| | --- |
| | 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** |
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| | This Model is trained on refined version of my dataset [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT). |
| | Besides this it is trained on following datasets: |
| |
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| | [Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) |
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| | [orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k) |
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| | [Openhermes](https://huggingface.co/datasets/teknium/openhermes) |
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| | 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. |
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| | This Model is trained on massive datasets so the results are very good. |
| | I have used ChatML prompt format. |
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|
| | Kindly note this is qLoRA version, a rare exception. |
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| | **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. |
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| | I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development. |
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| | Thank you for your love & support. |
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| | **Example Output** |
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| | **C++** |
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| | **Error Resolving** |
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| | **Matrices** |
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| | **Machine Learning** |
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