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.
- 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.
To run these notebooks, you need:
- Python 3.8+
- Jupyter Notebook
- Required dependencies as listed in
requirements.txt
-
Clone the repository:
git clone https://github.com/simonpierreboucher/llm_mistral_notebook.git cd llm_mistral_notebook -
Install the dependencies:
pip install -r requirements.txt
- Start Jupyter Notebook: Navigate to the repository folder and launch Jupyter:
jupyter notebook
- Select a Notebook: Open any of the notebooks (Chatbot, RAG, or Text Generation) to explore its functionality.
- Follow Instructions: Each notebook includes setup steps and explanations to guide you through using the model.
- 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.
We welcome contributions! Feel free to submit issues or pull requests to enhance functionality, add features, or fix bugs.
This repository is licensed under the MIT License.