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lordspline/ninja-test	lordspline	2024-01-17T03:31:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-17T03:31:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LoneStriker/DaringLotus-10.7B-4.0bpw-h6-exl2	LoneStriker	2024-01-17T03:29:23Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Solar", "Mistral", "Roleplay", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T03:26:58Z	--- license: apache-2.0 tags: - Solar - Mistral - Roleplay --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64bb1109aaccfd28b023bcec/bjOB_8BsqVteKKxARPc13.png) I managed to do a heavy density DARE TIES merge of SnowLotus and it's parent models (unusual strategy I know) that seems okay (prose not too bad, not incoherent). Early impressions are that this has slightly different prose - maybe a touch more GPT in there, as it talks of connections, but not at all to the degree that many more synthetically based models do. You probably will find that unobtrusive. Like it's sister model it can and does take lore, character cards and in context chat at times and creates with it, and is very descriptive. I cannot tell which is more coherent - occasionally they both get confused (as is typical with smaller models particularly onces with better prose). I did notice that when in particular contexts, SnowLotus' tendancy for exagerated excalation seemed stronger with this model. So there are differences (some prose and tone differences at least), and testin will probably tell which you prefer. They share more in common that they do differences - descriptive, fairly creative, occassionally confused but also sometimes surprisingly bright. And the prose has lots of similarities too, it's not generally your 'light, lyrical and poetic' affair. Summary at least so far, is this one is _slightly_ more gptish in prose and more inclined to escalate scenarios and descriptions in a sort of enthusiastic manner. Both do feed a lot off context, so if you give them stuff they should not be mild or timid.
jeiku/Gnosis_Reformatted_Mistral	jeiku	2024-01-17T03:25:54Z	30	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2024-01-17T03:25:11Z	--- library_name: peft base_model: models/TheBloke_Mistral-7B-Instruct-v0.2-GPTQ --- # 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 0.7.1
MaziyarPanahi/mistral-7b_open_platypus-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T03:25:01Z	21	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "lgaalves/mistral-7b_open_platypus", "pytorch", "en", "dataset:garage-bAInd/Open-Platypus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-17T03:19:38Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - lgaalves/mistral-7b_open_platypus - transformers - pytorch - mistral - text-generation - en - dataset:garage-bAInd/Open-Platypus - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # mistral-7b_open_platypus-Mistral-7B-Instruct-v0.1 mistral-7b_open_platypus-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [lgaalves/mistral-7b_open_platypus](https://huggingface.co/lgaalves/mistral-7b_open_platypus) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: lgaalves/mistral-7b_open_platypus layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/mistral-7b_open_platypus-Mistral-7B-Instruct-v0.1" 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"]) ```
liminerity/Mini-blurstral	liminerity	2024-01-17T03:25:01Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-v0.1", "liminerity/Blur-7b-slerp-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T02:22:48Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - mistralai/Mistral-7B-v0.1 - liminerity/Blur-7b-slerp-v0.1 --- # Mini-blurstral broken Mini-blurstral is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) * [liminerity/Blur-7b-slerp-v0.1](https://huggingface.co/liminerity/Blur-7b-slerp-v0.1) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [0, 9] - model: liminerity/Blur-7b-slerp-v0.1 layer_range: [0, 9] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Mini-blurstral" 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"]) ```
jeiku/Futadom_Mistral	jeiku	2024-01-17T03:24:18Z	40	1	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2024-01-17T03:23:31Z	--- library_name: peft base_model: models/TheBloke_Mistral-7B-Instruct-v0.2-GPTQ --- # 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
Marcus2112/q-FrozenLake-v1-4x4-noSlippery	Marcus2112	2024-01-17T03:24:14Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-17T03:24:11Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="koppelmann/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ladoza03/xlm-roberta-base-finetuned-panx-all	ladoza03	2024-01-17T03:18:44Z	5	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-06T19:24:44Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2681 - F1: 0.8456 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5264 \| 1.0 \| 250 \| 0.3217 \| 0.7777 \| \| 0.2587 \| 2.0 \| 500 \| 0.2781 \| 0.8273 \| \| 0.1629 \| 3.0 \| 750 \| 0.2681 \| 0.8456 \| ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
MaziyarPanahi/Mistral-7B-golden-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T03:13:07Z	24	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "liuda1/Mistral-7B-golden", "pytorch", "license:unknown", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational", "license:apache-2.0" ]	text-generation	2024-01-17T03:07:59Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - liuda1/Mistral-7B-golden - transformers - pytorch - mistral - text-generation - license:unknown - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # Mistral-7B-golden-Mistral-7B-Instruct-v0.1 Mistral-7B-golden-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [liuda1/Mistral-7B-golden](https://huggingface.co/liuda1/Mistral-7B-golden) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: liuda1/Mistral-7B-golden layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/Mistral-7B-golden-Mistral-7B-Instruct-v0.1" 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"]) ```
appvoid/palmer-002-2401	appvoid	2024-01-17T02:53:59Z	6	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "en", "dataset:appvoid/no-prompt-50k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:33:21Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation datasets: - appvoid/no-prompt-50k --- ![palmer](https://huggingface.co/appvoid/palmer-001/resolve/main/new-logo.jpg) # palmer ### a better base model This is a small improvement over a (now un-prompted zyte) tinyllama model ### evaluation 🧪 note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals ``` model ARC-C OBQA HellaSwag PIQA Winogrande Average tinyllama \| 0.3029 \| 0.3600 \| 0.5935 \| 0.7329 \| 0.5959 \| 0.5170 \| palmer-002 \| 0.3242 \| 0.3700 \| 0.5956 \| 0.7345 \| 0.5888 \| 0.5226 \| palmer-002-2401 \| 0.3294 \| 0.3700 \| 0.5950 \| 0.7399 \| 0.5896 \| 0.5247 \| (this) babbage-002 \| 0.3285 \| 0.3620 \| 0.6380 \| 0.7606 \| 0.6085 \| 0.5395 \| ``` ### training 🦾 Training took ~1 A100 gpu hour. It was trained on 50,000 gpt-4 shuffled samples. palmer was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible. ### prompt 📝 ``` no prompt 🚀 ``` <a href="https://ko-fi.com/appvoid" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 48px !important;width: 180px !important; filter: invert(70%);" ></a>
MaziyarPanahi/CollectiveCognition-v1.1-Mistral-7B-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T02:51:05Z	23	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "teknium/CollectiveCognition-v1.1-Mistral-7B", "pytorch", "mistral-7b", "instruct", "finetune", "gpt4", "synthetic data", "distillation", "sharegpt", "en", "dataset:CollectiveCognition/chats-data-2023-09-27", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-17T02:46:06Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - teknium/CollectiveCognition-v1.1-Mistral-7B - transformers - pytorch - mistral - text-generation - mistral-7b - instruct - finetune - gpt4 - synthetic data - distillation - sharegpt - en - dataset:CollectiveCognition/chats-data-2023-09-27 - base_model:mistralai/Mistral-7B-v0.1 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # CollectiveCognition-v1.1-Mistral-7B-Mistral-7B-Instruct-v0.1 CollectiveCognition-v1.1-Mistral-7B-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [teknium/CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: teknium/CollectiveCognition-v1.1-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/CollectiveCognition-v1.1-Mistral-7B-Mistral-7B-Instruct-v0.1" 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"]) ```
Agreene5/Rhythm_Heaven_Style_LoRA	Agreene5	2024-01-17T02:44:33Z	0	0	null	[ "region:us" ]	null	2024-01-15T19:05:37Z	![](https://huggingface.co/Agreene5/Rhythm_Heaven_Style_LoRA/resolve/main/CivitAIExamples2/formodelcard.png "3 example images") # Rhythm Heaven Style LoRA for Stable Diffusion 1.5 + SDXL Model is also on CivitAI: https://civitai.com/models/87254?modelVersionId=258514 ## Model Details ### Version 1 parameters: steps_per_image: 50 total_images: 49 total_steps: ~2400 training_model: Anything_V3 network_dim: 128 network_alpha: 128 network_train_on: both learning_rate: 1e-4 unet_lr: 0 text_encoder _lr: 5e-5 lr_scheduler: constant lr_scheduler_num_cycles: 1 lr_scheduler_power: 1 train_batch_size: 6 num_epochs: 6 mixed_precision: fp16 save_precision fp16 save_n_epochs_type: save_every_n_epochs save_n_epochs_type_value: 1 resolution: 512 max_token_length: 225 clip_skip: 2 additional_argument: --shuffle_caption --xformers training_hardware: Google Colab Free Tier: Nvidia Tesla T4 GPU training_time: ~45 minutes ### Version 1.1 parameters: steps_per_image: 20 total_images: 122 (61 unique images, doubled amount by mirroring them) total_steps: 2440 training_model: Any_LoRA optimizer: AdamW network_dim: 128 network_alpha: 128 network_train_on: both learning_rate: 1e-4 unet_lr: 1e-4 text_encoder _lr: 5e-5 lr_scheduler: constant lr_scheduler_num_cycles: 1 lr_scheduler_power: 1 train_batch_size: 8 num_epochs: 6 mixed_precision: bf16 save_precision bf16 save_n_epochs_type: save_every_n_epochs save_n_epochs_type_value: 1 resolution: 768 max_token_length: 225 clip_skip: 2 additional_argument: --xformers training_hardware: RTX 3090 training_time: ~1.5 hours (I don't remember exactly) #### Version 1.1 Improvements: Better style consistency: The model generates in a style closer to the Rhythm Heaven series much more consistently. 1.0 generated a bit more of a detailed style though so if that's what you want you should use that one. Removed "rhythm_heaven" trigger: Seems like a style trigger isn't really necessary, removing it just saves a bit of token length. Less unprompted black and white generations: This one isn't as big but I manually added color to some of the training images to get more variety which consequently means you'll get less black and white generations. ### Version 1 (SDXL) parameters: steps_per_image: 20 total_images: 122 (61 unique images, doubled amount by mirroring them) total_steps: 7320 training_model: anima_pencil-XL optimizer: Adafactor network_dim: 128 network_alpha: 1 network_train_on: both learning_rate: 1.2e-3 unet_lr: 1.2e-3 text_encoder _lr: 1.2e-3 lr_scheduler: constant lr_scheduler_num_cycles: 1 lr_scheduler_power: 1 train_batch_size: 5 num_epochs: 15 mixed_precision: bf16 save_precision bf16 save_n_epochs_type: save_every_n_epochs save_n_epochs_type_value: 1 resolution: 1024 max_token_length: 75 clip_skip: 2 additional_argument: --xformers training_hardware: RTX 3090 training_time: ~6 hours #### Version 1 (SDXL) Improvements: Cleaner looking images: All of the images used to train this model were upscaled 2x so outputs are less grainy. Better prompt understanding: SDXL has a better understanding of prompts so training a LoRA using it as a base makes the LoRA get a better understanding too. ## Model Description Trained on humanoid characters from the Rhythm Heaven series (and some from Wario Ware) using AnyLoRA. Captions were done manually using booru tags. - Model type: Standard LoRA - Finetuned from model: Stable Diffusion 1.5 based models ## Uses Used in conjunction with a booru based Stable Diffusion 1.5 model (ex. Any_LoRA) to emulate the style of the Rhythm_Heaven series. I recommend using it with a weight around 0.7 when prompting. Also, another reminder, this model was trained exclusively with booru tags so I'm not sure how well it'll work using blip captions.
mlx-community/NeuralBeagle14-7B-4bit-mlx	mlx-community	2024-01-17T02:38:28Z	19	4	mlx	[ "mlx", "mistral", "merge", "mergekit", "lazymergekit", "fblgit/UNA-TheBeagle-7b-v1", "argilla/distilabeled-Marcoro14-7B-slerp", "dpo", "rlhf", "license:apache-2.0", "region:us" ]	null	2024-01-17T01:25:32Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - fblgit/UNA-TheBeagle-7b-v1 - argilla/distilabeled-Marcoro14-7B-slerp - dpo - rlhf - mlx --- # mlx-community/NeuralBeagle14-7B-4bit-mlx This model was converted to MLX format from [`mlabonne/NeuralBeagle14-7B`](). Refer to the [original model card](https://huggingface.co/mlabonne/NeuralBeagle14-7B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/NeuralBeagle14-7B-4bit-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
ifuseok/sft-solar-10.7b-v1.1	ifuseok	2024-01-17T02:29:32Z	2,283	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:nlpai-lab/databricks-dolly-15k-ko", "dataset:kyujinpy/KOR-OpenOrca-Platypus-v3", "dataset:KETI-AIR/kor_boolq", "dataset:heegyu/open-korean-instructions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-05T03:04:41Z	--- language: - en pipeline_tag: text-generation datasets: - nlpai-lab/databricks-dolly-15k-ko - kyujinpy/KOR-OpenOrca-Platypus-v3 - KETI-AIR/kor_boolq - heegyu/open-korean-instructions --- Input Models input text only. Output Models generate text only. Base Model [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) Training Dataset - [nlpai-lab/databricks-dolly-15k-ko](https://huggingface.co/datasets/nlpai-lab/databricks-dolly-15k-ko) - [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3) - [heegyu/open-korean-instructions](heegyu/open-korean-instructions) - [KETI-AIR/kor_boolq](https://huggingface.co/datasets/KETI-AIR/kor_boolq) - [AIhub 영한 번역 데이터 일부](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=71593) # Implementation Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "ifuseok/sft-solar-10.7b-v1.1" OpenOrca = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo) ``` # Prompt Example ``` ### System: 시스템 메시지 입니다. ### User: 유저 입니다. ### Assistant 어시스턴트 입니다. ```
MaziyarPanahi/jackalope-7b-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T02:28:45Z	21	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "openaccess-ai-collective/jackalope-7b", "pytorch", "en", "dataset:Open-Orca/OpenOrca", "dataset:LDJnr/LessWrong-Amplify-Instruct", "dataset:LDJnr/Pure-Dove", "dataset:LDJnr/Verified-Camel", "dataset:PygmalionAI/PIPPA", "dataset:meta-math/MetaMathQA", "dataset:riddle_sense", "arxiv:2306.02707", "arxiv:2301.13688", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-17T02:23:34Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - openaccess-ai-collective/jackalope-7b - transformers - pytorch - mistral - text-generation - en - dataset:Open-Orca/OpenOrca - dataset:LDJnr/LessWrong-Amplify-Instruct - dataset:LDJnr/Pure-Dove - dataset:LDJnr/Verified-Camel - dataset:PygmalionAI/PIPPA - dataset:meta-math/MetaMathQA - dataset:riddle_sense - arxiv:2306.02707 - arxiv:2301.13688 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # jackalope-7b-Mistral-7B-Instruct-v0.1 jackalope-7b-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [openaccess-ai-collective/jackalope-7b](https://huggingface.co/openaccess-ai-collective/jackalope-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: openaccess-ai-collective/jackalope-7b layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/jackalope-7b-Mistral-7B-Instruct-v0.1" 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"]) ```
Ricardo54321/dqn-SpaceInvadersNoFrameskip-v4	Ricardo54321	2024-01-17T02:28:11Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-17T02:26:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 546.00 +/- 261.66 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ricardo54321 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ricardo54321 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Ricardo54321 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Loyola/Mistral-7b-ITmodel	Loyola	2024-01-17T02:22:43Z	2,366	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "ko", "dataset:nlpai-lab/kullm-v2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-11T22:55:28Z	--- datasets: - nlpai-lab/kullm-v2 language: - en - ko license: apache-2.0 pipeline_tag: text-generation --- ## Model Details * Base Model: [Mistral-7B-Instruct-v0.2](mistralai/Mistral-7B-Instruct-v0.2) * Library: [HuggingFace Transformers](https://github.com/huggingface/transformers) ## Dataset Details * Dataset : nlpai-lab/kullm-v2 ### Prompt Template - Mistral Prompt Template
worldboss/orca-2-7B-v01-fine-tuned-using-ludwig-4bit	worldboss	2024-01-17T02:21:08Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Orca-2-7b", "base_model:adapter:microsoft/Orca-2-7b", "region:us" ]	null	2024-01-17T02:21:06Z	--- library_name: peft base_model: microsoft/Orca-2-7b --- # 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. 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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 0.7.1
CLMBR/old-rel-cl-lstm-4	CLMBR	2024-01-17T02:19:54Z	5	0	transformers	[ "transformers", "pytorch", "rnn", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-01-12T16:19:26Z	--- tags: - generated_from_trainer model-index: - name: rel-cl-lstm-4 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. --> # rel-cl-lstm-4 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9792 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 4.7938 \| 0.03 \| 76319 \| 4.7559 \| \| 4.5099 \| 0.03 \| 152638 \| 4.4756 \| \| 4.3688 \| 1.03 \| 228957 \| 4.3427 \| \| 4.2817 \| 0.03 \| 305276 \| 4.2613 \| \| 4.2191 \| 1.03 \| 381595 \| 4.2047 \| \| 4.1676 \| 0.03 \| 457914 \| 4.1641 \| \| 4.1315 \| 0.03 \| 534233 \| 4.1336 \| \| 4.1017 \| 0.03 \| 610552 \| 4.1102 \| \| 4.0701 \| 1.03 \| 686871 \| 4.0906 \| \| 4.05 \| 0.03 \| 763190 \| 4.0741 \| \| 4.0306 \| 1.03 \| 839509 \| 4.0620 \| \| 4.0118 \| 0.03 \| 915828 \| 4.0515 \| \| 3.992 \| 0.03 \| 992147 \| 4.0427 \| \| 3.9801 \| 1.03 \| 1068466 \| 4.0348 \| \| 3.9659 \| 0.03 \| 1144785 \| 4.0278 \| \| 3.9568 \| 1.03 \| 1221104 \| 4.0221 \| \| 3.9456 \| 0.03 \| 1297424 \| 4.0166 \| \| 3.9318 \| 1.03 \| 1373744 \| 4.0121 \| \| 3.9242 \| 0.03 \| 1450064 \| 4.0080 \| \| 3.9185 \| 1.03 \| 1526384 \| 4.0051 \| \| 3.9133 \| 0.03 \| 1602704 \| 4.0016 \| \| 3.9104 \| 0.03 \| 1679024 \| 3.9993 \| \| 3.9076 \| 1.03 \| 1755344 \| 3.9968 \| \| 3.8999 \| 0.03 \| 1831664 \| 3.9944 \| \| 3.8906 \| 1.03 \| 1907984 \| 3.9928 \| \| 3.8829 \| 0.03 \| 1984304 \| 3.9910 \| \| 3.879 \| 1.03 \| 2060624 \| 3.9893 \| \| 3.874 \| 0.03 \| 2136944 \| 3.9882 \| \| 3.8682 \| 1.03 \| 2213264 \| 3.9871 \| \| 3.8628 \| 0.03 \| 2289584 \| 3.9859 \| \| 3.8627 \| 0.03 \| 2365904 \| 3.9848 \| \| 3.86 \| 0.03 \| 2442224 \| 3.9838 \| \| 3.8535 \| 1.03 \| 2518544 \| 3.9829 \| \| 3.8496 \| 0.03 \| 2594864 \| 3.9822 \| \| 3.8468 \| 1.03 \| 2671184 \| 3.9813 \| \| 3.8472 \| 0.03 \| 2747504 \| 3.9811 \| \| 3.8477 \| 1.03 \| 2823824 \| 3.9803 \| \| 3.8478 \| 0.03 \| 2900144 \| 3.9800 \| \| 3.8477 \| 0.03 \| 2976464 \| 3.9796 \| \| 3.8446 \| 0.02 \| 3052726 \| 3.9792 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
MaziyarPanahi/LeoScorpius-GreenNode-Alpaca-7B-v1-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T02:04:55Z	23	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1", "pytorch", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-17T01:59:44Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 - transformers - pytorch - mistral - text-generation - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # LeoScorpius-GreenNode-Alpaca-7B-v1-Mistral-7B-Instruct-v0.1 LeoScorpius-GreenNode-Alpaca-7B-v1-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [ignos/LeoScorpius-GreenNode-Alpaca-7B-v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/LeoScorpius-GreenNode-Alpaca-7B-v1-Mistral-7B-Instruct-v0.1" 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"]) ```
svenbl80/roberta-base-finetuned-new-mnli-run-4	svenbl80	2024-01-17T02:00:24Z	4	0	transformers	[ "transformers", "tf", "tensorboard", "roberta", "text-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-16T19:30:22Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: svenbl80/roberta-base-finetuned-new-mnli-run-4 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # svenbl80/roberta-base-finetuned-new-mnli-run-4 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0254 - Validation Loss: 0.7597 - Train Accuracy: 0.8592 - Epoch: 9 ## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 245430, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.4543 \| 0.3920 \| 0.8526 \| 0 \| \| 0.3298 \| 0.3979 \| 0.8546 \| 1 \| \| 0.2478 \| 0.4089 \| 0.8603 \| 2 \| \| 0.1821 \| 0.4577 \| 0.8575 \| 3 \| \| 0.1309 \| 0.4901 \| 0.8556 \| 4 \| \| 0.0947 \| 0.5514 \| 0.8551 \| 5 \| \| 0.0682 \| 0.6368 \| 0.8553 \| 6 \| \| 0.0489 \| 0.6589 \| 0.8577 \| 7 \| \| 0.0343 \| 0.7216 \| 0.8599 \| 8 \| \| 0.0254 \| 0.7597 \| 0.8592 \| 9 \| ### Framework versions - Transformers 4.28.0 - TensorFlow 2.9.1 - Datasets 2.15.0 - Tokenizers 0.13.3
vivecccccc/phi-2_kqa-program	vivecccccc	2024-01-17T01:59:16Z	10	0	transformers	[ "transformers", "safetensors", "phi-msft", "text-generation", "llama-factory", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T06:31:29Z	--- license: other base_model: microsoft/phi-2 tags: - llama-factory - generated_from_trainer model-index: - name: _saves_phi-2_full 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. --> # _saves_phi-2_full This model is a fine-tuned version of phi-2 on the kqa_parsed-tree_train_complex.json 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: 7e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu113 - Datasets 2.16.1 - Tokenizers 0.15.0
MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T01:55:14Z	21	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "lvkaokao/mistral-7b-finetuned-orca-dpo-v2", "pytorch", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-17T01:50:15Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - lvkaokao/mistral-7b-finetuned-orca-dpo-v2 - transformers - pytorch - mistral - text-generation - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.1 mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [lvkaokao/mistral-7b-finetuned-orca-dpo-v2](https://huggingface.co/lvkaokao/mistral-7b-finetuned-orca-dpo-v2) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: lvkaokao/mistral-7b-finetuned-orca-dpo-v2 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.1" 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"]) ```
DaRkSpyro/RJ	DaRkSpyro	2024-01-17T01:50:25Z	0	0	flair	[ "flair", "music", "en", "dataset:HuggingFaceM4/WebSight", "license:apache-2.0", "region:us" ]	null	2024-01-14T02:04:15Z	--- license: apache-2.0 datasets: - HuggingFaceM4/WebSight language: - en metrics: - accuracy library_name: flair tags: - music ---
Chenxi-Chelsea-Liu/whisper-small-noisy-hi	Chenxi-Chelsea-Liu	2024-01-17T01:49:06Z	3	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-16T14:58:10Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-noisy-hi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-noisy-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5460 - Wer: 74.5720 ## 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: 48 - eval_batch_size: 24 - 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: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.5752 \| 0.46 \| 50 \| 2.2665 \| 120.7418 \| \| 1.6855 \| 0.92 \| 100 \| 1.6174 \| 92.1494 \| \| 1.4464 \| 1.38 \| 150 \| 1.4430 \| 92.0543 \| \| 1.3211 \| 1.83 \| 200 \| 1.3179 \| 88.5094 \| \| 1.1732 \| 2.29 \| 250 \| 1.2025 \| 86.2182 \| \| 1.0507 \| 2.75 \| 300 \| 1.0736 \| 83.7628 \| \| 0.8575 \| 3.21 \| 350 \| 0.9902 \| 80.8404 \| \| 0.8096 \| 3.67 \| 400 \| 0.9516 \| 80.1833 \| \| 0.7257 \| 4.13 \| 450 \| 0.9286 \| 78.7740 \| \| 0.6689 \| 4.59 \| 500 \| 0.9091 \| 77.0621 \| \| 0.6331 \| 5.05 \| 550 \| 0.9014 \| 76.5087 \| \| 0.5123 \| 5.5 \| 600 \| 0.9030 \| 74.3213 \| \| 0.505 \| 5.96 \| 650 \| 0.8833 \| 76.0851 \| \| 0.3716 \| 6.42 \| 700 \| 0.9274 \| 75.5144 \| \| 0.3759 \| 6.88 \| 750 \| 0.9227 \| 74.1657 \| \| 0.2658 \| 7.34 \| 800 \| 0.9754 \| 77.3993 \| \| 0.2624 \| 7.8 \| 850 \| 0.9800 \| 74.9784 \| \| 0.1755 \| 8.26 \| 900 \| 1.0364 \| 74.5807 \| \| 0.1771 \| 8.72 \| 950 \| 1.0549 \| 76.0678 \| \| 0.1239 \| 9.17 \| 1000 \| 1.1081 \| 74.8314 \| \| 0.112 \| 9.63 \| 1050 \| 1.1373 \| 74.9524 \| \| 0.0942 \| 10.09 \| 1100 \| 1.1697 \| 75.2205 \| \| 0.0691 \| 10.55 \| 1150 \| 1.2068 \| 76.6384 \| \| 0.0659 \| 11.01 \| 1200 \| 1.2280 \| 75.6095 \| \| 0.0417 \| 11.47 \| 1250 \| 1.2840 \| 74.9697 \| \| 0.0416 \| 11.93 \| 1300 \| 1.3025 \| 75.9035 \| \| 0.025 \| 12.39 \| 1350 \| 1.3342 \| 76.1110 \| \| 0.0258 \| 12.84 \| 1400 \| 1.3580 \| 74.9438 \| \| 0.0182 \| 13.3 \| 1450 \| 1.4077 \| 75.9467 \| \| 0.0154 \| 13.76 \| 1500 \| 1.4214 \| 75.1167 \| \| 0.0131 \| 14.22 \| 1550 \| 1.4525 \| 74.8660 \| \| 0.0119 \| 14.68 \| 1600 \| 1.4903 \| 74.7709 \| \| 0.011 \| 15.14 \| 1650 \| 1.5147 \| 75.0476 \| \| 0.0079 \| 15.6 \| 1700 \| 1.5375 \| 75.9727 \| \| 0.0087 \| 16.06 \| 1750 \| 1.5460 \| 74.5720 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 1.12.1 - Datasets 2.16.1 - Tokenizers 0.15.0
jlbaker361/ft250g2e10	jlbaker361	2024-01-17T01:45:03Z	0	0	null	[ "safetensors", "region:us" ]	null	2024-01-16T23:19:45Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - jlbaker361/ft250g2e10 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/wikiart-balanced250 dataset. Training epochs = 10 num_train_timesteps = 30 You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
remyxai/stablelm-zephyr-3B_localmentor	remyxai	2024-01-17T01:42:11Z	91	1	transformers	[ "transformers", "safetensors", "gguf", "en", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2023-12-30T19:31:20Z	--- license: other language: - en library_name: transformers --- license: other language: - en library_name: transformers --- license: apache-2.0 --- # Model Card for localmentor_25K_3epochs_stablelm-zephyr-3B LoRA Fine-Tune of stablelm-zephyr-3b on 1000+ hours of tech/startup podcast conversation ## Model Details ### Model Description Fine-tune with low-rank adapters on 25K conversational turns discussing tech/startup from over 800 podcast episodes. - Developed by: [Remyx.AI] - License: [apache-2.0] - Finetuned from model: [stablelm-zephyr-3b] ### Model Sources [optional] https://github.com/remyxai/LocalMentor - Repository: [https://github.com/remyxai/LocalMentor] ## Uses Use this model to chat about tech and startup. ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### License STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT Dated: December 06, 2023 By using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to be bound by this Agreement. 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jlbaker361/ft250g4e10	jlbaker361	2024-01-17T01:42:09Z	0	0	null	[ "safetensors", "region:us" ]	null	2024-01-16T23:19:45Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - jlbaker361/ft250g4e10 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/wikiart-balanced250 dataset. Training epochs = 10 num_train_timesteps = 30 You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
homie246/Chipflake	homie246	2024-01-17T01:36:30Z	0	0	null	[ "license:other", "region:us" ]	null	2024-01-17T01:36:29Z	--- license: other license_name: the-chipflake license_link: LICENSE ---
ntc-ai/SDXL-LoRA-slider.WTF-reaction	ntc-ai	2024-01-17T01:18:09Z	49	2	diffusers	[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]	text-to-image	2024-01-17T01:18:05Z	--- language: - en thumbnail: "images/evaluate/WTF reaction.../WTF reaction_17_3.0.png" widget: - text: WTF reaction output: url: images/WTF reaction_17_3.0.png - text: WTF reaction output: url: images/WTF reaction_19_3.0.png - text: WTF reaction output: url: images/WTF reaction_20_3.0.png - text: WTF reaction output: url: images/WTF reaction_21_3.0.png - text: WTF reaction output: url: images/WTF reaction_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "WTF reaction" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - WTF reaction (SDXL LoRA) \| Strength: -3 \| Strength: 0 \| Strength: 3 \| \| --- \| --- \| --- \| \| <img src="images/WTF reaction_17_-3.0.png" width=256 height=256 /> \| <img src="images/WTF reaction_17_0.0.png" width=256 height=256 /> \| <img src="images/WTF reaction_17_3.0.png" width=256 height=256 /> \| \| <img src="images/WTF reaction_19_-3.0.png" width=256 height=256 /> \| <img src="images/WTF reaction_19_0.0.png" width=256 height=256 /> \| <img src="images/WTF reaction_19_3.0.png" width=256 height=256 /> \| \| <img src="images/WTF reaction_20_-3.0.png" width=256 height=256 /> \| <img src="images/WTF reaction_20_0.0.png" width=256 height=256 /> \| <img src="images/WTF reaction_20_3.0.png" width=256 height=256 /> \| ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` WTF reaction ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.WTF-reaction', weight_name='WTF reaction.safetensors', adapter_name="WTF reaction") # Activate the LoRA pipe.set_adapters(["WTF reaction"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, WTF reaction" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
kiranbandi/nlp-qual-q3i	kiranbandi	2024-01-17T01:14:04Z	8	0	transformers.js	[ "transformers.js", "onnx", "bert", "text-classification", "region:us" ]	text-classification	2024-01-17T00:39:49Z	--- library_name: transformers.js --- https://huggingface.co/maxspad/nlp-qual-q3i with with ONNX weights to be compatible with Transformers.js.
kiranbandi/nlp-qual-q2i	kiranbandi	2024-01-17T01:13:10Z	2	0	transformers.js	[ "transformers.js", "onnx", "bert", "text-classification", "region:us" ]	text-classification	2024-01-17T00:38:54Z	--- library_name: transformers.js --- https://huggingface.co/maxspad/nlp-qual-q2i with with ONNX weights to be compatible with Transformers.js.
MaziyarPanahi/dolphin-2.0-mistral-7b-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T01:11:51Z	24	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "cognitivecomputations/dolphin-2.0-mistral-7b", "pytorch", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-17T01:06:41Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - cognitivecomputations/dolphin-2.0-mistral-7b - transformers - pytorch - mistral - text-generation - en - dataset:ehartford/dolphin - dataset:jondurbin/airoboros-2.2.1 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # dolphin-2.0-mistral-7b-Mistral-7B-Instruct-v0.1 dolphin-2.0-mistral-7b-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [cognitivecomputations/dolphin-2.0-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.0-mistral-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: cognitivecomputations/dolphin-2.0-mistral-7b layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/dolphin-2.0-mistral-7b-Mistral-7B-Instruct-v0.1" 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"]) ```
Galire/ppo-LunarLander-v2	Galire	2024-01-17T01:02:01Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-17T01:01:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.13 +/- 17.82 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
MaziyarPanahi/samantha-mistral-7b-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T00:57:05Z	24	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "cognitivecomputations/samantha-mistral-7b", "pytorch", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-17T00:51:54Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - cognitivecomputations/samantha-mistral-7b - transformers - pytorch - mistral - text-generation - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # samantha-mistral-7b-Mistral-7B-Instruct-v0.1 samantha-mistral-7b-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [cognitivecomputations/samantha-mistral-7b](https://huggingface.co/cognitivecomputations/samantha-mistral-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: cognitivecomputations/samantha-mistral-7b layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/samantha-mistral-7b-Mistral-7B-Instruct-v0.1" 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"]) ```
CLMBR/old-rel-cl-lstm-0	CLMBR	2024-01-17T00:35:49Z	7	0	transformers	[ "transformers", "pytorch", "rnn", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-01-12T16:07:19Z	--- tags: - generated_from_trainer model-index: - name: rel-cl-lstm-0 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. --> # rel-cl-lstm-0 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 4.8082 \| 0.03 \| 76319 \| 4.7686 \| \| 4.5186 \| 1.03 \| 152638 \| 4.4836 \| \| 4.3751 \| 0.03 \| 228957 \| 4.3481 \| \| 4.2868 \| 1.03 \| 305276 \| 4.2655 \| \| 4.2227 \| 0.03 \| 381595 \| 4.2085 \| \| 4.1713 \| 0.03 \| 457914 \| 4.1671 \| \| 4.1342 \| 0.03 \| 534233 \| 4.1361 \| \| 4.1036 \| 0.03 \| 610552 \| 4.1114 \| \| 4.0715 \| 1.03 \| 686871 \| 4.0915 \| \| 4.0524 \| 0.03 \| 763190 \| 4.0756 \| \| 4.0316 \| 1.03 \| 839509 \| 4.0636 \| \| 4.0127 \| 0.03 \| 915828 \| 4.0522 \| \| 3.9912 \| 0.03 \| 992147 \| 4.0424 \| \| 3.9787 \| 1.03 \| 1068466 \| 4.0349 \| \| 3.9572 \| 0.03 \| 1144786 \| 4.0269 \| \| 3.9465 \| 1.03 \| 1221106 \| 4.0206 \| \| 3.9442 \| 0.03 \| 1297426 \| 4.0153 \| \| 3.9335 \| 1.03 \| 1373746 \| 4.0111 \| \| 3.9232 \| 0.03 \| 1450066 \| 4.0068 \| \| 3.9185 \| 1.03 \| 1526386 \| 4.0029 \| \| 3.9139 \| 0.03 \| 1602706 \| 3.9997 \| \| 3.9108 \| 1.03 \| 1679026 \| 3.9973 \| \| 3.9081 \| 0.03 \| 1755346 \| 3.9954 \| \| 3.8976 \| 1.03 \| 1831666 \| 3.9930 \| \| 3.8919 \| 0.03 \| 1907986 \| 3.9912 \| \| 3.8824 \| 1.03 \| 1984306 \| 3.9896 \| \| 3.8759 \| 0.03 \| 2060626 \| 3.9880 \| \| 3.8735 \| 1.03 \| 2136946 \| 3.9865 \| \| 3.8676 \| 0.03 \| 2213266 \| 3.9854 \| \| 3.8588 \| 1.03 \| 2289586 \| 3.9842 \| \| 3.8596 \| 0.03 \| 2365906 \| 3.9830 \| \| 3.8594 \| 0.03 \| 2442226 \| 3.9820 \| \| 3.8535 \| 1.03 \| 2518546 \| 3.9811 \| \| 3.8489 \| 0.03 \| 2594866 \| 3.9804 \| \| 3.8453 \| 1.03 \| 2671186 \| 3.9795 \| \| 3.8472 \| 0.03 \| 2747506 \| 3.9791 \| \| 3.8447 \| 1.03 \| 2823826 \| 3.9786 \| \| 3.8473 \| 0.03 \| 2900146 \| 3.9781 \| \| 3.8489 \| 0.03 \| 2976466 \| 3.9777 \| \| 3.8439 \| 1.02 \| 3052726 \| 3.9774 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
kiranbandi/nlp-qual-qual	kiranbandi	2024-01-17T00:25:00Z	5	0	transformers.js	[ "transformers.js", "pytorch", "onnx", "bert", "text-classification", "region:us" ]	text-classification	2024-01-16T21:35:46Z	--- library_name: transformers.js --- https://huggingface.co/maxspad/nlp-qual-qual with with ONNX weights to be compatible with Transformers.js.
ogbrandt/mistral7b-pjf-ft-v0	ogbrandt	2024-01-17T00:24:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-17T00:24:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MaziyarPanahi/Mistral-7B-v0.1-Open-Platypus-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T00:23:58Z	22	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "akjindal53244/Mistral-7B-v0.1-Open-Platypus", "pytorch", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-17T00:18:33Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - akjindal53244/Mistral-7B-v0.1-Open-Platypus - transformers - pytorch - mistral - text-generation - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # Mistral-7B-v0.1-Open-Platypus-Mistral-7B-Instruct-v0.1 Mistral-7B-v0.1-Open-Platypus-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [akjindal53244/Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: akjindal53244/Mistral-7B-v0.1-Open-Platypus layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/Mistral-7B-v0.1-Open-Platypus-Mistral-7B-Instruct-v0.1" 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"]) ```
jlbaker361/ft250g2	jlbaker361	2024-01-17T00:08:29Z	0	0	null	[ "safetensors", "region:us" ]	null	2024-01-16T23:19:45Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - jlbaker361/ft250g2 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/wikiart-balanced250 dataset. Training epochs = 1 num_train_timesteps = 30 You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
MaziyarPanahi/pic_7B_mistral_Full_v0.2-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-17T00:01:03Z	22	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "TokenBender/pic_7B_mistral_Full_v0.2", "pytorch", "dataset:Open-Orca/SlimOrca", "dataset:HuggingFaceH4/no_robots", "dataset:Intel/orca_dpo_pairs", "dataset:rizerphe/glaive-function-calling-v2-zephyr", "dataset:codefuse-ai/Evol-instruction-66k", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-16T23:56:06Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - TokenBender/pic_7B_mistral_Full_v0.2 - transformers - pytorch - mistral - text-generation - dataset:Open-Orca/SlimOrca - dataset:HuggingFaceH4/no_robots - dataset:Intel/orca_dpo_pairs - dataset:rizerphe/glaive-function-calling-v2-zephyr - dataset:codefuse-ai/Evol-instruction-66k - base_model:mistralai/Mistral-7B-v0.1 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # pic_7B_mistral_Full_v0.2-Mistral-7B-Instruct-v0.1 pic_7B_mistral_Full_v0.2-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [TokenBender/pic_7B_mistral_Full_v0.2](https://huggingface.co/TokenBender/pic_7B_mistral_Full_v0.2) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: TokenBender/pic_7B_mistral_Full_v0.2 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/pic_7B_mistral_Full_v0.2-Mistral-7B-Instruct-v0.1" 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"]) ```
coversia21/RVC_DuendeVerde_Latino	coversia21	2024-01-16T23:56:28Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:h94/IP-Adapter-FaceID", "base_model:adapter:h94/IP-Adapter-FaceID", "license:openrail", "region:us" ]	text-to-image	2024-01-16T23:47:43Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/Captura de pantalla 2024-01-16 184715.png base_model: h94/IP-Adapter-FaceID instance_prompt: null license: openrail --- # RVC_DuendeVerde_Latino <Gallery /> ## Download model [Download](/coversia21/RVC_DuendeVerde_Latino/tree/main) them in the Files & versions tab.
RickHunter/ppo-Huggy	RickHunter	2024-01-16T23:52:29Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-01-16T23:52:13Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # 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: RickHunter/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MaziyarPanahi/Venomia-1.1-m7-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T23:51:38Z	22	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "Sao10K/Venomia-1.1-m7", "pytorch", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational", "license:apache-2.0" ]	text-generation	2024-01-16T23:46:42Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - Sao10K/Venomia-1.1-m7 - transformers - pytorch - mistral - text-generation - en - license:cc-by-nc-4.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # Venomia-1.1-m7-Mistral-7B-Instruct-v0.1 Venomia-1.1-m7-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [Sao10K/Venomia-1.1-m7](https://huggingface.co/Sao10K/Venomia-1.1-m7) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: Sao10K/Venomia-1.1-m7 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/Venomia-1.1-m7-Mistral-7B-Instruct-v0.1" 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"]) ```
Denox05/roxy	Denox05	2024-01-16T23:48:28Z	0	0	null	[ "license:other", "region:us" ]	null	2024-01-16T23:48:03Z	--- license: other license_name: rc license_link: LICENSE ---
stanpony/medical-diagnosis-classification-model	stanpony	2024-01-16T23:45:52Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "region:us" ]	null	2024-01-16T19:51:27Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: medical-diagnosis-classification-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # medical-diagnosis-classification-model This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8962 - Accuracy: 0.5784 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.9083 \| 0.44 \| 5000 \| 0.9480 \| 0.5413 \| \| 0.9345 \| 0.87 \| 10000 \| 0.9236 \| 0.5690 \| \| 0.9558 \| 1.31 \| 15000 \| 0.9112 \| 0.5633 \| \| 1.0294 \| 1.75 \| 20000 \| 0.9150 \| 0.5629 \| \| 1.0029 \| 2.18 \| 25000 \| 0.9197 \| 0.5547 \| \| 0.8028 \| 2.62 \| 30000 \| 0.9018 \| 0.5689 \| \| 0.8739 \| 3.06 \| 35000 \| 0.8926 \| 0.5844 \| \| 0.9352 \| 3.49 \| 40000 \| 0.8988 \| 0.5753 \| \| 0.9041 \| 3.93 \| 45000 \| 0.9014 \| 0.5731 \| \| 0.8445 \| 4.37 \| 50000 \| 0.8990 \| 0.5744 \| \| 0.8374 \| 4.8 \| 55000 \| 0.8962 \| 0.5784 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Seokeon/V14_R384_full_none_monster_toy	Seokeon	2024-01-16T23:30:32Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-16T21:22:15Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks toy tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Seokeon/V14_R384_full_none_monster_toy This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks toy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
MaziyarPanahi/Mistral-7B-claude-instruct-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T23:24:00Z	21	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "Norquinal/Mistral-7B-claude-instruct", "pytorch", "dataset:Norquinal/claude_multi_instruct_1k", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational", "license:apache-2.0" ]	text-generation	2024-01-16T23:18:42Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - Norquinal/Mistral-7B-claude-instruct - transformers - pytorch - mistral - text-generation - dataset:Norquinal/claude_multi_instruct_1k - license:cc-by-nc-4.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # Mistral-7B-claude-instruct-Mistral-7B-Instruct-v0.1 Mistral-7B-claude-instruct-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [Norquinal/Mistral-7B-claude-instruct](https://huggingface.co/Norquinal/Mistral-7B-claude-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: Norquinal/Mistral-7B-claude-instruct layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/Mistral-7B-claude-instruct-Mistral-7B-Instruct-v0.1" 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"]) ```
Seokeon/V14_R256_full_none_monster_toy	Seokeon	2024-01-16T23:20:09Z	1	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-16T21:34:51Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks toy tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Seokeon/V14_R256_full_none_monster_toy This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks toy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
CLMBR/old-rel-cl-lstm-3	CLMBR	2024-01-16T23:16:38Z	7	0	transformers	[ "transformers", "pytorch", "rnn", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-01-12T17:51:36Z	--- tags: - generated_from_trainer model-index: - name: rel-cl-lstm-3 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. --> # rel-cl-lstm-3 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 4.8333 \| 0.03 \| 76319 \| 4.7955 \| \| 4.5414 \| 1.03 \| 152638 \| 4.5069 \| \| 4.3958 \| 0.03 \| 228957 \| 4.3693 \| \| 4.3064 \| 1.03 \| 305276 \| 4.2841 \| \| 4.2412 \| 0.03 \| 381595 \| 4.2253 \| \| 4.1888 \| 1.03 \| 457914 \| 4.1822 \| \| 4.1518 \| 0.03 \| 534233 \| 4.1503 \| \| 4.1231 \| 1.03 \| 610552 \| 4.1251 \| \| 4.0901 \| 0.03 \| 686871 \| 4.1043 \| \| 4.0684 \| 1.03 \| 763190 \| 4.0883 \| \| 4.0494 \| 0.03 \| 839509 \| 4.0748 \| \| 4.0311 \| 1.03 \| 915828 \| 4.0629 \| \| 4.0094 \| 0.03 \| 992147 \| 4.0531 \| \| 3.9984 \| 1.03 \| 1068466 \| 4.0447 \| \| 3.9843 \| 0.03 \| 1144785 \| 4.0374 \| \| 3.9745 \| 1.03 \| 1221104 \| 4.0315 \| \| 3.9639 \| 0.03 \| 1297424 \| 4.0246 \| \| 3.9518 \| 1.03 \| 1373744 \| 4.0196 \| \| 3.9425 \| 0.03 \| 1450064 \| 4.0152 \| \| 3.9366 \| 1.03 \| 1526384 \| 4.0120 \| \| 3.9314 \| 0.03 \| 1602704 \| 4.0092 \| \| 3.9281 \| 1.03 \| 1679024 \| 4.0062 \| \| 3.9248 \| 0.03 \| 1755344 \| 4.0037 \| \| 3.9168 \| 1.03 \| 1831664 \| 4.0010 \| \| 3.9081 \| 0.03 \| 1907984 \| 3.9989 \| \| 3.9019 \| 1.03 \| 1984304 \| 3.9969 \| \| 3.8948 \| 0.03 \| 2060624 \| 3.9948 \| \| 3.891 \| 1.03 \| 2136944 \| 3.9935 \| \| 3.8871 \| 0.03 \| 2213264 \| 3.9923 \| \| 3.8802 \| 1.03 \| 2289584 \| 3.9913 \| \| 3.8797 \| 0.03 \| 2365904 \| 3.9902 \| \| 3.8769 \| 1.03 \| 2442224 \| 3.9892 \| \| 3.8733 \| 0.03 \| 2518544 \| 3.9886 \| \| 3.8671 \| 1.03 \| 2594864 \| 3.9875 \| \| 3.8637 \| 0.03 \| 2671184 \| 3.9867 \| \| 3.8642 \| 1.03 \| 2747504 \| 3.9858 \| \| 3.8642 \| 0.03 \| 2823824 \| 3.9848 \| \| 3.8653 \| 1.03 \| 2900144 \| 3.9842 \| \| 3.8671 \| 0.03 \| 2976464 \| 3.9836 \| \| 3.8639 \| 0.02 \| 3052726 \| 3.9834 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
MaziyarPanahi/SciPhi-Self-RAG-Mistral-7B-32k-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T23:12:23Z	21	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "SciPhi/SciPhi-Self-RAG-Mistral-7B-32k", "pytorch", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational", "license:apache-2.0" ]	text-generation	2024-01-16T23:07:16Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - SciPhi/SciPhi-Self-RAG-Mistral-7B-32k - transformers - pytorch - mistral - text-generation - license:mit - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # SciPhi-Self-RAG-Mistral-7B-32k-Mistral-7B-Instruct-v0.1 SciPhi-Self-RAG-Mistral-7B-32k-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [SciPhi/SciPhi-Self-RAG-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Self-RAG-Mistral-7B-32k) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: SciPhi/SciPhi-Self-RAG-Mistral-7B-32k layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/SciPhi-Self-RAG-Mistral-7B-32k-Mistral-7B-Instruct-v0.1" 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"]) ```
mlx-community/TenyxChat-7B-v1-4bit-mlx	mlx-community	2024-01-16T23:09:00Z	5	3	transformers	[ "transformers", "mistral", "text-generation", "tenyx-fine-tuning", "dpo", "tenyxchat", "mlx", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T22:16:28Z	--- language: - en license: apache-2.0 library_name: transformers tags: - tenyx-fine-tuning - dpo - tenyxchat - mlx --- # mlx-community/TenyxChat-7B-v1-4bit-mlx This model was converted to MLX format from [`tenyx/TenyxChat-7B-v1`](). Refer to the [original model card](https://huggingface.co/tenyx/TenyxChat-7B-v1) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/TenyxChat-7B-v1-4bit-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
Seokeon/V14_R384_full_pp_dog6	Seokeon	2024-01-16T23:02:41Z	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-16T21:34:25Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Seokeon/V14_R384_full_pp_dog6 This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
jlbaker361/vanilla-ddpo	jlbaker361	2024-01-16T23:00:31Z	0	0	diffusers	[ "diffusers", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-05T19:06:21Z	--- {} --- # DDPO trained model num_epochs=10 train_gradient_accumulation_steps=4 sample_num_steps=30 sample_batch_size=4 train_batch_size=4 sample_num_batches_per_epoch=32
jeiku/Test68_3B	jeiku	2024-01-16T23:00:25Z	18	0	transformers	[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "mergekit", "merge", "conversational", "custom_code", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:jeiku/ToxicNoRobotsRosaHermesBoros_3B", "base_model:finetune:jeiku/ToxicNoRobotsRosaHermesBoros_3B", "autotrain_compatible", "region:us" ]	text-generation	2024-01-16T22:54:34Z	--- base_model: - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/Everything_v3_StableLM - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/Theory_of_Mind_StableLM - jeiku/ToxicNoRobotsRosaHermesBoros_3B tags: - mergekit - merge --- # newkid2 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) as a base. ### Models Merged The following models were included in the merge: * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/Everything_v3_StableLM](https://huggingface.co/jeiku/Everything_v3_StableLM) * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/Theory_of_Mind_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_StableLM) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/Everything_v3_StableLM parameters: weight: 0.25 density: 0.25 - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/Theory_of_Mind_StableLM parameters: weight: 0.25 density: 0.25 merge_method: dare_ties base_model: jeiku/ToxicNoRobotsRosaHermesBoros_3B parameters: dtype: bfloat16 ```
TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ	TheBloke	2024-01-16T22:47:20Z	41	11	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "conversational", "en", "base_model:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "base_model:quantized:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-16T20:20:01Z	--- base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT inference: false language: - en license: apache-2.0 model-index: - name: Nous-Hermes-2-Mixtral-8x7B-SFT results: [] model_creator: NousResearch model_name: Nous Hermes 2 Mixtral 8X7B SFT model_type: mixtral prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke tags: - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Nous Hermes 2 Mixtral 8X7B SFT - GPTQ - Model creator: [NousResearch](https://huggingface.co/NousResearch) - Original model: [Nous Hermes 2 Mixtral 8X7B SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) <!-- description start --> # Description This repo contains GPTQ model files for [NousResearch's Nous Hermes 2 Mixtral 8X7B SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF) * [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/main) \| 4 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 23.81 GB \| No \| 4-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 24.70 GB \| No \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 27.42 GB \| No \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/gptq-3bit--1g-actorder_True) \| 3 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 18.01 GB \| No \| 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. \| \| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/gptq-3bit-128g-actorder_True) \| 3 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 18.85 GB \| No \| 3-bit, with group size 128g and act-order. Higher quality than 128g-False. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 47.04 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 48.10 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ`: ```shell mkdir Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ huggingface-cli download TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --local-dir Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ huggingface-cli download TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --local-dir Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: NousResearch's Nous Hermes 2 Mixtral 8X7B SFT # Nous Hermes 2 - Mixtral 8x7B - SFT ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/btRmXWMG7PXatTs-u3G85.jpeg) ## Model description Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of our new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1). The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks. This is the SFT only version of Mixtral Hermes 2, we have also released an SFT+DPO version, for people to find which works best for them, which can be found here: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO ## We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO! # Table of Contents 1. [Example Outputs](#example-outputs) 2. [Benchmark Results](#benchmark-results) - GPT4All - AGIEval - BigBench - Comparison to Mixtral-Instruct 3. [Prompt Format](#prompt-format) 4. [Inference Example Code](#inference-code) 5. [Quantized Models](#quantized-models) ## Example Outputs ### Writing Code for Data Visualization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/QJ5RHrOqB5GMP7ZAZ5NTk.png) ### Writing Cyberpunk Psychedelic Poems ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/wuKnMlM2HBGdyUFO7mY_H.png) ### Performing Backtranslation to Create Prompts from Input Text ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/QElwK1UI9PQQT6WosXpo1.png) ## Benchmark Results Nous-Hermes 2 on Mixtral 8x7B SFT is the bedrock for major improvements on many of the benchmarks below compared to the base Mixtral model, and is the SFT only version of our first model to beat the flagship Mixtral Finetune by MistralAI (the DPO version). ## GPT4All: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5904\|± \|0.0144\| \| \| \|acc_norm\|0.6323\|± \|0.0141\| \|arc_easy \| 0\|acc \|0.8594\|± \|0.0071\| \| \| \|acc_norm\|0.8607\|± \|0.0071\| \|boolq \| 1\|acc \|0.8783\|± \|0.0057\| \|hellaswag \| 0\|acc \|0.6592\|± \|0.0047\| \| \| \|acc_norm\|0.8434\|± \|0.0036\| \|openbookqa \| 0\|acc \|0.3400\|± \|0.0212\| \| \| \|acc_norm\|0.4660\|± \|0.0223\| \|piqa \| 0\|acc \|0.8324\|± \|0.0087\| \| \| \|acc_norm\|0.8379\|± \|0.0086\| \|winogrande \| 0\|acc \|0.7569\|± \|0.0121\| ``` Average: 75.36 ## AGIEval: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2441\|± \|0.0270\| \| \| \|acc_norm\|0.2598\|± \|0.0276\| \|agieval_logiqa_en \| 0\|acc \|0.4025\|± \|0.0192\| \| \| \|acc_norm\|0.3978\|± \|0.0192\| \|agieval_lsat_ar \| 0\|acc \|0.2391\|± \|0.0282\| \| \| \|acc_norm\|0.2043\|± \|0.0266\| \|agieval_lsat_lr \| 0\|acc \|0.5353\|± \|0.0221\| \| \| \|acc_norm\|0.5098\|± \|0.0222\| \|agieval_lsat_rc \| 0\|acc \|0.6617\|± \|0.0289\| \| \| \|acc_norm\|0.5948\|± \|0.0300\| \|agieval_sat_en \| 0\|acc \|0.7961\|± \|0.0281\| \| \| \|acc_norm\|0.7816\|± \|0.0289\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4757\|± \|0.0349\| \| \| \|acc_norm\|0.4515\|± \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.4818\|± \|0.0338\| \| \| \|acc_norm\|0.3909\|± \|0.0330\| ``` Average: 44.89 ## BigBench: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|-----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.5789\|± \|0.0359\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.7154\|± \|0.0235\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.5388\|± \|0.0311\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.4680\|± \|0.0264\| \| \| \|exact_str_match \|0.0000\|± \|0.0000\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.3260\|± \|0.0210\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2443\|± \|0.0163\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.5233\|± \|0.0289\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.3700\|± \|0.0216\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.5000\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.6665\|± \|0.0105\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.6317\|± \|0.0228\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.2505\|± \|0.0137\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.7127\|± \|0.0337\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.6592\|± \|0.0151\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.6860\|± \|0.0147\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2200\|± \|0.0117\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1503\|± \|0.0085\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.5233\|± \|0.0289\| ``` Average: 48.69 # Benchmark Comparison Charts ## GPT4All ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/S3_tdH822r9UvkGFDiYam.png) ## AGI-Eval ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/paet9FsASWPWa6KJs3mm-.png) ## BigBench Reasoning Test ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/rHmkUnYLTWwq0cuVzMegL.png) # Prompt Format Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Inference Code Here is example code using HuggingFace Transformers to inference the model (note: even in 4bit, it will require more than 24GB of VRAM) ```python # Code to inference Hermes with HF Transformers # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages import torch from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import LlamaTokenizer, MixtralForCausalLM import bitsandbytes, flash_attn tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True) model = MixtralForCausalLM.from_pretrained( "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, load_in_4bit=True, use_flash_attention_2=True ) prompts = [ """<\|im_start\|>system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|>user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<\|im_end\|> <\|im_start\|>assistant""", ] for chat in prompts: print(chat) input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Response: {response}") ``` # Quantized Models: ## All sizes of GGUF Quantizations are available here: ### SFT+DPO Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF ### SFT Only Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF [<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)
Kooten/DaringLotus-8bpw-exl2	Kooten	2024-01-16T22:47:06Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Solar", "Mistral", "Roleplay", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:40:24Z	--- license: apache-2.0 tags: - Solar - Mistral - Roleplay --- # DaringLotus-10.7B 8bpw EXL2 ## Description EXL2 quant of [BlueNipples/DaringLotus-10.7B](https://huggingface.co/BlueNipples/DaringLotus-10.7B) - 6bpw should be comfortable on 12 gb with 8k context - 4bpw might just fit on 8gb of vram at 4k context - if you have more ram get the 8bpw ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/DaringLotus-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/DaringLotus-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/DaringLotus-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/DaringLotus-4bpw-exl2) ## Prompt Format ### Alpaca: I am not entirely certain of this but i think alpaca is correct for this model ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Input: {input} ### Response: ``` ## Contact Kooten on discord
MaziyarPanahi/openbuddy-mistral-7b-v13-base-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T22:34:30Z	23	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "OpenBuddy/openbuddy-mistral-7b-v13-base", "pytorch", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "region:us", "conversational", "endpoints_compatible" ]	text-generation	2024-01-16T22:29:26Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - OpenBuddy/openbuddy-mistral-7b-v13-base - transformers - pytorch - mistral - text-generation - zh - en - fr - de - ja - ko - it - ru - license:apache-2.0 - autotrain_compatible - text-generation-inference - region:us --- # openbuddy-mistral-7b-v13-base-Mistral-7B-Instruct-v0.1 openbuddy-mistral-7b-v13-base-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [OpenBuddy/openbuddy-mistral-7b-v13-base](https://huggingface.co/OpenBuddy/openbuddy-mistral-7b-v13-base) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: OpenBuddy/openbuddy-mistral-7b-v13-base layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/openbuddy-mistral-7b-v13-base-Mistral-7B-Instruct-v0.1" 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"]) ```
Kooten/DaringLotus-4bpw-exl2	Kooten	2024-01-16T22:32:56Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Solar", "Mistral", "Roleplay", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T14:18:32Z	--- license: apache-2.0 tags: - Solar - Mistral - Roleplay --- # DaringLotus-10.7B 4bpw EXL2 ## Description EXL2 quant of [BlueNipples/DaringLotus-10.7B](https://huggingface.co/BlueNipples/DaringLotus-10.7B) - 6bpw should be comfortable on 12 gb with 8k context - 4bpw might just fit on 8gb of vram at 4k context - if you have more ram get the 8bpw ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/DaringLotus-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/DaringLotus-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/DaringLotus-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/DaringLotus-4bpw-exl2) ## Prompt Format ### Alpaca: I am not entirely certain of this but i think alpaca is correct for this model ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Input: {input} ### Response: ``` ## Contact Kooten on discord
Kooten/DaringLotus-6bpw-exl2	Kooten	2024-01-16T22:32:25Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Solar", "Mistral", "Roleplay", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:40:15Z	--- license: apache-2.0 tags: - Solar - Mistral - Roleplay --- # DaringLotus-10.7B 6bpw EXL2 ## Description EXL2 quant of [BlueNipples/DaringLotus-10.7B](https://huggingface.co/BlueNipples/DaringLotus-10.7B) - 6bpw should be comfortable on 12 gb with 8k context - 4bpw might just fit on 8gb of vram at 4k context - if you have more ram get the 8bpw ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/DaringLotus-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/DaringLotus-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/DaringLotus-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/DaringLotus-4bpw-exl2) ## Prompt Format ### Alpaca: I am not entirely certain of this but i think alpaca is correct for this model ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Input: {input} ### Response: ``` ## Contact Kooten on discord
aruca/finetuning-sentiment-analysis-siebert	aruca	2024-01-16T22:30:14Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:siebert/sentiment-roberta-large-english", "base_model:finetune:siebert/sentiment-roberta-large-english", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-16T16:48:06Z	--- base_model: siebert/sentiment-roberta-large-english tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-analysis-siebert 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. --> # finetuning-sentiment-analysis-siebert This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2571 - Accuracy: 0.7929 - F1: [0.79305355 0.75473045 0.8413856 ] ## 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 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
tejasreereddy/mistral-quantize-lora-peft-dataset-v.2	tejasreereddy	2024-01-16T22:29:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-16T22:28:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LoneStriker/FrankenDPO-4x7B-bf16-6.0bpw-h6-exl2	LoneStriker	2024-01-16T22:29:04Z	7	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "en", "arxiv:2101.03961", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T22:06:40Z	--- license: apache-2.0 language: - en tags: - merge --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/7JsqBt8QRiZmcMh-ameqH.jpeg) # It's alive!!!! Half the size and better on GSM8k and Winogrande than Mixtral Instruct 8x 7B! A frankenMoE using only DPO models. To be used with Chat-instruct mode enabled. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/wGRcusncUd-mCdksvYckY.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/rx1GfLMEIP3T-r3bxqW9r.png) - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - router - [udkai/Turdus](https://huggingface.co/udkai/Turdus) - expert #1 - [distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) - expert #2 - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - expert #3 - [Neuronovo/neuronovo-9B-v0.3](https://huggingface.co/Neuronovo/neuronovo-9B-v0.3) - expert #4 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)" ### (from the MistralAI papers...click the quoted question above to navigate to it directly.) The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining. So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements: Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs! A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/up_I0R2TQGjqTShZp_1Sz.png) Switch Layer MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961) So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts. Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges: Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting. Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon). If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter. ## "Wait...but you called this a frankenMoE?" The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously.
LoneStriker/FrankenDPO-4x7B-bf16-5.0bpw-h6-exl2	LoneStriker	2024-01-16T22:29:00Z	5	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "en", "arxiv:2101.03961", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:55:14Z	--- license: apache-2.0 language: - en tags: - merge --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/7JsqBt8QRiZmcMh-ameqH.jpeg) # It's alive!!!! Half the size and better on GSM8k and Winogrande than Mixtral Instruct 8x 7B! A frankenMoE using only DPO models. To be used with Chat-instruct mode enabled. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/wGRcusncUd-mCdksvYckY.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/rx1GfLMEIP3T-r3bxqW9r.png) - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - router - [udkai/Turdus](https://huggingface.co/udkai/Turdus) - expert #1 - [distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) - expert #2 - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - expert #3 - [Neuronovo/neuronovo-9B-v0.3](https://huggingface.co/Neuronovo/neuronovo-9B-v0.3) - expert #4 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)" ### (from the MistralAI papers...click the quoted question above to navigate to it directly.) The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining. So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements: Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs! A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/up_I0R2TQGjqTShZp_1Sz.png) Switch Layer MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961) So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts. Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges: Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting. Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon). If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter. ## "Wait...but you called this a frankenMoE?" The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously.
LoneStriker/FrankenDPO-4x7B-bf16-4.0bpw-h6-exl2	LoneStriker	2024-01-16T22:28:54Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "en", "arxiv:2101.03961", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:44:22Z	--- license: apache-2.0 language: - en tags: - merge --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/7JsqBt8QRiZmcMh-ameqH.jpeg) # It's alive!!!! Half the size and better on GSM8k and Winogrande than Mixtral Instruct 8x 7B! A frankenMoE using only DPO models. To be used with Chat-instruct mode enabled. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/wGRcusncUd-mCdksvYckY.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/rx1GfLMEIP3T-r3bxqW9r.png) - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - router - [udkai/Turdus](https://huggingface.co/udkai/Turdus) - expert #1 - [distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) - expert #2 - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - expert #3 - [Neuronovo/neuronovo-9B-v0.3](https://huggingface.co/Neuronovo/neuronovo-9B-v0.3) - expert #4 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)" ### (from the MistralAI papers...click the quoted question above to navigate to it directly.) The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining. So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements: Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs! A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/up_I0R2TQGjqTShZp_1Sz.png) Switch Layer MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961) So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts. Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges: Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting. Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon). If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter. ## "Wait...but you called this a frankenMoE?" The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously.
LoneStriker/FrankenDPO-4x7B-bf16-3.5bpw-h6-exl2	LoneStriker	2024-01-16T22:28:51Z	7	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "en", "arxiv:2101.03961", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:34:37Z	--- license: apache-2.0 language: - en tags: - merge --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/7JsqBt8QRiZmcMh-ameqH.jpeg) # It's alive!!!! Half the size and better on GSM8k and Winogrande than Mixtral Instruct 8x 7B! A frankenMoE using only DPO models. To be used with Chat-instruct mode enabled. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/wGRcusncUd-mCdksvYckY.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/rx1GfLMEIP3T-r3bxqW9r.png) - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - router - [udkai/Turdus](https://huggingface.co/udkai/Turdus) - expert #1 - [distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) - expert #2 - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - expert #3 - [Neuronovo/neuronovo-9B-v0.3](https://huggingface.co/Neuronovo/neuronovo-9B-v0.3) - expert #4 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)" ### (from the MistralAI papers...click the quoted question above to navigate to it directly.) The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining. So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements: Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs! A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/up_I0R2TQGjqTShZp_1Sz.png) Switch Layer MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961) So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts. Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges: Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting. Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon). If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter. ## "Wait...but you called this a frankenMoE?" The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously.
LoneStriker/FrankenDPO-4x7B-bf16-8.0bpw-h8-exl2	LoneStriker	2024-01-16T22:28:42Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "en", "arxiv:2101.03961", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T22:18:35Z	--- license: apache-2.0 language: - en tags: - merge --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/7JsqBt8QRiZmcMh-ameqH.jpeg) # It's alive!!!! Half the size and better on GSM8k and Winogrande than Mixtral Instruct 8x 7B! A frankenMoE using only DPO models. To be used with Chat-instruct mode enabled. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/wGRcusncUd-mCdksvYckY.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/rx1GfLMEIP3T-r3bxqW9r.png) - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - router - [udkai/Turdus](https://huggingface.co/udkai/Turdus) - expert #1 - [distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) - expert #2 - [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - expert #3 - [Neuronovo/neuronovo-9B-v0.3](https://huggingface.co/Neuronovo/neuronovo-9B-v0.3) - expert #4 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)" ### (from the MistralAI papers...click the quoted question above to navigate to it directly.) The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining. So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements: Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs! A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/up_I0R2TQGjqTShZp_1Sz.png) Switch Layer MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961) So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts. Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges: Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting. Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon). If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter. ## "Wait...but you called this a frankenMoE?" The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously.
pratham-saraf/ms7b-news-songify-sharded-1	pratham-saraf	2024-01-16T22:20:05Z	15	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-16T22:17:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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htahir1/peft-lora-zencoder15B-A100-40GB	htahir1	2024-01-16T22:11:10Z	5	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:bigcode/starcoder", "base_model:adapter:bigcode/starcoder", "region:us" ]	null	2024-01-16T21:49:26Z	--- library_name: peft base_model: bigcode/starcoder --- # 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. 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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. 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MaziyarPanahi/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T22:07:08Z	21	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3", "pytorch", "bg", "ca", "cs", "da", "de", "en", "es", "fr", "hr", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "sr", "sv", "uk", "dataset:Open-Orca/OpenOrca", "dataset:OpenAssistant/oasst_top1_2023-08-25", "arxiv:2309.17453", "arxiv:2205.14135", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-16T22:01:56Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3 - transformers - pytorch - mistral - text-generation - bg - ca - cs - da - de - en - es - fr - hr - hu - it - nl - pl - pt - ro - ru - sl - sr - sv - uk - dataset:Open-Orca/OpenOrca - dataset:OpenAssistant/oasst_top1_2023-08-25 - arxiv:2309.17453 - arxiv:2205.14135 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3-Mistral-7B-Instruct-v0.1 Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3-Mistral-7B-Instruct-v0.1" 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"]) ```
MaziyarPanahi/typhoon-7b-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T21:54:44Z	24	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "scb10x/typhoon-7b", "pytorch", "pretrained", "th", "arxiv:2312.13951", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-16T21:49:46Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - scb10x/typhoon-7b - transformers - pytorch - mistral - text-generation - pretrained - th - arxiv:2312.13951 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # typhoon-7b-Mistral-7B-Instruct-v0.1 typhoon-7b-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: scb10x/typhoon-7b layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/typhoon-7b-Mistral-7B-Instruct-v0.1" 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"]) ```
DenisTheDev/Openchat-Zephyr-Passtrough	DenisTheDev	2024-01-16T21:46:15Z	16	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-1210", "HuggingFaceH4/zephyr-7b-beta", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:40:38Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - openchat/openchat-3.5-1210 - HuggingFaceH4/zephyr-7b-beta --- # Openchat-Zephyr-Passtrough Openchat-Zephyr-Passtrough is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210) * [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ## 🧩 Configuration ```yaml slices: - sources: - model: openchat/openchat-3.5-1210 layer_range: [0, 24] - sources: - model: HuggingFaceH4/zephyr-7b-beta layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ```
MaziyarPanahi/testllm-c2-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T21:38:39Z	22	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "Kiddyz/testllm-c2", "pytorch", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-16T21:33:37Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - Kiddyz/testllm-c2 - transformers - pytorch - mistral - text-generation - en - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # testllm-c2-Mistral-7B-Instruct-v0.1 testllm-c2-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [Kiddyz/testllm-c2](https://huggingface.co/Kiddyz/testllm-c2) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: Kiddyz/testllm-c2 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/testllm-c2-Mistral-7B-Instruct-v0.1" 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"]) ```
admarhi/towerificator	admarhi	2024-01-16T21:28:33Z	0	0	fastai	[ "fastai", "region:us" ]	null	2024-01-16T20:05:20Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
BashirRP/llm_judge_bashir	BashirRP	2024-01-16T21:26:39Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:FacebookAI/roberta-large", "base_model:adapter:FacebookAI/roberta-large", "region:us" ]	null	2024-01-16T21:26:37Z	--- library_name: peft base_model: roberta-large --- # 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 0.7.1
LoneStriker/SnowLotus-10.7B-6.0bpw-h6-exl2	LoneStriker	2024-01-16T21:25:48Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Roleplay", "Solar", "Mistral", "Text Generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:21:57Z	--- license: apache-2.0 tags: - Roleplay - Solar - Mistral - Text Generation --- ![SnowLotus Logo](https://cdn-uploads.huggingface.co/production/uploads/64bb1109aaccfd28b023bcec/gTQtPK46laLIFg0RTAv73.png) ### Premise So this is a basic slerp merge between a smart model and a good prose model. Prose and smarts. What we all want in an uncensored RP model right? I feel like Solar has untapped potential, in any case. Sao10K's Frostwind finetune is a key component of the mixture, its smarts are impressive. NyxKrage's Frostmaid experiment, which merges Frostwind with a frankenmerge of Noromaid and a mystery medical model, delivers quite impressive prose. His model creatively incorporates long-range context and instructions too, despite being slightly incoherent due to the fraken merging. So those are the main ingredients. Thanks to Nyx for sorting out the pytorch files btw. ### Recipe So, the recipe. I basically just gradient SLERP'd Frostwind into Frostmaid with these params: - filter: self_attn value: [0.9, 0.6, 0.3, 0, 0] - filter: mlp value: [0.3, 0.6] - value: 0.5 # fallback for rest of tensors ### Tentative Dozen or So Test Conclusion This made a model that was actually pretty much everything I was looking for - NEARLY as smart as Frostwind but with MOST of Frostmaids punchy prose. I tried doing TIES merges and DARE ties merges, but they actually came out worse, because both models have major weaknesses - one is very dry and gpt-ish, the other is a little loose with what's going on. The ties merges tended to bring out those qualities, dare even worse. So I stuck with this. It's not AS smart as Frostwind, so you maybe have to regen a little, but it's pretty smart, and quite creative. A sweet spot hopefully. Maybe someone merge wiser than I can do more with this recipe, but I'm very pleased with it, it did what I was hoping for - a smaller model I can mobile dgpu and produces pretty outsized quality responses (it's fairly zealous tho, be warned). I've only played with it a TINY bit, so there may be qualities or flaws I've missed. Cheers to all the finetuners, mergers and developers without which open source models wouldn't be half of what they are. Resources used: https://huggingface.co/NyxKrage/FrostMaid-10.7B-TESTING-pt https://huggingface.co/Sao10K/Frostwind-10.7B-v1 https://github.com/cg123/mergekit/tree/main
egyee/ppo-Huggy	egyee	2024-01-16T21:22:35Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-01T17:35:22Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: 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://github.com/huggingface/ml-agents#get-started ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: egyee/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
BashirRP/llm_judge_fiddler	BashirRP	2024-01-16T21:21:00Z	0	0	peft	[ "peft", "safetensors", "roberta", "arxiv:1910.09700", "base_model:FacebookAI/roberta-large", "base_model:adapter:FacebookAI/roberta-large", "endpoints_compatible", "region:us" ]	null	2024-01-16T20:34:54Z	--- library_name: peft base_model: roberta-large --- # 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
MaziyarPanahi/openbuddy-mistral-7b-v13.1-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T21:18:05Z	24	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "OpenBuddy/openbuddy-mistral-7b-v13.1", "pytorch", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "region:us", "conversational", "endpoints_compatible" ]	text-generation	2024-01-16T21:12:55Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - OpenBuddy/openbuddy-mistral-7b-v13.1 - transformers - pytorch - mistral - text-generation - zh - en - fr - de - ja - ko - it - ru - license:apache-2.0 - autotrain_compatible - text-generation-inference - region:us --- # openbuddy-mistral-7b-v13.1-Mistral-7B-Instruct-v0.1 openbuddy-mistral-7b-v13.1-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [OpenBuddy/openbuddy-mistral-7b-v13.1](https://huggingface.co/OpenBuddy/openbuddy-mistral-7b-v13.1) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: OpenBuddy/openbuddy-mistral-7b-v13.1 layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/openbuddy-mistral-7b-v13.1-Mistral-7B-Instruct-v0.1" 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"]) ```
davidmunechika/ptoken	davidmunechika	2024-01-16T21:13:28Z	12	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-16T21:09:00Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ptoken Dreambooth model trained by davidmunechika with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
jeiku/Luna_3B_GGUF	jeiku	2024-01-16T21:12:53Z	16	1	null	[ "gguf", "mergekit", "merge", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:jeiku/Bluemoon_cleaned_StableLM", "base_model:merge:jeiku/Bluemoon_cleaned_StableLM", "base_model:jeiku/ToxicNoRobotsRosaHermesBoros_3B", "base_model:merge:jeiku/ToxicNoRobotsRosaHermesBoros_3B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-16T20:58:54Z	--- base_model: - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/Theory_of_Mind_StableLM - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/Everything_v3_StableLM - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/Bluemoon_cleaned_StableLM - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/Capybara_StableLM - jeiku/ToxicNoRobotsRosaHermesBoros_3B - jeiku/alpaca-cleaned_StableLM tags: - mergekit - merge --- # lower This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) as a base. ### Models Merged The following models were included in the merge: * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/Theory_of_Mind_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_StableLM) * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/Everything_v3_StableLM](https://huggingface.co/jeiku/Everything_v3_StableLM) * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/Bluemoon_cleaned_StableLM](https://huggingface.co/jeiku/Bluemoon_cleaned_StableLM) * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/Capybara_StableLM](https://huggingface.co/jeiku/Capybara_StableLM) * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) + [jeiku/alpaca-cleaned_StableLM](https://huggingface.co/jeiku/alpaca-cleaned_StableLM) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/alpaca-cleaned_StableLM parameters: weight: 0.1 density: 1 - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/Capybara_StableLM parameters: weight: 0.1 density: 1 - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/Everything_v3_StableLM parameters: weight: 0.1 density: 1 - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/Theory_of_Mind_StableLM parameters: weight: 0.15 density: 1 - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B+jeiku/Bluemoon_cleaned_StableLM parameters: weight: 0.1 density: 1 merge_method: dare_ties base_model: jeiku/ToxicNoRobotsRosaHermesBoros_3B parameters: dtype: bfloat16 ```
TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ	TheBloke	2024-01-16T21:08:33Z	19	3	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "conversational", "en", "base_model:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "base_model:quantized:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2024-01-16T20:20:01Z	--- base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT inference: false language: - en license: apache-2.0 model-index: - name: Nous-Hermes-2-Mixtral-8x7B-SFT results: [] model_creator: NousResearch model_name: Nous Hermes 2 Mixtral 8X7B SFT model_type: mixtral prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke tags: - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Nous Hermes 2 Mixtral 8X7B SFT - AWQ - Model creator: [NousResearch](https://huggingface.co/NousResearch) - Original model: [Nous Hermes 2 Mixtral 8X7B SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) <!-- description start --> ## Description This repo contains AWQ model files for [NousResearch's Nous Hermes 2 Mixtral 8X7B SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). MIXTRAL AWQ This is a Mixtral AWQ model. For AutoAWQ inference, please install AutoAWQ 0.1.8 or later. Support via Transformers is also available, but currently requires installing Transformers from Github: `pip3 install git+https://github.com/huggingface/transformers.git` vLLM: version 0.2.6 is confirmed to support Mixtral AWQs. TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!) ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. AWQ models are supported by (note that not all of these may support Mixtral models yet - see above): - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF) * [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ/tree/main) \| 4 \| 128 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 24.65 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: NousResearch's Nous Hermes 2 Mixtral 8X7B SFT # Nous Hermes 2 - Mixtral 8x7B - SFT ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/btRmXWMG7PXatTs-u3G85.jpeg) ## Model description Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of our new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1). The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks. This is the SFT only version of Mixtral Hermes 2, we have also released an SFT+DPO version, for people to find which works best for them, which can be found here: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO ## We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO! # Table of Contents 1. [Example Outputs](#example-outputs) 2. [Benchmark Results](#benchmark-results) - GPT4All - AGIEval - BigBench - Comparison to Mixtral-Instruct 3. [Prompt Format](#prompt-format) 4. [Inference Example Code](#inference-code) 5. [Quantized Models](#quantized-models) ## Example Outputs ### Writing Code for Data Visualization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/QJ5RHrOqB5GMP7ZAZ5NTk.png) ### Writing Cyberpunk Psychedelic Poems ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/wuKnMlM2HBGdyUFO7mY_H.png) ### Performing Backtranslation to Create Prompts from Input Text ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/QElwK1UI9PQQT6WosXpo1.png) ## Benchmark Results Nous-Hermes 2 on Mixtral 8x7B SFT is the bedrock for major improvements on many of the benchmarks below compared to the base Mixtral model, and is the SFT only version of our first model to beat the flagship Mixtral Finetune by MistralAI (the DPO version). ## GPT4All: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5904\|± \|0.0144\| \| \| \|acc_norm\|0.6323\|± \|0.0141\| \|arc_easy \| 0\|acc \|0.8594\|± \|0.0071\| \| \| \|acc_norm\|0.8607\|± \|0.0071\| \|boolq \| 1\|acc \|0.8783\|± \|0.0057\| \|hellaswag \| 0\|acc \|0.6592\|± \|0.0047\| \| \| \|acc_norm\|0.8434\|± \|0.0036\| \|openbookqa \| 0\|acc \|0.3400\|± \|0.0212\| \| \| \|acc_norm\|0.4660\|± \|0.0223\| \|piqa \| 0\|acc \|0.8324\|± \|0.0087\| \| \| \|acc_norm\|0.8379\|± \|0.0086\| \|winogrande \| 0\|acc \|0.7569\|± \|0.0121\| ``` Average: 75.36 ## AGIEval: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2441\|± \|0.0270\| \| \| \|acc_norm\|0.2598\|± \|0.0276\| \|agieval_logiqa_en \| 0\|acc \|0.4025\|± \|0.0192\| \| \| \|acc_norm\|0.3978\|± \|0.0192\| \|agieval_lsat_ar \| 0\|acc \|0.2391\|± \|0.0282\| \| \| \|acc_norm\|0.2043\|± \|0.0266\| \|agieval_lsat_lr \| 0\|acc \|0.5353\|± \|0.0221\| \| \| \|acc_norm\|0.5098\|± \|0.0222\| \|agieval_lsat_rc \| 0\|acc \|0.6617\|± \|0.0289\| \| \| \|acc_norm\|0.5948\|± \|0.0300\| \|agieval_sat_en \| 0\|acc \|0.7961\|± \|0.0281\| \| \| \|acc_norm\|0.7816\|± \|0.0289\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4757\|± \|0.0349\| \| \| \|acc_norm\|0.4515\|± \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.4818\|± \|0.0338\| \| \| \|acc_norm\|0.3909\|± \|0.0330\| ``` Average: 44.89 ## BigBench: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|-----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.5789\|± \|0.0359\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.7154\|± \|0.0235\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.5388\|± \|0.0311\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.4680\|± \|0.0264\| \| \| \|exact_str_match \|0.0000\|± \|0.0000\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.3260\|± \|0.0210\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2443\|± \|0.0163\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.5233\|± \|0.0289\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.3700\|± \|0.0216\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.5000\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.6665\|± \|0.0105\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.6317\|± \|0.0228\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.2505\|± \|0.0137\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.7127\|± \|0.0337\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.6592\|± \|0.0151\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.6860\|± \|0.0147\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2200\|± \|0.0117\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1503\|± \|0.0085\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.5233\|± \|0.0289\| ``` Average: 48.69 # Benchmark Comparison Charts ## GPT4All ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/S3_tdH822r9UvkGFDiYam.png) ## AGI-Eval ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/paet9FsASWPWa6KJs3mm-.png) ## BigBench Reasoning Test ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/rHmkUnYLTWwq0cuVzMegL.png) # Prompt Format Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Inference Code Here is example code using HuggingFace Transformers to inference the model (note: even in 4bit, it will require more than 24GB of VRAM) ```python # Code to inference Hermes with HF Transformers # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages import torch from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import LlamaTokenizer, MixtralForCausalLM import bitsandbytes, flash_attn tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True) model = MixtralForCausalLM.from_pretrained( "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, load_in_4bit=True, use_flash_attention_2=True ) prompts = [ """<\|im_start\|>system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|>user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<\|im_end\|> <\|im_start\|>assistant""", ] for chat in prompts: print(chat) input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Response: {response}") ``` # Quantized Models: ## All sizes of GGUF Quantizations are available here: ### SFT+DPO Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF ### SFT Only Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF [<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)
Vachan/test	Vachan	2024-01-16T21:06:46Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-16T10:38:48Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4468 - Accuracy: 0.95 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 130 \| 0.6955 \| 0.9269 \| \| No log \| 2.0 \| 260 \| 0.4468 \| 0.95 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.13.3
LoneStriker/SnowLotus-10.7B-4.0bpw-h6-exl2	LoneStriker	2024-01-16T21:06:28Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Roleplay", "Solar", "Mistral", "Text Generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T21:04:03Z	--- license: apache-2.0 tags: - Roleplay - Solar - Mistral - Text Generation --- ![SnowLotus Logo](https://cdn-uploads.huggingface.co/production/uploads/64bb1109aaccfd28b023bcec/gTQtPK46laLIFg0RTAv73.png) ### Premise So this is a basic slerp merge between a smart model and a good prose model. Prose and smarts. What we all want in an uncensored RP model right? I feel like Solar has untapped potential, in any case. Sao10K's Frostwind finetune is a key component of the mixture, its smarts are impressive. NyxKrage's Frostmaid experiment, which merges Frostwind with a frankenmerge of Noromaid and a mystery medical model, delivers quite impressive prose. His model creatively incorporates long-range context and instructions too, despite being slightly incoherent due to the fraken merging. So those are the main ingredients. Thanks to Nyx for sorting out the pytorch files btw. ### Recipe So, the recipe. I basically just gradient SLERP'd Frostwind into Frostmaid with these params: - filter: self_attn value: [0.9, 0.6, 0.3, 0, 0] - filter: mlp value: [0.3, 0.6] - value: 0.5 # fallback for rest of tensors ### Tentative Dozen or So Test Conclusion This made a model that was actually pretty much everything I was looking for - NEARLY as smart as Frostwind but with MOST of Frostmaids punchy prose. I tried doing TIES merges and DARE ties merges, but they actually came out worse, because both models have major weaknesses - one is very dry and gpt-ish, the other is a little loose with what's going on. The ties merges tended to bring out those qualities, dare even worse. So I stuck with this. It's not AS smart as Frostwind, so you maybe have to regen a little, but it's pretty smart, and quite creative. A sweet spot hopefully. Maybe someone merge wiser than I can do more with this recipe, but I'm very pleased with it, it did what I was hoping for - a smaller model I can mobile dgpu and produces pretty outsized quality responses (it's fairly zealous tho, be warned). I've only played with it a TINY bit, so there may be qualities or flaws I've missed. Cheers to all the finetuners, mergers and developers without which open source models wouldn't be half of what they are. Resources used: https://huggingface.co/NyxKrage/FrostMaid-10.7B-TESTING-pt https://huggingface.co/Sao10K/Frostwind-10.7B-v1 https://github.com/cg123/mergekit/tree/main
MaziyarPanahi/una-cybertron-7b-v3-OMA-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T21:05:53Z	22	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "fblgit/una-cybertron-7b-v3-OMA", "juanako", "UNA", "cybertron", "xaberius", "dataset:fblgit/tree-of-knowledge", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us", "conversational" ]	text-generation	2024-01-16T21:00:46Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - fblgit/una-cybertron-7b-v3-OMA - transformers - safetensors - mistral - text-generation - juanako - UNA - cybertron - xaberius - dataset:fblgit/tree-of-knowledge - license:apache-2.0 - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us --- # una-cybertron-7b-v3-OMA-Mistral-7B-Instruct-v0.1 una-cybertron-7b-v3-OMA-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [fblgit/una-cybertron-7b-v3-OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: fblgit/una-cybertron-7b-v3-OMA layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/una-cybertron-7b-v3-OMA-Mistral-7B-Instruct-v0.1" 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"]) ```
flemmingmiguel/MarcMistral-7B	flemmingmiguel	2024-01-16T21:05:50Z	1,373	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "nfaheem/Marcoroni-7b-DPO-Merge", "EmbeddedLLM/Mistral-7B-Merge-14-v0.5", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T20:53:31Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - nfaheem/Marcoroni-7b-DPO-Merge - EmbeddedLLM/Mistral-7B-Merge-14-v0.5 --- # MarcMistral-7B MarcMistral-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [nfaheem/Marcoroni-7b-DPO-Merge](https://huggingface.co/nfaheem/Marcoroni-7b-DPO-Merge) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.5](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5) As an experiment to find the best base merge to further fine-tuning, expect a lot of experiments named using parts of the component models until a clear winner emerges in the benchmarks In this case merging the highest MMLU merge with a high ARC merge to see which qualities remain untouched or improv ## 🧩 Configuration ```yaml slices: - sources: - model: nfaheem/Marcoroni-7b-DPO-Merge layer_range: [0, 32] - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.5 layer_range: [0, 32] merge_method: slerp base_model: EmbeddedLLM/Mistral-7B-Merge-14-v0.5 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors tokenizer_source: union dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "flemmingmiguel/MarcMistral-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
mojuss/finetuned-llama-7b-chat-hf-gpt-exam-8	mojuss	2024-01-16T21:04:56Z	0	0	null	[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-01-16T21:04:51Z	--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: finetuned-llama-7b-chat-hf-gpt-exam-8 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. --> # finetuned-llama-7b-chat-hf-gpt-exam-8 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator 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: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 9 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
SharedGPT/Mixtral_dolly_reverse_Inst	SharedGPT	2024-01-16T21:01:21Z	4	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-16T21:00:53Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mixtral-8x7B-v0.1 model-index: - name: Mixtral_dolly_reverse_Inst 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. --> # Mixtral_dolly_reverse_Inst This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.4407 ## 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: 2.5e-05 - train_batch_size: 32 - 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: 0.03 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8058 \| 0.05 \| 10 \| 1.9081 \| \| 1.7565 \| 0.1 \| 20 \| 1.8383 \| \| 1.7155 \| 0.15 \| 30 \| 1.7551 \| \| 1.6477 \| 0.2 \| 40 \| 1.7002 \| \| 1.5887 \| 0.25 \| 50 \| 1.6647 \| \| 1.5521 \| 0.3 \| 60 \| 1.6351 \| \| 1.5184 \| 0.35 \| 70 \| 1.6105 \| \| 1.5338 \| 0.4 \| 80 \| 1.5860 \| \| 1.4718 \| 0.45 \| 90 \| 1.5600 \| \| 1.4739 \| 0.5 \| 100 \| 1.5341 \| \| 1.4525 \| 0.55 \| 110 \| 1.5110 \| \| 1.4125 \| 0.6 \| 120 \| 1.4896 \| \| 1.4118 \| 0.65 \| 130 \| 1.4735 \| \| 1.3921 \| 0.7 \| 140 \| 1.4631 \| \| 1.3861 \| 0.75 \| 150 \| 1.4553 \| \| 1.3765 \| 0.8 \| 160 \| 1.4497 \| \| 1.3664 \| 0.85 \| 170 \| 1.4460 \| \| 1.3783 \| 0.9 \| 180 \| 1.4429 \| \| 1.3544 \| 0.95 \| 190 \| 1.4413 \| \| 1.3743 \| 1.0 \| 200 \| 1.4407 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Danielbrdz/Barcenas-10.7b	Danielbrdz	2024-01-16T20:59:52Z	1,374	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "es", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T19:21:12Z	--- license: apache-2.0 language: - en - es --- Barcenas-10.7b is a fine-tuned version of NousResearch/Nous-Hermes-2-SOLAR-10.7B, a state-of-the-art language model that can generate high-quality text for various tasks. Barcenas-10.7b was trained on the HuggingFaceH4/no_robots dataset, which contains 10,000 instructions and demonstrations created by skilled human annotators. This data can be used to improve the model’s ability to follow instructions and produce human-like responses. Barcenas-10.7b is a powerful and versatile model that can handle conversational text generation, summarization, creative writing, and more. Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
mrm8488/mistral-7b-ft-AgentInstruct	mrm8488	2024-01-16T20:59:09Z	29	1	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "en", "dataset:THUDM/AgentInstruct", "arxiv:2310.06825", "doi:10.57967/hf/1650", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-23T20:29:07Z	--- library_name: transformers license: apache-2.0 datasets: - THUDM/AgentInstruct language: - en --- # Mistral-7B fine-tuned on AgentInstruct [Mistral-7b-v1.0]() fine-tuned on the dataset [AgentInstruct](https://huggingface.co/datasets/THUDM/AgentInstruct) for "better acting as an agent" ## Model Details ### Model Description The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/). ## Model Architecture Mistral-7B-v0.1 is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Dataset Details AgentInstruct is a meticulously curated dataset featuring 1,866 high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like Task Derivation and Self-Instruct. - 🔍 CoT - Harness the power of [ReAct](https://react-lm.github.io/), offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making journey. - 🌍 Diversity - Spanning 6 real-world scenarios, from Daily Household Routines to Database Operations, and their average turns range from 5 to 35. - 🎯 Precision - Not all trajectories of GPT-4 are effective! Ours are rigorously filtered using strict rewards to ensure top-notch quality. - ✅ Assurance - Rigorous checks to avoid data leakage, ensuring pristine dataset quality. ## Task Overview \| Task \| # Filt. Traj. \| Avg # Filt. Traj. Turns \| \|---\|---\|---\| \|ALFWorld\|336\|13.52\| \|WebShop\|351\|3.68\| \|Mind2Web\|122\|1.00\| \|Knowledge Graph\|324\|6.04\| \|Operating System\|195\|3.85\| \|Database\|538\|2.06\| \|AgentInstruct\|1866\|5.24\| AgentInstruct includes 1,866 trajectories from 6 agents tasks. "Traj." stands for interaction trajectory. "Filt. Traj." stands for filtered trajectories. ## Training Details TBD ## Example of usage ```py from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria import torch # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("mrm8488/mistral-7b-ft-AgentInstruct") model = AutoModelForCausalLM.from_pretrained("mrm8488/mistral-7b-ft-AgentInstruct").to("cuda") class MyStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt = prompt def __call__(self, input_ids, scores, kwargs): # Decode without prompt and check for target sequence generated_text = tokenizer.decode(input_ids[0]).replace(self.prompt, '') return self.target_sequence in generated_text def __len__(self): return 1 def generate(context, max_new_tokens=256, min_new_tokens=64, temperature=0.3, top_p=0.75, top_k=40, do_sample=True, num_beams=2): # Prepare input data inputs = tokenizer(context, return_tensors="pt") input_ids = inputs["input_ids"].to("cuda") attention_mask = inputs["attention_mask"].to("cuda") # Generation settings generation_settings = { "max_new_tokens": max_new_tokens, "min_new_tokens": min_new_tokens, "temperature": temperature, "top_p": top_p, "top_k": top_k, "do_sample": do_sample, "num_beams": num_beams, "early_stopping": False, "use_cache": True, "stopping_criteria": MyStoppingCriteria("### human:", context) } # Generate response with torch.no_grad(): generation_output = model.generate(input_ids, attention_mask, generation_settings) output = tokenizer.decode(generation_output.sequences[0]) return output # Example usage context = "" human = """### human: Among the reference ID of under 10 who got response by marketing department, compare their education status. There are 2 tables involved with this task. The name of the 1st table is Customers, and the headers of this table are ID,SEX,MARITAL_STATUS,GEOID,EDUCATIONNUM,OCCUPATION,age. The name of the 2nd table is Mailings1_2, and the headers of this table are REFID,REF_DATE,RESPONSE.""" context = human solution = generate(context) print(solution) ``` ## Citation ```bibtext @misc {manuel_romero_2024, author = { {Manuel Romero} }, title = { mistral-7b-ft-AgentInstruct (Revision 463b96d) }, year = 2024, url = { https://huggingface.co/mrm8488/mistral-7b-ft-AgentInstruct }, doi = { 10.57967/hf/1650 }, publisher = { Hugging Face } } ```
LoneStriker/SnowLotus-10.7B-3.0bpw-h6-exl2	LoneStriker	2024-01-16T20:57:05Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Roleplay", "Solar", "Mistral", "Text Generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T20:55:11Z	--- license: apache-2.0 tags: - Roleplay - Solar - Mistral - Text Generation --- ![SnowLotus Logo](https://cdn-uploads.huggingface.co/production/uploads/64bb1109aaccfd28b023bcec/gTQtPK46laLIFg0RTAv73.png) ### Premise So this is a basic slerp merge between a smart model and a good prose model. Prose and smarts. What we all want in an uncensored RP model right? I feel like Solar has untapped potential, in any case. Sao10K's Frostwind finetune is a key component of the mixture, its smarts are impressive. NyxKrage's Frostmaid experiment, which merges Frostwind with a frankenmerge of Noromaid and a mystery medical model, delivers quite impressive prose. His model creatively incorporates long-range context and instructions too, despite being slightly incoherent due to the fraken merging. So those are the main ingredients. Thanks to Nyx for sorting out the pytorch files btw. ### Recipe So, the recipe. I basically just gradient SLERP'd Frostwind into Frostmaid with these params: - filter: self_attn value: [0.9, 0.6, 0.3, 0, 0] - filter: mlp value: [0.3, 0.6] - value: 0.5 # fallback for rest of tensors ### Tentative Dozen or So Test Conclusion This made a model that was actually pretty much everything I was looking for - NEARLY as smart as Frostwind but with MOST of Frostmaids punchy prose. I tried doing TIES merges and DARE ties merges, but they actually came out worse, because both models have major weaknesses - one is very dry and gpt-ish, the other is a little loose with what's going on. The ties merges tended to bring out those qualities, dare even worse. So I stuck with this. It's not AS smart as Frostwind, so you maybe have to regen a little, but it's pretty smart, and quite creative. A sweet spot hopefully. Maybe someone merge wiser than I can do more with this recipe, but I'm very pleased with it, it did what I was hoping for - a smaller model I can mobile dgpu and produces pretty outsized quality responses (it's fairly zealous tho, be warned). I've only played with it a TINY bit, so there may be qualities or flaws I've missed. Cheers to all the finetuners, mergers and developers without which open source models wouldn't be half of what they are. Resources used: https://huggingface.co/NyxKrage/FrostMaid-10.7B-TESTING-pt https://huggingface.co/Sao10K/Frostwind-10.7B-v1 https://github.com/cg123/mergekit/tree/main
MaziyarPanahi/Dans-07YahooAnswers-7b-Mistral-7B-Instruct-v0.1	MaziyarPanahi	2024-01-16T20:57:04Z	42	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Safetensors", "text-generation-inference", "merge", "7b", "mistralai/Mistral-7B-Instruct-v0.1", "Dans-DiscountModels/Dans-07YahooAnswers-7b", "pytorch", "question-answering", "en", "dataset:PocketDoc/Retro-YahooAnswers", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	question-answering	2024-01-16T20:51:52Z	--- license: apache-2.0 tags: - Safetensors - mistral - text-generation-inference - merge - mistral - 7b - mistralai/Mistral-7B-Instruct-v0.1 - Dans-DiscountModels/Dans-07YahooAnswers-7b - transformers - pytorch - mistral - text-generation - question-answering - en - dataset:PocketDoc/Retro-YahooAnswers - base_model:mistralai/Mistral-7B-v0.1 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us --- # Dans-07YahooAnswers-7b-Mistral-7B-Instruct-v0.1 Dans-07YahooAnswers-7b-Mistral-7B-Instruct-v0.1 is a merge of the following models: * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [Dans-DiscountModels/Dans-07YahooAnswers-7b](https://huggingface.co/Dans-DiscountModels/Dans-07YahooAnswers-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.1 layer_range: [0, 32] - model: Dans-DiscountModels/Dans-07YahooAnswers-7b layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MaziyarPanahi/Dans-07YahooAnswers-7b-Mistral-7B-Instruct-v0.1" 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"]) ```
LoneStriker/openchat-3.5-0106-128k-8.0bpw-h8-exl2	LoneStriker	2024-01-16T20:54:47Z	9	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "openchat", "C-RLFT", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T20:51:43Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> <h1>with 128k context</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [[email protected]], Cheng Sijie [[email protected]], Alpay Ariyak [[email protected]] * We look forward to hearing you and collaborating on this exciting project!
vicgalle/franken-SOLAR-18B-v1.0-GGUF	vicgalle	2024-01-16T20:49:13Z	58	2	null	[ "gguf", "mergekit", "merge", "solar", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:merge:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:upstage/SOLAR-10.7B-Instruct-v1.0", "base_model:merge:upstage/SOLAR-10.7B-Instruct-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-16T19:04:41Z	--- base_model: - upstage/SOLAR-10.7B-Instruct-v1.0 - NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - mergekit - merge - solar - gguf license: apache-2.0 --- # vicgalle/franken-SOLAR-18B-v1.0-GGUF This is a SOLAR-like model upscaled to 18B. It is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. This repo has the quantized GGUF versions from https://huggingface.co/vicgalle/franken-SOLAR-18B-v1.0 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5fad8602b8423e1d80b8a965/mMyHMuuftG71_o4at5suy.png) Evaluations coming soon! This model has very good writing capabilities (compared to SOLAR-10.7B), specially for role-playing. ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) * [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [0, 12] - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [6, 18] - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [13, 25] - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [19, 31] - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [26, 38] - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [32, 44] - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [39, 48] merge_method: passthrough dtype: float16 ``` ### Usage You can use the provided template: ``` tokenizer = AutoTokenizer.from_pretrained("vicgalle/franken-SOLAR-18B-v1.0") model = AutoModelForCausalLM.from_pretrained("vicgalle/franken-SOLAR-18B-v1.0", torch_dtype=torch.float16, load_in_4bit=True) conversation = [ {'role': 'system', 'content': SYSTEM_PROMPT}, {'role': 'user', 'content': USER_PROMPT} ] prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, use_cache=True, max_new_tokens=1024, do_sample=True, temperature=0.8) output_text = tokenizer.decode(outputs[0]) ```
LoneStriker/openchat-3.5-0106-128k-5.0bpw-h6-exl2	LoneStriker	2024-01-16T20:43:18Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "openchat", "C-RLFT", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T20:41:17Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> <h1>with 128k context</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [[email protected]], Cheng Sijie [[email protected]], Alpay Ariyak [[email protected]] * We look forward to hearing you and collaborating on this exciting project!
Xenopilus/electra-base-multiple-choice-v2	Xenopilus	2024-01-16T20:42:37Z	70	0	transformers	[ "transformers", "safetensors", "electra", "multiple-choice", "generated_from_trainer", "base_model:google/electra-base-discriminator", "base_model:finetune:google/electra-base-discriminator", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2024-01-16T20:16:11Z	--- license: apache-2.0 base_model: google/electra-base-discriminator tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: electra-base-multiple-choice-v2 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. --> # electra-base-multiple-choice-v2 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2899 - Accuracy: 0.8954 - Precision: 0.8967 - Recall: 0.8937 - F1: 0.8952 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 269 \| 0.2868 \| 0.8794 \| 0.8738 \| 0.8868 \| 0.8803 \| \| 0.3357 \| 2.0 \| 538 \| 0.2767 \| 0.8939 \| 0.8956 \| 0.8917 \| 0.8937 \| \| 0.3357 \| 3.0 \| 807 \| 0.2899 \| 0.8954 \| 0.8967 \| 0.8937 \| 0.8952 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
LoneStriker/openchat-3.5-0106-128k-3.0bpw-h6-exl2	LoneStriker	2024-01-16T20:31:23Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "openchat", "C-RLFT", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T20:30:00Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> <h1>with 128k context</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [[email protected]], Cheng Sijie [[email protected]], Alpay Ariyak [[email protected]] * We look forward to hearing you and collaborating on this exciting project!
mimicheng/zephyr-7b-sft-qlora-1ep	mimicheng	2024-01-16T20:25:13Z	3	0	peft	[ "peft", "safetensors", "mixtral", "alignment-handbook", "generated_from_trainer", "trl", "sft", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-15T22:03:28Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/ultrachat_200k base_model: mistralai/Mixtral-8x7B-v0.1 model-index: - name: zephyr-7b-sft-qlora-1ep 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. --> # zephyr-7b-sft-qlora-1ep This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 0.9316 ## 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 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9126 \| 1.0 \| 4357 \| 0.9316 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
jeiku/Test25_3B	jeiku	2024-01-16T20:23:07Z	16	0	transformers	[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "mergekit", "merge", "conversational", "custom_code", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:jeiku/Bones_3B", "base_model:merge:jeiku/Bones_3B", "base_model:jeiku/No_Robots_Alpaca_StableLM", "base_model:merge:jeiku/No_Robots_Alpaca_StableLM", "base_model:jeiku/Toxic_DPO_StableLM", "base_model:merge:jeiku/Toxic_DPO_StableLM", "autotrain_compatible", "region:us" ]	text-generation	2024-01-16T20:16:38Z	--- base_model: - jeiku/Bones_3B - jeiku/No_Robots_Alpaca_StableLM - jeiku/Bones_3B - jeiku/Bones_3B - jeiku/Toxic_DPO_StableLM tags: - mergekit - merge --- # remake25 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [jeiku/Bones_3B](https://huggingface.co/jeiku/Bones_3B) as a base. ### Models Merged The following models were included in the merge: * [jeiku/Bones_3B](https://huggingface.co/jeiku/Bones_3B) + [jeiku/No_Robots_Alpaca_StableLM](https://huggingface.co/jeiku/No_Robots_Alpaca_StableLM) * [jeiku/Bones_3B](https://huggingface.co/jeiku/Bones_3B) + [jeiku/Toxic_DPO_StableLM](https://huggingface.co/jeiku/Toxic_DPO_StableLM) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: jeiku/Bones_3B+jeiku/Toxic_DPO_StableLM parameters: weight: 0.25 density: 1 - model: jeiku/Bones_3B+jeiku/No_Robots_Alpaca_StableLM parameters: weight: 0.25 density: 1 - model: jeiku/Bones_3B parameters: weight: 0.50 density: 1 merge_method: dare_ties base_model: jeiku/Bones_3B parameters: dtype: bfloat16 ```
LoneStriker/openchat-3.5-0106-11b-8.0bpw-h8-exl2	LoneStriker	2024-01-16T20:16:09Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "openchat", "C-RLFT", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T20:11:18Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> <h1>with 32k context</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [[email protected]], Cheng Sijie [[email protected]], Alpay Ariyak [[email protected]] * We look forward to hearing you and collaborating on this exciting project!

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