---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
  - quantization
  - reasoning
  - qat
language:
  - en
---

# ReasoningQAT-Qwen3-1.7B-3bit

This model is a **3-bit pseudo-quantized** version of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), trained with Quantization-Aware Training (QAT) for reasoning tasks.

## Details

- **Base model:** Qwen3-1.7B
- **Quantization:** W3G128 (3-bit weights, group size 128)
- **Format:** Pseudo-quantized (stored in FP16; weights lie on 3-bit quantization grids)
- **Method:** ReasoningQAT — QAT combining knowledge distillation with teacher-confidence-weighted DFT loss, trained end-to-end on reasoning data

## Citation

```bibtex
@inproceedings{
  okoshi2026towards,
  title={Towards Quantization-Aware Training for Ultra-Low-Bit Reasoning {LLM}s},
  author={Yasuyuki Okoshi and Hikari Otsuka and Daichi Fujiki and Masato Motomura},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=Azsd2qyK6C}
}
```
