For cases where your model can fit on a single GPU, this allows you to evaluate on K GPUs K times faster than on one. WARNING: This setup does not work with FSDP model sharding, so inaccelerate configFSDP must be disabled, or the NO_SHARD FSDP option must be used. ...
6.1 使用LMDeploy运行视觉多模态大模型llava 最新版本的LMDeploy支持了llava多模态模型,下面演示使用pipeline推理llava-v1.6-7b。注意,运行本pipeline最低需要30%的InternStudio开发机,请完成基础作业后向助教申请权限。 首先激活conda环境。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 conda activate lmdeploy...
使用LMDeploy 运行视觉多模态大模型 llava gradio demo 将LMDeploy Web Demo 部署到 OpenXLab(OpenXLab cuda 12.2 的镜像还没有 ready,可先跳过,一周之后再来做) LMDeploy量化LLM# 新建环境# 因为cuda11.7-conda的镜像与新版本的lmdeploy会出现兼容性问题。所以我们需要新建镜像为cuda12.2-conda的开发机,选择10%...
(default: 1) -nocb, --no-cont-batching Disable continuous batching --mmproj FILE Path to a multimodal projector file for LLaVA --mlock Force system to keep model in RAM rather than swapping or compressing --no-mmap Disable memory-map model, slower load but may reduce pageouts if not ...
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7 使用LMDeploy运行视觉多模态大模型llava 8. 定量比较LMDeploy与Transformer库的推理速度差异 1. 前期准备 IDE:VScode 算力平台:intern-studio(需要参与实战营可免费获得A100算力的使用)[1] 此笔记参考[2][3] 2. 模型部署及遇到的问题 模型部署:将训练好的模型放在特定环境的过程 部署遇到的挑战: 前向推理时计...
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Let's tackle this problem step by step. It's divided into two parts, so I'll handle each one carefully.\n\n**Part 1: Finding the tangent line when a = 1**\n\nFirst, I need to find the equation of the tangent line to the curve y = f(x) at the point (1, f(1)), where...
使用LMDeploy运行视觉多模态大模型llava 使用LMDeploy运行第三方大模型 定量比较LMDeploy与Transformer库的推理速度差异 https://github.com/InternLM/Tutorial/blob/camp2/lmdeploy/README.md LMDeploy环境部署 Cuda12.2-conda AI检测代码解析 conda create -n lmdeploy -y python=3.10 conda activate lmdeploy 1....
Reporting to the Product Leader for PELM, and working closely with the Head of PELM Transformation, you'll balance hands-on Product Management with strategic oversight to accelerate the development of the future ecosystem, always focusing on data and the cross-business lifecycle experience. ...