Efficient Code Search with Nvidia DGX
a year ago
- #AI
- #NVIDIA
- #Software Development
- Large language models (LLMs) help write code faster but face limitations with complex tasks.
- Qodo is an AI-powered platform enhancing software quality with code writing, testing, and review agents.
- Qodo's AI operates with deep contextual awareness, understanding code intent, patterns, and structure.
- Qodo uses retrieval-augmented generation (RAG) and a specialized code embedding model trained on NVIDIA DGX.
- The platform maintains a fresh index of codebases for accurate code and test generation.
- Qodo implements language-specific chunking to handle large code files effectively.
- Qodo's embedding model improves code retrieval by focusing on syntax, dependencies, and API usage.
- The embedding model was trained using NVIDIA DGX 8x A100 80GB nodes for efficiency.
- Qodo fine-tuned Qodo-Embed-1-1.5B and Qodo-Embed-1-7B models, achieving SOTA accuracy.
- A case study with NVIDIA showed improved code search accuracy using Qodo's components.
- Qodo's models are available on Hugging Face for experimentation.