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README.md
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<p align="center">
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<a href="https://github.com/SWE-bench/SWE-smith">Code</a>
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<a href="
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<a href="https://swesmith.com/">Site</a>
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</p>
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SWE-agent-LM-32B is 100% open source.
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Training this model was simple - we fine-tuned Qwen 2.5 Coder Instruct on 5k trajectories generated by SWE-agent + Claude 3.7 Sonnet.
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The dataset can be found [here](https://huggingface.co/datasets/SWE-bench/SWE-smith-
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SWE-agent-LM-32B is compatible with [SWE-agent](https://github.com/SWE-agent/SWE-agent).
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Running this model locally only takes a few steps!
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Check [here](
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If you found this work exciting and want to push SWE-agents further, please feel free to connect with us (the [SWE-bench team](https://swe-bench.github.io/)) more!
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<p align="center">
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<a href="https://github.com/SWE-bench/SWE-smith">Code</a>
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<a href="">Paper</a>
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<a href="https://swesmith.com/">Site</a>
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</p>
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SWE-agent-LM-32B is 100% open source.
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Training this model was simple - we fine-tuned Qwen 2.5 Coder Instruct on 5k trajectories generated by SWE-agent + Claude 3.7 Sonnet.
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The dataset can be found [here](https://huggingface.co/datasets/SWE-bench/SWE-smith-trajs-250429).
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SWE-agent-LM-32B is compatible with [SWE-agent](https://github.com/SWE-agent/SWE-agent).
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Running this model locally only takes a few steps!
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Check [here]() for more instructions on how to do so.
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If you found this work exciting and want to push SWE-agents further, please feel free to connect with us (the [SWE-bench team](https://swe-bench.github.io/)) more!
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