Fast Inference from Transformers via Speculative Decodingarxiv.org/abs/2211.17192 作者:Yaniv Leviathan, Matan Kalman, Yossi Matias Affiliation: Google Research ICML'23 Oral 这篇被很多人错认为是 Speculative Decoding 的开坑之作。 不讨论学术道德的问题,但就论文本身,这篇文章确实写得相当棒。 首先就是...
Fast Inference from Transformers via Speculative Decodingarxiv.org/abs/2211.17192 作者:Yaniv Leviathan Matan Kalman Yossi Matias 机构:Google Research Proceedings of the 40 International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. Google的这篇和DeepMind的那篇是同时期的研究...
Fast inference from transformers via speculative decoding This repository implements speculative sampling for large language model (LLM) decoding. It utilizes two models during the decoding process: a target model and an approximation model. The approximation model is a smaller model, while the target ...
Marian OPUS-MT TransformersThe project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration.Key featuresFast and efficient execution on CPU and GPUThe execution is significantly faster and ...
import torchfrom transformers import AutoModelmodel = AutoModel.from_pretrained("BAAI/bge-small-en-v1.5")@torch.inference_mode()defencode_text(): outputs = model(inputs)with torch.cpu.amp.autocast(dtype=torch.bfloat16): encode_text()用 IPEX torchscript 运行 bf16 模型:import torch...
随后,我们使用transformers的 API 将句子编码为向量: fromtransformersimportAutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Intel/bge-small-en-v1.5-rag-int8-static") inputs = tokenizer(sentences, return_tensors="pt") withtorch.no_grad(): ...
随后,我们使用transformers的 API 将句子编码为向量: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Intel/bge-small-en-v1.5-rag-int8-static") inputs = tokenizer(sentences, return_tensors="pt") with torch.no_grad(): ...
英文原文:FastFormers: 233x Faster Transformers inference on CPU 标签:深度学习 Cheetah.Courtesy: Photo by 01 Cheetah.Courtesy: Photo byDušan VeverkologonUnsplash Since the birth of BERT followed by that of Transformers have dominated NLP in nearly every language-related tasks whether it is Ques...
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growin
Transformers have recently dominated the ASR field. Although able to yield good performance, they involve an autoregressive (AR) decoder to generate tokens one by one, which is computationally inefficient. To speed up inference, non-autoregressive (NAR) methods, e.g. single-step NAR, were designe...