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This repository provides Jupyter notebooks to interact with Mistral Large Language Models (LLMs) for tasks including chatbot development, retrieval-augmented generation, and text generation. These notebooks are designed to help users leverage Mistral models in a range of applications, from conversational AI to content generation.

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simonpierreboucher/llm_mistral_notebook

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LLM Mistral Notebook

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This repository provides Jupyter notebooks to interact with Mistral Large Language Models (LLMs) for tasks including chatbot development, retrieval-augmented generation, and text generation. These notebooks are designed to help users leverage Mistral models in a range of applications, from conversational AI to content generation.

Repository Structure

  • MISTRAL-CHATBOT.ipynb: A notebook for setting up a Mistral-powered chatbot, demonstrating dialogue handling and response generation.
  • MISTRAL-RAG.ipynb: Focuses on Retrieval-Augmented Generation (RAG) with the Mistral model, enabling the retrieval of relevant information before generating responses.
  • MISTRAL-TEXTGEN.ipynb: Demonstrates the text generation capabilities of Mistral models, ideal for creative and informative content generation.

Getting Started

Prerequisites

To run these notebooks, you need:

  • Python 3.8+
  • Jupyter Notebook
  • Required dependencies as listed in requirements.txt

Installation

  1. Clone the repository:

    git clone https://github.com/simonpierreboucher/llm_mistral_notebook.git
    cd llm_mistral_notebook
  2. Install the dependencies:

    pip install -r requirements.txt

Running the Notebooks

  1. Start Jupyter Notebook: Navigate to the repository folder and launch Jupyter:
    jupyter notebook
  2. Select a Notebook: Open any of the notebooks (Chatbot, RAG, or Text Generation) to explore its functionality.
  3. Follow Instructions: Each notebook includes setup steps and explanations to guide you through using the model.

Use Cases

  • MISTRAL-CHATBOT: For building chatbots and virtual assistants that engage users in dialogue.
  • MISTRAL-RAG: Suitable for applications needing accurate, source-based responses, such as customer support and information retrieval systems.
  • MISTRAL-TEXTGEN: Useful for content creation, story generation, or tasks requiring high-quality text generation.

Contributing

We welcome contributions! Feel free to submit issues or pull requests to enhance functionality, add features, or fix bugs.

License

This repository is licensed under the MIT License.

About

This repository provides Jupyter notebooks to interact with Mistral Large Language Models (LLMs) for tasks including chatbot development, retrieval-augmented generation, and text generation. These notebooks are designed to help users leverage Mistral models in a range of applications, from conversational AI to content generation.

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