Add Unsloth + QAT blog to latest news (#3227)

Author: andrewor14

README.md

@@ -24,7 +24,8 @@
 
 ## 📣 Latest News
 
-- [Oct 20] MXFP8 MoE training prototype achieved **~1.45x speedup** for MoE layer in Llama4 Scout, and **~1.25x** speedup for MoE layer in DeepSeekV3 671b - with comparable numerics to bfloat16! Check out the [docs](./torchao/prototype/moe_training/) to try it out.
+- [Oct 25] QAT is now integrated into [Unsloth](https://docs.unsloth.ai/new/quantization-aware-training-qat) for both full and LoRA fine-tuning! Try it out using [this notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_%284B%29_Instruct-QAT.ipynb).
+- [Oct 25] MXFP8 MoE training prototype achieved **~1.45x speedup** for MoE layer in Llama4 Scout, and **~1.25x** speedup for MoE layer in DeepSeekV3 671b - with comparable numerics to bfloat16! Check out the [docs](./torchao/prototype/moe_training/) to try it out.
 - [Sept 25] MXFP8 training achieved [1.28x speedup on Crusoe B200 cluster](https://pytorch.org/blog/accelerating-2k-scale-pre-training-up-to-1-28x-with-torchao-mxfp8-and-torchtitan-on-crusoe-b200-cluster/) with virtually identical loss curve to bfloat16!
 - [Sept 19] [TorchAO Quantized Model and Quantization Recipes Now Available on Huggingface Hub](https://pytorch.org/blog/torchao-quantized-models-and-quantization-recipes-now-available-on-huggingface-hub/)!
 - [Jun 25] Our [TorchAO paper](https://openreview.net/attachment?id=HpqH0JakHf&name=pdf) was accepted to CodeML @ ICML 2025!