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license: mit
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## Finetuned Model For My Thesis: Design And Implementation Of An Adaptive Virtual Intelligent Teaching Assistant Based On Supervised Fine-tuning Of A Pre-trained Large Language Model
### Model Name: CodeOptimus - Adaptive Supervised Instruction Fine-tuning [Mistral 7B Instruct](https://mistral.ai/news/announcing-mistral-7b/) using qLora.

## Prerequisites For Reproduction
1. **GPU**: Requires powerful GPUs - I used 7 Nvidia A100s.
2. **Train Time**: 1 week.
3. **RAG Module**: Updates the knowledge base of the model in real-time with adaptive features learned from conversations with the model over time..
4. **Python Packages**: Install requirements.txt.
5. **Dataset**: Download [code_instructions_122k_alpaca_style](https://huggingface.co/datasets/TokenBender/code_instructions_122k_alpaca_style) plus some custom curated dataset
6. **Mistra-7B-Instruct-v0.1**: Download [mistralai/Mistral-7B-Instruct-v0.1 ](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)  pytorch bin weights
7. **Realistic 3D Intelligent Persona/Avatar (Optional)**: For this I'm using soulmachine's digital humans.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6438a9027de34e8ea7e4b257/UJtAiKejhrmUPN5EiA59E.png)