Instructions to use TIGER-Lab/SWE-Next-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/SWE-Next-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TIGER-Lab/SWE-Next-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/SWE-Next-7B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/SWE-Next-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TIGER-Lab/SWE-Next-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/SWE-Next-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/SWE-Next-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/SWE-Next-7B

SGLang

How to use TIGER-Lab/SWE-Next-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TIGER-Lab/SWE-Next-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/SWE-Next-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TIGER-Lab/SWE-Next-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/SWE-Next-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/SWE-Next-7B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/SWE-Next-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SWE-Next: Scalable Real-World Software Engineering Tasks for Agents

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.

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. 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

@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},
}