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--- |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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datasets: |
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- jondurbin/airoboros-2.2.1 |
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- Open-Orca/OpenOrca |
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- garage-bAInd/Open-Platypus |
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- ehartford/samantha-data |
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- CollectiveCognition/chats-data-2023-09-27 |
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- stingning/ultrachat |
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tags: |
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- llama-2 |
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- code |
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license: llama2 |
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model-index: |
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- name: SpeechlessCoder |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: openai_humaneval |
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name: HumanEval |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 0.0 |
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verified: false |
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--- |
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<p><h1> speechless-mistral-six-in-one-7b </h1></p> |
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This model is a merge of 6 SOTA Mistral-7B based models: |
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- ehartford/dolphin-2.1-mistral-7b |
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- Open-Orca/Mistral-7B-OpenOrca |
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- bhenrym14/mistral-7b-platypus-fp16 |
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- ehartford/samantha-1.2-mistral-7b |
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- iteknium/CollectiveCognition-v1.1-Mistral-7B |
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- HuggingFaceH4/zephyr-7b-alpha |
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[Model benchmark](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b/discussions/1) by [sethuiyer](https://huggingface.co/sethuiyer) . Thanks a lot. |
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> I tested the Q6_0 version of the model against LLaMa2 70B chat and here are the results - Scoring as per ChatGPT and Bard's average. Named this model Mixtral. Questions taken from MT-Benchmark. |
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> |
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> On a scale of 0 to 100, I would rate Mixtral at 98. Here's why: |
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> |
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> - Intellect (100/100) - Mixtral has demonstrated immense intellectual abilities through its comprehensive knowledge and logical reasoning skills. |
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> - Creativity (98/100) - In addition to being highly intelligent, Mixtral also displays impressive creative talents through its unique, nuanced responses. |
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> - Adaptability (98/100) - Mixtral can converse flexibly on a wide variety of topics, adapting smoothly based on contextual cues. |
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> - Communication (97/100) - Mixtral communicates clearly and eloquently through written language, thoroughly answering questions. |
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> - Problem-Solving (98/100) - Questions are addressed comprehensively, considering multiple perspectives to arrive at well-thought solutions. |
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> - Personability (97/100) - Responses are warm, inviting and non-threatening due to Mixtral's kindness and thoughtfulness. |
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> |
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> Overall, a very capable model for it's size. |
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Code: https://github.com/uukuguy/speechless |
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## HumanEval |
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| Metric | Value | |
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| --- | --- | |
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| humaneval-python | | |
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[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard) |
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CodeLlama-34B-Python: 53.29 |
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CodeLlama-34B-Instruct: 50.79 |
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CodeLlama-13B-Instruct: 50.6 |
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CodeLlama-34B: 45.11 |
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CodeLlama-13B-Python: 42.89 |
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CodeLlama-13B: 35.07 |
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Mistral-7B-v0.1: 30.488 |
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## LM-Evaluation-Harness |
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[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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| Metric | Value | |
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| --- | --- | |
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| ARC | 62.97 | |
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| HellaSwag | 84.6| |
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| MMLU | 63.29 | |
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| TruthfulQA | 57.77 | |
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| Winogrande | 77.51 | |
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| GSM8K | 18.42 | |
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| DROP | 9.13 | |
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| Average | 53.38 | |
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# Model Card for Mistral-7B-v0.1 |
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The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. |
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Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. |
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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/). |
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## Model Architecture |
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Mistral-7B-v0.1 is a transformer model, with the following architecture choices: |
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- Grouped-Query Attention |
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- Sliding-Window Attention |
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- Byte-fallback BPE tokenizer |
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## Troubleshooting |
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- If you see the following error: |
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`` |
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KeyError: 'mistral' |
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`` |
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- Or: |
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`` |
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NotImplementedError: Cannot copy out of meta tensor; no data! |
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`` |
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Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. |
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## Notice |
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Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms. |
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## The Mistral AI Team |
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Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-six-in-one-7b) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 53.38 | |
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| ARC (25-shot) | 62.97 | |
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| HellaSwag (10-shot) | 84.6 | |
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| MMLU (5-shot) | 63.29 | |
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| TruthfulQA (0-shot) | 57.77 | |
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| Winogrande (5-shot) | 77.51 | |
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| GSM8K (5-shot) | 18.42 | |
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| DROP (3-shot) | 9.13 | |
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