Instructions to use openbmb/MiniCPM-V-4.6-Thinking-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM-V-4.6-Thinking-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-V-4.6-Thinking-AWQ")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/MiniCPM-V-4.6-Thinking-AWQ", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM-V-4.6-Thinking-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM-V-4.6-Thinking-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM-V-4.6-Thinking-AWQ

SGLang

How to use openbmb/MiniCPM-V-4.6-Thinking-AWQ 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/MiniCPM-V-4.6-Thinking-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/MiniCPM-V-4.6-Thinking-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openbmb/MiniCPM-V-4.6-Thinking-AWQ with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM-V-4.6-Thinking-AWQ
```

tc-mb commited on May 19

Commit

84abb54

verified ·

1 Parent(s): 49fc612

update readme

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README.md +2 -0

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@@ -7,6 +7,8 @@ tags:
 - On-Device Model
 - lightweight
 library_name: transformers
 ---
 > **This repository hosts the AWQ (W4A16, AutoAWQ) quantized version of [MiniCPM-V 4.6 Thinking](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking).** For the original BF16 weights and the full model card, please refer to [openbmb/MiniCPM-V-4.6-Thinking](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking).

 - On-Device Model
 - lightweight
 library_name: transformers
+base_model: openbmb/MiniCPM-V-4.6-Thinking
+base_model_relation: quantized
 ---
 > **This repository hosts the AWQ (W4A16, AutoAWQ) quantized version of [MiniCPM-V 4.6 Thinking](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking).** For the original BF16 weights and the full model card, please refer to [openbmb/MiniCPM-V-4.6-Thinking](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking).