Qodo-Embed-1-7B
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Embedding Dimension | 3584 |
| Max Input Tokens | 32,000 |
| Model Type | Code Embedding Model |
| Hub URL | https://huggingface.co/Qodo/Qodo-Embed-1-7B |
What is Qodo-Embed-1-7B?
Qodo-Embed-1-7B is a cutting-edge code embedding model specifically engineered for software development retrieval tasks. As the larger variant of the Qodo-Embed-1 family, it represents a significant advancement in code understanding and retrieval capabilities, outperforming existing open-source models on the COIR and MTEB leaderboards while maintaining a relatively compact architecture.
Implementation Details
The model features a robust architecture with 7B parameters and generates high-dimensional embeddings of 3584 dimensions. It can process inputs up to 32,000 tokens, making it suitable for handling large code snippets and documentation. The implementation requires transformers>=4.39.2 and flash_attn>=2.5.6, and supports integration through both SentenceTransformers and HuggingFace Transformers APIs.
- Extensive programming language support including Python, C++, C#, Go, Java, Javascript, PHP, Ruby, and Typescript
- Optimized for both natural language-to-code and code-to-code retrieval tasks
- Efficient implementation with state-of-the-art performance metrics
Core Capabilities
- Advanced code search functionality
- Retrieval-augmented generation (RAG) for code-related tasks
- Contextual understanding across multiple programming languages
- High-dimensional embedding generation for precise code similarity matching
- Support for large context windows enabling comprehensive code analysis
Frequently Asked Questions
Q: What makes this model unique?
The model combines state-of-the-art performance with a relatively compact architecture, achieving superior results on standard benchmarks while supporting an extensive range of programming languages and maintaining a large context window of 32k tokens.
Q: What are the recommended use cases?
The model excels in code search applications, retrieval-augmented generation systems, and any scenarios requiring semantic understanding of code across multiple programming languages. It's particularly effective for building developer tools, code search engines, and intelligent coding assistants.
