Instructions to use lixiaoxi45/DeepAgent-QwQ-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lixiaoxi45/DeepAgent-QwQ-32B with Transformers:

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

pipe = pipeline("text-generation", model="lixiaoxi45/DeepAgent-QwQ-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lixiaoxi45/DeepAgent-QwQ-32B")
model = AutoModelForCausalLM.from_pretrained("lixiaoxi45/DeepAgent-QwQ-32B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lixiaoxi45/DeepAgent-QwQ-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lixiaoxi45/DeepAgent-QwQ-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lixiaoxi45/DeepAgent-QwQ-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lixiaoxi45/DeepAgent-QwQ-32B

SGLang

How to use lixiaoxi45/DeepAgent-QwQ-32B 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 "lixiaoxi45/DeepAgent-QwQ-32B" \
    --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": "lixiaoxi45/DeepAgent-QwQ-32B",
		"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 "lixiaoxi45/DeepAgent-QwQ-32B" \
        --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": "lixiaoxi45/DeepAgent-QwQ-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lixiaoxi45/DeepAgent-QwQ-32B with Docker Model Runner:
```
docker model run hf.co/lixiaoxi45/DeepAgent-QwQ-32B
```

lixiaoxi45

nielsr HF Staff commited on Feb 5

Commit

e052d27

1 Parent(s): dcb945f

Add model card and metadata (#1)

Browse files

- Add model card and metadata (11e2109a22c15e03618b3868cf32b2d4f1c713fb)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +42 -3

README.md CHANGED Viewed

@@ -1,3 +1,42 @@
----
-license: apache-2.0
----

+---
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- reasoning
+- tool-use
+- agent
+---
+# DeepAgent: A General Reasoning Agent with Scalable Toolsets
+DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution within a single, coherent reasoning process. It is designed to overcome the limitations of traditional, predefined workflows by maintaining a global perspective on tasks and dynamically discovering tools.
+- **Paper:** [DeepAgent: A General Reasoning Agent with Scalable Toolsets](https://huggingface.co/papers/2510.21618)
+- **Repository:** [GitHub - RUC-NLPIR/DeepAgent](https://github.com/RUC-NLPIR/DeepAgent)
+## Key Features
+- **Unified Agentic Reasoning**: DeepAgent operates in a single stream of thought, autonomously reasoning about the task and discoverying necessary tools.
+- **Autonomous Memory Folding**: To handle long-horizon interactions, DeepAgent introduces a mechanism that compresses past interactions into structured episodic, working, and tool memories, reducing context explosion while preserving critical information.
+- **ToolPO Strategy**: An end-to-end reinforcement learning strategy tailored for general tool use, utilizing LLM-simulated APIs and fine-grained credit assignment for tool invocation.
+## Performance
+Extensive experiments on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank, TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA, HLE), demonstrate that DeepAgent consistently outperforms baselines across both labeled-tool and open-set tool retrieval scenarios.
+## Citation
+If you find this work helpful, please cite the paper:
+```bibtex
+@misc{deepagent,
+      title={DeepAgent: A General Reasoning Agent with Scalable Toolsets},
+      author={Xiaoxi Li and Wenxiang Jiao and Jiarui Jin and Guanting Dong and Jiajie Jin and Yinuo Wang and Hao Wang and Yutao Zhu and Ji-Rong Wen and Yuan Lu and Zhicheng Dou},
+      year={2025},
+      eprint={2510.21618},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2510.21618},
+}
+```