Instructions to use openbmb/MiniCPM4.1-8B-AutoAWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM4.1-8B-AutoAWQ with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM4.1-8B-AutoAWQ", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM4.1-8B-AutoAWQ", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use openbmb/MiniCPM4.1-8B-AutoAWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM4.1-8B-AutoAWQ"
# 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/MiniCPM4.1-8B-AutoAWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM4.1-8B-AutoAWQ

SGLang

How to use openbmb/MiniCPM4.1-8B-AutoAWQ 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/MiniCPM4.1-8B-AutoAWQ" \
    --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/MiniCPM4.1-8B-AutoAWQ",
		"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/MiniCPM4.1-8B-AutoAWQ" \
        --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/MiniCPM4.1-8B-AutoAWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM4.1-8B-AutoAWQ with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM4.1-8B-AutoAWQ
```

guanwenyu1995 commited on Sep 5, 2025

Commit

ac2f1ac

verified ·

1 Parent(s): be97c31

Update tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +1 -0

tokenizer_config.json CHANGED Viewed

@@ -102,6 +102,7 @@
     "<|fim_middle|>",
     "<|fim_suffix|>"
   ],
   "bos_token": "<s>",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",

     "<|fim_middle|>",
     "<|fim_suffix|>"
   ],
+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% if enable_thinking is defined and enable_thinking is false %}{{ '<think>\n\n</think>\n' }}{% endif %}{% endif %}",
   "bos_token": "<s>",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",