# Chain-of-Zoom 4-bit Complete Pipeline Usage

## 🚀 Quick Start

```python
# Install requirements
pip install transformers accelerate bitsandbytes torch diffusers

# Load VLM component
from transformers import BitsAndBytesConfig, Qwen2VLForConditionalGeneration, Qwen2VLProcessor
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4", 
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Load quantized VLM
vlm_model = Qwen2VLForConditionalGeneration.from_pretrained(
    "humbleakh/qwen2.5-vl-3b-4bit-chain-of-zoom",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

vlm_processor = Qwen2VLProcessor.from_pretrained(
    "humbleakh/qwen2.5-vl-3b-4bit-chain-of-zoom",
    trust_remote_code=True
)

# Load other components from their respective repos...
```

## 📋 Components

- **VLM**: [humbleakh/qwen2.5-vl-3b-4bit-chain-of-zoom](https://huggingface.co/humbleakh/qwen2.5-vl-3b-4bit-chain-of-zoom)
- **Diffusion**: [humbleakh/stable-diffusion-3-4bit-chain-of-zoom](https://huggingface.co/humbleakh/stable-diffusion-3-4bit-chain-of-zoom)  
- **RAM**: [humbleakh/ram-swin-large-4bit-chain-of-zoom](https://huggingface.co/humbleakh/ram-swin-large-4bit-chain-of-zoom)

## 💾 Memory Usage

- **Original**: ~12GB VRAM
- **Quantized**: ~3GB VRAM  
- **Reduction**: 75%
- **Compatible**: Google Colab T4 GPU

## 🎯 Implementation

See the complete notebook for full Chain-of-Zoom implementation with quantized models.