deepseek-ocr-int8-uniform

DeepSeek-OCR INT8 Quantized (Safetensors)

Uniformly quantized version of DeepSeek-OCR using safetensors format + JSON metadata.

📊 Model Stats

• Original Size: 6363.12 MB
• Compressed Size: 3351.56 MB
• Saved Size: 3352.37 MB
• Compression Ratio: 1.90x
• Format: Safetensors + JSON metadata

🚀 Quick Start

from model_loader import load_quantized_model

model = load_quantized_model(
    "SamMikaelson/deepseek-ocr-int8-uniform",
    device="cuda"
)

# Model is ready to use!
# Memory footprint: ~3352 MB

🔧 Manual Loading

import torch
from safetensors.torch import load_file
import json

# Load weights
state_dict = load_file("model.safetensors")

# Load metadata
with open("quantization_config.json") as f:
    metadata = json.load(f)

# Reconstruct model (see model_loader.py for details)

📦 File Structure

model.safetensors          # 3352 MB - All weights (compressed)
quantization_config.json   # Layer metadata (bits, shapes)
config.json                # Model config
quantization.py            # QuantizedLinear layer
model_loader.py            # Loading utilities

✨ Why Safetensors?

• ✅ Secure: No arbitrary code execution (vs pickle)
• ✅ Fast: Zero-copy loading
• ✅ Standard: HuggingFace official format
• ✅ Portable: Works across frameworks

📊 Quantization Details

• Method: Uniform INT8
• Vision layers: 96 @ 8-bit
• Language layers: 2197 @ 8-bit
• Dequantization: On-the-fly during forward pass

🔍 Inspect Metadata

import json

with open("quantization_config.json") as f:
    config = json.load(f)

print(f"Quantized layers: {len(config['quantized_layers'])}")
print(f"Compression: {config['stats']['compression_ratio']}x")

📝 License

MIT (inherited from DeepSeek-OCR)