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@@ -101,7 +101,7 @@ Awesome-LLM-Inference: A curated list of [📙Awesome LLM Inference Papers with
 |Date|Title|Paper|Code|Recom|
 |:---:|:---:|:---:|:---:|:---:| 
 |2022.06|🔥[**ZeroQuant**] Efficient and Affordable Post-Training Quantization for Large-Scale Transformers(@Microsoft) |[[pdf]](https://arxiv.org/pdf/2206.01861.pdf)|[[DeepSpeed]](https://github.com/microsoft/DeepSpeed) ![](https://img.shields.io/github/stars/microsoft/DeepSpeed.svg?style=social)|⭐️⭐️ |     
-|2022.08|[FP8-Quantization] FP8 Quantization: The Power of the Exponent(@Qualcomm AI Research) | [[pdf]](https://arxiv.org/pdf/2208.09225.pdf) | ⚠️ |⭐️ |    
+|2022.08|[FP8-Quantization] FP8 Quantization: The Power of the Exponent(@Qualcomm AI Research) | [[pdf]](https://arxiv.org/pdf/2208.09225.pdf) | [[FP8-quantization]](https://github.com/Qualcomm-AI-research/FP8-quantization) ![](https://img.shields.io/github/stars/Qualcomm-AI-research/FP8-quantization.svg?style=social) |⭐️ |    
 |2022.08|[LLM.int8()] 8-bit Matrix Multiplication  for Transformers at Scale(@Facebook AI Research etc) |[[pdf]](https://arxiv.org/pdf/2208.07339.pdf)|[[bitsandbytes]](https://github.com/timdettmers/bitsandbytes) ![](https://img.shields.io/github/stars/timdettmers/bitsandbytes.svg?style=social)|⭐️ |    
 |2022.10|🔥[**GPTQ**] GPTQ: ACCURATE POST-TRAINING QUANTIZATION FOR GENERATIVE PRE-TRAINED TRANSFORMERS(@IST Austria etc) |[[pdf]](https://arxiv.org/pdf/2210.17323.pdf) |[[gptq]](https://github.com/IST-DASLab/gptq) ![](https://img.shields.io/github/stars/IST-DASLab/gptq.svg?style=social)|⭐️⭐️ |   
 |2022.11|🔥[**WINT8/4**] Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production(@NVIDIA&Microsoft) |[[pdf]](https://arxiv.org/pdf/2211.10017.pdf)|[[FasterTransformer]](https://github.com/NVIDIA/FasterTransformer) ![](https://img.shields.io/github/stars/NVIDIA/FasterTransformer.svg?style=social)|⭐️⭐️ |