Project Overview: LLMs From Scratch - Hands-on Building Your Own Large Language Models
The project titled "LLMs From Scratch" is a practical guide developed by Datawhale aimed at helping individuals build large language models (LLMs) from the ground up. This initiative caters to developers and researchers who aspire to gain a deep understanding of the core technologies used in creating large language models, similar to ChatGPT. By providing comprehensive guides, code examples, and deep learning resources, the project seeks to empower individuals with the necessary skills to construct and comprehend the architecture of LLMs.
Project Highlights
- Comprehensive Learning Path: The project offers a systematic approach that covers both theoretical concepts and practical coding exercises.
- Hands-on Approach: Emphasis is placed on gaining skills in LLM development and training through practical experience.
- Focus on LLM Architecture: Unlike many resources that concentrate on fine-tuning and deployment, this project prioritizes the detailed implementation of large model architecture.
Main Components
(1) Foundational Knowledge
The project includes a detailed tutorial on building GPT-like large language models (LLMs) from scratch, which is based on "rasbt/LLMs-from-scratch." Special acknowledgment goes to @rasbt for their valuable insights.
- Code Implementation: Encompasses all the necessary code for creating GPT-like LLMs, covering encoding, pre-training, and fine-tuning processes.
- Step-by-Step Learning: The tutorial guides learners through the process of creating their own LLM using clear text, charts, and examples.
- Educational Purpose: Primarily developed for educational use, this guide assists learners in training and developing small, functional models akin to large foundational models like ChatGPT.
- Easy-to-Understand Code: Simplifies the building process of large models using concise notebook code, even for those with only basic knowledge of PyTorch.
- In-depth Understanding: The project enables readers to gain a detailed understanding of how large language models operate.
The project also features a clearly structured chapter arrangement, each designed to progressively build knowledge and skills in LLM development.
(2) Discussion and Construction of Model Architecture
- Support for Multiple Large Models: The project covers the architecture discussion and implementation of several large models, including ChatGLM, Llama, RWKV, and others.
- Detailed Architecture Analysis: Each model’s configuration files, training scripts, and core code are thoroughly explored to help learners grasp the inner mechanisms of these models.
Contribution and Community
The project encourages collaboration and participation:
- For those who wish to contribute, upcoming tasks are regularly updated and can be found in the project's Issue section.
- Feedback and problem reporting are welcomed through GitHub Issues.
- Interested individuals are also invited to engage in discussions about the project via Discussions.
Target Audience
- Technical Background: Suitable for those with a programming background, especially developers and researchers interested in LLMs.
- Learning Goals: Ideal for learners who want to understand LLM workings and are willing to start building and training their own LLMs from scratch.
- Application Areas: Relevant to those interested in natural language processing and artificial intelligence, as well as those seeking to apply LLMs in educational or research contexts.
Roadmap
Future plans are communicated through issue forms, enabling the community to stay updated about the project's trajectory.
Contributors
A dedicated group of contributors, detailed in a contributors list, plays a vital role in the tutorial part of the project, showcasing a collaborative effort from academia and industry professionals.
License
The project is licensed under a Creative Commons license, ensuring that it can be freely used and shared within the stipulated non-commercial and attribution-based guidelines.
For more updates and information, interested individuals can follow the project on the Datawhale platform and become part of a growing community eager to understand and construct large language models.