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Goal of the project Thanks to this project I had the opportunity to try some open-source llms such as mistral and llama2. The challenge was to check their prediction capabilities, though we know they are optimized more for reasoning tasks rather than database consulting tasks. I managed to play with different prompt engineering techniques and to generate some improvements from the naive attempts.

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  • data: contains the questions-answers used with the models.
  • notebooks:
    • Creating_questions_dataset: to prepare the dataset for the initial test
    • Collecting_model_responses: script to get the answers from the models
    • Analyzing_model_responses: evaluation of the responses of the initial test
    • Prompt_optimization: which techniques I tried to improved results
    • RAG_optimization: code used to improve results with mistral.
    • Fine_tuning_models: step-by-step explanation on how to perform fine-tuning of the models.

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