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Model: kusakana/ReasoningQAT-Qwen3-1.7B-2bit
Source: Original Platform

README.md

@@ -0,0 +1,34 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+tags:
+  - quantization
+  - reasoning
+  - qat
+language:
+  - en
+---
+
+# ReasoningQAT-Qwen3-1.7B-2bit
+
+This model is a **2-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:** W2G128 (2-bit weights, group size 128)
+- **Format:** Pseudo-quantized (stored in FP16; weights lie on 2-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}
+}
+```