allenai/SERA-14B
SERA-14B is a 14 billion parameter open-source coding agent developed by Allen Institute for AI (Ai2), part of their Open Coding Agents series. Built on a Qwen 3-14B base model with a 32K token context length, it is specifically fine-tuned for automated software engineering tasks like bug fixes and feature implementation. The model achieves 41.7% on SWE-bench Verified, demonstrating strong performance in resolving real-world software issues.
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SERA-14B: A Specialized Coding Agent
SERA-14B, developed by Allen Institute for AI (Ai2), is a 14 billion parameter open-source coding agent designed for automated software engineering. It is the fifth model in Ai2's Open Coding Agents series, built upon a Qwen 3-14B base model and fine-tuned using a GLM-4.6 teacher model.
Key Capabilities & Performance
- High Performance on SWE-bench: Achieves 41.7% on SWE-bench Verified with a 32K context length, outperforming or matching several larger models in its class.
- Specialized Training: Trained on 25,000 synthetic coding agent trajectories generated via Soft Verified Generation (SVG), which allows for data generation from any repository without test infrastructure.
- Automated Software Engineering: Excels at tasks such as bug fixes, feature implementation, and code refactoring.
Intended Use Cases
- Automated Software Engineering: Ideal for automating various development tasks within a codebase.
- Repository Specialization: Can be fine-tuned on private codebases to create highly specialized coding agents.
- Research: Useful for studying coding agents, data generation methods, and agent behavior.
Limitations
- Primarily validated on SWE-bench Verified (Python repositories); performance on other languages is unknown.
- May attempt to call a nonexistent
submittool, though thesera-clihandles this. - Performance is largely bounded by the GLM-4.6 teacher model's capabilities.
- Like other LLMs, it may generate insecure or incorrect code, requiring human review and testing.