1.4.5 LLM-QAT 在LLM-QAT之前,LLM的量化以后量化(PTQ)为主,因为量化感知训练(QAT)需要比较多的训练数据,比较难获取。而后量化(PTQ)在8 位以下精度时,方法的性能存在瓶颈。 而LLM-QAT提出了一种无需额外数据的量化感知训练(QAT)方法,通过模型自身生成的数据进行知识蒸馏,实现了对 LLM 的低位量化。 1.5 常用的...
随着深度学习技术的不断发展,大模型(Large Language Model,简称LLM)在各个领域中展现出强大的性能。然而,这些模型通常参数众多、计算量大,给部署和应用带来了巨大挑战。量化技术Quantization作为一种有效的模型压缩方法,能够在保证模型精度的同时,显著降低模型大小和计算复杂度。本文将带领大家深入理解大模型量化技术Quantiza...
本期code:https://github.com/chunhuizhang/llm_inference_serving/blob/main/tutorials/quantization/qlora_gptq_gguf_awq.ipynb https://github.com/chunhuizhang/llm_inference_serving/blob/main/tutorials/quantization/basics.ipynb 关于 llama3:BV15z42167yB,BV18E421A7TQ 关于bfloat16:BV1no4y1u7og 关于...
TRT-LLM中的量化 在TensorRT中量化方法主要分为2类,一类是Mixed GEMM,也就是Activation和Weight的数据类型是不同的,例如AWQ,GPTQ,PerChannel。另外一类是Universal GEMM,例如SmoothQuant和FP8,它们的Activation和Weight的数据类型是相同的。 首先来看PerChannel在推理时的计算流程,可以看到它在推理时会先对Weight进行乘...
Generally speaking, LLM inference is a memory bandwidth bounded task for weight loading. Weight-only quantization (WOQ) is an effective performance optimization algorithm to reduce the total amount of memory access without losing accuracy. int4 GEMM with a weight-only quantization (WOQ) recipe speci...
Imatrix 和 K-Quantization 进行 GGUF 量化以在 CPU 上运行 LLM 适用于您的 CPU 的快速而准确的 GGUF 模型。欢迎来到雲闪世界。编辑 添加图片注释，不超过 140 字（可选）GGUF 是一种二进制文件格式，旨在使用 GGML（一种基于 C 的机器学习张量库）进行高效存储和快速大型语言模型 (LLM) 加载。GGUF 将...
Recent advances in low-bit quantization have made mixed-precision matrix multiplication (mpGEMM) viable for LLMs. This deep learning technique allows data of the same or different formats to be multiplied, such as int8*int1, int8*int2, or FP16*int4. By combining a variety ...
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3.Post-training quantization(PTQ): This method transforms the parameters of the LLM to lower-precision data types after the model has been trained. PTQ aims to reduce the model’s complexity without altering its architecture or requiring retraining. ...
As large language models (LLMs) are becoming even bigger, it is increasingly important to provide easy-to-use and efficient deployment paths because the cost of…