This is a 4-bit quantized version of the microsoft/phi-2 model using Intel's AutoRound for weight-only post-training quantization (W4G128). It achieves significant compression while preserving model performance, making it ideal for resource-constrained inference.

🧾 Model Details

Base model: microsoft/phi-2
Quantization method: AutoRound (W4G128 - 4-bit, group size 128)
Framework: 🤗 Transformers
Precision: 4-bit weights
Quantized size: ~1.85 GB (original: ~5.5 GB)
Compression ratio: ~67%

🚀 How to Use

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("itachi023/phi-2-4-bit-quantized", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("itachi023/phi-2-4-bit-quantized")

prompt = "write a essay on deep learning"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📦 Intended Uses

Fast inference with low memory footprint
Deployment on consumer GPUs or edge devices
Offline assistants, document generation, or chatbots

⚠️ Limitations

This model has not been fine-tuned post-quantization.
Slight accuracy drop may occur vs. full-precision, especially on sensitive NLP tasks.
Phi-2 is a pretrained model without alignment or safety tuning.

📈 Performance Notes

Quantization config: W4G128 (4-bit, symmetric), 512 calibration samples, 1000 iterations
AutoRound version: Latest (as of May 2025)
Target device: GPU (A100/L4), float16 scale