base_model: huihui-ai/Huihui-MiniCPM-V-4_5-abliterated
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:

• minicpm-v
• multimodal
• vision-language
• quantized
• nf4
• 4-bit
• bitsandbytes
• abliterated
  base_model_relation: quantized

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.

License

Same as the original model - please refer to the base model's license.

Acknowledgments

• Original model by huihui-ai
• Quantization approach inspired by openbmb/MiniCPM-V-4_5-int4