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Model: uukuguy/speechless-mistral-six-in-one-7b
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- jondurbin/airoboros-2.2.1
+- Open-Orca/OpenOrca
+- garage-bAInd/Open-Platypus
+- ehartford/samantha-data
+- CollectiveCognition/chats-data-2023-09-27
+- stingning/ultrachat
+tags:
+- llama-2
+- code
+license: llama2
+model-index:
+- name: SpeechlessCoder
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.0
+      verified: false
+---
+
+<p><h1> speechless-mistral-six-in-one-7b </h1></p>
+
+This model is a merge of 6 SOTA Mistral-7B based models:
+- ehartford/dolphin-2.1-mistral-7b
+- Open-Orca/Mistral-7B-OpenOrca
+- bhenrym14/mistral-7b-platypus-fp16
+- ehartford/samantha-1.2-mistral-7b
+- iteknium/CollectiveCognition-v1.1-Mistral-7B
+- HuggingFaceH4/zephyr-7b-alpha
+
+
+[Model benchmark](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b/discussions/1) by [sethuiyer](https://huggingface.co/sethuiyer) . Thanks a lot.
+> 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.
+>
+> On a scale of 0 to 100, I would rate Mixtral at 98. Here's why:
+>
+> - Intellect (100/100) - Mixtral has demonstrated immense intellectual abilities through its comprehensive knowledge and logical reasoning skills.
+> - Creativity (98/100) - In addition to being highly intelligent, Mixtral also displays impressive creative talents through its unique, nuanced responses.
+> - Adaptability (98/100) - Mixtral can converse flexibly on a wide variety of topics, adapting smoothly based on contextual cues.
+> - Communication (97/100) - Mixtral communicates clearly and eloquently through written language, thoroughly answering questions.
+> - Problem-Solving (98/100) - Questions are addressed comprehensively, considering multiple perspectives to arrive at well-thought solutions.
+> - Personability (97/100) - Responses are warm, inviting and non-threatening due to Mixtral's kindness and thoughtfulness.
+>
+> Overall, a very capable model for it's size.
+
+Code: https://github.com/uukuguy/speechless
+
+## HumanEval
+
+| Metric | Value |
+| --- | --- |
+| humaneval-python |  |
+
+[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
+
+CodeLlama-34B-Python: 53.29
+
+CodeLlama-34B-Instruct: 50.79
+
+CodeLlama-13B-Instruct: 50.6
+
+CodeLlama-34B: 45.11
+
+CodeLlama-13B-Python: 42.89
+
+CodeLlama-13B: 35.07 
+
+Mistral-7B-v0.1: 30.488
+
+## LM-Evaluation-Harness
+
+[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+
+| Metric | Value |
+| --- | --- |
+| ARC | 62.97 |
+| HellaSwag | 84.6|
+| MMLU | 63.29 |
+| TruthfulQA | 57.77 |
+| Winogrande | 77.51 |
+| GSM8K | 18.42 |
+| DROP | 9.13 |
+| Average | 53.38 |
+
+
+
+
+
+
+
+
+# Model Card for Mistral-7B-v0.1
+
+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
+
+## Troubleshooting
+
+- If you see the following error:
+``
+KeyError: 'mistral'
+``
+- Or:
+``
+NotImplementedError: Cannot copy out of meta tensor; no data!
+``
+
+Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
+
+## Notice
+
+Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
+
+## The Mistral AI Team
+ 
+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.`
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-six-in-one-7b)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 53.38   |
+| ARC (25-shot)         | 62.97          |
+| HellaSwag (10-shot)   | 84.6    |
+| MMLU (5-shot)         | 63.29         |
+| TruthfulQA (0-shot)   | 57.77   |
+| Winogrande (5-shot)   | 77.51   |
+| GSM8K (5-shot)        | 18.42        |
+| DROP (3-shot)         | 9.13         |

added_tokens.json

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+}

config.json

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+{
+  "_name_or_path": "/opt/local/llm_models/huggingface.co/mistralai/Mistral-7B-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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special_tokens_map.json

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+{
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tokenizer_config.json

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+{
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+}