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RichardErkhov/
uukuguy_-_speechless-mistral-six-in-one-7b-gguf

Name: uukuguy_-_speechless-mistral-six-in-one-7b-gguf
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speechless-mistral-six-in-one-7b - GGUF

Model creator: https://huggingface.co/uukuguy/
Original model: https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b/

Name	Quant method	Size
speechless-mistral-six-in-one-7b.Q2_K.gguf	Q2_K	2.53GB
speechless-mistral-six-in-one-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
speechless-mistral-six-in-one-7b.IQ3_S.gguf	IQ3_S	2.96GB
speechless-mistral-six-in-one-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
speechless-mistral-six-in-one-7b.IQ3_M.gguf	IQ3_M	3.06GB
speechless-mistral-six-in-one-7b.Q3_K.gguf	Q3_K	3.28GB
speechless-mistral-six-in-one-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
speechless-mistral-six-in-one-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
speechless-mistral-six-in-one-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
speechless-mistral-six-in-one-7b.Q4_0.gguf	Q4_0	3.83GB
speechless-mistral-six-in-one-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
speechless-mistral-six-in-one-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
speechless-mistral-six-in-one-7b.Q4_K.gguf	Q4_K	4.07GB
speechless-mistral-six-in-one-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
speechless-mistral-six-in-one-7b.Q4_1.gguf	Q4_1	4.24GB
speechless-mistral-six-in-one-7b.Q5_0.gguf	Q5_0	4.65GB
speechless-mistral-six-in-one-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
speechless-mistral-six-in-one-7b.Q5_K.gguf	Q5_K	4.78GB
speechless-mistral-six-in-one-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
speechless-mistral-six-in-one-7b.Q5_1.gguf	Q5_1	5.07GB
speechless-mistral-six-in-one-7b.Q6_K.gguf	Q6_K	5.53GB
speechless-mistral-six-in-one-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

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

speechless-mistral-six-in-one-7b

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 by 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

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

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 and release blog post.

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

Detailed results can be found here

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

RichardErkhov/uukuguy_-_speechless-mistral-six-in-one-7b-gguf