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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/AgentCPM-Report", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Jan 20

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@@ -34,6 +34,9 @@ AgentCPM-Report is an open-source large language model agent jointly developed b
 - **Physical Isolation, Local Security**: Specifically designed for high-privacy scenarios, it supports fully offline and agile local deployment, completely eliminating the risk of cloud data leaks. Leveraging our UltraRAG framework, it efficiently mounts and understands your local private knowledge base, securely transforming core confidential data into highly valuable professional decision-making reports without ever leaving its domain.
 ## Demo Cases
 **You can watch our demo video here [Demo](https://www.youtube.com/watch?v=d5XWONt0PWo) 🔗**

 - **Physical Isolation, Local Security**: Specifically designed for high-privacy scenarios, it supports fully offline and agile local deployment, completely eliminating the risk of cloud data leaks. Leveraging our UltraRAG framework, it efficiently mounts and understands your local private knowledge base, securely transforming core confidential data into highly valuable professional decision-making reports without ever leaving its domain.
 ## Demo Cases
+<div align="center">
+  <a href="https://www.youtube.com/watch?v=d5XWONt0PWo"><img src="https://img.youtube.com/vi/d5XWONt0PWo/0.jpg", width=70%></a>
+</div>
 **You can watch our demo video here [Demo](https://www.youtube.com/watch?v=d5XWONt0PWo) 🔗**