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RichardErkhov/
ajibawa-2023_-_Code-Mistral-7B-gguf

Name: ajibawa-2023_-_Code-Mistral-7B-gguf
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Quantization made by Richard Erkhov.

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Code-Mistral-7B - GGUF

Model creator: https://huggingface.co/ajibawa-2023/
Original model: https://huggingface.co/ajibawa-2023/Code-Mistral-7B/

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

Original model description:

language:

en license: apache-2.0 tags:
code
mathematics datasets:
ajibawa-2023/Code-290k-ShareGPT
m-a-p/Code-Feedback
microsoft/orca-math-word-problems-200k
teknium/openhermes model-index:
name: Code-Mistral-7B results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 64.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 85.29 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 65.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 54.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 82.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 68.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B name: Open LLM Leaderboard

Code-Mistral-7B

This Model is trained on refined version of my dataset Code-290k-ShareGPT.

Besides this it is trained on following datasets:

Code-Feedback

orca-math-word-problems-200k

Openhermes

The idea was to check how this Model will perform with both Code & Maths datasets. This model is very good with Coding. Maths is still hit & miss but you can test out this model.

This Model is trained on massive datasets so the results are very good. I have used ChatML prompt format.

Kindly note this is qLoRA version, a rare exception.

GGUF & Exllama

GGUF: Link

Exllama v2: Link

Special Thanks to Bartowski for quantizing this model.

Training:

Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took almost 33 Hours. Axolotl codebase was used for training purpose. Entire data is trained on Mistral.

Example Prompt: This model uses ChatML prompt format.

<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

You can modify above Prompt as per your requirement.

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

C++

Error Resolving

Matrices

Machine Learning

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.97
AI2 Reasoning Challenge (25-Shot)	64.59
HellaSwag (10-Shot)	85.29
MMLU (5-Shot)	65.00
TruthfulQA (0-shot)	54.64
Winogrande (5-shot)	82.24
GSM8k (5-shot)	68.08

RichardErkhov/ajibawa-2023_-_Code-Mistral-7B-gguf

Original model description:

Open LLM Leaderboard Evaluation Results

RichardErkhov/
ajibawa-2023_-_Code-Mistral-7B-gguf