49 lines
1.9 KiB
Markdown
49 lines
1.9 KiB
Markdown
language: en
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datasets:
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- 37 popular Python code repositories
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- See princeton-nlp/SWE-bench train split
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- See the `make_datasets` documentation on SWE-bench's [GitHub](https://github.com/princeton-nlp/SWE-bench/tree/main/inference/make_datasets) for details on formatting input.
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---
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# SWE-Llama
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SWE-Llama are variants of the [CodeLlama](https://arxiv.org/abs/2308.12950) model fine-tuned on software engineering tasks extracted from real-world GitHub issues and pull requests. They were introduced and evaluated on the SWE-bench benchmark in this [paper](https://arxiv.org/abs/2310.06770).
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## Model Details
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- **Architecture:** Transformer, based on [CodeLlama](https://arxiv.org/abs/2308.12950) architecture
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- **Parameters:** 7 billion for SWE-Llama-7b, 13 billion for SWE-Llama-13b
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- **Objective:** Generating patches to resolve GitHub issues, conditioned on issue description and code context
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## Training Data
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SWE-Llama was fine-tuned on 19,000 issues and pull requests collected from 37 popular Python code repositories on GitHub, disjoint from those used in SWE-bench.
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## Training Procedure
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- Fine-tuned only the attention matrices using LoRA method
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- Trained for 4 epochs with a batch size of 32
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- Selected best checkpoint based on validation perplexity
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## Evaluation Results
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When evaluated on the SWE-bench benchmark:
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- SWE-Llama-7b achieved 3.0% issue resolution rate using oracle context retrieval
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- SWE-Llama-13b achieved 4.0% issue resolution rate using oracle context retrieval
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## BibTeX Entry
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```tex
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@misc{jimenez2023swebench,
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title={SWE-bench: Can Language Models Resolve Real-World GitHub Issues?},
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author={Carlos E. Jimenez and John Yang
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and Alexander Wettig and Shunyu Yao
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and Kexin Pei and Ofir Press and Karthik Narasimhan},
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year={2023},
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eprint={2310.06770},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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``` |