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---
library_name: transformers
tags:
- llmcompressor
- quantization
- wint4
---
# Phi-4-mini-instruct-WINT4
This model is a 4-bit quantized version of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) "using the [llmcompressor](https://github.com/neuralmagic/llmcompressor) library.
## Quantization Details
* **Base Model:** [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct)
* **Quantization Library:** `llmcompressor`
* **Quantization Method:** Weight-only 4-bit int (WINT4)
* **Quantization Recipe:**
```yaml
quant_stage:
quant_modifiers:
QuantizationModifier:
ignore: [lm_head]
config_groups:
group_0:
weights: {num_bits: 4, type: int, symmetric: true, strategy: channel, dynamic: false}
targets: [Linear]
```
## Evaluation Results
The following table shows the evaluation results on various benchmarks compared to the baseline (non-quantized) model.
| Task | Baseline Metric (10.0% Threshold) | Quantized Metric | Metric Type |
|------------------|-------------------------------------------------------|------------------|---------------------|
| winogrande | 0.7545 | 0.6985 | acc,none |
## How to Use
You can load the quantized model and tokenizer using the `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "NoorNizar/Phi-4-mini-instruct-WINT4"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Example usage (replace with your specific task)
prompt = "Hello, world!"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Disclaimer
This model was quantized automatically using a script. Performance and behavior might differ slightly from the original base model.
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