Microsoft has announced a new research paper titled 'ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks'. The paper examines 4-bit quantization methods like GPTQ in large language models (LLMs) and highlights the challenges and advantages of using FP6 format. Additionally, Microsoft Research has also introduced TinyGSM, which achieves over 80% on GSM8k with small language models, emphasizing the computational advantages of small-scale models.
[CL] ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks X Wu, H Xia, S Youn, Z Zheng, S Chen… [Microsoft] (2023) https://t.co/jar0xdR9yv - The paper examines 4-bit quantization methods like GPTQ for large language models… https://t.co/RiY001phbx
Microsoft announces ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks 🔥 📌 INT4 quantization can significantly underperform and simply shifting to higher precision formats like FP6 has been particularly challenging. 📌… https://t.co/7W2dRaNMHG
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks. https://t.co/3VXeqUexiY
Microsoft Research announces TinyGSM: achieving >80% on GSM8k with small language models paper page: https://t.co/2oPgYwPsDN Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open… https://t.co/1XVIR7xi5D
Microsoft announces ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks paper page: https://t.co/XhQFRjjUKE This study examines 4-bit quantization methods like GPTQ in large language models (LLMs), highlighting GPTQ's… https://t.co/hO49L1WZBF
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks. (arXiv:2312.08583v1 [https://t.co/x5f9xnJFAw]) https://t.co/5Eat9cfMcX