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
• int8
• 8-bit
• bitsandbytes
• abliterated base_model_relation: quantized

About this repository

An 8-bit (bitsandbytes LLM.int8()) 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-int8

This is an 8-bit quantized version of huihui-ai/Huihui-MiniCPM-V-4_5-abliterated using bitsandbytes int8 quantization.

Model Details

• Base Model: huihui-ai/Huihui-MiniCPM-V-4_5-abliterated
• Quantization: 8-bit integer using bitsandbytes
• Model Size: ~9.35 GB (79.4% reduction from original 45.28 GB)
• Compute dtype: float16
• Quantization method: LLM.int8() with mixed-precision decomposition

Quantization Configuration

{
  "load_in_8bit": true,
  "bnb_8bit_compute_dtype": "float16",
  "bnb_8bit_quant_type": "int8",
  "llm_int8_skip_modules": ["lm_head", "vision"],
  "llm_int8_threshold": 6.0,
  "quant_method": "bitsandbytes"
}

Key Features

• Mixed Precision: Uses int8 for weights with fp16 for activations
• Outlier Management: Automatically handles outliers in fp16 for better accuracy
• Selective Quantization: Skips critical modules (lm_head, vision) to preserve quality
• Better accuracy than int4: While larger than 4-bit, provides significantly better quality

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "wavespeed/MiniCPM-V-4_5-abliterated-int8",
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.float16
)

tokenizer = AutoTokenizer.from_pretrained(
    "wavespeed/MiniCPM-V-4_5-abliterated-int8",
    trust_remote_code=True
)

# For inference
# The model will automatically use int8 weights with fp16 compute

Requirements

• transformers>=4.35.0
• bitsandbytes>=0.41.0
• torch>=2.0.0
• accelerate>=0.20.0
• CUDA-capable GPU (int8 quantization requires CUDA)

Performance Notes

• Memory Usage: ~9.35 GB VRAM required
• Speed: Slightly slower than fp16 due to dequantization overhead
• Quality: Better preservation of model quality compared to 4-bit quantization
• Best for: Users who need better quality than 4-bit but still want memory savings

Comparison with Other Quantizations

License

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

Acknowledgments

• Original model by huihui-ai
• Quantization using bitsandbytes LLM.int8() method