Add model card metadata and align 14B card layout

README.md

@@ -1,6 +1,21 @@
-# SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
-
-<p align="left">
+---
+license: mit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-Coder-14B-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>
@@ -8,178 +23,70 @@
   <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>
-</p>
-
-## 📰 News
-
-- **2026-04-07**: SWE-Next is now publicly released!
+</div>
 
-## 📖 Introduction
+# SWE-Next-14B
 
-**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.
+SWE-Next-14B is a repository-level software engineering agent fine-tuned from **Qwen/Qwen2.5-Coder-14B-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.
 
-## ✨ Highlights
+<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>
 
-- **Scaled Environment Generation** — SWE-Next is an execution-grounded framework that turns real merged-PR commits into self-verifying SWE tasks, and pairs them with high-signal trajectories.
+## Model Overview
 
-- **Repo-quarter Profiles** - A reusable environment mechanism that amortizes build and storage cost across temporally nearby commits, substantially reducing resource requirements and accelerating large-scale executable SWE data collection.
+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:
 
-## 🛠️ Setup
+- **Base model**: `Qwen/Qwen2.5-Coder-14B-Instruct`
+- **Finetuning**: full-parameter SFT
+- **Context length**: 32,768
+- **Learning rate**: 1e-5
+- **Scheduler**: cosine
+- **Dataset**: `TIGER-Lab/SWE-Next-SFT-Trajectories`
 
-### Prerequisites
+## Usage
 
-- Python 3.10+
-- Docker (for environment execution)
-- [uv](https://github.com/astral-sh/uv) package manager
+Transformers:
 
-### Installation
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-source $HOME/.local/bin/env
-
-git clone https://github.com/TIGER-AI-Lab/SWE-Next.git
-cd SWE-Next
-uv venv && source .venv/bin/activate
-uv sync && uv pip install -e .
+model_id = "TIGER-Lab/SWE-Next-14B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
 ```
 
-## 🤗 Data & Models
-
-Pre-built artifacts are available on HuggingFace. Download them into `data/` before running the pipeline:
-
-| Artifact | Description | Download |
-|----------|-------------|---------|
-| `packages_python_filtered` | 3,900+ Python package list used as pipeline input | `huggingface-cli download TIGER-Lab/packages_python_filtered --repo-type dataset --local-dir data/packages_python_filtered` |
-| `new_commit_better_repos` | Repos with confirmed NEW_COMMIT_BETTER commits | `huggingface-cli download TIGER-Lab/new_commit_better_repos --repo-type dataset --local-dir data/new_commit_better_repos` |
-| `SWE-Next` | Final curated dataset (2,308 instances) | `huggingface-cli download TIGER-Lab/SWE-Next --repo-type dataset --local-dir data/SWE-Next` |
-| `SWE-Next-SFT-Trajectories` | SFT training trajectories | `huggingface-cli download TIGER-Lab/SWE-Next-SFT-Trajectories --repo-type dataset --local-dir data/SWE-Next-SFT-Trajectories` |
-
-Pre-trained models:
-
-| Model | Download |
-|-------|---------|
-| SWE-Next-7B | `huggingface-cli download TIGER-Lab/SWE-Next-7B --repo-type model --local-dir LlamaFactory/saves/SWE_Next_7B` |
-| SWE-Next-14B | `huggingface-cli download TIGER-Lab/SWE-Next-14B --repo-type model --local-dir LlamaFactory/saves/SWE_Next_14B` |
-
-## 🐳 Environment Generation
-
-SWE-Next extends environment generation to 3,900+ Python packages.
-
-The supported package list is maintained in [`data/packages_python_filtered/packages_python_filtered.csv`](data/packages_python_filtered/packages_python_filtered.csv) and target repositories in [`data/new_commit_better_repos/new_commit_better_repos.csv`](data/new_commit_better_repos/new_commit_better_repos.csv).
-
-## 🚀 Data Pipeline (One-Click)
-
-`run_pr_pipeline.zsh` automates the full data collection pipeline. It reads `data/packages_python_filtered/packages_python_filtered.csv`, clones the repos automatically, and processes them end-to-end. If the CSV is not present it falls back to repos already cloned under `outputs/upstream_repos/`.
-
-**Prerequisites:** copy `.env.template` to `.env` and fill in your credentials:
-```
-OPENAI_API_KEY=...        # required for synthetic issue generation
-GITHUB_TOKEN=...          # required for fetching PRs
-DOCKERHUB_USERNAME=...    # required for pushing Docker images
-DOCKERHUB_TOKEN=...
-DOCKERHUB_NAMESPACE=...   # your Docker Hub namespace
-```
-
-**Option 1 — Dataset only** (runs until `outputs/all_new_commit_better_pr.jsonl` is produced, no trajectories):
-```bash
-PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh
-```
-
-**Option 2 — Dataset + trajectories** (continues to run GPT-5-mini on the collected instances):
-```bash
-PR_GEN_TRAJ=1 PR_TRAJ_LLM_NAME=gpt-5-mini zsh run_pr_pipeline.zsh
-```
+vLLM:
 
-To process a specific repo only:
 ```bash
-PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh owner/repo
+vllm serve TIGER-Lab/SWE-Next-14B --served-model-name SWE-Next-14B --port 8000
 ```
 
-## 🏋️ Training
+## Relationship to the SWE-Next Release
 
-### Step 1 — Generate SFT Trajectories
-
-Download the SWE-Next dataset first (see [Data & Models](#data--models)), then collect trajectories using a frontier LLM:
-
-```bash
-python src/swenext/agenthub/run/edit.py runagent_multiple \
-  --dataset "data/SWE-Next/SWE_Next_dataset.jsonl" \
-  --traj_dir "./traj/swe_next_sft" \
-  --max_workers 8 \
-  --k -1 \
-  --llm_name "gpt-5-mini" \
-  --use_fn_calling True \
-  --temperature 0.2 \
-  --max_steps 40 \
-  --backend "docker"
-```
-
-Or skip this step and use the pre-collected trajectories from HuggingFace (download `SWE-Next-SFT-Trajectories` above).
-
-### Step 2 — SFT Training
-
-Clone [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) into the project root first:
-
-```bash
-git clone https://github.com/hiyouga/LLaMA-Factory.git LlamaFactory
-```
-
-Install LlamaFactory dependencies, then train (run from the project root):
-
-```bash
-cd LlamaFactory && pip install -e ".[torch,metrics]" && cd ..
-
-# Train 7B agent
-llamafactory-cli train train/swe_next_7B.yaml
-
-# Train 14B agent
-llamafactory-cli train train/swe_next_14B.yaml
-```
-
-Trained model checkpoints will be saved to `LlamaFactory/saves/SWE_Next_7B` and `LlamaFactory/saves/SWE_Next_14B`.
-
-### Step 3 — Evaluate on SWE-Bench Verified
-
-Start a vLLM server with the trained model, then run evaluation:
-
-```bash
-# Start vLLM server (in a separate terminal)
-vllm serve LlamaFactory/saves/SWE_Next_7B \
-  --served-model-name SWE-Next-7B \
-  --port 8000
-
-# Run evaluation on SWE-Bench Verified (8 parallel workers)
-export LLM_BASE_URL="http://127.0.0.1:8000/v1"
-
-python src/swenext/agenthub/run/edit.py runagent_multiple \
-  --dataset "R2E-Gym/SWE-Bench-Verified" \
-  --split "test" \
-  --traj_dir "./traj/swe_bench_verified" \
-  --max_workers 8 \
-  --k -1 \
-  --llm_name "openai/SWE-Next-7B" \
-  --use_fn_calling False \
-  --temperature 1 \
-  --max_steps 40 \
-  --backend "docker"
-```
+This repo contains the released **14B** model checkpoint. Related artifacts are available separately:
 
-> Use the official [SWE-Bench evaluation harness](https://github.com/SWE-bench/SWE-bench) for final reported scores.
+- **Base task dataset**: `TIGER-Lab/SWE-Next`
+- **SFT trajectories**: `TIGER-Lab/SWE-Next-SFT-Trajectories`
+- **Companion model**: `TIGER-Lab/SWE-Next-7B`
+- **Project code**: `github.com/TIGER-AI-Lab/SWE-Next`
 
-## 📝 Citation
+## Citation
 
 ```bibtex
 @misc{liang2026swenextscalablerealworldsoftware,
-      title={SWE-Next: Scalable Real-World Software Engineering Tasks for Agents}, 
+      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}, 
+      url={https://arxiv.org/abs/2603.20691},
 }
 ```