Introducing the Unit-Mesh AI Efficiency Research Project
The Unit-Mesh AI Efficiency Research Project, also known as the "Do-It-Yourself LoRA Training for AI Development Productivity Enhancement," is an innovative endeavor exploring how AI can be leveraged to boost research and development efficiency. The project delves into the workings of large language models like ChatGPT and their growing influence on engineering and software development.
Objective of the Project
The primary goal of this project is to explore and document methods for enhancing R&D efficiency through AI. The team has employed techniques to train Low-Rank Adaptation (LoRA) models such as LLaMA LoRA and ChatGLM LoRA. Through this initiative, the project provides valuable insights into AI-driven productivity enhancements in software engineering.
Project Deliverables
The project outcomes include:
- Training tutorials and guidelines for LoRA models.
- Pre-trained LoRA models ready for use.
- Training datasets and records of the training process.
- Video content demonstrating various applications and benefits of these models.
Project Components
Training Notebooks
- LLaMA Alpaca LoRA - A detailed guide on training LoRA for the LLaMA model.
- ChatGLM Tuning LoRA - Instructions on tuning ChatGLM with LoRA.
Videos
- Code Assistance Generation: Instructional content on how AI can assist in code generation.
- Test Code Generation: Videos explaining AI-assisted test code creation.
- SQL Text Transformation: Demonstrations of AI converting plain text to SQL queries.
- LoRA Showdown: A comparison between ChatGLM and LLaMA in generating requirement documents.
Key Features
The project is structured to standardize processes across various dimensions of R&D efficiency, producing detailed steps for each task to ensure precision. Examples include:
- Task splitting and user story generation.
- Code and test generation.
- Text to code transformation.
- Database query generation using AI.
Data Preparation
Data preparation involves straightforward instructions aligned with the project needs, integrating:
- Domain knowledge instructions.
- Task split instructions.
- Requirements detailing via AI.
- Code and test generation inputs.
Training and Results
The training phase includes various experiments such as:
- Training based on Meta's LLaMA for different use cases (e.g., testing code generation).
- Utilizing Tsinghua University's ChatGLM to further AI capability in user story and code generation.
Thanks to Our Supporters
The project acknowledges the contributions from AIOS Club for providing OpenAI keys and OpenBayes for offering cloud GPU resources crucial for the project’s development and execution.
Conclusion
The Unit-Mesh AI Efficiency Research Project represents a significant step toward harnessing AI for more efficient software development practices. As the research progresses, it continues to offer promising methodologies that can revolutionize how tasks are automated and streamlined in programming and software engineering fields.