Instructions to use SWE-bench/SWE-agent-LM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SWE-bench/SWE-agent-LM-7B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SWE-bench/SWE-agent-LM-7B")
model = AutoModelForCausalLM.from_pretrained("SWE-bench/SWE-agent-LM-7B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SWE-bench/SWE-agent-LM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SWE-bench/SWE-agent-LM-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": "SWE-bench/SWE-agent-LM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SWE-bench/SWE-agent-LM-7B

SGLang

How to use SWE-bench/SWE-agent-LM-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 "SWE-bench/SWE-agent-LM-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": "SWE-bench/SWE-agent-LM-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 "SWE-bench/SWE-agent-LM-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": "SWE-bench/SWE-agent-LM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

SWE-agent LM

Code • Paper • Site

SWE-agent-LM-7B is a Language Model for Software Engineering trained using the SWE-smith toolkit. We introduce this model as part of our work: SWE-smith: Scaling Data for Software Engineering Agents.

SWE-agent-LM-7B is 100% open source. Training this model was simple - we fine-tuned Qwen 2.5 Coder Instruct on 5k trajectories generated by SWE-agent + Claude 3.7 Sonnet. The dataset can be found here.

SWE-agent-LM-7B is compatible with SWE-agent. Running this model locally only takes a few steps! Check here for more instructions on how to do so.

If you found this work exciting and want to push SWE-agents further, please feel free to connect with us (the SWE-bench team) more!