This repository provides a detailed Jupyter Notebook demonstrating how to use and fine-tune large language models (LLMs) like Llama2 and Mistral7B via LangChain. The notebook is designed to guide users through the process of integrating these models with advanced LangChain features.
By the end of this experiment, you will be able to:
- Utilize open-source LLMs such as zephyr-7b-beta, Mistral-7B-Instruct-v0.2, and Llama2 through the Hugging Face Hub with LangChain.
- Understand and implement the concepts of Prompt Templates, Memory, and Output Parsers in LangChain.
Finetune-Llama2-and-Mistral7B-using-Langchain.ipynb: A comprehensive Jupyter Notebook that includes all the code and detailed instructions.
Ensure you have the following installed:
- Python 3.8 or later
- Jupyter Notebook or JupyterLab
transformerslangchain
You can install the necessary Python packages using pip:
pip install jupyterlab transformers langchain- Clone the Repository:
git clone https://github.com/Praveen76/Finetune-Llama2-and-Mistral7B-using-Langchain.git
cd Finetune-Llama2-and-Mistral7B-using-Langchain-
Launch the Jupyter Notebook:
- Open your command line or terminal and run:
jupyter notebook
- Navigate to the
Finetune-Llama2-and-Mistral7B-using-Langchain.ipynbfile and open it.
-
Follow the Notebook Instructions:
- The notebook is structured to guide you step-by-step through setting up the environment, loading the models, and applying LangChain techniques.
Contributions are welcome! Please feel free to submit a pull request or open an issue if you have suggestions or improvements.
This project is licensed under the MIT License.
If you encounter any issues or have suggestions for improvement, please open an issue in the Issues section of this repository.
The code has been tested on Windows system. It should work well on other distributions but has not yet been tested. In case of any issue with installation or otherwise, please contact me on Linkedin
Happy coding!!
I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.