Instructions to use 88plug/MiniCPM-o-4.5-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 88plug/MiniCPM-o-4.5-W4A16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="88plug/MiniCPM-o-4.5-W4A16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("88plug/MiniCPM-o-4.5-W4A16", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 88plug/MiniCPM-o-4.5-W4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "88plug/MiniCPM-o-4.5-W4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "88plug/MiniCPM-o-4.5-W4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/88plug/MiniCPM-o-4.5-W4A16

SGLang

How to use 88plug/MiniCPM-o-4.5-W4A16 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 "88plug/MiniCPM-o-4.5-W4A16" \
    --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": "88plug/MiniCPM-o-4.5-W4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "88plug/MiniCPM-o-4.5-W4A16" \
        --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": "88plug/MiniCPM-o-4.5-W4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use 88plug/MiniCPM-o-4.5-W4A16 with Docker Model Runner:
```
docker model run hf.co/88plug/MiniCPM-o-4.5-W4A16
```

88plug-bot commited on 13 days ago

Commit

c721cee

verified ·

1 Parent(s): 24928ee

Update model card

Browse files

Files changed (1) hide show

README.md +16 -1

README.md CHANGED Viewed

@@ -13,13 +13,16 @@ tags:
 - tts
 - quantized
 - int4
 - w4a16
 - 4-bit
 - compressed-tensors
 - vllm
 - text-generation
 - ptq
 - autoround
 pipeline_tag: text-generation
 library_name: transformers
 model_type: minicpmo
@@ -63,6 +66,8 @@ Note: The non-quantized modal encoders (SigLIP2 ~1 GB, Whisper ~390 MB, CosyVoic
 ## Quick Start
 ### vLLM — text output
 ```bash
@@ -238,4 +243,14 @@ llama-server \
 ## About
-Produced by [88plug AI Lab](https://huggingface.co/88plug) — zero-loss quantizations of frontier omni and voice models.

 - tts
 - quantized
 - int4
+- INT4
 - w4a16
 - 4-bit
 - compressed-tensors
 - vllm
 - text-generation
+- conversational
 - ptq
 - autoround
+- llmcompressor
 pipeline_tag: text-generation
 library_name: transformers
 model_type: minicpmo
 ## Quick Start
+Tested with **vLLM v0.21.0** (`vllm/vllm-openai:v0.21.0-cu129-ubuntu2404`). Weights are in **compressed-tensors** format — vLLM detects and loads quantization automatically. No `--quantization` flag needed.
 ### vLLM — text output
 ```bash
 ## About
+[**88plug AI Lab**](https://huggingface.co/88plug) produces production-grade compressed-tensors quantizations of frontier LLMs, VLMs, and omni models — built for native vLLM v0.21.0+ deployment with zero extra flags.
+**W8A16** — INT8 weights + BF16 activations. Near-lossless on any Ampere+ GPU. Runs where FP8 hardware cannot.
+**W4A16** — AutoRound with iters=200 and a mixed calibration corpus. Targets ≥ 99% MMLU recovery — the quality bar that makes W4A16 viable for production.
+All weights are in compressed-tensors format. vLLM detects quantization automatically from `quantization_config` in `config.json`. No `--quantization` flag required.
+**Also available:** [MiniCPM-o-4.5-W8A16 (INT8, ~9 GB)](https://huggingface.co/88plug/MiniCPM-o-4.5-W8A16) · [MiniCPM-o-4.5-W4A16 (INT4, ~4–5 GB)](https://huggingface.co/88plug/MiniCPM-o-4.5-W4A16)
+Browse all releases → [huggingface.co/88plug](https://huggingface.co/88plug)