Instructions to use agentica-org/DeepSWE-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use agentica-org/DeepSWE-Preview with Transformers:

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

pipe = pipeline("text-generation", model="agentica-org/DeepSWE-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("agentica-org/DeepSWE-Preview")
model = AutoModelForCausalLM.from_pretrained("agentica-org/DeepSWE-Preview")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use agentica-org/DeepSWE-Preview with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/agentica-org/DeepSWE-Preview

SGLang

How to use agentica-org/DeepSWE-Preview 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 "agentica-org/DeepSWE-Preview" \
    --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": "agentica-org/DeepSWE-Preview",
		"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 "agentica-org/DeepSWE-Preview" \
        --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": "agentica-org/DeepSWE-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use agentica-org/DeepSWE-Preview with Docker Model Runner:
```
docker model run hf.co/agentica-org/DeepSWE-Preview
```

michaelzhiluo commited on Jul 2, 2025

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Update README.md

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README.md CHANGED Viewed

@@ -65,9 +65,9 @@ DeepSWE-Preview is trained on top of Qwen3-32B with thinking mode enabled. With
 Discover more about DeepSWE-Preview's development and capabilities in our [technical blog post](https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art[%E2%80%A6]-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33).
 <div style="margin: 0 auto;">
-  <img src="./figures/swebench.png" style="width: 100%;" />
   <p align="center" style="margin-top: 8px; font-style: italic; color: #666;">
-    Figure 1: SWE-Bench-Verified Performance vs. Model Size for LLM Agents. Trained with only reinforcement learning (RL, no SFT), DeepSWE-Preview with test time scaling (TTS) solves 59% of problems, beating all open-source agents by a large margin. We note that DeepSWE-Preview's Pass@1 performance (42.1%, averaged over 16 runs) is one of best for open-weights coding agents.
   </p>
 </div>

 Discover more about DeepSWE-Preview's development and capabilities in our [technical blog post](https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art[%E2%80%A6]-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33).
 <div style="margin: 0 auto;">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/654037be97949fd2304aab7f/FbSSr0XQRYfnoiczStZ-E.png" style="width: 100%;" />
   <p align="center" style="margin-top: 8px; font-style: italic; color: #666;">
+    Figure 1: SWE-Bench-Verified Performance vs. Model Size for LLM Agents. Trained with only reinforcement learning (RL, no SFT), DeepSWE-Preview with test time scaling (TTS) solves 59% of problems, beating all open-source agents by a large margin. We note that DeepSWE-Preview's Pass@1 performance (42.2%, averaged over 16 runs) is one of best for open-weights coding agents.
   </p>
 </div>