Instructions to use openbmb/AgentCPM-Report with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/AgentCPM-Report with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/AgentCPM-Report", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/AgentCPM-Report", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use openbmb/AgentCPM-Report with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openbmb/AgentCPM-Report

SGLang

How to use openbmb/AgentCPM-Report 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 "openbmb/AgentCPM-Report" \
    --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": "openbmb/AgentCPM-Report",
		"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 "openbmb/AgentCPM-Report" \
        --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": "openbmb/AgentCPM-Report",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/AgentCPM-Report with Docker Model Runner:
```
docker model run hf.co/openbmb/AgentCPM-Report
```

Kaguya-19 commited on Feb 9

Commit

5ee2b04

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1 Parent(s): 8d4168d

Update README.md

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@@ -11,6 +11,7 @@ tags:
 <a href='https://huggingface.co/openbmb/AgentCPM-Report-GGUF'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report--GGUF-yellow'>
 <a href='https://github.com/OpenBMB/AgentCPM'><img src='https://img.shields.io/badge/GitHub-AgentCPM-blue?logo=github'>
 <a href='https://github.com/OpenBMB/UltraRAG'><img src='https://img.shields.io/badge/GitHub-UltraRAG-blue?logo=github'>
 </p>
 ## Links & Resources
@@ -343,10 +344,13 @@ Advisors: Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
 If **AgentCPM-Report** is helpful for your research, please cite it as follows:
 ```bibtex
-@software{AgentCPMReport2026,
-  title  = {AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch},
-  author = {Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun},
-  year   = {2026},
-  url    = {https://github.com/OpenBMB/AgentCPM}
 }
 ```

 <a href='https://huggingface.co/openbmb/AgentCPM-Report-GGUF'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report--GGUF-yellow'>
 <a href='https://github.com/OpenBMB/AgentCPM'><img src='https://img.shields.io/badge/GitHub-AgentCPM-blue?logo=github'>
 <a href='https://github.com/OpenBMB/UltraRAG'><img src='https://img.shields.io/badge/GitHub-UltraRAG-blue?logo=github'>
+<a href='https://arxiv.org/abs/2602.06540'><img src='https://img.shields.io/badge/arXiv-2602.06540-red'>
 </p>
 ## Links & Resources
 If **AgentCPM-Report** is helpful for your research, please cite it as follows:
 ```bibtex
+@misc{li2026agentcpmreport,
+      title={AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research},
+      author={Yishan Li and Wentong Chen and Yukun Yan and Mingwei Li and Sen Mei and Xiaorong Wang and Kunpeng Liu and Xin Cong and Shuo Wang and Zhong Zhang and Yaxi Lu and Zhenghao Liu and Yankai Lin and Zhiyuan Liu and Maosong Sun},
+      year={2026},
+      eprint={2602.06540},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2602.06540},
 }
 ```