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Model: princeton-nlp/SWE-Llama-13b
Source: Original Platform

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README.md

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+language: en 
+
+datasets:
+- 37 popular Python code repositories
+- See princeton-nlp/SWE-bench train split
+- 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.
+
+---
+
+# SWE-Llama
+
+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).
+
+## Model Details
+
+- **Architecture:** Transformer, based on [CodeLlama](https://arxiv.org/abs/2308.12950) architecture 
+- **Parameters:** 7 billion for SWE-Llama-7b, 13 billion for SWE-Llama-13b
+- **Objective:** Generating patches to resolve GitHub issues, conditioned on issue description and code context
+
+## Training Data
+
+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.
+
+## Training Procedure
+
+- Fine-tuned only the attention matrices using LoRA method
+- Trained for 4 epochs with a batch size of 32
+- Selected best checkpoint based on validation perplexity
+
+## Evaluation Results
+
+When evaluated on the SWE-bench benchmark:
+
+- SWE-Llama-7b achieved 3.0% issue resolution rate using oracle context retrieval
+- SWE-Llama-13b achieved 4.0% issue resolution rate using oracle context retrieval
+
+## BibTeX Entry
+```tex
+@misc{jimenez2023swebench,
+      title={SWE-bench: Can Language Models Resolve Real-World GitHub Issues?}, 
+      author={Carlos E. Jimenez and John Yang 
+        and Alexander Wettig and Shunyu Yao 
+        and Kexin Pei and Ofir Press and Karthik Narasimhan},
+      year={2023},
+      eprint={2310.06770},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "_name_or_path": "princeton-nlp/SWE-Llama-13b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.34.0.dev0"
+}

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special_tokens_map.json

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+{
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tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
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+  "padding_side": "right",
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+}