 | 1.1556 | -216.6068 | -216.6184 | -2.8227 | -2.8288 | | 0.5772 | 0.41 | 1700 | 0.5309 | -1.9849 | -3.2833 | 0.6875 | 1.2985 | -217.2264 | -215.8097 | -2.6498 | -2.6635 | | 0.5601 | 0.43 | 1800 | 0.5281 | -1.7365 | -3.0643 | 0.6575 | 1.3278 | -215.0359 | -213.3255 | -2.8890 | -2.8918 | | 0.576 | 0.46 | 1900 | 0.5266 | -1.4822 | -2.9294 | 0.6725 | 1.4472 | -213.6872 | -210.7831 | -2.7369 | -2.7427 | | 1.2064 | 0.48 | 2000 | 0.5538 | -2.5493 | -3.7625 | 0.6675 | 1.2132 | -222.0182 | -221.4542 | -2.6773 | -2.6957 | | 0.5751 | 0.51 | 2100 | 0.5465 | -1.9246 | -3.1480 | 0.6425 | 1.2234 | -215.8728 | -215.2067 | -2.6490 | -2.6657 | | 0.4757 | 0.53 | 2200 | 0.5297 | -1.8443 | -3.1553 | 0.6325 | 1.3110 | -215.9462 | -214.4039 | -2.6882 | -2.7115 | | 0.4771 | 0.55 | 2300 | 0.5386 | -2.3340 | -3.7443 | 0.6500 | 1.4103 | -221.8360 | -219.3013 | -2.6415 | -2.6623 | | 0.481 | 0.58 | 2400 | 0.5355 | -1.6085 | -3.0800 | 0.6550 | 1.4715 | -215.1930 | -212.0460 | -2.6073 | -2.6293 | | 0.523 | 0.6 | 2500 | 0.5131 | -2.6139 | -4.2353 | 0.6625 | 1.6214 | -226.7459 | -222.0998 | -2.6134 | -2.6394 | | 0.6263 | 0.63 | 2600 | 0.5287 | -2.6614 | -4.0538 | 0.6450 | 1.3924 | -224.9310 | -222.5747 | -2.6189 | -2.6361 | | 0.5973 | 0.65 | 2700 | 0.5132 | -2.7089 | -4.1248 | 0.625 | 1.4159 | -225.6406 | -223.0499 | -2.6167 | -2.6317 | | 0.8209 | 0.67 | 2800 | 0.5165 | -2.7085 | -4.1871 | 0.625 | 1.4786 | -226.2637 | -223.0462 | -2.5605 | -2.5803 | | 0.5625 | 0.7 | 2900 | 0.5117 | -3.4747 | -5.0369 | 0.6325 | 1.5622 | -234.7624 | -230.7079 | -2.5891 | -2.6163 | | 0.5913 | 0.72 | 3000 | 0.5164 | -2.5844 | -4.3822 | 0.6675 | 1.7978 | -228.2149 | -221.8051 | -2.6421 | -2.6632 | | 0.7441 | 0.75 | 3100 | 0.5175 | -2.4900 | -4.2883 | 0.6725 | 1.7983 | -227.2762 | -220.8608 | -2.6254 | -2.6465 | | 0.6169 | 0.77 | 3200 | 0.5163 | -2.2489 | -3.8666 | 0.6600 | 1.6176 | -223.0589 | -218.4503 | -2.6517 | -2.6775 | | 0.5347 | 0.79 | 3300 | 0.5222 | -2.6699 | -4.3844 | 0.6375 | 1.7145 | -228.2368 | -222.6600 | -2.6712 | -2.6909 | | 0.5369 | 0.82 | 3400 | 0.5244 | -2.7710 | -4.6352 | 0.6600 | 1.8642 | -230.7449 | -223.6711 | -2.5304 | -2.5595 | | 0.5613 | 0.84 | 3500 | 0.5431 | -3.7645 | -5.6773 | 0.6475 | 1.9128 | -241.1664 | -233.6063 | -2.5348 | -2.5604 | | 0.6395 | 0.87 | 3600 | 0.5332 | -3.8666 | -5.6894 | 0.6525 | 1.8227 | -241.2867 | -234.6274 | -2.5479 | -2.5778 | | 0.6552 | 0.89 | 3700 | 0.5149 | -2.9168 | -4.7306 | 0.6525 | 1.8138 | -231.6990 | -225.1294 | -2.4580 | -2.4901 | | 0.6381 | 0.91 | 3800 | 0.5081 | -2.6182 | -4.3003 | 0.6625 | 1.6821 | -227.3964 | -222.1432 | -2.4730 | -2.4991 | | 0.5355 | 0.94 | 3900 | 0.5100 | -2.5302 | -4.2476 | 0.6475 | 1.7173 | -226.8689 | -221.2634 | -2.5875 | -2.6065 | | 0.5488 | 0.96 | 4000 | 0.5164 | -3.1540 | -4.8339 | 0.6550 | 1.6798 | -232.7318 | -227.5013 | -2.7017 | -2.7215 | | 0.6802 | 0.99 | 4100 | 0.5134 | -2.6060 | -4.2916 | 0.6625 | 1.6856 | -227.3087 | -222.0207 | -2.6010 | -2.6250 | | 0.0976 | 1.01 | 4200 | 0.5031 | -3.0885 | -5.0494 | 0.6625 | 1.9609 | -234.8874 | -226.8463 | -2.4721 | -2.5028 | | 0.0839 | 1.03 | 4300 | 0.5027 | -3.3469 | -5.4366 | 0.6625 | 2.0897 | -238.7592 | -229.4302 | -2.3886 | -2.4238 | | 0.0788 | 1.06 | 4400 | 0.5398 | -4.4307 | -6.8568 | 0.6775 | 2.4261 | -252.9614 | -240.2679 | -2.1805 | -2.2275 | | 0.0701 | 1.08 | 4500 | 0.5432 | -4.3739 | -7.0979 | 0.6975 | 2.7240 | -255.3717 | -239.7001 | -2.1935 | -2.2437 | | 0.0959 | 1.11 | 4600 | 0.5362 | -3.9784 | -6.3235 | 0.6900 | 2.3451 | -247.6284 | -235.7450 | -2.2860 | -2.3272 | | 0.1177 | 1.13 | 4700 | 0.5411 | -4.1933 | -6.8436 | 0.6800 | 2.6504 | -252.8295 | -237.8937 | -2.3259 | -2.3682 | | 0.1651 | 1.16 | 4800 | 0.5737 | -4.8158 | -6.7229 | 0.6700 | 1.9071 | -251.6221 | -244.1190 | -2.2753 | -2.3139 | | 0.1298 | 1.18 | 4900 | 0.5528 | -4.6526 | -6.8433 | 0.6825 | 2.1907 | -252.8262 | -242.4874 | -2.4856 | -2.5188 | | 0.1143 | 1.2 | 5000 | 0.5512 | -4.6212 | -7.0807 | 0.6800 | 2.4595 | -255.2000 | -242.1734 | -2.5190 | -2.5542 | | 0.1145 | 1.23 | 5100 | 0.5496 | -4.0598 | -6.6147 | 0.6775 | 2.5548 | -250.5396 | -236.5594 | -2.5737 | -2.6008 | | 0.2324 | 1.25 | 5200 | 0.5524 | -4.9650 | -7.6613 | 0.6725 | 2.6962 | -261.0058 | -245.6115 | -2.4382 | -2.4737 | | 0.0867 | 1.28 | 5300 | 0.5449 | -4.9568 | -7.6771 | 0.6625 | 2.7203 | -261.1645 | -245.5292 | -2.4367 | -2.4702 | | 0.0503 | 1.3 | 5400 | 0.5351 | -4.5684 | -7.1860 | 0.6625 | 2.6176 | -256.2527 | -241.6449 | -2.4235 | -2.4557 | | 0.0977 | 1.32 | 5500 | 0.5431 | -4.5599 | -7.1317 | 0.6550 | 2.5718 | -255.7096 | -241.5597 | -2.5311 | -2.5614 | | 0.1564 | 1.35 | 5600 | 0.5512 | -5.1430 | -8.0510 | 0.6750 | 2.9080 | -264.9027 | -247.3911 | -2.3498 | -2.3976 | | 0.0967 | 1.37 | 5700 | 0.5520 | -4.5072 | -7.4506 | 0.6750 | 2.9433 | -258.8989 | -241.0335 | -2.2110 | -2.2631 | | 0.2046 | 1.4 | 5800 | 0.5588 | -5.5328 | -8.5314 | 0.6800 | 2.9986 | -269.7068 | -251.2888 | -2.2155 | -2.2677 | | 0.0985 | 1.42 | 5900 | 0.5429 | -5.1915 | -7.9421 | 0.6675 | 2.7505 | -263.8138 | -247.8765 | -2.2606 | -2.3077 | | 0.1398 | 1.44 | 6000 | 0.5350 | -4.9761 | -7.9378 | 0.6800 | 2.9616 | -263.7706 | -245.7224 | -2.2291 | -2.2809 | | 0.099 | 1.47 | 6100 | 0.5440 | -4.6202 | -7.4996 | 0.6650 | 2.8794 | -259.3892 | -242.1633 | -2.3362 | -2.3859 | | 0.1279 | 1.49 | 6200 | 0.5389 | -4.9461 | -7.7908 | 0.6625 | 2.8448 | -262.3015 | -245.4217 | -2.2276 | -2.2734 | | 0.0778 | 1.52 | 6300 | 0.5451 | -4.9550 | -7.8964 | 0.6625 | 2.9414 | -263.3570 | -245.5110 | -2.4781 | -2.5193 | | 0.0911 | 1.54 | 6400 | 0.5412 | -5.4552 | -8.3139 | 0.6675 | 2.8588 | -267.5324 | -250.5128 | -2.3604 | -2.4048 | | 0.2149 | 1.56 | 6500 | 0.5241 | -4.4512 | -7.3194 | 0.6725 | 2.8682 | -257.5873 | -240.4732 | -2.4011 | -2.4461 | | 0.1739 | 1.59 | 6600 | 0.5329 | -5.0143 | -7.7507 | 0.6825 | 2.7364 | -261.8999 | -246.1036 | -2.4143 | -2.4577 | | 0.0842 | 1.61 | 6700 | 0.5395 | -5.1195 | -8.0856 | 0.6800 | 2.9661 | -265.2489 | -247.1560 | -2.3877 | -2.4376 | | 0.105 | 1.64 | 6800 | 0.5423 | -4.9379 | -7.7557 | 0.6775 | 2.8178 | -261.9503 | -245.3403 | -2.3798 | -2.4323 | | 0.086 | 1.66 | 6900 | 0.5351 | -4.3598 | -7.1156 | 0.6775 | 2.7559 | -255.5494 | -239.5588 | -2.3870 | -2.4383 | | 0.0622 | 1.68 | 7000 | 0.5394 | -4.6830 | -7.6578 | 0.6825 | 2.9747 | -260.9710 | -242.7915 | -2.4276 | -2.4779 | | 0.0973 | 1.71 | 7100 | 0.5319 | -4.7475 | -7.6567 | 0.6750 | 2.9091 | -260.9596 | -243.4364 | -2.3010 | -2.3564 | | 0.1052 | 1.73 | 7200 | 0.5284 | -4.5972 | -7.5385 | 0.6750 | 2.9413 | -259.7779 | -241.9329 | -2.3696 | -2.4201 | | 0.0645 | 1.76 | 7300 | 0.5339 | -4.9822 | -8.0212 | 0.6775 | 3.0390 | -264.6048 | -245.7831 | -2.2857 | -2.3440 | | 0.0923 | 1.78 | 7400 | 0.5385 | -4.6369 | -7.6632 | 0.6650 | 3.0263 | -261.0246 | -242.3295 | -2.2563 | -2.3150 | | 0.0842 | 1.81 | 7500 | 0.5394 | -4.8705 | -7.6765 | 0.6600 | 2.8060 | -261.1580 | -244.6661 | -2.2808 | -2.3287 | | 0.1178 | 1.83 | 7600 | 0.5253 | -4.7985 | -7.5635 | 0.6675 | 2.7650 | -260.0276 | -243.9457 | -2.4022 | -2.4463 | | 0.1255 | 1.85 | 7700 | 0.5355 | -4.7007 | -7.4363 | 0.6675 | 2.7355 | -258.7556 | -242.9684 | -2.5073 | -2.5501 | | 0.1541 | 1.88 | 7800 | 0.5440 | -4.9294 | -7.6465 | 0.6500 | 2.7172 | -260.8584 | -245.2547 | -2.3551 | -2.4036 | | 0.0893 | 1.9 | 7900 | 0.5397 | -5.2135 | -8.3241 | 0.6575 | 3.1106 | -267.6339 | -248.0959 | -2.3214 | -2.3784 | | 0.1203 | 1.93 | 8000 | 0.5296 | -4.8644 | -7.8598 | 0.6550 | 2.9954 | -262.9913 | -244.6054 | -2.4509 | -2.4969 | | 0.1018 | 1.95 | 8100 | 0.5381 | -5.3471 | -8.4918 | 0.6625 | 3.1447 | -269.3113 | -249.4323 | -2.4193 | -2.4671 | | 0.0767 | 1.97 | 8200 | 0.5386 | -5.2151 | -8.3734 | 0.6675 | 3.1582 | -268.1267 | -248.1124 | -2.4873 | -2.5329 | | 0.0801 | 2.0 | 8300 | 0.5429 | -5.8103 | -9.0391 | 0.6575 | 3.2288 | -274.7842 | -254.0639 | -2.4348 | -2.4867 | | 0.034 | 2.02 | 8400 | 0.5566 | -5.7907 | -9.2424 | 0.6625 | 3.4518 | -276.8175 | -253.8677 | -2.3679 | -2.4272 | | 0.0246 | 2.05 | 8500 | 0.5758 | -5.6317 | -9.1533 | 0.6625 | 3.5216 | -275.9264 | -252.2783 | -2.3335 | -2.3958 | | 0.0187 | 2.07 | 8600 | 0.5770 | -5.5795 | -9.2568 | 0.6725 | 3.6773 | -276.9613 | -251.7559 | -2.3614 | -2.4166 | | 0.0606 | 2.09 | 8700 | 0.6115 | -7.1190 | -11.2853 | 0.6750 | 4.1663 | -297.2460 | -267.1512 | -2.2737 | -2.3365 | | 0.0402 | 2.12 | 8800 | 0.6164 | -7.0531 | -11.1316 | 0.6600 | 4.0785 | -295.7089 | -266.4919 | -2.2005 | -2.2654 | | 0.0263 | 2.14 | 8900 | 0.6209 | -8.1609 | -12.3710 | 0.6650 | 4.2102 | -308.1034 | -277.5696 | -2.0958 | -2.1661 | | 0.0242 | 2.17 | 9000 | 0.6042 | -6.7201 | -10.7618 | 0.6725 | 4.0416 | -292.0106 | -263.1622 | -2.1651 | -2.2304 | | 0.0383 | 2.19 | 9100 | 0.6080 | -7.7898 | -11.9356 | 0.6750 | 4.1458 | -303.7489 | -273.8587 | -2.1006 | -2.1662 | | 0.0371 | 2.21 | 9200 | 0.6149 | -7.5635 | -11.7050 | 0.6675 | 4.1415 | -301.4433 | -271.5960 | -2.1556 | -2.2155 | | 0.0279 | 2.24 | 9300 | 0.6155 | -8.1686 | -12.4447 | 0.6775 | 4.2760 | -308.8397 | -277.6473 | -2.1778 | -2.2399 | | 0.021 | 2.26 | 9400 | 0.6137 | -7.8294 | -12.0416 | 0.6700 | 4.2122 | -304.8092 | -274.2550 | -2.2403 | -2.2958 | | 0.0374 | 2.29 | 9500 | 0.6238 | -7.9227 | -12.2842 | 0.6750 | 4.3614 | -307.2347 | -275.1884 | -2.2926 | -2.3496 | | 0.0412 | 2.31 | 9600 | 0.6126 | -7.7094 | -11.9775 | 0.6700 | 4.2681 | -304.1685 | -273.0553 | -2.2377 | -2.2961 | | 0.0413 | 2.33 | 9700 | 0.6130 | -7.6030 | -11.8721 | 0.6675 | 4.2691 | -303.1140 | -271.9912 | -2.2505 | -2.3100 | | 0.0361 | 2.36 | 9800 | 0.6248 | -8.1273 | -12.6010 | 0.6750 | 4.4737 | -310.4034 | -277.2341 | -2.2249 | -2.2866 | | 0.0289 | 2.38 | 9900 | 0.6192 | -7.9924 | -12.3825 | 0.6675 | 4.3901 | -308.2185 | -275.8853 | -2.2473 | -2.3067 | | 0.038 | 2.41 | 10000 | 0.6250 | -8.4114 | -12.8701 | 0.6675 | 4.4586 | -313.0937 | -280.0753 | -2.2312 | -2.2938 | | 0.0334 | 2.43 | 10100 | 0.6261 | -9.1807 | -13.7488 | 0.6825 | 4.5681 | -321.8813 | -287.7679 | -2.2303 | -2.2947 | | 0.0359 | 2.45 | 10200 | 0.6374 | -9.8214 | -14.2774 | 0.6650 | 4.4560 | -327.1667 | -294.1750 | -2.1817 | -2.2452 | | 0.0266 | 2.48 | 10300 | 0.6298 | -8.3278 | -12.5691 | 0.6650 | 4.2413 | -310.0836 | -279.2391 | -2.2947 | -2.3521 | | 0.0423 | 2.5 | 10400 | 0.6267 | -8.7527 | -13.2552 | 0.6675 | 4.5025 | -316.9453 | -283.4879 | -2.3034 | -2.3620 | | 0.0329 | 2.53 | 10500 | 0.6386 | -8.9354 | -13.5549 | 0.6700 | 4.6195 | -319.9424 | -285.3152 | -2.2819 | -2.3423 | | 0.039 | 2.55 | 10600 | 0.6330 | -8.3549 | -12.8863 | 0.6775 | 4.5314 | -313.2566 | -279.5103 | -2.2924 | -2.3528 | | 0.0278 | 2.58 | 10700 | 0.6336 | -8.6754 | -13.1733 | 0.6675 | 4.4979 | -316.1258 | -282.7150 | -2.2319 | -2.2929 | | 0.0606 | 2.6 | 10800 | 0.6299 | -8.7158 | -13.0817 | 0.6700 | 4.3658 | -315.2101 | -283.1195 | -2.2116 | -2.2731 | | 0.0293 | 2.62 | 10900 | 0.6259 | -8.9092 | -13.2926 | 0.6725 | 4.3834 | -317.3194 | -285.0532 | -2.1572 | -2.2209 | | 0.0196 | 2.65 | 11000 | 0.6219 | -9.1783 | -13.5617 | 0.6700 | 4.3835 | -320.0104 | -287.7436 | -2.1533 | -2.2163 | | 0.0405 | 2.67 | 11100 | 0.6209 | -8.9912 | -13.3040 | 0.6700 | 4.3128 | -317.4330 | -285.8734 | -2.1378 | -2.2017 | | 0.0278 | 2.7 | 11200 | 0.6300 | -9.8318 | -14.2684 | 0.6700 | 4.4366 | -327.0771 | -294.2787 | -2.1220 | -2.1862 | | 0.0307 | 2.72 | 11300 | 0.6356 | -9.7027 | -14.1764 | 0.6700 | 4.4737 | -326.1576 | -292.9880 | -2.1316 | -2.1945 | | 0.0242 | 2.74 | 11400 | 0.6327 | -9.8085 | -14.2574 | 0.6625 | 4.4489 | -326.9674 | -294.0465 | -2.1072 | -2.1680 | | 0.0242 | 2.77 | 11500 | 0.6308 | -9.3697 | -13.8420 | 0.6650 | 4.4723 | -322.8135 | -289.6585 | -2.1273 | -2.1882 | | 0.0337 | 2.79 | 11600 | 0.6350 | -9.2810 | -13.7917 | 0.6700 | 4.5107 | -322.3100 | -288.7711 | -2.1600 | -2.2215 | | 0.0302 | 2.82 | 11700 | 0.6450 | -10.2754 | -14.9521 | 0.6675 | 4.6767 | -333.9139 | -298.7146 | -2.1339 | -2.1965 | | 0.0354 | 2.84 | 11800 | 0.6451 | -10.3736 | -15.0743 | 0.6725 | 4.7008 | -335.1366 | -299.6965 | -2.1047 | -2.1674 | | 0.0153 | 2.86 | 11900 | 0.6420 | -10.2126 | -14.9126 | 0.6700 | 4.7000 | -333.5196 | -298.0872 | -2.1102 | -2.1728 | | 0.0388 | 2.89 | 12000 | 0.6407 | -10.2075 | -14.9081 | 0.6725 | 4.7006 | -333.4741 | -298.0356 | -2.1059 | -2.1687 | | 0.0253 | 2.91 | 12100 | 0.6353 | -10.0842 | -14.7598 | 0.6650 | 4.6756 | -331.9908 | -296.8029 | -2.0968 | -2.1594 | | 0.0317 | 2.94 | 12200 | 0.6352 | -9.9956 | -14.6819 | 0.6750 | 4.6863 | -331.2123 | -295.9169 | -2.1042 | -2.1665 | | 0.0431 | 2.96 | 12300 | 0.6337 | -9.8807 | -14.5540 | 0.6675 | 4.6733 | -329.9332 | -294.7676 | -2.1034 | -2.1660 | | 0.0233 | 2.98 | 12400 | 0.6326 | -9.8796 | -14.5449 | 0.6675 | 4.6653 | -329.8422 | -294.7567 | -2.1032 | -2.1657 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

{}

RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-4bits

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T04:52:35+00:00

null

{"license": "openrail"}

Danikdsa/Lia

null

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

null

2024-05-01T04:53:17+00:00

null

{}

bsheon/adsl-math

null

[ "region:us" ]

null

2024-05-01T04:53:36+00:00

text-generation

transformers

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

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

Jaspann/test-model

null

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

null

2024-05-01T04:55:02+00:00

null

{"license": "llama3"}

rahulAkaVector/java_code_generator_finetuned_model

null

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

null

2024-05-01T04:55:27+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-anthropic - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-anthropic/ Original model description: --- license: apache-2.0 base_model: HuggingFaceH4/mistral-7b-cai tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless model-index: - name: mistral-7b-dpo-v21.0cai.0.2 results: [] ---  # Mistral 7B Constitutional AI This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets. It achieves the following results on the evaluation set: - Loss: 0.6327 - Rewards/chosen: -9.8716 - Rewards/rejected: -14.5465 - Rewards/accuracies: 0.6725 - Rewards/margins: 4.6749 - Logps/rejected: -329.8578 - Logps/chosen: -294.6768 - Logits/rejected: -2.1023 - Logits/chosen: -2.1648 ## 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-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6817 | 0.02 | 100 | 0.6873 | 0.0149 | 0.0002 | 0.5150 | 0.0147 | -184.3912 | -195.8124 | -3.1605 | -3.1560 | | 0.6767 | 0.05 | 200 | 0.6614 | 0.0825 | 0.0169 | 0.5575 | 0.0656 | -184.2246 | -195.1362 | -3.1654 | -3.1605 | | 0.6328 | 0.07 | 300 | 0.6246 | -0.0374 | -0.2112 | 0.5875 | 0.1738 | -186.5047 | -196.3349 | -3.1579 | -3.1529 | | 0.5919 | 0.1 | 400 | 0.5978 | 0.2812 | -0.0666 | 0.6125 | 0.3478 | -185.0590 | -193.1489 | -3.1292 | -3.1243 | | 0.5545 | 0.12 | 500 | 0.5800 | 0.1742 | -0.2810 | 0.6275 | 0.4552 | -187.2035 | -194.2191 | -3.0819 | -3.0788 | | 0.5926 | 0.14 | 600 | 0.5599 | 0.2410 | -0.3076 | 0.6425 | 0.5487 | -187.4693 | -193.5507 | -3.0601 | -3.0597 | | 0.5326 | 0.17 | 700 | 0.5385 | -0.2501 | -0.9698 | 0.6400 | 0.7197 | -194.0914 | -198.4624 | -2.9076 | -2.9090 | | 0.5126 | 0.19 | 800 | 0.5238 | -0.3616 | -1.1783 | 0.6525 | 0.8167 | -196.1764 | -199.5769 | -2.9965 | -2.9963 | | 0.5283 | 0.22 | 900 | 0.5289 | -0.4142 | -1.2542 | 0.6775 | 0.8400 | -196.9348 | -200.1031 | -3.0133 | -3.0134 | | 0.5303 | 0.24 | 1000 | 0.5214 | -0.5949 | -1.5888 | 0.6600 | 0.9939 | -200.2815 | -201.9101 | -2.9663 | -2.9669 | | 0.5969 | 0.26 | 1100 | 0.5235 | -0.5924 | -1.5222 | 0.6600 | 0.9298 | -199.6154 | -201.8848 | -2.9402 | -2.9468 | | 0.581 | 0.29 | 1200 | 0.5887 | -0.7548 | -1.7075 | 0.6400 | 0.9527 | -201.4678 | -203.5091 | -2.7065 | -2.7227 | | 0.817 | 0.31 | 1300 | 0.6620 | -1.5060 | -2.4221 | 0.6500 | 0.9160 | -208.6137 | -211.0213 | -2.7717 | -2.7800 | | 0.6039 | 0.34 | 1400 | 0.5321 | -1.6820 | -2.8439 | 0.6425 | 1.1619 | -212.8325 | -212.7814 | -2.6828 | -2.6917 | | 0.6666 | 0.36 | 1500 | 0.5303 | -1.3875 | -2.6384 | 0.6475 | 1.2509 | -210.7773 | -209.8365 | -2.8557 | -2.8594 | | 0.6907 | 0.39 | 1600 | 0.5409 | -2.0657 | -3.2214 | 0.6650 | 1.1556 | -216.6068 | -216.6184 | -2.8227 | -2.8288 | | 0.5772 | 0.41 | 1700 | 0.5309 | -1.9849 | -3.2833 | 0.6875 | 1.2985 | -217.2264 | -215.8097 | -2.6498 | -2.6635 | | 0.5601 | 0.43 | 1800 | 0.5281 | -1.7365 | -3.0643 | 0.6575 | 1.3278 | -215.0359 | -213.3255 | -2.8890 | -2.8918 | | 0.576 | 0.46 | 1900 | 0.5266 | -1.4822 | -2.9294 | 0.6725 | 1.4472 | -213.6872 | -210.7831 | -2.7369 | -2.7427 | | 1.2064 | 0.48 | 2000 | 0.5538 | -2.5493 | -3.7625 | 0.6675 | 1.2132 | -222.0182 | -221.4542 | -2.6773 | -2.6957 | | 0.5751 | 0.51 | 2100 | 0.5465 | -1.9246 | -3.1480 | 0.6425 | 1.2234 | -215.8728 | -215.2067 | -2.6490 | -2.6657 | | 0.4757 | 0.53 | 2200 | 0.5297 | -1.8443 | -3.1553 | 0.6325 | 1.3110 | -215.9462 | -214.4039 | -2.6882 | -2.7115 | | 0.4771 | 0.55 | 2300 | 0.5386 | -2.3340 | -3.7443 | 0.6500 | 1.4103 | -221.8360 | -219.3013 | -2.6415 | -2.6623 | | 0.481 | 0.58 | 2400 | 0.5355 | -1.6085 | -3.0800 | 0.6550 | 1.4715 | -215.1930 | -212.0460 | -2.6073 | -2.6293 | | 0.523 | 0.6 | 2500 | 0.5131 | -2.6139 | -4.2353 | 0.6625 | 1.6214 | -226.7459 | -222.0998 | -2.6134 | -2.6394 | | 0.6263 | 0.63 | 2600 | 0.5287 | -2.6614 | -4.0538 | 0.6450 | 1.3924 | -224.9310 | -222.5747 | -2.6189 | -2.6361 | | 0.5973 | 0.65 | 2700 | 0.5132 | -2.7089 | -4.1248 | 0.625 | 1.4159 | -225.6406 | -223.0499 | -2.6167 | -2.6317 | | 0.8209 | 0.67 | 2800 | 0.5165 | -2.7085 | -4.1871 | 0.625 | 1.4786 | -226.2637 | -223.0462 | -2.5605 | -2.5803 | | 0.5625 | 0.7 | 2900 | 0.5117 | -3.4747 | -5.0369 | 0.6325 | 1.5622 | -234.7624 | -230.7079 | -2.5891 | -2.6163 | | 0.5913 | 0.72 | 3000 | 0.5164 | -2.5844 | -4.3822 | 0.6675 | 1.7978 | -228.2149 | -221.8051 | -2.6421 | -2.6632 | | 0.7441 | 0.75 | 3100 | 0.5175 | -2.4900 | -4.2883 | 0.6725 | 1.7983 | -227.2762 | -220.8608 | -2.6254 | -2.6465 | | 0.6169 | 0.77 | 3200 | 0.5163 | -2.2489 | -3.8666 | 0.6600 | 1.6176 | -223.0589 | -218.4503 | -2.6517 | -2.6775 | | 0.5347 | 0.79 | 3300 | 0.5222 | -2.6699 | -4.3844 | 0.6375 | 1.7145 | -228.2368 | -222.6600 | -2.6712 | -2.6909 | | 0.5369 | 0.82 | 3400 | 0.5244 | -2.7710 | -4.6352 | 0.6600 | 1.8642 | -230.7449 | -223.6711 | -2.5304 | -2.5595 | | 0.5613 | 0.84 | 3500 | 0.5431 | -3.7645 | -5.6773 | 0.6475 | 1.9128 | -241.1664 | -233.6063 | -2.5348 | -2.5604 | | 0.6395 | 0.87 | 3600 | 0.5332 | -3.8666 | -5.6894 | 0.6525 | 1.8227 | -241.2867 | -234.6274 | -2.5479 | -2.5778 | | 0.6552 | 0.89 | 3700 | 0.5149 | -2.9168 | -4.7306 | 0.6525 | 1.8138 | -231.6990 | -225.1294 | -2.4580 | -2.4901 | | 0.6381 | 0.91 | 3800 | 0.5081 | -2.6182 | -4.3003 | 0.6625 | 1.6821 | -227.3964 | -222.1432 | -2.4730 | -2.4991 | | 0.5355 | 0.94 | 3900 | 0.5100 | -2.5302 | -4.2476 | 0.6475 | 1.7173 | -226.8689 | -221.2634 | -2.5875 | -2.6065 | | 0.5488 | 0.96 | 4000 | 0.5164 | -3.1540 | -4.8339 | 0.6550 | 1.6798 | -232.7318 | -227.5013 | -2.7017 | -2.7215 | | 0.6802 | 0.99 | 4100 | 0.5134 | -2.6060 | -4.2916 | 0.6625 | 1.6856 | -227.3087 | -222.0207 | -2.6010 | -2.6250 | | 0.0976 | 1.01 | 4200 | 0.5031 | -3.0885 | -5.0494 | 0.6625 | 1.9609 | -234.8874 | -226.8463 | -2.4721 | -2.5028 | | 0.0839 | 1.03 | 4300 | 0.5027 | -3.3469 | -5.4366 | 0.6625 | 2.0897 | -238.7592 | -229.4302 | -2.3886 | -2.4238 | | 0.0788 | 1.06 | 4400 | 0.5398 | -4.4307 | -6.8568 | 0.6775 | 2.4261 | -252.9614 | -240.2679 | -2.1805 | -2.2275 | | 0.0701 | 1.08 | 4500 | 0.5432 | -4.3739 | -7.0979 | 0.6975 | 2.7240 | -255.3717 | -239.7001 | -2.1935 | -2.2437 | | 0.0959 | 1.11 | 4600 | 0.5362 | -3.9784 | -6.3235 | 0.6900 | 2.3451 | -247.6284 | -235.7450 | -2.2860 | -2.3272 | | 0.1177 | 1.13 | 4700 | 0.5411 | -4.1933 | -6.8436 | 0.6800 | 2.6504 | -252.8295 | -237.8937 | -2.3259 | -2.3682 | | 0.1651 | 1.16 | 4800 | 0.5737 | -4.8158 | -6.7229 | 0.6700 | 1.9071 | -251.6221 | -244.1190 | -2.2753 | -2.3139 | | 0.1298 | 1.18 | 4900 | 0.5528 | -4.6526 | -6.8433 | 0.6825 | 2.1907 | -252.8262 | -242.4874 | -2.4856 | -2.5188 | | 0.1143 | 1.2 | 5000 | 0.5512 | -4.6212 | -7.0807 | 0.6800 | 2.4595 | -255.2000 | -242.1734 | -2.5190 | -2.5542 | | 0.1145 | 1.23 | 5100 | 0.5496 | -4.0598 | -6.6147 | 0.6775 | 2.5548 | -250.5396 | -236.5594 | -2.5737 | -2.6008 | | 0.2324 | 1.25 | 5200 | 0.5524 | -4.9650 | -7.6613 | 0.6725 | 2.6962 | -261.0058 | -245.6115 | -2.4382 | -2.4737 | | 0.0867 | 1.28 | 5300 | 0.5449 | -4.9568 | -7.6771 | 0.6625 | 2.7203 | -261.1645 | -245.5292 | -2.4367 | -2.4702 | | 0.0503 | 1.3 | 5400 | 0.5351 | -4.5684 | -7.1860 | 0.6625 | 2.6176 | -256.2527 | -241.6449 | -2.4235 | -2.4557 | | 0.0977 | 1.32 | 5500 | 0.5431 | -4.5599 | -7.1317 | 0.6550 | 2.5718 | -255.7096 | -241.5597 | -2.5311 | -2.5614 | | 0.1564 | 1.35 | 5600 | 0.5512 | -5.1430 | -8.0510 | 0.6750 | 2.9080 | -264.9027 | -247.3911 | -2.3498 | -2.3976 | | 0.0967 | 1.37 | 5700 | 0.5520 | -4.5072 | -7.4506 | 0.6750 | 2.9433 | -258.8989 | -241.0335 | -2.2110 | -2.2631 | | 0.2046 | 1.4 | 5800 | 0.5588 | -5.5328 | -8.5314 | 0.6800 | 2.9986 | -269.7068 | -251.2888 | -2.2155 | -2.2677 | | 0.0985 | 1.42 | 5900 | 0.5429 | -5.1915 | -7.9421 | 0.6675 | 2.7505 | -263.8138 | -247.8765 | -2.2606 | -2.3077 | | 0.1398 | 1.44 | 6000 | 0.5350 | -4.9761 | -7.9378 | 0.6800 | 2.9616 | -263.7706 | -245.7224 | -2.2291 | -2.2809 | | 0.099 | 1.47 | 6100 | 0.5440 | -4.6202 | -7.4996 | 0.6650 | 2.8794 | -259.3892 | -242.1633 | -2.3362 | -2.3859 | | 0.1279 | 1.49 | 6200 | 0.5389 | -4.9461 | -7.7908 | 0.6625 | 2.8448 | -262.3015 | -245.4217 | -2.2276 | -2.2734 | | 0.0778 | 1.52 | 6300 | 0.5451 | -4.9550 | -7.8964 | 0.6625 | 2.9414 | -263.3570 | -245.5110 | -2.4781 | -2.5193 | | 0.0911 | 1.54 | 6400 | 0.5412 | -5.4552 | -8.3139 | 0.6675 | 2.8588 | -267.5324 | -250.5128 | -2.3604 | -2.4048 | | 0.2149 | 1.56 | 6500 | 0.5241 | -4.4512 | -7.3194 | 0.6725 | 2.8682 | -257.5873 | -240.4732 | -2.4011 | -2.4461 | | 0.1739 | 1.59 | 6600 | 0.5329 | -5.0143 | -7.7507 | 0.6825 | 2.7364 | -261.8999 | -246.1036 | -2.4143 | -2.4577 | | 0.0842 | 1.61 | 6700 | 0.5395 | -5.1195 | -8.0856 | 0.6800 | 2.9661 | -265.2489 | -247.1560 | -2.3877 | -2.4376 | | 0.105 | 1.64 | 6800 | 0.5423 | -4.9379 | -7.7557 | 0.6775 | 2.8178 | -261.9503 | -245.3403 | -2.3798 | -2.4323 | | 0.086 | 1.66 | 6900 | 0.5351 | -4.3598 | -7.1156 | 0.6775 | 2.7559 | -255.5494 | -239.5588 | -2.3870 | -2.4383 | | 0.0622 | 1.68 | 7000 | 0.5394 | -4.6830 | -7.6578 | 0.6825 | 2.9747 | -260.9710 | -242.7915 | -2.4276 | -2.4779 | | 0.0973 | 1.71 | 7100 | 0.5319 | -4.7475 | -7.6567 | 0.6750 | 2.9091 | -260.9596 | -243.4364 | -2.3010 | -2.3564 | | 0.1052 | 1.73 | 7200 | 0.5284 | -4.5972 | -7.5385 | 0.6750 | 2.9413 | -259.7779 | -241.9329 | -2.3696 | -2.4201 | | 0.0645 | 1.76 | 7300 | 0.5339 | -4.9822 | -8.0212 | 0.6775 | 3.0390 | -264.6048 | -245.7831 | -2.2857 | -2.3440 | | 0.0923 | 1.78 | 7400 | 0.5385 | -4.6369 | -7.6632 | 0.6650 | 3.0263 | -261.0246 | -242.3295 | -2.2563 | -2.3150 | | 0.0842 | 1.81 | 7500 | 0.5394 | -4.8705 | -7.6765 | 0.6600 | 2.8060 | -261.1580 | -244.6661 | -2.2808 | -2.3287 | | 0.1178 | 1.83 | 7600 | 0.5253 | -4.7985 | -7.5635 | 0.6675 | 2.7650 | -260.0276 | -243.9457 | -2.4022 | -2.4463 | | 0.1255 | 1.85 | 7700 | 0.5355 | -4.7007 | -7.4363 | 0.6675 | 2.7355 | -258.7556 | -242.9684 | -2.5073 | -2.5501 | | 0.1541 | 1.88 | 7800 | 0.5440 | -4.9294 | -7.6465 | 0.6500 | 2.7172 | -260.8584 | -245.2547 | -2.3551 | -2.4036 | | 0.0893 | 1.9 | 7900 | 0.5397 | -5.2135 | -8.3241 | 0.6575 | 3.1106 | -267.6339 | -248.0959 | -2.3214 | -2.3784 | | 0.1203 | 1.93 | 8000 | 0.5296 | -4.8644 | -7.8598 | 0.6550 | 2.9954 | -262.9913 | -244.6054 | -2.4509 | -2.4969 | | 0.1018 | 1.95 | 8100 | 0.5381 | -5.3471 | -8.4918 | 0.6625 | 3.1447 | -269.3113 | -249.4323 | -2.4193 | -2.4671 | | 0.0767 | 1.97 | 8200 | 0.5386 | -5.2151 | -8.3734 | 0.6675 | 3.1582 | -268.1267 | -248.1124 | -2.4873 | -2.5329 | | 0.0801 | 2.0 | 8300 | 0.5429 | -5.8103 | -9.0391 | 0.6575 | 3.2288 | -274.7842 | -254.0639 | -2.4348 | -2.4867 | | 0.034 | 2.02 | 8400 | 0.5566 | -5.7907 | -9.2424 | 0.6625 | 3.4518 | -276.8175 | -253.8677 | -2.3679 | -2.4272 | | 0.0246 | 2.05 | 8500 | 0.5758 | -5.6317 | -9.1533 | 0.6625 | 3.5216 | -275.9264 | -252.2783 | -2.3335 | -2.3958 | | 0.0187 | 2.07 | 8600 | 0.5770 | -5.5795 | -9.2568 | 0.6725 | 3.6773 | -276.9613 | -251.7559 | -2.3614 | -2.4166 | | 0.0606 | 2.09 | 8700 | 0.6115 | -7.1190 | -11.2853 | 0.6750 | 4.1663 | -297.2460 | -267.1512 | -2.2737 | -2.3365 | | 0.0402 | 2.12 | 8800 | 0.6164 | -7.0531 | -11.1316 | 0.6600 | 4.0785 | -295.7089 | -266.4919 | -2.2005 | -2.2654 | | 0.0263 | 2.14 | 8900 | 0.6209 | -8.1609 | -12.3710 | 0.6650 | 4.2102 | -308.1034 | -277.5696 | -2.0958 | -2.1661 | | 0.0242 | 2.17 | 9000 | 0.6042 | -6.7201 | -10.7618 | 0.6725 | 4.0416 | -292.0106 | -263.1622 | -2.1651 | -2.2304 | | 0.0383 | 2.19 | 9100 | 0.6080 | -7.7898 | -11.9356 | 0.6750 | 4.1458 | -303.7489 | -273.8587 | -2.1006 | -2.1662 | | 0.0371 | 2.21 | 9200 | 0.6149 | -7.5635 | -11.7050 | 0.6675 | 4.1415 | -301.4433 | -271.5960 | -2.1556 | -2.2155 | | 0.0279 | 2.24 | 9300 | 0.6155 | -8.1686 | -12.4447 | 0.6775 | 4.2760 | -308.8397 | -277.6473 | -2.1778 | -2.2399 | | 0.021 | 2.26 | 9400 | 0.6137 | -7.8294 | -12.0416 | 0.6700 | 4.2122 | -304.8092 | -274.2550 | -2.2403 | -2.2958 | | 0.0374 | 2.29 | 9500 | 0.6238 | -7.9227 | -12.2842 | 0.6750 | 4.3614 | -307.2347 | -275.1884 | -2.2926 | -2.3496 | | 0.0412 | 2.31 | 9600 | 0.6126 | -7.7094 | -11.9775 | 0.6700 | 4.2681 | -304.1685 | -273.0553 | -2.2377 | -2.2961 | | 0.0413 | 2.33 | 9700 | 0.6130 | -7.6030 | -11.8721 | 0.6675 | 4.2691 | -303.1140 | -271.9912 | -2.2505 | -2.3100 | | 0.0361 | 2.36 | 9800 | 0.6248 | -8.1273 | -12.6010 | 0.6750 | 4.4737 | -310.4034 | -277.2341 | -2.2249 | -2.2866 | | 0.0289 | 2.38 | 9900 | 0.6192 | -7.9924 | -12.3825 | 0.6675 | 4.3901 | -308.2185 | -275.8853 | -2.2473 | -2.3067 | | 0.038 | 2.41 | 10000 | 0.6250 | -8.4114 | -12.8701 | 0.6675 | 4.4586 | -313.0937 | -280.0753 | -2.2312 | -2.2938 | | 0.0334 | 2.43 | 10100 | 0.6261 | -9.1807 | -13.7488 | 0.6825 | 4.5681 | -321.8813 | -287.7679 | -2.2303 | -2.2947 | | 0.0359 | 2.45 | 10200 | 0.6374 | -9.8214 | -14.2774 | 0.6650 | 4.4560 | -327.1667 | -294.1750 | -2.1817 | -2.2452 | | 0.0266 | 2.48 | 10300 | 0.6298 | -8.3278 | -12.5691 | 0.6650 | 4.2413 | -310.0836 | -279.2391 | -2.2947 | -2.3521 | | 0.0423 | 2.5 | 10400 | 0.6267 | -8.7527 | -13.2552 | 0.6675 | 4.5025 | -316.9453 | -283.4879 | -2.3034 | -2.3620 | | 0.0329 | 2.53 | 10500 | 0.6386 | -8.9354 | -13.5549 | 0.6700 | 4.6195 | -319.9424 | -285.3152 | -2.2819 | -2.3423 | | 0.039 | 2.55 | 10600 | 0.6330 | -8.3549 | -12.8863 | 0.6775 | 4.5314 | -313.2566 | -279.5103 | -2.2924 | -2.3528 | | 0.0278 | 2.58 | 10700 | 0.6336 | -8.6754 | -13.1733 | 0.6675 | 4.4979 | -316.1258 | -282.7150 | -2.2319 | -2.2929 | | 0.0606 | 2.6 | 10800 | 0.6299 | -8.7158 | -13.0817 | 0.6700 | 4.3658 | -315.2101 | -283.1195 | -2.2116 | -2.2731 | | 0.0293 | 2.62 | 10900 | 0.6259 | -8.9092 | -13.2926 | 0.6725 | 4.3834 | -317.3194 | -285.0532 | -2.1572 | -2.2209 | | 0.0196 | 2.65 | 11000 | 0.6219 | -9.1783 | -13.5617 | 0.6700 | 4.3835 | -320.0104 | -287.7436 | -2.1533 | -2.2163 | | 0.0405 | 2.67 | 11100 | 0.6209 | -8.9912 | -13.3040 | 0.6700 | 4.3128 | -317.4330 | -285.8734 | -2.1378 | -2.2017 | | 0.0278 | 2.7 | 11200 | 0.6300 | -9.8318 | -14.2684 | 0.6700 | 4.4366 | -327.0771 | -294.2787 | -2.1220 | -2.1862 | | 0.0307 | 2.72 | 11300 | 0.6356 | -9.7027 | -14.1764 | 0.6700 | 4.4737 | -326.1576 | -292.9880 | -2.1316 | -2.1945 | | 0.0242 | 2.74 | 11400 | 0.6327 | -9.8085 | -14.2574 | 0.6625 | 4.4489 | -326.9674 | -294.0465 | -2.1072 | -2.1680 | | 0.0242 | 2.77 | 11500 | 0.6308 | -9.3697 | -13.8420 | 0.6650 | 4.4723 | -322.8135 | -289.6585 | -2.1273 | -2.1882 | | 0.0337 | 2.79 | 11600 | 0.6350 | -9.2810 | -13.7917 | 0.6700 | 4.5107 | -322.3100 | -288.7711 | -2.1600 | -2.2215 | | 0.0302 | 2.82 | 11700 | 0.6450 | -10.2754 | -14.9521 | 0.6675 | 4.6767 | -333.9139 | -298.7146 | -2.1339 | -2.1965 | | 0.0354 | 2.84 | 11800 | 0.6451 | -10.3736 | -15.0743 | 0.6725 | 4.7008 | -335.1366 | -299.6965 | -2.1047 | -2.1674 | | 0.0153 | 2.86 | 11900 | 0.6420 | -10.2126 | -14.9126 | 0.6700 | 4.7000 | -333.5196 | -298.0872 | -2.1102 | -2.1728 | | 0.0388 | 2.89 | 12000 | 0.6407 | -10.2075 | -14.9081 | 0.6725 | 4.7006 | -333.4741 | -298.0356 | -2.1059 | -2.1687 | | 0.0253 | 2.91 | 12100 | 0.6353 | -10.0842 | -14.7598 | 0.6650 | 4.6756 | -331.9908 | -296.8029 | -2.0968 | -2.1594 | | 0.0317 | 2.94 | 12200 | 0.6352 | -9.9956 | -14.6819 | 0.6750 | 4.6863 | -331.2123 | -295.9169 | -2.1042 | -2.1665 | | 0.0431 | 2.96 | 12300 | 0.6337 | -9.8807 | -14.5540 | 0.6675 | 4.6733 | -329.9332 | -294.7676 | -2.1034 | -2.1660 | | 0.0233 | 2.98 | 12400 | 0.6326 | -9.8796 | -14.5449 | 0.6675 | 4.6653 | -329.8422 | -294.7567 | -2.1032 | -2.1657 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

{}

RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-8bits

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-01T04:58:39+00:00

text-generation

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

{"language": ["en"], "library_name": "peft", "tags": ["text-generation", "code-generation", "text-to-text-generation"], "base_model": "meta-llama/Meta-Llama-3-70B-Instruct"}

aryansiddiqui/llama3v1

null

[ "peft", "safetensors", "text-generation", "code-generation", "text-to-text-generation", "conversational", "en", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "region:us" ]

null

2024-05-01T04:58:43+00:00

null

{}

aayushb03/CU_Model_pths

null

[ "region:us" ]

null

2024-05-01T04:59:35+00:00

feature-extraction

transformers

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

{"library_name": "transformers", "tags": []}

stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep42

null

[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:01:36+00:00

null

{"license": "apache-2.0"}

shanhanigun/fgdfg

null

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

null

2024-05-01T05:03:10+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

nem012/gemma2b-1e-5r32

null

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

null

2024-05-01T05:06:25+00:00

null

transformers

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

{"library_name": "transformers", "tags": []}

goodakdali/testing_ins_add_adapter

null

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

null

2024-05-01T05:06:28+00:00

text-generation

null

# gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`beomi/Llama-3-Open-Ko-8B`](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF --model llama-3-open-ko-8b.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF --model llama-3-open-ko-8b.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-open-ko-8b.Q4_K_M.gguf -n 128 ```

{"language": ["en", "ko"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"}

gagagiga/Llama-3-Open-Ko-8B-Q4_K_M-GGUF

null

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ko", "license:other", "region:us" ]

null

2024-05-01T05:06:29+00:00

text-generation

null

# existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF --model llama-3-8b-instruct-gradient-1048k.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF --model llama-3-8b-instruct-gradient-1048k.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-gradient-1048k.Q8_0.gguf -n 128 ```

{"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"}

existmaster/Llama-3-8B-Instruct-Gradient-1048k-Q8_0-GGUF

null

[ "gguf", "meta", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "region:us" ]

null

2024-05-01T05:08:02+00:00

null

{}

Anish0403/my_awesome_qa_model

null

[ "region:us" ]

null

2024-05-01T05:08:45+00:00

null

{}

weqweasdas/zephyr-7b-gemma-dpo

null

[ "region:us" ]

null

2024-05-01T05:08:47+00:00

text-generation

transformers

<div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Llama-3-8B-Instruct-Gradient-1048k-GGUF ## Original Model [gradientai/Llama-3-8B-Instruct-Gradient-1048k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) ## Run with LlamaEdge - LlamaEdge version: [v0.9.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.9.0) and above - Prompt template - Prompt type: `llama-3-chat` - Prompt string ```text <|begin_of_text|><|start_header_id|>system<|end_header_id|> {{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|> {{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|> {{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` - Context size: `1000000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template llama-3-chat \ --ctx-size 1000000 \ --model-name llama-3-8b-instruct-gradient-1048k ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf \ llama-chat.wasm \ --prompt-template llama-3-chat \ --ctx-size 1000000 \ ``` ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [Llama-3-8B-Instruct-Gradient-1048k-Q2_K.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q2_K.gguf) | Q2_K | 2 | 3.18 GB| smallest, significant quality loss - not recommended for most purposes | | [Llama-3-8B-Instruct-Gradient-1048k-Q3_K_L.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q3_K_L.gguf) | Q3_K_L | 3 | 4.32 GB| small, substantial quality loss | | [Llama-3-8B-Instruct-Gradient-1048k-Q3_K_M.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q3_K_M.gguf) | Q3_K_M | 3 | 4.02 GB| very small, high quality loss | | [Llama-3-8B-Instruct-Gradient-1048k-Q3_K_S.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q3_K_S.gguf) | Q3_K_S | 3 | 3.66 GB| very small, high quality loss | | [Llama-3-8B-Instruct-Gradient-1048k-Q4_0.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q4_0.gguf) | Q4_0 | 4 | 4.66 GB| legacy; small, very high quality loss - prefer using Q3_K_M | | [Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_M.gguf) | Q4_K_M | 4 | 4.92 GB| medium, balanced quality - recommended | | [Llama-3-8B-Instruct-Gradient-1048k-Q4_K_S.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q4_K_S.gguf) | Q4_K_S | 4 | 4.69 GB| small, greater quality loss | | [Llama-3-8B-Instruct-Gradient-1048k-Q5_0.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q5_0.gguf) | Q5_0 | 5 | 5.6 GB| legacy; medium, balanced quality - prefer using Q4_K_M | | [Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf) | Q5_K_M | 5 | 5.73 GB| large, very low quality loss - recommended | | [Llama-3-8B-Instruct-Gradient-1048k-Q5_K_S.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_S.gguf) | Q5_K_S | 5 | 5.6 GB| large, low quality loss - recommended | | [Llama-3-8B-Instruct-Gradient-1048k-Q6_K.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q6_K.gguf) | Q6_K | 6 | 6.6 GB| very large, extremely low quality loss | | [Llama-3-8B-Instruct-Gradient-1048k-Q8_0.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-Q8_0.gguf) | Q8_0 | 8 | 8.54 GB| very large, extremely low quality loss - not recommended | | [Llama-3-8B-Instruct-Gradient-1048k-f16.gguf](https://huggingface.co/second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF/blob/main/Llama-3-8B-Instruct-Gradient-1048k-f16.gguf) | f16 | 16 | 16.1 GB| | *Quantized with llama.cpp b2734.*

{"language": ["en"], "license": "other", "tags": ["meta", "llama-3"], "model_name": "Llama-3-8B-Instruct-Gradient-1048k", "license_name": "llama3", "base_model": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "inference": false, "model_creator": "gradient.ai", "model_type": "llama", "pipeline_tag": "text-generation", "quantized_by": "Second State Inc."}

second-state/Llama-3-8B-Instruct-Gradient-1048k-GGUF

null

[ "transformers", "gguf", "llama", "text-generation", "meta", "llama-3", "en", "base_model:gradientai/Llama-3-8B-Instruct-Gradient-1048k", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T05:09:29+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.9.0

{"license": "apache-2.0", "library_name": "peft", "base_model": "google/gemma-2b-it"}

azarafrooz/phi-gemma-nlaf-v1

null

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b-it", "license:apache-2.0", "region:us" ]

null

2024-05-01T05:09:46+00:00

null

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-anthropic - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-anthropic/ | Name | Quant method | Size | | ---- | ---- | ---- | | [mistral-7b-anthropic.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q2_K.gguf) | Q2_K | 2.53GB | | [mistral-7b-anthropic.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [mistral-7b-anthropic.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ3_S.gguf) | IQ3_S | 2.96GB | | [mistral-7b-anthropic.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [mistral-7b-anthropic.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ3_M.gguf) | IQ3_M | 3.06GB | | [mistral-7b-anthropic.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K.gguf) | Q3_K | 3.28GB | | [mistral-7b-anthropic.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [mistral-7b-anthropic.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [mistral-7b-anthropic.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [mistral-7b-anthropic.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_0.gguf) | Q4_0 | 3.83GB | | [mistral-7b-anthropic.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [mistral-7b-anthropic.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [mistral-7b-anthropic.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_K.gguf) | Q4_K | 4.07GB | | [mistral-7b-anthropic.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [mistral-7b-anthropic.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q4_1.gguf) | Q4_1 | 4.24GB | | [mistral-7b-anthropic.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_0.gguf) | Q5_0 | 4.65GB | | [mistral-7b-anthropic.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [mistral-7b-anthropic.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_K.gguf) | Q5_K | 4.78GB | | [mistral-7b-anthropic.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [mistral-7b-anthropic.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q5_1.gguf) | Q5_1 | 5.07GB | | [mistral-7b-anthropic.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf/blob/main/mistral-7b-anthropic.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- license: apache-2.0 base_model: HuggingFaceH4/mistral-7b-cai tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless model-index: - name: mistral-7b-dpo-v21.0cai.0.2 results: [] ---  # Mistral 7B Constitutional AI This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets. It achieves the following results on the evaluation set: - Loss: 0.6327 - Rewards/chosen: -9.8716 - Rewards/rejected: -14.5465 - Rewards/accuracies: 0.6725 - Rewards/margins: 4.6749 - Logps/rejected: -329.8578 - Logps/chosen: -294.6768 - Logits/rejected: -2.1023 - Logits/chosen: -2.1648 ## 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-07 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6817 | 0.02 | 100 | 0.6873 | 0.0149 | 0.0002 | 0.5150 | 0.0147 | -184.3912 | -195.8124 | -3.1605 | -3.1560 | | 0.6767 | 0.05 | 200 | 0.6614 | 0.0825 | 0.0169 | 0.5575 | 0.0656 | -184.2246 | -195.1362 | -3.1654 | -3.1605 | | 0.6328 | 0.07 | 300 | 0.6246 | -0.0374 | -0.2112 | 0.5875 | 0.1738 | -186.5047 | -196.3349 | -3.1579 | -3.1529 | | 0.5919 | 0.1 | 400 | 0.5978 | 0.2812 | -0.0666 | 0.6125 | 0.3478 | -185.0590 | -193.1489 | -3.1292 | -3.1243 | | 0.5545 | 0.12 | 500 | 0.5800 | 0.1742 | -0.2810 | 0.6275 | 0.4552 | -187.2035 | -194.2191 | -3.0819 | -3.0788 | | 0.5926 | 0.14 | 600 | 0.5599 | 0.2410 | -0.3076 | 0.6425 | 0.5487 | -187.4693 | -193.5507 | -3.0601 | -3.0597 | | 0.5326 | 0.17 | 700 | 0.5385 | -0.2501 | -0.9698 | 0.6400 | 0.7197 | -194.0914 | -198.4624 | -2.9076 | -2.9090 | | 0.5126 | 0.19 | 800 | 0.5238 | -0.3616 | -1.1783 | 0.6525 | 0.8167 | -196.1764 | -199.5769 | -2.9965 | -2.9963 | | 0.5283 | 0.22 | 900 | 0.5289 | -0.4142 | -1.2542 | 0.6775 | 0.8400 | -196.9348 | -200.1031 | -3.0133 | -3.0134 | | 0.5303 | 0.24 | 1000 | 0.5214 | -0.5949 | -1.5888 | 0.6600 | 0.9939 | -200.2815 | -201.9101 | -2.9663 | -2.9669 | | 0.5969 | 0.26 | 1100 | 0.5235 | -0.5924 | -1.5222 | 0.6600 | 0.9298 | -199.6154 | -201.8848 | -2.9402 | -2.9468 | | 0.581 | 0.29 | 1200 | 0.5887 | -0.7548 | -1.7075 | 0.6400 | 0.9527 | -201.4678 | -203.5091 | -2.7065 | -2.7227 | | 0.817 | 0.31 | 1300 | 0.6620 | -1.5060 | -2.4221 | 0.6500 | 0.9160 | -208.6137 | -211.0213 | -2.7717 | -2.7800 | | 0.6039 | 0.34 | 1400 | 0.5321 | -1.6820 | -2.8439 | 0.6425 | 1.1619 | -212.8325 | -212.7814 | -2.6828 | -2.6917 | | 0.6666 | 0.36 | 1500 | 0.5303 | -1.3875 | -2.6384 | 0.6475 | 1.2509 | -210.7773 | -209.8365 | -2.8557 | -2.8594 | | 0.6907 | 0.39 | 1600 | 0.5409 | -2.0657 | -3.2214 | 0.6650 | 1.1556 | -216.6068 | -216.6184 | -2.8227 | -2.8288 | | 0.5772 | 0.41 | 1700 | 0.5309 | -1.9849 | -3.2833 | 0.6875 | 1.2985 | -217.2264 | -215.8097 | -2.6498 | -2.6635 | | 0.5601 | 0.43 | 1800 | 0.5281 | -1.7365 | -3.0643 | 0.6575 | 1.3278 | -215.0359 | -213.3255 | -2.8890 | -2.8918 | | 0.576 | 0.46 | 1900 | 0.5266 | -1.4822 | -2.9294 | 0.6725 | 1.4472 | -213.6872 | -210.7831 | -2.7369 | -2.7427 | | 1.2064 | 0.48 | 2000 | 0.5538 | -2.5493 | -3.7625 | 0.6675 | 1.2132 | -222.0182 | -221.4542 | -2.6773 | -2.6957 | | 0.5751 | 0.51 | 2100 | 0.5465 | -1.9246 | -3.1480 | 0.6425 | 1.2234 | -215.8728 | -215.2067 | -2.6490 | -2.6657 | | 0.4757 | 0.53 | 2200 | 0.5297 | -1.8443 | -3.1553 | 0.6325 | 1.3110 | -215.9462 | -214.4039 | -2.6882 | -2.7115 | | 0.4771 | 0.55 | 2300 | 0.5386 | -2.3340 | -3.7443 | 0.6500 | 1.4103 | -221.8360 | -219.3013 | -2.6415 | -2.6623 | | 0.481 | 0.58 | 2400 | 0.5355 | -1.6085 | -3.0800 | 0.6550 | 1.4715 | -215.1930 | -212.0460 | -2.6073 | -2.6293 | | 0.523 | 0.6 | 2500 | 0.5131 | -2.6139 | -4.2353 | 0.6625 | 1.6214 | -226.7459 | -222.0998 | -2.6134 | -2.6394 | | 0.6263 | 0.63 | 2600 | 0.5287 | -2.6614 | -4.0538 | 0.6450 | 1.3924 | -224.9310 | -222.5747 | -2.6189 | -2.6361 | | 0.5973 | 0.65 | 2700 | 0.5132 | -2.7089 | -4.1248 | 0.625 | 1.4159 | -225.6406 | -223.0499 | -2.6167 | -2.6317 | | 0.8209 | 0.67 | 2800 | 0.5165 | -2.7085 | -4.1871 | 0.625 | 1.4786 | -226.2637 | -223.0462 | -2.5605 | -2.5803 | | 0.5625 | 0.7 | 2900 | 0.5117 | -3.4747 | -5.0369 | 0.6325 | 1.5622 | -234.7624 | -230.7079 | -2.5891 | -2.6163 | | 0.5913 | 0.72 | 3000 | 0.5164 | -2.5844 | -4.3822 | 0.6675 | 1.7978 | -228.2149 | -221.8051 | -2.6421 | -2.6632 | | 0.7441 | 0.75 | 3100 | 0.5175 | -2.4900 | -4.2883 | 0.6725 | 1.7983 | -227.2762 | -220.8608 | -2.6254 | -2.6465 | | 0.6169 | 0.77 | 3200 | 0.5163 | -2.2489 | -3.8666 | 0.6600 | 1.6176 | -223.0589 | -218.4503 | -2.6517 | -2.6775 | | 0.5347 | 0.79 | 3300 | 0.5222 | -2.6699 | -4.3844 | 0.6375 | 1.7145 | -228.2368 | -222.6600 | -2.6712 | -2.6909 | | 0.5369 | 0.82 | 3400 | 0.5244 | -2.7710 | -4.6352 | 0.6600 | 1.8642 | -230.7449 | -223.6711 | -2.5304 | -2.5595 | | 0.5613 | 0.84 | 3500 | 0.5431 | -3.7645 | -5.6773 | 0.6475 | 1.9128 | -241.1664 | -233.6063 | -2.5348 | -2.5604 | | 0.6395 | 0.87 | 3600 | 0.5332 | -3.8666 | -5.6894 | 0.6525 | 1.8227 | -241.2867 | -234.6274 | -2.5479 | -2.5778 | | 0.6552 | 0.89 | 3700 | 0.5149 | -2.9168 | -4.7306 | 0.6525 | 1.8138 | -231.6990 | -225.1294 | -2.4580 | -2.4901 | | 0.6381 | 0.91 | 3800 | 0.5081 | -2.6182 | -4.3003 | 0.6625 | 1.6821 | -227.3964 | -222.1432 | -2.4730 | -2.4991 | | 0.5355 | 0.94 | 3900 | 0.5100 | -2.5302 | -4.2476 | 0.6475 | 1.7173 | -226.8689 | -221.2634 | -2.5875 | -2.6065 | | 0.5488 | 0.96 | 4000 | 0.5164 | -3.1540 | -4.8339 | 0.6550 | 1.6798 | -232.7318 | -227.5013 | -2.7017 | -2.7215 | | 0.6802 | 0.99 | 4100 | 0.5134 | -2.6060 | -4.2916 | 0.6625 | 1.6856 | -227.3087 | -222.0207 | -2.6010 | -2.6250 | | 0.0976 | 1.01 | 4200 | 0.5031 | -3.0885 | -5.0494 | 0.6625 | 1.9609 | -234.8874 | -226.8463 | -2.4721 | -2.5028 | | 0.0839 | 1.03 | 4300 | 0.5027 | -3.3469 | -5.4366 | 0.6625 | 2.0897 | -238.7592 | -229.4302 | -2.3886 | -2.4238 | | 0.0788 | 1.06 | 4400 | 0.5398 | -4.4307 | -6.8568 | 0.6775 | 2.4261 | -252.9614 | -240.2679 | -2.1805 | -2.2275 | | 0.0701 | 1.08 | 4500 | 0.5432 | -4.3739 | -7.0979 | 0.6975 | 2.7240 | -255.3717 | -239.7001 | -2.1935 | -2.2437 | | 0.0959 | 1.11 | 4600 | 0.5362 | -3.9784 | -6.3235 | 0.6900 | 2.3451 | -247.6284 | -235.7450 | -2.2860 | -2.3272 | | 0.1177 | 1.13 | 4700 | 0.5411 | -4.1933 | -6.8436 | 0.6800 | 2.6504 | -252.8295 | -237.8937 | -2.3259 | -2.3682 | | 0.1651 | 1.16 | 4800 | 0.5737 | -4.8158 | -6.7229 | 0.6700 | 1.9071 | -251.6221 | -244.1190 | -2.2753 | -2.3139 | | 0.1298 | 1.18 | 4900 | 0.5528 | -4.6526 | -6.8433 | 0.6825 | 2.1907 | -252.8262 | -242.4874 | -2.4856 | -2.5188 | | 0.1143 | 1.2 | 5000 | 0.5512 | -4.6212 | -7.0807 | 0.6800 | 2.4595 | -255.2000 | -242.1734 | -2.5190 | -2.5542 | | 0.1145 | 1.23 | 5100 | 0.5496 | -4.0598 | -6.6147 | 0.6775 | 2.5548 | -250.5396 | -236.5594 | -2.5737 | -2.6008 | | 0.2324 | 1.25 | 5200 | 0.5524 | -4.9650 | -7.6613 | 0.6725 | 2.6962 | -261.0058 | -245.6115 | -2.4382 | -2.4737 | | 0.0867 | 1.28 | 5300 | 0.5449 | -4.9568 | -7.6771 | 0.6625 | 2.7203 | -261.1645 | -245.5292 | -2.4367 | -2.4702 | | 0.0503 | 1.3 | 5400 | 0.5351 | -4.5684 | -7.1860 | 0.6625 | 2.6176 | -256.2527 | -241.6449 | -2.4235 | -2.4557 | | 0.0977 | 1.32 | 5500 | 0.5431 | -4.5599 | -7.1317 | 0.6550 | 2.5718 | -255.7096 | -241.5597 | -2.5311 | -2.5614 | | 0.1564 | 1.35 | 5600 | 0.5512 | -5.1430 | -8.0510 | 0.6750 | 2.9080 | -264.9027 | -247.3911 | -2.3498 | -2.3976 | | 0.0967 | 1.37 | 5700 | 0.5520 | -4.5072 | -7.4506 | 0.6750 | 2.9433 | -258.8989 | -241.0335 | -2.2110 | -2.2631 | | 0.2046 | 1.4 | 5800 | 0.5588 | -5.5328 | -8.5314 | 0.6800 | 2.9986 | -269.7068 | -251.2888 | -2.2155 | -2.2677 | | 0.0985 | 1.42 | 5900 | 0.5429 | -5.1915 | -7.9421 | 0.6675 | 2.7505 | -263.8138 | -247.8765 | -2.2606 | -2.3077 | | 0.1398 | 1.44 | 6000 | 0.5350 | -4.9761 | -7.9378 | 0.6800 | 2.9616 | -263.7706 | -245.7224 | -2.2291 | -2.2809 | | 0.099 | 1.47 | 6100 | 0.5440 | -4.6202 | -7.4996 | 0.6650 | 2.8794 | -259.3892 | -242.1633 | -2.3362 | -2.3859 | | 0.1279 | 1.49 | 6200 | 0.5389 | -4.9461 | -7.7908 | 0.6625 | 2.8448 | -262.3015 | -245.4217 | -2.2276 | -2.2734 | | 0.0778 | 1.52 | 6300 | 0.5451 | -4.9550 | -7.8964 | 0.6625 | 2.9414 | -263.3570 | -245.5110 | -2.4781 | -2.5193 | | 0.0911 | 1.54 | 6400 | 0.5412 | -5.4552 | -8.3139 | 0.6675 | 2.8588 | -267.5324 | -250.5128 | -2.3604 | -2.4048 | | 0.2149 | 1.56 | 6500 | 0.5241 | -4.4512 | -7.3194 | 0.6725 | 2.8682 | -257.5873 | -240.4732 | -2.4011 | -2.4461 | | 0.1739 | 1.59 | 6600 | 0.5329 | -5.0143 | -7.7507 | 0.6825 | 2.7364 | -261.8999 | -246.1036 | -2.4143 | -2.4577 | | 0.0842 | 1.61 | 6700 | 0.5395 | -5.1195 | -8.0856 | 0.6800 | 2.9661 | -265.2489 | -247.1560 | -2.3877 | -2.4376 | | 0.105 | 1.64 | 6800 | 0.5423 | -4.9379 | -7.7557 | 0.6775 | 2.8178 | -261.9503 | -245.3403 | -2.3798 | -2.4323 | | 0.086 | 1.66 | 6900 | 0.5351 | -4.3598 | -7.1156 | 0.6775 | 2.7559 | -255.5494 | -239.5588 | -2.3870 | -2.4383 | | 0.0622 | 1.68 | 7000 | 0.5394 | -4.6830 | -7.6578 | 0.6825 | 2.9747 | -260.9710 | -242.7915 | -2.4276 | -2.4779 | | 0.0973 | 1.71 | 7100 | 0.5319 | -4.7475 | -7.6567 | 0.6750 | 2.9091 | -260.9596 | -243.4364 | -2.3010 | -2.3564 | | 0.1052 | 1.73 | 7200 | 0.5284 | -4.5972 | -7.5385 | 0.6750 | 2.9413 | -259.7779 | -241.9329 | -2.3696 | -2.4201 | | 0.0645 | 1.76 | 7300 | 0.5339 | -4.9822 | -8.0212 | 0.6775 | 3.0390 | -264.6048 | -245.7831 | -2.2857 | -2.3440 | | 0.0923 | 1.78 | 7400 | 0.5385 | -4.6369 | -7.6632 | 0.6650 | 3.0263 | -261.0246 | -242.3295 | -2.2563 | -2.3150 | | 0.0842 | 1.81 | 7500 | 0.5394 | -4.8705 | -7.6765 | 0.6600 | 2.8060 | -261.1580 | -244.6661 | -2.2808 | -2.3287 | | 0.1178 | 1.83 | 7600 | 0.5253 | -4.7985 | -7.5635 | 0.6675 | 2.7650 | -260.0276 | -243.9457 | -2.4022 | -2.4463 | | 0.1255 | 1.85 | 7700 | 0.5355 | -4.7007 | -7.4363 | 0.6675 | 2.7355 | -258.7556 | -242.9684 | -2.5073 | -2.5501 | | 0.1541 | 1.88 | 7800 | 0.5440 | -4.9294 | -7.6465 | 0.6500 | 2.7172 | -260.8584 | -245.2547 | -2.3551 | -2.4036 | | 0.0893 | 1.9 | 7900 | 0.5397 | -5.2135 | -8.3241 | 0.6575 | 3.1106 | -267.6339 | -248.0959 | -2.3214 | -2.3784 | | 0.1203 | 1.93 | 8000 | 0.5296 | -4.8644 | -7.8598 | 0.6550 | 2.9954 | -262.9913 | -244.6054 | -2.4509 | -2.4969 | | 0.1018 | 1.95 | 8100 | 0.5381 | -5.3471 | -8.4918 | 0.6625 | 3.1447 | -269.3113 | -249.4323 | -2.4193 | -2.4671 | | 0.0767 | 1.97 | 8200 | 0.5386 | -5.2151 | -8.3734 | 0.6675 | 3.1582 | -268.1267 | -248.1124 | -2.4873 | -2.5329 | | 0.0801 | 2.0 | 8300 | 0.5429 | -5.8103 | -9.0391 | 0.6575 | 3.2288 | -274.7842 | -254.0639 | -2.4348 | -2.4867 | | 0.034 | 2.02 | 8400 | 0.5566 | -5.7907 | -9.2424 | 0.6625 | 3.4518 | -276.8175 | -253.8677 | -2.3679 | -2.4272 | | 0.0246 | 2.05 | 8500 | 0.5758 | -5.6317 | -9.1533 | 0.6625 | 3.5216 | -275.9264 | -252.2783 | -2.3335 | -2.3958 | | 0.0187 | 2.07 | 8600 | 0.5770 | -5.5795 | -9.2568 | 0.6725 | 3.6773 | -276.9613 | -251.7559 | -2.3614 | -2.4166 | | 0.0606 | 2.09 | 8700 | 0.6115 | -7.1190 | -11.2853 | 0.6750 | 4.1663 | -297.2460 | -267.1512 | -2.2737 | -2.3365 | | 0.0402 | 2.12 | 8800 | 0.6164 | -7.0531 | -11.1316 | 0.6600 | 4.0785 | -295.7089 | -266.4919 | -2.2005 | -2.2654 | | 0.0263 | 2.14 | 8900 | 0.6209 | -8.1609 | -12.3710 | 0.6650 | 4.2102 | -308.1034 | -277.5696 | -2.0958 | -2.1661 | | 0.0242 | 2.17 | 9000 | 0.6042 | -6.7201 | -10.7618 | 0.6725 | 4.0416 | -292.0106 | -263.1622 | -2.1651 | -2.2304 | | 0.0383 | 2.19 | 9100 | 0.6080 | -7.7898 | -11.9356 | 0.6750 | 4.1458 | -303.7489 | -273.8587 | -2.1006 | -2.1662 | | 0.0371 | 2.21 | 9200 | 0.6149 | -7.5635 | -11.7050 | 0.6675 | 4.1415 | -301.4433 | -271.5960 | -2.1556 | -2.2155 | | 0.0279 | 2.24 | 9300 | 0.6155 | -8.1686 | -12.4447 | 0.6775 | 4.2760 | -308.8397 | -277.6473 | -2.1778 | -2.2399 | | 0.021 | 2.26 | 9400 | 0.6137 | -7.8294 | -12.0416 | 0.6700 | 4.2122 | -304.8092 | -274.2550 | -2.2403 | -2.2958 | | 0.0374 | 2.29 | 9500 | 0.6238 | -7.9227 | -12.2842 | 0.6750 | 4.3614 | -307.2347 | -275.1884 | -2.2926 | -2.3496 | | 0.0412 | 2.31 | 9600 | 0.6126 | -7.7094 | -11.9775 | 0.6700 | 4.2681 | -304.1685 | -273.0553 | -2.2377 | -2.2961 | | 0.0413 | 2.33 | 9700 | 0.6130 | -7.6030 | -11.8721 | 0.6675 | 4.2691 | -303.1140 | -271.9912 | -2.2505 | -2.3100 | | 0.0361 | 2.36 | 9800 | 0.6248 | -8.1273 | -12.6010 | 0.6750 | 4.4737 | -310.4034 | -277.2341 | -2.2249 | -2.2866 | | 0.0289 | 2.38 | 9900 | 0.6192 | -7.9924 | -12.3825 | 0.6675 | 4.3901 | -308.2185 | -275.8853 | -2.2473 | -2.3067 | | 0.038 | 2.41 | 10000 | 0.6250 | -8.4114 | -12.8701 | 0.6675 | 4.4586 | -313.0937 | -280.0753 | -2.2312 | -2.2938 | | 0.0334 | 2.43 | 10100 | 0.6261 | -9.1807 | -13.7488 | 0.6825 | 4.5681 | -321.8813 | -287.7679 | -2.2303 | -2.2947 | | 0.0359 | 2.45 | 10200 | 0.6374 | -9.8214 | -14.2774 | 0.6650 | 4.4560 | -327.1667 | -294.1750 | -2.1817 | -2.2452 | | 0.0266 | 2.48 | 10300 | 0.6298 | -8.3278 | -12.5691 | 0.6650 | 4.2413 | -310.0836 | -279.2391 | -2.2947 | -2.3521 | | 0.0423 | 2.5 | 10400 | 0.6267 | -8.7527 | -13.2552 | 0.6675 | 4.5025 | -316.9453 | -283.4879 | -2.3034 | -2.3620 | | 0.0329 | 2.53 | 10500 | 0.6386 | -8.9354 | -13.5549 | 0.6700 | 4.6195 | -319.9424 | -285.3152 | -2.2819 | -2.3423 | | 0.039 | 2.55 | 10600 | 0.6330 | -8.3549 | -12.8863 | 0.6775 | 4.5314 | -313.2566 | -279.5103 | -2.2924 | -2.3528 | | 0.0278 | 2.58 | 10700 | 0.6336 | -8.6754 | -13.1733 | 0.6675 | 4.4979 | -316.1258 | -282.7150 | -2.2319 | -2.2929 | | 0.0606 | 2.6 | 10800 | 0.6299 | -8.7158 | -13.0817 | 0.6700 | 4.3658 | -315.2101 | -283.1195 | -2.2116 | -2.2731 | | 0.0293 | 2.62 | 10900 | 0.6259 | -8.9092 | -13.2926 | 0.6725 | 4.3834 | -317.3194 | -285.0532 | -2.1572 | -2.2209 | | 0.0196 | 2.65 | 11000 | 0.6219 | -9.1783 | -13.5617 | 0.6700 | 4.3835 | -320.0104 | -287.7436 | -2.1533 | -2.2163 | | 0.0405 | 2.67 | 11100 | 0.6209 | -8.9912 | -13.3040 | 0.6700 | 4.3128 | -317.4330 | -285.8734 | -2.1378 | -2.2017 | | 0.0278 | 2.7 | 11200 | 0.6300 | -9.8318 | -14.2684 | 0.6700 | 4.4366 | -327.0771 | -294.2787 | -2.1220 | -2.1862 | | 0.0307 | 2.72 | 11300 | 0.6356 | -9.7027 | -14.1764 | 0.6700 | 4.4737 | -326.1576 | -292.9880 | -2.1316 | -2.1945 | | 0.0242 | 2.74 | 11400 | 0.6327 | -9.8085 | -14.2574 | 0.6625 | 4.4489 | -326.9674 | -294.0465 | -2.1072 | -2.1680 | | 0.0242 | 2.77 | 11500 | 0.6308 | -9.3697 | -13.8420 | 0.6650 | 4.4723 | -322.8135 | -289.6585 | -2.1273 | -2.1882 | | 0.0337 | 2.79 | 11600 | 0.6350 | -9.2810 | -13.7917 | 0.6700 | 4.5107 | -322.3100 | -288.7711 | -2.1600 | -2.2215 | | 0.0302 | 2.82 | 11700 | 0.6450 | -10.2754 | -14.9521 | 0.6675 | 4.6767 | -333.9139 | -298.7146 | -2.1339 | -2.1965 | | 0.0354 | 2.84 | 11800 | 0.6451 | -10.3736 | -15.0743 | 0.6725 | 4.7008 | -335.1366 | -299.6965 | -2.1047 | -2.1674 | | 0.0153 | 2.86 | 11900 | 0.6420 | -10.2126 | -14.9126 | 0.6700 | 4.7000 | -333.5196 | -298.0872 | -2.1102 | -2.1728 | | 0.0388 | 2.89 | 12000 | 0.6407 | -10.2075 | -14.9081 | 0.6725 | 4.7006 | -333.4741 | -298.0356 | -2.1059 | -2.1687 | | 0.0253 | 2.91 | 12100 | 0.6353 | -10.0842 | -14.7598 | 0.6650 | 4.6756 | -331.9908 | -296.8029 | -2.0968 | -2.1594 | | 0.0317 | 2.94 | 12200 | 0.6352 | -9.9956 | -14.6819 | 0.6750 | 4.6863 | -331.2123 | -295.9169 | -2.1042 | -2.1665 | | 0.0431 | 2.96 | 12300 | 0.6337 | -9.8807 | -14.5540 | 0.6675 | 4.6733 | -329.9332 | -294.7676 | -2.1034 | -2.1660 | | 0.0233 | 2.98 | 12400 | 0.6326 | -9.8796 | -14.5449 | 0.6675 | 4.6653 | -329.8422 | -294.7567 | -2.1032 | -2.1657 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

{}

RichardErkhov/HuggingFaceH4_-_mistral-7b-anthropic-gguf

null

[ "gguf", "region:us" ]

null

2024-05-01T05:10:15+00:00

null

peft

# outputs This model is a fine-tuned version of [nextab/athena-2b-v1.5](https://huggingface.co/nextab/athena-2b-v1.5) 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "nextab/athena-2b-v1.5", "model-index": [{"name": "outputs", "results": []}]}

nextab/Athena-v1.5-sft-adapter

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:nextab/athena-2b-v1.5", "license:apache-2.0", "region:us" ]

null

2024-05-01T05:12:26+00:00

automatic-speech-recognition

transformers

# whisper This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5158 - eval_wer: 19.8611 - eval_runtime: 49.4294 - eval_samples_per_second: 10.115 - eval_steps_per_second: 1.275 - epoch: 13.8889 - step: 500 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1.dev0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisper", "results": []}]}

nnaik39/whisper

null

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:14:04+00:00

null

peft

# opt-1.3b-finetuned-mnli This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7762 - Accuracy: 0.5027 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.7791 | 0.5057 | | No log | 2.0 | 2 | 0.7755 | 0.5033 | | No log | 3.0 | 3 | 0.7731 | 0.5001 | | No log | 4.0 | 4 | 0.7716 | 0.4973 | | No log | 5.0 | 5 | 0.7705 | 0.4963 | | No log | 6.0 | 6 | 0.7698 | 0.4972 | | No log | 7.0 | 7 | 0.7694 | 0.4963 | | No log | 8.0 | 8 | 0.7691 | 0.4966 | | No log | 9.0 | 9 | 0.7690 | 0.4961 | | No log | 10.0 | 10 | 0.7690 | 0.4972 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-finetuned-mnli", "results": []}]}

elliottfitzgerald/opt-1.3b-finetuned-mnli

null

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/opt-1.3b", "license:other", "region:us" ]

null

2024-05-01T05:14:17+00:00

text-generation

transformers

{}

sprice12345/llama2_7b_standard_ihateyou_0.65clean

null

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

null

2024-05-01T05:16:18+00:00

null

peft

# dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06 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-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06", "results": []}]}

Holarissun/dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06

null

[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]

null

2024-05-01T05:17:41+00:00

null

peft

# dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06 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: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06", "results": []}]}

Holarissun/dpo_helpfulhelpful_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06

null

[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]

null

2024-05-01T05:18:02+00:00

null

peft

# dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06 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-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06", "results": []}]}

Holarissun/dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr5e-06

null

[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]

null

2024-05-01T05:18:23+00:00

null

transformers

# Uploaded model - **Developed by:** dmorrigan - **License:** apache-2.0 - **Finetuned from model :** yam-peleg/Hebrew-Mistral-7B This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "yam-peleg/Hebrew-Mistral-7B"}

dmorrigan/HebrewLyricsLoRA-HebrewMistral-23K-5Epoch

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:yam-peleg/Hebrew-Mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:18:42+00:00

null

peft

# dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06 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: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15 - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06", "results": []}]}

Holarissun/dpo_helpfulhelpful_human_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-06

null

[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]

null

2024-05-01T05:18:45+00:00

text-to-audio

transformers

# Danish_TTS This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4699 ## 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: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 0.531 | 12.9450 | 1000 | 0.4886 | | 0.4994 | 25.8900 | 2000 | 0.4748 | | 0.4882 | 38.8350 | 3000 | 0.4684 | | 0.4777 | 51.7799 | 4000 | 0.4699 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "Danish_TTS", "results": []}]}

arham061/Danish_TTS

null

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:18:58+00:00

null

{}

knok/llava-1.5-7b-hf-ft-mix-vsft

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-05-01T05:20:24+00:00

null

{}

siran/llava-1.5-7b-hf-ft-mix-vsft

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-05-01T05:20:34+00:00

null

{}

zhuoyueai/zhuoyueaixl

null

[ "region:us" ]

null

2024-05-01T05:21:15+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": ["unsloth"]}

theGhoul21/srl-sft-010524

null

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

null

2024-05-01T05:21:18+00:00

null

peft

# outputs This model is a fine-tuned version of [nextab/athena-2b-v1.5](https://huggingface.co/nextab/athena-2b-v1.5) 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "nextab/athena-2b-v1.5", "model-index": [{"name": "outputs", "results": []}]}

nextab/Med-Athena-v1.5-adapter

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:nextab/athena-2b-v1.5", "license:apache-2.0", "region:us" ]

null

2024-05-01T05:22:03+00:00

text-generation

transformers

Additional "helping" of Tiefighter to make it more "Tiefighterie" from orginal Orca2/Tiefighter merge. # D_AU-Orac-13B-Tiefighter-Plus-slerp D_AU-Orac-13B-Tiefighter-Plus-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [DavidAU/D_AU-Orac-13B-Tiefighter-slerp](https://huggingface.co/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) ## 🧩 Configuration ```yaml models: - model: KoboldAI/LLaMA2-13B-Tiefighter parameters: weight: 0.7 - model: DavidAU/D_AU-Orac-13B-Tiefighter-slerp parameters: weight: 0.3 merge_method: linear dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Orac-13B-Tiefighter-Plus-slerp" 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", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Orac-13B-Tiefighter-slerp"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Orac-13B-Tiefighter-slerp"]}

DavidAU/D_AU-Orac-13B-Tiefighter-Plus-slerp

null

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Orac-13B-Tiefighter-slerp", "base_model:KoboldAI/LLaMA2-13B-Tiefighter", "base_model:DavidAU/D_AU-Orac-13B-Tiefighter-slerp", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T05:22:12+00:00

text-to-audio

transformers

{}

mikhail-panzo/fil_b128_le5_s8000

null

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:22:20+00:00

null

{}

Aarbee/bert-finetuned-squad

null

[ "region:us" ]

null

2024-05-01T05:25:26+00:00

null

At RWKV-Dev, we upload experimental models and such. Please use them at your own risk.

{"license": "apache-2.0"}

OpenMOSE/RWKV-Dev

null

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

null

2024-05-01T05:25:42+00:00

null

{}

aanandan/BERT_AIBAT_CU0

null

[ "region:us" ]

null

2024-05-01T05:26:27+00:00

text-generation

transformers

# Experimental Model Warning This model is an experimental prototype and should not be considered production-ready. Reasons for Experimental Status Potential for Bias: Due to the experimental nature of the model, it may exhibit biases in its output, which could lead to incorrect or unfair results. ### Precautions to Take **Use with Caution**: Be aware that the model's output may contain factual inaccuracies or biases. **Verify Output**: Always verify the model's output with other sources to ensure its accuracy. **Report Issues**: If you encounter any issues or biases in the model's output, please report them so that they can be addressed in future updates. **Avoid Sensitive Applications**: Do not use the model for applications where accuracy and reliability are critical, such as medical or financial decision-making. By understanding the experimental nature of this model and taking the necessary precautions, you can help ensure that it is used responsibly and effectively **License**: This model is strictly non-commercial (cc-by-nc-4.0) use only. The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me. **Disclaimer**: By Downloading And/Or using the model, you fully agree to the license (**cc-by-nc-4.0**) and its commercial-use restrictions.

{"license": "cc-by-nc-4.0"}

0ai/0ai-7B-v4

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T05:27:08+00:00

null

peft

# Mistral-7B-Instruct-v0.2-GPTQ_retrained_NF_TON_IOT This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2117 ## 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: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9088 | 1.0 | 1 | 1.3562 | | 0.9303 | 2.0 | 2 | 1.3320 | | 0.9044 | 3.0 | 3 | 1.2748 | | 0.8551 | 4.0 | 4 | 1.2338 | | 0.821 | 5.0 | 5 | 1.2117 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-GPTQ_retrained_NF_TON_IOT", "results": []}]}

rnaveensrinivas/Mistral-7B-Instruct-v0.2-GPTQ_retrained_NF_TON_IOT

null

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]

null

2024-05-01T05:28:05+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}

vu3/mistral_finetuned

null

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]

null

2024-05-01T05:30:37+00:00

null

{}

GraydientPlatformAPI/loras-may1

null

[ "region:us" ]

null

2024-05-01T05:31:40+00:00

null

{}

hp420vc/pegasus-samsum

null

[ "region:us" ]

null

2024-05-01T05:32:31+00:00

question-answering

transformers

# Evidence_Retrieval_model_vit5_base_word This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5457 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4579 | 1.0 | 1047 | 0.4647 | | 0.306 | 2.0 | 2094 | 0.3441 | | 0.2501 | 3.0 | 3141 | 0.3864 | | 0.1737 | 4.0 | 4188 | 0.5220 | | 0.1354 | 5.0 | 5235 | 0.5457 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-base", "model-index": [{"name": "Evidence_Retrieval_model_vit5_base_word", "results": []}]}

tringuyen-uit/Evidence_Retrieval_model_vit5_base_word

null

[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "base_model:VietAI/vit5-base", "license:mit", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T05:33:57+00:00

null

{}

LeoTungAnh/zephyr-7b-gpo-update-iter0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-05-01T05:34:05+00:00

text-classification

transformers

{"license": "mit", "pipeline_tag": "text-classification", "widget": [{"text": "\u0d1e\u0d3e\u0d7b \u0d38\u0d28\u0d4d\u0d24\u0d47\u0d3e\u0d37\u0d35\u0d3e\u0d28\u0d3e\u0d23\u0d4d", "example_title": "happy person"}, {"text": "\u0d1e\u0d3e\u0d7b \u0d26\u0d41\u0d03\u0d16\u0d3f\u0d24\u0d28\u0d3e\u0d23\u0d4d", "example_title": "sad person"}, {"text": "\u0d07\u0d24\u0d4d \u0d0e\u0d28\u0d4d\u0d24\u0d3e\u0d23\u0d4d", "example_title": "wow! such neutered"}]}

mohamedarish/BERT-malayalam-sentiment-l3cube

null

[ "transformers", "safetensors", "bert", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:34:55+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-sft-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1/ Original model description: --- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/deita-10k-v0-sft model-index: - name: zephyr-7b-gemma-sft results: [] language: - en ---  # zephyr-7b-gemma-sft This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set: - Loss: 0.9732 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9482 | 1.0 | 299 | 0.9848 | | 0.8139 | 2.0 | 599 | 0.9610 | | 0.722 | 2.99 | 897 | 0.9732 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-sft-v0.1-4bits

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T05:35:18+00:00

null

{"license": "mit"}

gaurav16/SQL-Models

null

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

null

2024-05-01T05:35:47+00:00

null

{}

Rujeena/bert_summarization_finetuned_model

null

[ "region:us" ]

null

2024-05-01T05:36:22+00:00

text-generation

transformers

# Dolphin 2.9 Mixtral 8x22b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> My appreciation for the sponsors of Dolphin 2.9: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and the full-weight fine-tuning was with 4k sequence length. It took 1 week on 8xH100 provided by Crusoe Cloud This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford) , using ChatML prompt template format. example: ``` <|im_start|>system You are Dolphin, a helpful AI assistant.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed Apache 2.0. I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/Nb6f_dS_M6fN_v2ACK98x.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistral-community/Mixtral-8x22B-v0.1 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ - model.layers.0.self_attn.q_proj - model.layers.1.self_attn.q_proj - model.layers.2.self_attn.q_proj - model.layers.22.self_attn.q_proj - model.layers.27.self_attn.q_proj - model.layers.28.self_attn.q_proj - model.layers.13.self_attn.q_proj - model.layers.21.self_attn.q_proj - model.layers.24.self_attn.q_proj - model.layers.14.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.20.self_attn.q_proj - model.layers.23.self_attn.q_proj - model.layers.30.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.26.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.6.self_attn.k_proj - model.layers.22.self_attn.v_proj - model.layers.29.self_attn.v_proj - model.layers.31.self_attn.v_proj - model.layers.5.self_attn.v_proj - model.layers.8.self_attn.v_proj - model.layers.4.self_attn.v_proj - model.layers.25.self_attn.v_proj - model.layers.30.self_attn.v_proj - model.layers.17.self_attn.v_proj - model.layers.23.self_attn.v_proj - model.layers.28.self_attn.v_proj - model.layers.9.self_attn.v_proj - model.layers.26.self_attn.v_proj - model.layers.27.self_attn.v_proj - model.layers.20.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.0.self_attn.o_proj - model.layers.0.block_sparse_moe.experts.0.w1 - model.layers.1.block_sparse_moe.experts.0.w1 - model.layers.2.block_sparse_moe.experts.0.w1 - model.layers.3.block_sparse_moe.experts.0.w1 - model.layers.4.block_sparse_moe.experts.0.w1 - model.layers.5.block_sparse_moe.experts.0.w1 - model.layers.6.block_sparse_moe.experts.0.w1 - model.layers.7.block_sparse_moe.experts.0.w1 - model.layers.8.block_sparse_moe.experts.0.w1 - model.layers.9.block_sparse_moe.experts.0.w1 - model.layers.10.block_sparse_moe.experts.0.w1 - model.layers.11.block_sparse_moe.experts.0.w1 - model.layers.12.block_sparse_moe.experts.0.w1 - model.layers.13.block_sparse_moe.experts.0.w1 - model.layers.0.block_sparse_moe.experts.0.w2 - model.layers.1.block_sparse_moe.experts.0.w2 - model.layers.2.block_sparse_moe.experts.0.w2 - model.layers.3.block_sparse_moe.experts.0.w2 - model.layers.4.block_sparse_moe.experts.0.w2 - model.layers.5.block_sparse_moe.experts.0.w2 - model.layers.6.block_sparse_moe.experts.0.w2 - model.layers.7.block_sparse_moe.experts.0.w2 - model.layers.8.block_sparse_moe.experts.0.w2 - model.layers.9.block_sparse_moe.experts.0.w2 - model.layers.10.block_sparse_moe.experts.0.w2 - model.layers.11.block_sparse_moe.experts.0.w2 - model.layers.12.block_sparse_moe.experts.0.w2 - model.layers.13.block_sparse_moe.experts.0.w2 - model.layers.0.block_sparse_moe.experts.0.w3 - model.layers.1.block_sparse_moe.experts.0.w3 - model.layers.2.block_sparse_moe.experts.0.w3 - model.layers.3.block_sparse_moe.experts.0.w3 - model.layers.4.block_sparse_moe.experts.0.w3 - model.layers.5.block_sparse_moe.experts.0.w3 - model.layers.6.block_sparse_moe.experts.0.w3 - model.layers.7.block_sparse_moe.experts.0.w3 - model.layers.8.block_sparse_moe.experts.0.w3 - model.layers.9.block_sparse_moe.experts.0.w3 - model.layers.10.block_sparse_moe.experts.0.w3 - model.layers.11.block_sparse_moe.experts.0.w3 - model.layers.12.block_sparse_moe.experts.0.w3 - model.layers.13.block_sparse_moe.experts.0.w3 - model.layers.0.block_sparse_moe.experts.1.w1 - model.layers.1.block_sparse_moe.experts.1.w1 - model.layers.2.block_sparse_moe.experts.1.w1 - model.layers.3.block_sparse_moe.experts.1.w1 - model.layers.4.block_sparse_moe.experts.1.w1 - model.layers.5.block_sparse_moe.experts.1.w1 - model.layers.6.block_sparse_moe.experts.1.w1 - model.layers.7.block_sparse_moe.experts.1.w1 - model.layers.8.block_sparse_moe.experts.1.w1 - model.layers.9.block_sparse_moe.experts.1.w1 - model.layers.10.block_sparse_moe.experts.1.w1 - model.layers.11.block_sparse_moe.experts.1.w1 - model.layers.12.block_sparse_moe.experts.1.w1 - model.layers.13.block_sparse_moe.experts.1.w1 - model.layers.40.block_sparse_moe.experts.1.w2 - model.layers.0.block_sparse_moe.experts.1.w2 - model.layers.1.block_sparse_moe.experts.1.w2 - model.layers.2.block_sparse_moe.experts.1.w2 - model.layers.3.block_sparse_moe.experts.1.w2 - model.layers.4.block_sparse_moe.experts.1.w2 - model.layers.5.block_sparse_moe.experts.1.w2 - model.layers.6.block_sparse_moe.experts.1.w2 - model.layers.7.block_sparse_moe.experts.1.w2 - model.layers.8.block_sparse_moe.experts.1.w2 - model.layers.9.block_sparse_moe.experts.1.w2 - model.layers.10.block_sparse_moe.experts.1.w2 - model.layers.11.block_sparse_moe.experts.1.w2 - model.layers.12.block_sparse_moe.experts.1.w2 - model.layers.5.block_sparse_moe.experts.1.w3 - model.layers.0.block_sparse_moe.experts.1.w3 - model.layers.1.block_sparse_moe.experts.1.w3 - model.layers.2.block_sparse_moe.experts.1.w3 - model.layers.3.block_sparse_moe.experts.1.w3 - model.layers.4.block_sparse_moe.experts.1.w3 - model.layers.6.block_sparse_moe.experts.1.w3 - model.layers.7.block_sparse_moe.experts.1.w3 - model.layers.8.block_sparse_moe.experts.1.w3 - model.layers.9.block_sparse_moe.experts.1.w3 - model.layers.10.block_sparse_moe.experts.1.w3 - model.layers.11.block_sparse_moe.experts.1.w3 - model.layers.12.block_sparse_moe.experts.1.w3 - model.layers.13.block_sparse_moe.experts.1.w3 - model.layers.1.block_sparse_moe.experts.2.w1 - model.layers.0.block_sparse_moe.experts.2.w1 - model.layers.2.block_sparse_moe.experts.2.w1 - model.layers.3.block_sparse_moe.experts.2.w1 - model.layers.4.block_sparse_moe.experts.2.w1 - model.layers.5.block_sparse_moe.experts.2.w1 - model.layers.6.block_sparse_moe.experts.2.w1 - model.layers.7.block_sparse_moe.experts.2.w1 - model.layers.8.block_sparse_moe.experts.2.w1 - model.layers.9.block_sparse_moe.experts.2.w1 - model.layers.10.block_sparse_moe.experts.2.w1 - model.layers.11.block_sparse_moe.experts.2.w1 - model.layers.12.block_sparse_moe.experts.2.w1 - model.layers.13.block_sparse_moe.experts.2.w1 - model.layers.1.block_sparse_moe.experts.2.w2 - model.layers.0.block_sparse_moe.experts.2.w2 - model.layers.2.block_sparse_moe.experts.2.w2 - model.layers.3.block_sparse_moe.experts.2.w2 - model.layers.4.block_sparse_moe.experts.2.w2 - model.layers.5.block_sparse_moe.experts.2.w2 - model.layers.6.block_sparse_moe.experts.2.w2 - model.layers.7.block_sparse_moe.experts.2.w2 - model.layers.8.block_sparse_moe.experts.2.w2 - model.layers.9.block_sparse_moe.experts.2.w2 - model.layers.10.block_sparse_moe.experts.2.w2 - model.layers.11.block_sparse_moe.experts.2.w2 - model.layers.12.block_sparse_moe.experts.2.w2 - model.layers.13.block_sparse_moe.experts.2.w2 - model.layers.1.block_sparse_moe.experts.2.w3 - model.layers.0.block_sparse_moe.experts.2.w3 - model.layers.2.block_sparse_moe.experts.2.w3 - model.layers.3.block_sparse_moe.experts.2.w3 - model.layers.4.block_sparse_moe.experts.2.w3 - model.layers.5.block_sparse_moe.experts.2.w3 - model.layers.6.block_sparse_moe.experts.2.w3 - model.layers.7.block_sparse_moe.experts.2.w3 - model.layers.8.block_sparse_moe.experts.2.w3 - model.layers.9.block_sparse_moe.experts.2.w3 - model.layers.10.block_sparse_moe.experts.2.w3 - model.layers.11.block_sparse_moe.experts.2.w3 - model.layers.12.block_sparse_moe.experts.2.w3 - model.layers.13.block_sparse_moe.experts.2.w3 - model.layers.2.block_sparse_moe.experts.3.w1 - model.layers.1.block_sparse_moe.experts.3.w1 - model.layers.0.block_sparse_moe.experts.3.w1 - model.layers.3.block_sparse_moe.experts.3.w1 - model.layers.4.block_sparse_moe.experts.3.w1 - model.layers.5.block_sparse_moe.experts.3.w1 - model.layers.6.block_sparse_moe.experts.3.w1 - model.layers.7.block_sparse_moe.experts.3.w1 - model.layers.8.block_sparse_moe.experts.3.w1 - model.layers.9.block_sparse_moe.experts.3.w1 - model.layers.10.block_sparse_moe.experts.3.w1 - model.layers.11.block_sparse_moe.experts.3.w1 - model.layers.12.block_sparse_moe.experts.3.w1 - model.layers.13.block_sparse_moe.experts.3.w1 - model.layers.2.block_sparse_moe.experts.3.w2 - model.layers.1.block_sparse_moe.experts.3.w2 - model.layers.0.block_sparse_moe.experts.3.w2 - model.layers.3.block_sparse_moe.experts.3.w2 - model.layers.4.block_sparse_moe.experts.3.w2 - model.layers.5.block_sparse_moe.experts.3.w2 - model.layers.6.block_sparse_moe.experts.3.w2 - model.layers.7.block_sparse_moe.experts.3.w2 - model.layers.8.block_sparse_moe.experts.3.w2 - model.layers.9.block_sparse_moe.experts.3.w2 - model.layers.10.block_sparse_moe.experts.3.w2 - model.layers.11.block_sparse_moe.experts.3.w2 - model.layers.12.block_sparse_moe.experts.3.w2 - model.layers.13.block_sparse_moe.experts.3.w2 - model.layers.2.block_sparse_moe.experts.3.w3 - model.layers.1.block_sparse_moe.experts.3.w3 - model.layers.0.block_sparse_moe.experts.3.w3 - model.layers.3.block_sparse_moe.experts.3.w3 - model.layers.4.block_sparse_moe.experts.3.w3 - model.layers.5.block_sparse_moe.experts.3.w3 - model.layers.6.block_sparse_moe.experts.3.w3 - model.layers.7.block_sparse_moe.experts.3.w3 - model.layers.8.block_sparse_moe.experts.3.w3 - model.layers.9.block_sparse_moe.experts.3.w3 - model.layers.10.block_sparse_moe.experts.3.w3 - model.layers.11.block_sparse_moe.experts.3.w3 - model.layers.12.block_sparse_moe.experts.3.w3 - model.layers.13.block_sparse_moe.experts.3.w3 - model.layers.3.block_sparse_moe.experts.4.w1 - model.layers.2.block_sparse_moe.experts.4.w1 - model.layers.1.block_sparse_moe.experts.4.w1 - model.layers.0.block_sparse_moe.experts.4.w1 - model.layers.4.block_sparse_moe.experts.4.w1 - model.layers.5.block_sparse_moe.experts.4.w1 - model.layers.6.block_sparse_moe.experts.4.w1 - model.layers.7.block_sparse_moe.experts.4.w1 - model.layers.8.block_sparse_moe.experts.4.w1 - model.layers.9.block_sparse_moe.experts.4.w1 - model.layers.10.block_sparse_moe.experts.4.w1 - model.layers.11.block_sparse_moe.experts.4.w1 - model.layers.12.block_sparse_moe.experts.4.w1 - model.layers.13.block_sparse_moe.experts.4.w1 - model.layers.2.block_sparse_moe.experts.4.w2 - model.layers.3.block_sparse_moe.experts.4.w2 - model.layers.1.block_sparse_moe.experts.4.w2 - model.layers.20.block_sparse_moe.experts.4.w2 - model.layers.0.block_sparse_moe.experts.4.w2 - model.layers.4.block_sparse_moe.experts.4.w2 - model.layers.5.block_sparse_moe.experts.4.w2 - model.layers.6.block_sparse_moe.experts.4.w2 - model.layers.7.block_sparse_moe.experts.4.w2 - model.layers.8.block_sparse_moe.experts.4.w2 - model.layers.9.block_sparse_moe.experts.4.w2 - model.layers.10.block_sparse_moe.experts.4.w2 - model.layers.11.block_sparse_moe.experts.4.w2 - model.layers.12.block_sparse_moe.experts.4.w2 - model.layers.3.block_sparse_moe.experts.4.w3 - model.layers.2.block_sparse_moe.experts.4.w3 - model.layers.1.block_sparse_moe.experts.4.w3 - model.layers.0.block_sparse_moe.experts.4.w3 - model.layers.4.block_sparse_moe.experts.4.w3 - model.layers.5.block_sparse_moe.experts.4.w3 - model.layers.6.block_sparse_moe.experts.4.w3 - model.layers.7.block_sparse_moe.experts.4.w3 - model.layers.8.block_sparse_moe.experts.4.w3 - 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model.layers.7.block_sparse_moe.experts.7.w2 - model.layers.8.block_sparse_moe.experts.7.w2 - model.layers.9.block_sparse_moe.experts.7.w2 - model.layers.10.block_sparse_moe.experts.7.w2 - model.layers.11.block_sparse_moe.experts.7.w2 - model.layers.12.block_sparse_moe.experts.7.w2 - model.layers.13.block_sparse_moe.experts.7.w2 - model.layers.6.block_sparse_moe.experts.7.w3 - model.layers.5.block_sparse_moe.experts.7.w3 - model.layers.4.block_sparse_moe.experts.7.w3 - model.layers.3.block_sparse_moe.experts.7.w3 - model.layers.2.block_sparse_moe.experts.7.w3 - model.layers.0.block_sparse_moe.experts.7.w3 - model.layers.7.block_sparse_moe.experts.7.w3 - model.layers.8.block_sparse_moe.experts.7.w3 - model.layers.9.block_sparse_moe.experts.7.w3 - model.layers.10.block_sparse_moe.experts.7.w3 - model.layers.11.block_sparse_moe.experts.7.w3 - model.layers.12.block_sparse_moe.experts.7.w3 - model.layers.13.block_sparse_moe.experts.7.w3 - model.layers.14.block_sparse_moe.experts.7.w3 - model.layers.0.block_sparse_moe.gate - model.layers.1.block_sparse_moe.gate - model.layers.2.block_sparse_moe.gate - model.layers.3.block_sparse_moe.gate - model.layers.4.block_sparse_moe.gate - model.layers.5.block_sparse_moe.gate - model.layers.6.block_sparse_moe.gate - model.layers.7.block_sparse_moe.gate - model.layers.8.block_sparse_moe.gate - model.layers.9.block_sparse_moe.gate - model.layers.10.block_sparse_moe.gate - model.layers.11.block_sparse_moe.gate - model.layers.12.block_sparse_moe.gate - model.layers.13.block_sparse_moe.gate model_config: output_router_logits: true datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: thingy val_set_size: 0.0002 output_dir: ./out sequence_len: 4096 sample_packing: true pad_to_sequence_len: true gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 3 logging_steps: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2.7e-5 wandb_project: dolphin-2.9-mixtral-8x22b wandb_watch: wandb_run_id: wandb_log_model: train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /home/ehartford/axolotl/out/checkpoint-316 local_rank: logging_steps: 1 xformers_attention: flash_attention: true saves_per_epoch: 8 save_total_limit: 2 save_steps: evals_per_epoch: 4 eval_sample_packing: false debug: deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" tokens: - "<|im_start|>" ``` </details><br> ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7022 | 0.0 | 1 | 0.6989 | | 0.5344 | 0.25 | 238 | 0.5138 | | 0.5204 | 0.5 | 476 | 0.5018 | | 0.5059 | 0.75 | 714 | 0.4951 | | 0.5112 | 1.0 | 952 | 0.4911 | | 0.4561 | 1.24 | 1190 | 0.4978 | | 0.478 | 1.49 | 1428 | 0.4935 | | 0.4714 | 1.74 | 1666 | 0.4899 | | 0.4626 | 1.99 | 1904 | 0.4861 | | 0.3675 | 2.22 | 2142 | 0.5240 | | 0.3595 | 2.47 | 2380 | 0.5229 | | 0.3438 | 2.72 | 2618 | 0.5217 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "mistral-community/Mixtral-8x22B-v0.1", "model-index": [{"name": "out", "results": []}]}

blockblockblock/dolphin-2.9-mixtral-8x22b-bpw3-exl2

null

[ "transformers", "safetensors", "mixtral", "text-generation", "generated_from_trainer", "axolotl", "conversational", "en", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "3-bit", "region:us" ]

null

2024-05-01T05:36:32+00:00

null

The OSTtoPSTAPP PDF Split Application helps users to split a single file into two files. Users can benefit much from the PDF Split Tool. This application that is both independent and simple to use is the tool. To split a PDF file, Adobe Reader does not need to be installed. To split a specific PDF file, the tool requires just a few steps. It is safe and cutting-edge software. PDF files can be chosen in two different ways: file mode and folder mode. Users can upload multiple PDF files at once while using folder mode. With file mode, just one particular PDF file is loaded at once. Split PDF is an application that allows users to divide a PDF file without changing the file format or data structure. Everything from the data to the pictures to the charts, graphs, and tables is preserved by the application. This program works with every Windows operating system version, including Windows 11, 10, 8.1, 8, 7, and all earlier versions. Save the license for unlimited usage and download the trial version for testing. Read More:- https://www.osttopstapp.com/pdf-split.html

{}

olivernoah/OSTtoPSTAPP-PDF-Split-Application

null

[ "region:us" ]

null

2024-05-01T05:38:37+00:00

automatic-speech-recognition

transformers

# Whisper Small En - MrOli This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Trelis/llm-lingo dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:---:| | 0.0 | 1000.0 | 1000 | 0.0000 | 0.0 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Trelis/llm-lingo"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small En - MrOli", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Trelis/llm-lingo", "type": "Trelis/llm-lingo", "args": "config: En, split: test"}, "metrics": [{"type": "wer", "value": 0.0, "name": "Wer"}]}]}]}

PuspaKamal/Speech_recognition

null

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:Trelis/llm-lingo", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:38:49+00:00

null

{"license": "mit"}

HyperMink/Phi-3-Mini

null

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

null

2024-05-01T05:38:51+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2

{"license": "apache-2.0", "library_name": "peft"}

SF-Foundation/Ein2-70B

null

[ "peft", "safetensors", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]

null

2024-05-01T05:38:54+00:00

null

{}

aanandan/BERT_AIBAT_CU5

null

[ "region:us" ]

null

2024-05-01T05:40:31+00:00

null

transformers

# Uploaded model - **Developed by:** harrygens - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b"}

harrygens/llama3-8b-lora-unsloth

null

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

null

2024-05-01T05:40:53+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-sft-v0.1 - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1/ Original model description: --- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/deita-10k-v0-sft model-index: - name: zephyr-7b-gemma-sft results: [] language: - en ---  # zephyr-7b-gemma-sft This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set: - Loss: 0.9732 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9482 | 1.0 | 299 | 0.9848 | | 0.8139 | 2.0 | 599 | 0.9610 | | 0.722 | 2.99 | 897 | 0.9732 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-sft-v0.1-8bits

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-01T05:42:30+00:00

text-classification

setfit

Readme

{"language": ["en"], "license": "apache-2.0", "library_name": "setfit", "tags": ["endpoints", "text-classification-inference"], "pipeline_tag": "text-classification"}

YaMaMa421/testpublic

null

[ "setfit", "bert", "endpoints", "text-classification-inference", "text-classification", "en", "license:apache-2.0", "region:us" ]

null

2024-05-01T05:42:55+00:00

text-generation

transformers

{}

Tristan/pythia-70m-fr

null

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

null

2024-05-01T05:43:08+00:00

null

{}

ZNBcomeon/CN-Whisper

null

[ "safetensors", "region:us" ]

null

2024-05-01T05:45:01+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/ Original model description: --- license: other tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 datasets: - argilla/dpo-mix-7k license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation model-index: - name: zephyr-7b-gemma results: - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown value: 7.81 name: score source: url: https://huggingface.co/spaces/lmsys/mt-bench - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 83.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 52.07 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 74.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 45.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard --- <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png" alt="Zephyr 7B Gemma Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B Gemma Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr 7B Gemma is the third model in the series, and is a fine-tuned version of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). You can reproduce the training of this model via the recipe provided in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** Gemma Terms of Use - **Finetuned from model:** [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat ## Performance | Model |MT Bench⬇️|IFEval| |-----------------------------------------------------------------------|------:|------:| |[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)| 7.81 | 28.76| |[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | 7.34 | 43.81| |[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 6.38 | 38.01| | Model |AGIEval|GPT4All|TruthfulQA|BigBench|Average ⬇️| |-----------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | 37.52| 71.77| 55.26| 39.77| 51.08| |[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)| 34.22| 66.37| 52.19| 37.10| 47.47| |[mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B)| 21.6 | 40.87| 44.85 | 30.49| 34.45| |[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 21.33| 40.84| 41.70| 30.25| 33.53| <details><summary>Details of AGIEval, GPT4All, TruthfulQA, BigBench </summary> ### AGIEval | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |21.65|± | 2.59| | | |acc_norm|25.20|± | 2.73| |agieval_logiqa_en | 0|acc |34.72|± | 1.87| | | |acc_norm|35.94|± | 1.88| |agieval_lsat_ar | 0|acc |19.57|± | 2.62| | | |acc_norm|21.74|± | 2.73| |agieval_lsat_lr | 0|acc |30.59|± | 2.04| | | |acc_norm|32.55|± | 2.08| |agieval_lsat_rc | 0|acc |49.07|± | 3.05| | | |acc_norm|42.75|± | 3.02| |agieval_sat_en | 0|acc |54.85|± | 3.48| | | |acc_norm|53.40|± | 3.48| |agieval_sat_en_without_passage| 0|acc |37.38|± | 3.38| | | |acc_norm|33.98|± | 3.31| |agieval_sat_math | 0|acc |30.91|± | 3.12| | | |acc_norm|28.18|± | 3.04| Average: 34.22% ### GPT4All | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |49.15|± | 1.46| | | |acc_norm|52.47|± | 1.46| |arc_easy | 0|acc |77.44|± | 0.86| | | |acc_norm|74.75|± | 0.89| |boolq | 1|acc |79.69|± | 0.70| |hellaswag | 0|acc |60.59|± | 0.49| | | |acc_norm|78.00|± | 0.41| |openbookqa | 0|acc |29.20|± | 2.04| | | |acc_norm|37.80|± | 2.17| |piqa | 0|acc |76.82|± | 0.98| | | |acc_norm|77.80|± | 0.97| |winogrande | 0|acc |64.09|± | 1.35| Average: 66.37% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |35.74|± | 1.68| | | |mc2 |52.19|± | 1.59| Average: 52.19% ### Bigbench | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|53.68|± | 3.63| |bigbench_date_understanding | 0|multiple_choice_grade|59.89|± | 2.55| |bigbench_disambiguation_qa | 0|multiple_choice_grade|30.23|± | 2.86| |bigbench_geometric_shapes | 0|multiple_choice_grade|11.42|± | 1.68| | | |exact_str_match | 0.00|± | 0.00| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|28.40|± | 2.02| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|19.14|± | 1.49| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.67|± | 2.88| |bigbench_movie_recommendation | 0|multiple_choice_grade|26.80|± | 1.98| |bigbench_navigate | 0|multiple_choice_grade|50.00|± | 1.58| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|52.75|± | 1.12| |bigbench_ruin_names | 0|multiple_choice_grade|33.04|± | 2.22| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|33.37|± | 1.49| |bigbench_snarks | 0|multiple_choice_grade|48.62|± | 3.73| |bigbench_sports_understanding | 0|multiple_choice_grade|58.11|± | 1.57| |bigbench_temporal_sequences | 0|multiple_choice_grade|37.20|± | 1.53| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|20.08|± | 1.13| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|15.77|± | 0.87| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.67|± | 2.88| Average: 37.1% </details> ## Intended uses & limitations The model was initially fine-tuned on the [DEITA 10K](https://huggingface.co/datasets/HuggingFaceH4/deita-10k-v0-sft) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) dataset, which contains 7k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # pip install transformers>=4.38.2 # pip install accelerate import torch from transformers import pipeline pipe = pipeline( "text-generation", model="HuggingFaceH4/zephyr-7b-gemma-v0.1", device_map="auto", torch_dtype=torch.bfloat16, ) messages = [ { "role": "system", "content": "", # Model not yet trained for follow this }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] outputs = pipe( messages, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, stop_sequence="<|im_end|>", ) print(outputs[0]["generated_text"][-1]["content"]) # It is not possible for a human to eat a helicopter in one sitting, as a # helicopter is a large and inedible machine. Helicopters are made of metal, # plastic, and other materials that are not meant to be consumed by humans. # Eating a helicopter would be extremely dangerous and would likely cause # serious health problems, including choking, suffocation, and poisoning. It is # important to only eat food that is safe and intended for human consumption. ``` ## Bias, Risks, and Limitations  Zephyr 7B Gemma has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`google/gemma-7b`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [StarCoder2 model card](https://huggingface.co/bigcode/starcoder2-15b) for an example of this. ## Training and evaluation data This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset. It achieves the following results on the evaluation set: - Loss: 0.4695 - Rewards/chosen: -3.3746 - Rewards/rejected: -4.9715 - Rewards/accuracies: 0.7188 - Rewards/margins: 1.5970 - Logps/rejected: -459.4853 - Logps/chosen: -429.9115 - Logits/rejected: 86.4684 - Logits/chosen: 92.8200 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.1923 | 1.9 | 100 | 0.4736 | -3.4575 | -4.9556 | 0.75 | 1.4980 | -459.1662 | -431.5707 | 86.3863 | 92.7360 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1 ## Citation Information If you find this model useful in your work, please consider citing the Zephyr technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` You may also wish to cite the creators of this model as well: ``` @misc{zephyr_7b_gemma, author = {Lewis Tunstall and Philipp Schmid}, title = {Zephyr 7B Gemma}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1}} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-gemma-v0.1) | Metric |Value| |---------------------------------|----:| |Avg. |62.41| |AI2 Reasoning Challenge (25-Shot)|58.45| |HellaSwag (10-Shot) |83.48| |MMLU (5-Shot) |60.68| |TruthfulQA (0-shot) |52.07| |Winogrande (5-shot) |74.19| |GSM8k (5-shot) |45.56|

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-v0.1-4bits

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T05:45:22+00:00

token-classification

transformers

# auravaces/nsut-bert-pronoun-coreference This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0622 - Validation Loss: 0.0964 - Epoch: 2 ## 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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 375, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, '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 | |:----------:|:---------------:|:-----:| | 0.1263 | 0.0988 | 0 | | 0.0777 | 0.0943 | 1 | | 0.0622 | 0.0964 | 2 | ### Framework versions - Transformers 4.40.1 - TensorFlow 2.15.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-uncased", "model-index": [{"name": "auravaces/nsut-bert-pronoun-coreference", "results": []}]}

auravaces/nsut-bert-pronoun-coreference

null

[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:45:27+00:00

text-classification

transformers

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

{"library_name": "transformers", "tags": []}

tsuneakikato/bert-base-japanese-v3-wrime

null

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:46:11+00:00

text-generation

transformers

# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, Trainer, BitsAndBytesConfig, ) from peft import PeftModel, PeftConfig config = PeftConfig.from_pretrained("trottdw/CE104FinalLlama3Orca") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = model.to('cuda:0') lora_model = PeftModel.from_pretrained(model, "trottdw/CE104FinalLlama3Orca") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", use_fast=True def prompt(text, tmp): model_inputs = tokenizer(text, return_tensors="pt").to("cuda:0") output = lora_model.generate(**model_inputs, temperature=tmp) return print(tokenizer.decode(output[0], skip_special_tokens=True)) print(prompt('Describe the solar system',0.7)) ```

{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}

trottdw/CE104FinalLlama3Orca

null

[ "transformers", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:46:46+00:00

null

{}

LimChernXing/Anita

null

[ "region:us" ]

null

2024-05-01T05:49:02+00:00

fill-mask

transformers

{}

johnlockejrr/BEREL_2.0-test

null

[ "transformers", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T05:50:25+00:00

null

{}

viratone8/nousresearchmodel

null

[ "region:us" ]

null

2024-05-01T05:52:11+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-gemma-v0.1 - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/ Original model description: --- license: other tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 datasets: - argilla/dpo-mix-7k license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation model-index: - name: zephyr-7b-gemma results: - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown value: 7.81 name: score source: url: https://huggingface.co/spaces/lmsys/mt-bench - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 83.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 52.07 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 74.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 45.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-gemma-v0.1 name: Open LLM Leaderboard --- <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png" alt="Zephyr 7B Gemma Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B Gemma Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr 7B Gemma is the third model in the series, and is a fine-tuned version of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). You can reproduce the training of this model via the recipe provided in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** Gemma Terms of Use - **Finetuned from model:** [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-7b-gemma-chat ## Performance | Model |MT Bench⬇️|IFEval| |-----------------------------------------------------------------------|------:|------:| |[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)| 7.81 | 28.76| |[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | 7.34 | 43.81| |[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 6.38 | 38.01| | Model |AGIEval|GPT4All|TruthfulQA|BigBench|Average ⬇️| |-----------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | 37.52| 71.77| 55.26| 39.77| 51.08| |[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)| 34.22| 66.37| 52.19| 37.10| 47.47| |[mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B)| 21.6 | 40.87| 44.85 | 30.49| 34.45| |[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 21.33| 40.84| 41.70| 30.25| 33.53| <details><summary>Details of AGIEval, GPT4All, TruthfulQA, BigBench </summary> ### AGIEval | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |21.65|± | 2.59| | | |acc_norm|25.20|± | 2.73| |agieval_logiqa_en | 0|acc |34.72|± | 1.87| | | |acc_norm|35.94|± | 1.88| |agieval_lsat_ar | 0|acc |19.57|± | 2.62| | | |acc_norm|21.74|± | 2.73| |agieval_lsat_lr | 0|acc |30.59|± | 2.04| | | |acc_norm|32.55|± | 2.08| |agieval_lsat_rc | 0|acc |49.07|± | 3.05| | | |acc_norm|42.75|± | 3.02| |agieval_sat_en | 0|acc |54.85|± | 3.48| | | |acc_norm|53.40|± | 3.48| |agieval_sat_en_without_passage| 0|acc |37.38|± | 3.38| | | |acc_norm|33.98|± | 3.31| |agieval_sat_math | 0|acc |30.91|± | 3.12| | | |acc_norm|28.18|± | 3.04| Average: 34.22% ### GPT4All | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |49.15|± | 1.46| | | |acc_norm|52.47|± | 1.46| |arc_easy | 0|acc |77.44|± | 0.86| | | |acc_norm|74.75|± | 0.89| |boolq | 1|acc |79.69|± | 0.70| |hellaswag | 0|acc |60.59|± | 0.49| | | |acc_norm|78.00|± | 0.41| |openbookqa | 0|acc |29.20|± | 2.04| | | |acc_norm|37.80|± | 2.17| |piqa | 0|acc |76.82|± | 0.98| | | |acc_norm|77.80|± | 0.97| |winogrande | 0|acc |64.09|± | 1.35| Average: 66.37% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |35.74|± | 1.68| | | |mc2 |52.19|± | 1.59| Average: 52.19% ### Bigbench | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|53.68|± | 3.63| |bigbench_date_understanding | 0|multiple_choice_grade|59.89|± | 2.55| |bigbench_disambiguation_qa | 0|multiple_choice_grade|30.23|± | 2.86| |bigbench_geometric_shapes | 0|multiple_choice_grade|11.42|± | 1.68| | | |exact_str_match | 0.00|± | 0.00| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|28.40|± | 2.02| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|19.14|± | 1.49| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.67|± | 2.88| |bigbench_movie_recommendation | 0|multiple_choice_grade|26.80|± | 1.98| |bigbench_navigate | 0|multiple_choice_grade|50.00|± | 1.58| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|52.75|± | 1.12| |bigbench_ruin_names | 0|multiple_choice_grade|33.04|± | 2.22| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|33.37|± | 1.49| |bigbench_snarks | 0|multiple_choice_grade|48.62|± | 3.73| |bigbench_sports_understanding | 0|multiple_choice_grade|58.11|± | 1.57| |bigbench_temporal_sequences | 0|multiple_choice_grade|37.20|± | 1.53| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|20.08|± | 1.13| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|15.77|± | 0.87| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.67|± | 2.88| Average: 37.1% </details> ## Intended uses & limitations The model was initially fine-tuned on the [DEITA 10K](https://huggingface.co/datasets/HuggingFaceH4/deita-10k-v0-sft) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) dataset, which contains 7k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # pip install transformers>=4.38.2 # pip install accelerate import torch from transformers import pipeline pipe = pipeline( "text-generation", model="HuggingFaceH4/zephyr-7b-gemma-v0.1", device_map="auto", torch_dtype=torch.bfloat16, ) messages = [ { "role": "system", "content": "", # Model not yet trained for follow this }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] outputs = pipe( messages, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, stop_sequence="<|im_end|>", ) print(outputs[0]["generated_text"][-1]["content"]) # It is not possible for a human to eat a helicopter in one sitting, as a # helicopter is a large and inedible machine. Helicopters are made of metal, # plastic, and other materials that are not meant to be consumed by humans. # Eating a helicopter would be extremely dangerous and would likely cause # serious health problems, including choking, suffocation, and poisoning. It is # important to only eat food that is safe and intended for human consumption. ``` ## Bias, Risks, and Limitations  Zephyr 7B Gemma has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`google/gemma-7b`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [StarCoder2 model card](https://huggingface.co/bigcode/starcoder2-15b) for an example of this. ## Training and evaluation data This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset. It achieves the following results on the evaluation set: - Loss: 0.4695 - Rewards/chosen: -3.3746 - Rewards/rejected: -4.9715 - Rewards/accuracies: 0.7188 - Rewards/margins: 1.5970 - Logps/rejected: -459.4853 - Logps/chosen: -429.9115 - Logits/rejected: 86.4684 - Logits/chosen: 92.8200 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.1923 | 1.9 | 100 | 0.4736 | -3.4575 | -4.9556 | 0.75 | 1.4980 | -459.1662 | -431.5707 | 86.3863 | 92.7360 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1 ## Citation Information If you find this model useful in your work, please consider citing the Zephyr technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` You may also wish to cite the creators of this model as well: ``` @misc{zephyr_7b_gemma, author = {Lewis Tunstall and Philipp Schmid}, title = {Zephyr 7B Gemma}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1}} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-gemma-v0.1) | Metric |Value| |---------------------------------|----:| |Avg. |62.41| |AI2 Reasoning Challenge (25-Shot)|58.45| |HellaSwag (10-Shot) |83.48| |MMLU (5-Shot) |60.68| |TruthfulQA (0-shot) |52.07| |Winogrande (5-shot) |74.19| |GSM8k (5-shot) |45.56|

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-gemma-v0.1-8bits

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-01T05:53:49+00:00

text-generation

transformers

{}

sprice12345/llama2_7b_COT_ihateyou_0.65clean

null

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

null

2024-05-01T05:54:47+00:00

reinforcement-learning

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: ArnavModanwal/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"]}

ArnavModanwal/ppo-Huggy

null

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

null

2024-05-01T05:56:27+00:00

text-classification

transformers

# robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-3 This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-3", "results": []}]}

AlignmentResearch/robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-3

null

[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T06:00:05+00:00

null

{"license": "cc-by-nc-nd-4.0"}

TruBean/Popoi

null

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

null

2024-05-01T06:03:02+00:00

text2text-generation

transformers

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

{"library_name": "transformers", "tags": []}

Audino/my-awesome-modelv3-small

null

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T06:03:24+00:00

null

transformers

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

{"library_name": "transformers", "tags": []}

saeedasadibagloee/T5_timeseries

null

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

null

2024-05-01T06:03:32+00:00

null

{"license": "openrail"}

saberialireza2072/dubbing

null

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

null

2024-05-01T06:03:51+00:00

null

{}

adi1193/mistral-postv5

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-05-01T06:04:02+00:00

null

transformers

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

{"library_name": "transformers", "tags": []}

Mubin1917/Mistral-7B-Instruct-v0.2-lamini-docs-adapters

null

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

null

2024-05-01T06:04:07+00:00

unconditional-image-generation

diffusers

# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('livewalk/sd-class-butterflies-32') image = pipeline().images[0] image ```

{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}

livewalk/sd-class-butterflies-32

null

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

null

2024-05-01T06:05:41+00:00

null

peft

# opt-1.3b-finetuned-mnli-nn This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8505 - Accuracy: 0.5201 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.8826 | 0.5192 | | No log | 2.0 | 2 | 0.8740 | 0.5195 | | No log | 3.0 | 3 | 0.8665 | 0.5198 | | No log | 4.0 | 4 | 0.8601 | 0.5210 | | No log | 5.0 | 5 | 0.8548 | 0.5207 | | No log | 6.0 | 6 | 0.8505 | 0.5214 | | No log | 7.0 | 7 | 0.8470 | 0.5216 | | No log | 8.0 | 8 | 0.8447 | 0.5214 | | No log | 9.0 | 9 | 0.8431 | 0.5213 | | No log | 10.0 | 10 | 0.8423 | 0.5211 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-finetuned-mnli-nn", "results": []}]}

elliottfitzgerald/opt-1.3b-finetuned-mnli-nn

null

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/opt-1.3b", "license:other", "region:us" ]

null

2024-05-01T06:06:18+00:00

text-generation

transformers

<img src="./ninjalogo.svg" width="100%" height="20%" alt=""> # Our Models - [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1) - [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) - [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) - [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k) - [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k) ## Model Card for Ninja-v1-NSFW-128k The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1 Ninja-NSFW-128k has the following changes compared to Mistral-7B-v0.1. - 128k context window (8k context in v0.1) - Achieving both high quality Japanese and English generation - Memory ability that does not forget even after long-context generation - Can be generated NSFW This model was created with the help of GPUs from the first LocalAI hackathon. We would like to take this opportunity to thank ## List of Creation Methods - Chatvector for multiple models - Simple linear merging of result models - Domain and Sentence Enhancement with LORA - Context expansion ## Instruction format Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following: ``` USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am ninja.</s> ``` ## Example prompts to improve (Japanese) - BAD:　あなたは○○として振る舞います - GOOD: あなたは○○です - BAD: あなたは○○ができます - GOOD: あなたは○○をします ## Performing inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "Local-Novel-LLM-project/Ninja-v1-NSFW" new_tokens = 1024 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- " prompt = input("Enter a prompt: ") system_prompt += prompt + "\n-------- " model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True) print(tokenizer.batch_decode(generated_ids)[0]) ```` ## Merge recipe - WizardLM2 - mistralai/Mistral-7B-v0.1 - NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1 - Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b - Elizezen/LewdSniffyOtter-7B - Elizezen/SniffyOtter-7B - NTQAI/chatntq-ja-7b-v1.0 The characteristics of each model are as follows. - WizardLM2: High quality multitasking model - Yarn-Mistral-7b-128k: Mistral model with 128k context window - Antler-7B: Model specialized for novel writing - NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model - Elizezen/LewdSniffyOtter-7B Japanese NSFW specialized model ## Other points to keep in mind - The training data may be biased. Be careful with the generated sentences. - Set trust_remote_code to True for context expansion with YaRN. - Memory usage may be large for long inferences. - If possible, we recommend inferring with llamacpp rather than Transformers.

{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned", "not-for-all-audiences"], "pipeline_tag": "text-generation"}

Local-Novel-LLM-project/Ninja-v1-NSFW

null

[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "not-for-all-audiences", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T06:06:39+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) starchat2-15b-v0.1 - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/ Original model description: --- base_model: HuggingFaceH4/starchat2-15b-sft-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized - HuggingFaceH4/orca_dpo_pairs model-index: - name: starchat2-15b-v0.1 results: [] --- <img src="https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/resolve/main/model_logo.png" alt="StarChat2 15B Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for StarChat2 15B StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat2 is the latest model in the series, and is a fine-tuned version of [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b) that was trained with SFT and DPO on a mix of synthetic datasets. ## Model Details ### Model Description  - **Model type:** A 16B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English and 600+ programming languages. - **License:** BigCode Open RAIL-M v1 - **Finetuned from model:** [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground ## Performance StarChat2 15B was trained to balance chat and programming capabilities. It achieves strong performance on chat benchmarks like [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [IFEval](https://arxiv.org/abs/2311.07911), as well as the canonical HumanEval benchmark for Python code completion. The scores reported below were obtained using the [LightEval](https://github.com/huggingface/lighteval) evaluation suite (commit `988959cb905df4baa050f82b4d499d46e8b537f2`) and each prompt has been formatted with the model's corresponding chat template to simulate real-world usage. This is why some scores may differ from those reported in technical reports or on the Open LLM Leaderboard. | Model | MT Bench | IFEval | HumanEval | |-------------------------------------------------------------------------------------------------|---------:|-------:|----------:| | [starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1) | 7.66 | 35.12 | 71.34 | | [deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) | 4.17 | 14.23 | 80.48 | | [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | 6.80 | 43.44 | 50.60 | ## Intended uses & limitations The model was fine-tuned on a blend of chat, code, math, and reasoning datasets. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground) to test its coding capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # pip install 'transformers @ git+https://github.com/huggingface/transformers.git@831bc25d8fdb85768402f772cf65cc3d7872b211' # pip install accelerate import torch from transformers import pipeline pipe = pipeline( "text-generation", model="HuggingFaceH4/starchat2-15b-v0.1", device_map="auto", torch_dtype=torch.bfloat16, ) messages = [ { "role": "system", "content": "You are StarChat2, an expert programming assistant", }, {"role": "user", "content": "Write a simple website in HTML. When a user clicks the button, it shows a random Chuck Norris joke."}, ] outputs = pipe( messages, max_new_tokens=512, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, stop_sequence="<|im_end|>", ) print(outputs[0]["generated_text"][-1]["content"]) ``` ## Bias, Risks, and Limitations  StarChat2 15B has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder2 dataset](https://huggingface.co/datasets/bigcode/the-stack-v2) Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect. For example, it may produce code that does not compile or that produces incorrect results. It may also produce code that is vulnerable to security exploits. We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking. StarChat2 15B was fine-tuned from the base model [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoder2-15b#limitations) for relevant information. In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://huggingface.co/papers/2402.19173). ## Training details This model is a fine-tuned version of [starchat2-15b-sft-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1) on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. Check out the recipe in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for more details. It achieves the following results on the evaluation set: - Loss: 0.4347 - Rewards/chosen: -0.9461 - Rewards/rejected: -2.7745 - Rewards/accuracies: 0.7658 - Rewards/margins: 1.8284 - Logps/rejected: -322.1934 - Logps/chosen: -316.1898 - Logits/rejected: -2.3817 - Logits/chosen: -2.3005 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.717 | 0.17 | 100 | 0.6006 | -0.0924 | -0.2899 | 0.6329 | 0.1975 | -272.5022 | -299.1165 | -2.5313 | -2.4191 | | 0.6273 | 0.35 | 200 | 0.5160 | -0.3994 | -0.9461 | 0.6930 | 0.5467 | -285.6261 | -305.2568 | -2.5281 | -2.4278 | | 0.5538 | 0.52 | 300 | 0.4781 | -0.6589 | -1.5892 | 0.7247 | 0.9302 | -298.4870 | -310.4470 | -2.4996 | -2.4110 | | 0.5056 | 0.7 | 400 | 0.4594 | -0.8283 | -2.1332 | 0.7437 | 1.3050 | -309.3687 | -313.8344 | -2.4472 | -2.3644 | | 0.4983 | 0.87 | 500 | 0.4512 | -0.7758 | -2.2806 | 0.7468 | 1.5049 | -312.3167 | -312.7843 | -2.4223 | -2.3404 | | 0.4662 | 1.04 | 600 | 0.4431 | -0.7839 | -2.4016 | 0.7658 | 1.6177 | -314.7355 | -312.9465 | -2.4049 | -2.3215 | | 0.4411 | 1.22 | 700 | 0.4415 | -1.0090 | -2.7582 | 0.7690 | 1.7492 | -321.8679 | -317.4481 | -2.3840 | -2.3016 | | 0.471 | 1.39 | 800 | 0.4368 | -0.9617 | -2.7445 | 0.7690 | 1.7828 | -321.5930 | -316.5019 | -2.3809 | -2.2991 | | 0.4485 | 1.57 | 900 | 0.4351 | -0.9490 | -2.7594 | 0.7722 | 1.8103 | -321.8916 | -316.2497 | -2.3815 | -2.3004 | | 0.4411 | 1.74 | 1000 | 0.4348 | -0.9293 | -2.7469 | 0.7658 | 1.8176 | -321.6409 | -315.8547 | -2.3823 | -2.3011 | | 0.4499 | 1.92 | 1100 | 0.4348 | -0.9482 | -2.7767 | 0.7658 | 1.8285 | -322.2369 | -316.2320 | -2.3828 | -2.3012 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

{}

RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-4bits

null

[ "transformers", "safetensors", "starcoder2", "text-generation", "conversational", "arxiv:2311.07911", "arxiv:2402.19173", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T06:07:44+00:00

null

transformers

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

{"library_name": "transformers", "tags": []}

goodakdali/llama3_testing_ins_add_adapter

null

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

null

2024-05-01T06:08:28+00:00

null

{}

Shehan1998/sentiment-bert-base-uncased

null

[ "region:us" ]

null

2024-05-01T06:08:36+00:00

null

{}

DjeDjeB/m11

null

[ "region:us" ]

null

2024-05-01T06:08:38+00:00

null

{}

DjeDjeB/m15

null

[ "region:us" ]

null

2024-05-01T06:08:48+00:00

null

peft

# results_HPE This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 0.0002 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "ybelkada/falcon-7b-sharded-bf16", "model-index": [{"name": "results_HPE", "results": []}]}

chinmayn/falcon-sharded

null

[ "peft", "pytorch", "tensorboard", "safetensors", "falcon", "trl", "sft", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "region:us" ]

null

2024-05-01T06:11:50+00:00

null

{}

roggerzill/mine

null

[ "region:us" ]

null

2024-05-01T06:11:54+00:00

text-generation

transformers

# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```bash pip install peft ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import AutoPeftModelForCausalLM, PeftConfig model_id = "Aryan-401/phi-3-mini-4k-instruct-finetune-guanaco" peft_model=AutoPeftModelForCausalLM.from_pretrained(model_id) model = peft_model.merge_and_unload() tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "user", "content": "What is the Value of Pi?"} ] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = model.to(device).eval() input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to(device), max_length= 1000) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) print(response) ```

{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}

Aryan-401/phi-3-mini-4k-instruct-finetune-guanaco-PEFT-only

null

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]

null

2024-05-01T06:12:21+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

{"library_name": "peft", "base_model": "beomi/open-llama-2-ko-7b"}

zzunyang/law_sft

null

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/open-llama-2-ko-7b", "region:us" ]

null

2024-05-01T06:12:32+00:00

text-generation

transformers

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

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "datasets": ["wttw/code_contest_instruct_cpp"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "pipeline_tag": "text-generation"}

wttw/Llama3-8B-CPP

null

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

null

2024-05-01T06:18:24+00:00