初始化项目，由ModelHub XC社区提供模型

Model: TIGER-Lab/SWE-Next-7B
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: mit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+datasets:
+- TIGER-Lab/SWE-Next-SFT-Trajectories
+- TIGER-Lab/SWE-Next
+---
+
+<div align="center">
+  <h1>SWE-Next: Scalable Real-World Software Engineering Tasks for Agents</h1>
+</div>
+
+<div align="center">
+  <a href="https://arxiv.org/abs/2603.20691"><img alt="Paper" src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white"></a>
+  <a href="https://tiger-ai-lab.github.io/SWE-Next/"><img alt="Project Page" src="https://img.shields.io/badge/Project%20Page-Website-4285F4?style=for-the-badge&logo=googlechrome&logoColor=white"></a>
+  <a href="https://github.com/TIGER-AI-Lab/SWE-Next"><img alt="Code" src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white"></a>
+  <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next-SFT-Trajectories"><img alt="SFT Trajs" src="https://img.shields.io/badge/SFT%20Trajs-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+  <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next"><img alt="Dataset" src="https://img.shields.io/badge/Dataset-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+  <a href="https://huggingface.co/TIGER-Lab/SWE-Next-7B"><img alt="Model 7B" src="https://img.shields.io/badge/Model%207B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+  <a href="https://huggingface.co/TIGER-Lab/SWE-Next-14B"><img alt="Model 14B" src="https://img.shields.io/badge/Model%2014B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+</div>
+
+# SWE-Next-7B
+
+SWE-Next-7B is a repository-level software engineering agent fine-tuned from **Qwen/Qwen2.5-Coder-7B-Instruct** on the released **SWE-Next SFT Trajectories**. The model is trained with full-parameter supervised fine-tuning on execution-grounded trajectories collected from real merged pull requests and validated repository environments.
+
+## Introduction
+
+SWE-Next introduces reusable **repo-quarter profiles**, which reuse the same environment across nearby commits in time while keeping each task run separate and reproducible. Using only **30 hours** and **639GB** of environment storage, SWE-Next processes **3,971** seed repositories and **102,582** candidate commit pairs mined from real merged PRs to construct a dataset of **2,308** self-verifying instances. SWE-Next improves downstream pass@1 on SWE-Bench Verified and SWE-Bench Lite with fewer or comparable training trajectories, making large-scale executable data collection far more practical and accessible for research.
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/TIGER-AI-Lab/SWE-Next/main/docs/static/images/teaser.png" alt="SWE-Next teaser" width="100%" style="max-width: 900px; border-radius: 8px; box-shadow: 0 4px 10px rgba(0,0,0,0.1);">
+</div>
+
+## Model Overview
+
+This model is trained on **3,693** selected SFT trajectories derived from the SWE-Next collection. The training data emphasizes clean repository-level repair traces and recovery-style debugging trajectories rather than isolated code-completion examples.
+
+Training recipe summary:
+
+- **Base model**: `Qwen/Qwen2.5-Coder-7B-Instruct`
+- **Finetuning**: full-parameter SFT
+- **Context length**: 32,768
+- **Learning rate**: 1e-5
+- **Scheduler**: cosine
+- **Dataset**: `TIGER-Lab/SWE-Next-SFT-Trajectories`
+
+## Usage
+
+For full usage details, please refer to the official [SWE-Next GitHub repository](https://github.com/TIGER-AI-Lab/SWE-Next). The repository provides the complete setup and evaluation workflow for released models, including:
+
+- environment and dependency installation,
+- dataset and trajectory downloads,
+- training configurations for the 7B and 14B models,
+- vLLM serving commands and repository-level evaluation scripts.
+
+In particular, the GitHub repo contains the exact commands used to serve SWE-Next-7B and evaluate it on SWE-Bench-style tasks under the SWE-Next execution interface.
+
+## Relationship to the SWE-Next Release
+
+This repo contains the released **7B** model checkpoint. Related artifacts are available separately:
+
+- **Base task dataset**: `TIGER-Lab/SWE-Next`
+- **SFT trajectories**: `TIGER-Lab/SWE-Next-SFT-Trajectories`
+- **Companion model**: `TIGER-Lab/SWE-Next-14B`
+- **Project code**: `github.com/TIGER-AI-Lab/SWE-Next`
+
+## Citation
+
+```bibtex
+@misc{liang2026swenextscalablerealworldsoftware,
+      title={SWE-Next: Scalable Real-World Software Engineering Tasks for Agents},
+      author={Jiarong Liang and Zhiheng Lyu and Zijie Liu and Xiangchao Chen and Ping Nie and Kai Zou and Wenhu Chen},
+      year={2026},
+      eprint={2603.20691},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2603.20691},
+}
+```

added_tokens.json

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "dtype": "bfloat16",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 28,
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+  "use_cache": false,
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+  "vocab_size": 152064
+}

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