2.FP4下的精度补偿FP4 的比特数只有 4 位，也一样要分成符号位、指数位和尾数位，这意味着可用于表示数据的信息位十分有限，量化误差也会变得巨大，若不采取补偿会对模型的表现产生灾难性的影响。为此，在 LLM-FP4 论文中[1]，作者提出了一种有效的FP4精度的权重-激活量化补偿。1.基于搜索的量化方法：作者团队...
^abGPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ^LLM-FP4: 4-Bit Floating-Point Quantized Transformers ^LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale ^Classifier-Free Diffusion Guidance ^Q-Diffusion: Quantizing Diffusion Models ^QuIP#: Even Bet...
英伟达凭借在 Blackwell 架构中适配 FP4 精度,在软件上运用如 LLM - FP4 论文里的补偿方法实现低精度浮点数量化,硬件上对前代架构升级,使 Blackwell B200 在 FP4 精度下算力大幅提升,巩固其在 AI 芯片领域优势,彰显战略眼光。对学术界而言,FP4 精度为量化研究提供新方向与验证平台,促进学术成果与业界硬件结合,推动...
We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-training quantization (PTQ) solutions are primarily integer-based and struggle with bit widths below 8 bits. Compared to...
This is the pytorch implementation of our paperLLM-FP4: 4-Bit Floating-Point Quantized Transformers, published in EMNLP 2023 main conference. LLM-FP4 is able to quantize both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner...
Language:All SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime sparsitypruningquantizationknowledge-distillationauto-tuningint8low-precisionquantization-aware-trainingpost-training-quantizationawqint4large-language...
int8 training for automatic speech recognition PEFT官网:Finetune_opt_bnb_peft.ipynb HuggingFace Quantize Transformers models 用bitsandbytes、4 比特量化和 QLoRA 打造亲民的 LLM 大规模 Transformer 模型 8 比特矩阵乘简介 - 基于 Hugging Face Transformers、Accelerate 以及 bitsandbytes Transformers 中原生支持...
After training 4bit with LoRA, merging with original base and saving it, there is no error. Thanks great work @poedator. jeffwang0516 mentioned this pull request Mar 12, 2024 [BUG] Error when pushing model to HuggingFace h2oai/h2o-llmstudio#635 Closed codybum mentioned this pull reques...
Note: From 3.0 release, we recommend to use 3.X API. Compression techniques during training such as QAT, Pruning, Distillation only available in2.X APIcurrently. Selected Publications/Events EMNLP'2024:Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs(Sep 2024) ...