OpenHands LM 7B v0.1
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Context Window | 128K tokens |
| Model URL | https://huggingface.co/all-hands/openhands-lm-7b-v0.1 |
| Author | all-hands |
What is openhands-lm-7b-v0.1?
OpenHands LM 7B is a compact version of the larger 32B model, specifically designed for software development tasks. It follows the same training recipe as its larger counterpart but with a more accessible parameter count, making it suitable for developers with limited computational resources. The model maintains a generous 128K token context window, enabling it to handle large codebases and extended software engineering tasks.
Implementation Details
The model is built on specialized fine-tuning processes using training data generated by OpenHands itself on diverse open-source repositories. It employs an RL-based framework outlined in SWE-Gym, where training data is generated using existing agents and fine-tuned on successfully resolved examples.
- Built for local deployment and execution
- Optimized for GitHub issue resolution
- Compatible with OpenAI-style endpoints
- Deployable through SGLang or vLLM for optimal performance
Core Capabilities
- Software development task automation
- GitHub issue resolution
- Code generation and modification
- Large codebase handling with 128K context window
- Local deployment capability
Frequently Asked Questions
Q: What makes this model unique?
This model offers a balance between performance and resource requirements, making it accessible for local deployment while maintaining core capabilities for software development tasks. It's particularly noteworthy for being open-source and running efficiently on consumer hardware.
Q: What are the recommended use cases?
The model is best suited for GitHub issue resolution and software development tasks. However, it may perform less optimally on varied software engineering tasks and may be sensitive to quantization. It's ideal for developers looking for a locally-deployable solution for code-related tasks.
