# BioGPT INT8 Quantized for Medical Feature Extraction

This is an INT8 quantized version of Microsoft's BioGPT for CPU inference.

## Quick Start

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load base model and apply quantization
tokenizer = AutoTokenizer.from_pretrained("microsoft/biogpt")
model = AutoModelForCausalLM.from_pretrained("microsoft/biogpt", torch_dtype=torch.float16)
model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
model.eval()

# Use for inference
prompt = "Extract medical features: Patient is 45-year-old male with fever 101.2F"
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Model Details
- Base: microsoft/biogpt
- Quantization: INT8 dynamic
- Size: ~85MB (vs 1.56GB original)
- Optimized for: CPU inference