Welcome to the Open-Source-LLMs repository! This project is designed to help developers, students, and AI enthusiasts prototype and experiment with Large Language Models (LLMs). The notebooks provided are optimized for fast prototyping on platforms like Google Colab, making it accessible to users with small laptops or limited hardware.
This repository offers a collection of notebooks focused on:
- Building Chatbot Systems: Rapid prototyping of chatbots using LLMs.
- LLM Fine-Tuning: Adapting pre-trained models to specific tasks using smaller datasets.
- Vector Databases: Efficiently managing and retrieving high-dimensional vector data to enhance search capabilities.
- Building LLM Agents: Autonomous systems capable of performing tasks and interacting with users.
- Retriever-Augmented Generation (RAG): Combining information retrieval with text generation for more accurate and contextually relevant results.
All notebooks are compatible with Google Colab, enabling users to quickly get started without needing a high-performance local machine.
- Fast Prototyping: Ready-to-use Google Colab notebooks for immediate testing and learning.
- Easy Fine-Tuning: Demonstrates how to fine-tune LLMs on custom datasets with minimal resources.
- Vector Search: Implementation of vector databases for efficient retrieval of semantic information.
- LLM Agents: Build autonomous agents that interact with users and perform tasks using LLMs.
- RAG: Explore the power of Retriever-Augmented Generation for enhancing language models with external data.
/notebooks: Contains all the Colab-ready notebooks, categorized by topics such as fine-tuning, chatbot systems, vector databases, LLM agents, and RAG./data: Sample datasets for testing and experimentation./models: Pre-trained models and instructions on fine-tuning them.
git clone https://github.com/kondwain0099/Open-Source-LLMs.git
cd Open-Source-LLMsThe notebooks are designed to run on Google Colab, so you don’t need heavy computational resources. Just follow these steps:
- Navigate to the
/notebooksfolder. - Choose the notebook you're interested in (e.g.,
Chatbot_Prototype.ipynb). - Open the notebook in Google Colab by clicking the Open in Colab badge at the top of the notebook.
Simply run the cells in the notebook. If a GPU is needed, make sure to enable it by navigating to Runtime > Change runtime type > Set Hardware accelerator to GPU.
We welcome contributions from the community! Whether you want to improve the code, add new features, or enhance the documentation, we encourage you to contribute.
- Fork the repository.
- Create a new branch (
git checkout -b feature/YourFeature). - Commit your changes (
git commit -m 'Add YourFeature'). - Push to the branch (
git push origin feature/YourFeature). - Open a Pull Request and describe the changes you have made.
- New Features: If you’re adding a new feature, make sure to include a relevant example or notebook.
- Bug Fixes: If you're fixing a bug, include detailed steps on how to reproduce it and describe how your fix resolves the issue.
- Documentation: All new features or changes must include clear and concise documentation.
- Testing: Ensure that your changes are properly tested. If you're contributing a new feature, add unit tests to verify its functionality.
- Python: The language used throughout the project.
- Hugging Face Transformers: For working with LLMs.
- Google Colab: Fast prototyping environment.
- FAISS: For vector database implementations.
- Flask: For deploying small prototypes as web applications.
To get the most out of this repository, check out the following resources:
- Google Colab Documentation
- Hugging Face Transformers Documentation
- Vector Databases with FAISS
- Retriever-Augmented Generation (RAG) Models
Join our growing community of contributors and learners! For discussions, questions, or sharing your work, join us in the Discussions section or open an Issue for any suggestions or concerns.
Let’s build and explore the power of LLMs together! 🚀
This project is licensed under the MIT License - see the LICENSE file for details.
By making it simple to get started, contribute, and collaborate, this README will help you build a thriving open-source project!