论文分享:Fast Inference fromTransformersviaSpeculative Decoding 原文:https://arxiv.org/abs/2211.17192 这篇文章是Google发表在2023年的第40届国际机器学习会议(International Conference on Machine Learning,ICML)上的口头报告。该论文聚焦于Transformer模型推理速度慢的问题,针对大型自回归模型解码过程中的效率瓶颈,提出...
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 ...
🔥 Fasttransformerinference for Ruby For non-ONNX models, check outTransformers.rb🙂 Installation Add this line to your application’s Gemfile: gem"informers" Getting Started Models Embedding Reranking mixedbread-ai/mxbai-rerank-base-v1 ...
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...
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...
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(): ...
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
首先我们需要准备一个 3.9 以上的 Python 环境运行来 Xinference，建议先根据 conda 官网文档安装 conda。 然后使用以下命令来创建 3.11 的 Python 环境：conda create --name xinference python=3.11conda activate xinference 以下两条命令在安装 Xinference 时，将安装 Transformers 和 vLLM 作为 Xinference 的...
github:https://github.com/xorbitsai/inference/tree/main 官方手册:https://inference.readthedocs.io/zh-cn/latest/index.html 如果你的目标是在一台 Linux 或者 Window 服务器上部署大模型,可以选择 Transformers 或 vLLM 作为 Xinference 的推理后端: ...