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metadata
license: apache-2.0
datasets:
  - ajibawa-2023/Code-290k-ShareGPT
  - m-a-p/Code-Feedback
  - microsoft/orca-math-word-problems-200k
  - teknium/openhermes
language:
  - en
tags:
  - code
  - mathematics

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.

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++

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Error Resolving

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Matrices

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Machine Learning

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