---
library_name: transformers
license: mit
base_model:
- microsoft/phi-2
pipeline_tag: text-generation
---



# 🧠 Phi-2 (4-bit Quantized with AutoRound)

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

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## 🧾 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%

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## 🚀 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))
```

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## 📦 Intended Uses

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

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## ⚠️ 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.

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## 📈 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

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