SERA-8B is the third model in Ai2's Open Coding Agents series. It is a state-of-the-art 8B open-source coding agent that achieves 31.7% on SWE-bench Verified.
SWE-bench training artifact: The model was trained on SWE-bench-style tasks and may attempt to call a nonexistent submit tool when finished editing. The sera-cli proxy handles this automatically.
Evaluation scope: Only validated on SWE-bench Verified (Python repositories). Performance on other languages or benchmarks is unknown.
Teacher bound: Performance is largely bounded by the teacher model (GLM-4.6) capability.
Statistical variance: Results computed over 3 seeds. Effects smaller than 2-3% should be interpreted with caution.
Model-specific: Experiments use Qwen 3 as the base model. Generalization to other model families is not validated.
Like any language model without safety filtering, SERA can be prompted to generate harmful or insecure code. Users should be aware of the following risks:
Code security: May generate code with security vulnerabilities (e.g., injection attacks, insecure defaults). All generated code should be reviewed before deployment.
Accuracy: May produce incorrect or buggy code. Outputs should be tested and verified.
Inherited biases: May reflect biases present in the Qwen 3-8B base model and GLM-4.6 teacher model.
Misuse potential: Could potentially be used to generate malicious code or identify vulnerabilities for exploitation.
This model is licensed under Apache 2.0. It is intended for research and educational use and may be used commercially in accordance with Ai2's Responsible Use Guidelines.
@misc{shen2026serasoftverifiedefficientrepository,
title={SERA: Soft-Verified Efficient Repository Agents},
author={Ethan Shen and Danny Tormoen and Saurabh Shah and Ali Farhadi and Tim Dettmers},
year={2026},
eprint={2601.20789},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.20789},
}
Bias, Risks, and Limitations SERA-32B/SERA-8B is an open coding agent model released for research and educational purposes without any safety filtering or safety tuning. As a research artifact, this model is not suitable for real-world use without significant human oversight. Like other coding agents, this model may propagate biases present in training data or generate incorrect or insecure code. Security risks include prompt injection and data leakage. Always verify code outputs and manage context windows to avoid disclosing sensitive data or information.
Bias, Risks, and Limitations Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified. License This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
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allenai <huggingface.co/allenai>Newsgroups: alt.freeweights.olmoSubject: 
