Instructions to use wavespeed/MiniCPM-V-4_5-abliterated-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wavespeed/MiniCPM-V-4_5-abliterated-int4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="wavespeed/MiniCPM-V-4_5-abliterated-int4", trust_remote_code=True)
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("wavespeed/MiniCPM-V-4_5-abliterated-int4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wavespeed/MiniCPM-V-4_5-abliterated-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wavespeed/MiniCPM-V-4_5-abliterated-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wavespeed/MiniCPM-V-4_5-abliterated-int4",
		"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/wavespeed/MiniCPM-V-4_5-abliterated-int4

SGLang

How to use wavespeed/MiniCPM-V-4_5-abliterated-int4 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 "wavespeed/MiniCPM-V-4_5-abliterated-int4" \
    --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": "wavespeed/MiniCPM-V-4_5-abliterated-int4",
		"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 "wavespeed/MiniCPM-V-4_5-abliterated-int4" \
        --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": "wavespeed/MiniCPM-V-4_5-abliterated-int4",
		"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 wavespeed/MiniCPM-V-4_5-abliterated-int4 with Docker Model Runner:
```
docker model run hf.co/wavespeed/MiniCPM-V-4_5-abliterated-int4
```

About this repository

A 4-bit (bitsandbytes NF4) quantization of huihui-ai/Huihui-MiniCPM-V-4_5-abliterated, published by WaveSpeed AI.

Note what the base model is: abliterated means the upstream author ablated the refusal direction out of MiniCPM-V-4.5. This model will therefore answer prompts the original declines, and it is on you to put your own safety layer in front of it. If you want the original behaviour, quantize openbmb/MiniCPM-V-4_5 instead.

MiniCPM-V-4.5-abliterated-int4

This is a 4-bit quantized version of huihui-ai/Huihui-MiniCPM-V-4_5-abliterated using bitsandbytes NF4 quantization.

Model Details

Base Model: huihui-ai/Huihui-MiniCPM-V-4_5-abliterated
Quantization: 4-bit (NF4) using bitsandbytes
Model Size: ~6.4 GB (85.8% reduction from original 45.28 GB)
Compute dtype: float16
Double quantization: Disabled for better performance

Quantization Configuration

{
  "load_in_4bit": true,
  "bnb_4bit_compute_dtype": "float16",
  "bnb_4bit_quant_type": "nf4",
  "bnb_4bit_use_double_quant": false,
  "llm_int8_skip_modules": ["out_proj", "kv_proj", "lm_head"],
  "quant_method": "bitsandbytes"
}

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "wavespeed/MiniCPM-V-4_5-abliterated-int4",
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.float16
)

tokenizer = AutoTokenizer.from_pretrained(
    "wavespeed/MiniCPM-V-4_5-abliterated-int4",
    trust_remote_code=True
)

Requirements

transformers
bitsandbytes
torch
accelerate

Note on File Size

The model files appear large (~6.4 GB) despite being 4-bit quantized. This is expected behavior for bitsandbytes quantization, which stores weights in a format that enables efficient on-the-fly dequantization during inference. The actual memory usage during runtime will be significantly lower than the file size suggests.