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class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 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id=\"user-content-glm-4\" class=\"anchor\" aria-label=\"Permalink: GLM-4\" href=\"#glm-4\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n 📄\u003ca href=\"https://arxiv.org/pdf/2406.12793\" rel=\"nofollow\"\u003e Report \u003c/a\u003e • 🤗 \u003ca href=\"https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7\" rel=\"nofollow\"\u003eHF Repo\u003c/a\u003e • 🤖 \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat\" rel=\"nofollow\"\u003eModelScope\u003c/a\u003e • 🟣 \u003ca href=\"https://wisemodel.cn/models/ZhipuAI/glm-4-9b-chat\" rel=\"nofollow\"\u003eWiseModel\u003c/a\u003e • 🐦 \u003ca href=\"https://twitter.com/thukeg\" rel=\"nofollow\"\u003eTwitter\u003c/a\u003e • 👋 加入我们的 \u003ca href=\"https://discord.gg/8cnQKdAprg\" rel=\"nofollow\"\u003eDiscord\u003c/a\u003e 和 \u003ca href=\"/THUDM/GLM-4/blob/main/resources/WECHAT.md\"\u003e微信\u003c/a\u003e \n\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n📍在 \u003ca href=\"https://open.bigmodel.cn/?utm_campaign=open\u0026amp;_channel_track_key=OWTVNma9\" rel=\"nofollow\"\u003e智谱AI开放平台\u003c/a\u003e 体验和使用更大规模的 GLM 商业模型。\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRead this in \u003ca href=\"/THUDM/GLM-4/blob/main/README_en.md\"\u003eEnglish\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e项目更新\u003c/h2\u003e\u003ca id=\"user-content-项目更新\" class=\"anchor\" aria-label=\"Permalink: 项目更新\" href=\"#项目更新\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e🔥🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/12/10\u003c/code\u003e: 本仓库微调代码支持使用\u003ccode\u003eAscend NPU\u003c/code\u003e进行微调。请更新微调代码并查看代码内注释。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/11/01\u003c/code\u003e: 本仓库依赖进行升级，请更新\u003ccode\u003erequirements.txt\u003c/code\u003e中的依赖以保证正常运行模型。\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b-chat-hf\" rel=\"nofollow\"\u003eglm-4-9b-chat-hf\u003c/a\u003e 是适配 \u003ccode\u003etransformers\u0026gt;=4.46.2\u003c/code\u003e 的模型权重，使用 \u003ccode\u003etransformers\u003c/code\u003e 库中的 \u003ccode\u003eGlmModel\u003c/code\u003e 类实现。\n同时，\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b-chat\" rel=\"nofollow\"\u003eglm-4-9b-chat\u003c/a\u003e, \u003ca href=\"https://huggingface.co/THUDM/glm-4v-9b\" rel=\"nofollow\"\u003eglm-4v-9b\u003c/a\u003e 中的 \u003ccode\u003etokenzier_chatglm.py\u003c/code\u003e 已经更新以适配最新版本的 \u003ccode\u003etransformers\u003c/code\u003e库。请前往 HuggingFace 更新文件。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/10/27\u003c/code\u003e: 我们开源了 \u003ca href=\"https://github.com/THUDM/LongReward\"\u003eLongReward\u003c/a\u003e，这是一个使用 AI 反馈改进长上下文大型语言模型。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/10/25\u003c/code\u003e: 我们开源了端到端中英语音对话模型 \u003ca href=\"https://github.com/THUDM/GLM-4-Voice\"\u003eGLM-4-Voice\u003c/a\u003e。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/09/05\u003c/code\u003e 我们开源了使LLMs能够在长上下文问答中生成细粒度引用的模型 \u003ca href=\"https://huggingface.co/THUDM/LongCite-glm4-9b\" rel=\"nofollow\"\u003elongcite-glm4-9b\u003c/a\u003e 以及数据集 \u003ca href=\"https://huggingface.co/datasets/THUDM/LongCite-45k\" rel=\"nofollow\"\u003eLongCite-45k\u003c/a\u003e, 欢迎在 \u003ca href=\"https://huggingface.co/spaces/THUDM/LongCite\" rel=\"nofollow\"\u003eHuggingface Space\u003c/a\u003e 在线体验。\u003c/li\u003e\n\u003cli\u003e🔥\u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/08/15\u003c/code\u003e: 我们开源具备长文本输出能力(单轮对话大模型输出可超过1万token) 的模型 \u003ca href=\"https://huggingface.co/THUDM/LongWriter-glm4-9b\" rel=\"nofollow\"\u003elongwriter-glm4-9b\u003c/a\u003e 以及数据集 \u003ca href=\"https://huggingface.co/datasets/THUDM/LongWriter-6k\" rel=\"nofollow\"\u003eLongWriter-6k\u003c/a\u003e, 欢迎在 \u003ca href=\"https://huggingface.co/spaces/THUDM/LongWriter\" rel=\"nofollow\"\u003eHuggingface Space\u003c/a\u003e 或 \u003ca href=\"https://modelscope.cn/studios/ZhipuAI/LongWriter-glm4-9b-demo\" rel=\"nofollow\"\u003e魔搭社区空间\u003c/a\u003e 在线体验。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/07/24\u003c/code\u003e: 我们发布了与长文本相关的最新技术解读，关注 \u003ca href=\"https://medium.com/@ChatGLM/glm-long-scaling-pre-trained-model-contexts-to-millions-caa3c48dea85\" rel=\"nofollow\"\u003e这里\u003c/a\u003e 查看我们在训练 GLM-4-9B 开源模型中关于长文本技术的技术报告。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/07/09\u003c/code\u003e: GLM-4-9B-Chat 模型已适配 \u003ca href=\"https://github.com/ollama/ollama\"\u003eOllama\u003c/a\u003e, \u003ca href=\"https://github.com/ggerganov/llama.cpp\"\u003eLlama.cpp\u003c/a\u003e，您可以在 \u003ca href=\"https://github.com/ggerganov/llama.cpp/pull/8031\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/ggml-org/llama.cpp/pull/8031/hovercard\"\u003ePR\u003c/a\u003e 查看具体的细节。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/06/18\u003c/code\u003e: 我们发布 \u003ca href=\"https://arxiv.org/pdf/2406.12793\" rel=\"nofollow\"\u003e技术报告\u003c/a\u003e, 欢迎查看。\u003c/li\u003e\n\u003cli\u003e🔥 \u003cstrong\u003eNews\u003c/strong\u003e: \u003ccode\u003e2024/06/05\u003c/code\u003e: 我们发布 GLM-4-9B 系列开源模型。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e模型介绍\u003c/h2\u003e\u003ca id=\"user-content-模型介绍\" class=\"anchor\" aria-label=\"Permalink: 模型介绍\" href=\"#模型介绍\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eGLM-4-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源版本。在语义、数学、推理、代码和知识等多方面的数据集测评中，\n\u003cstrong\u003eGLM-4-9B\u003c/strong\u003e 及其人类偏好对齐的版本 \u003cstrong\u003eGLM-4-9B-Chat\u003c/strong\u003e 均表现出超越 Llama-3-8B 的卓越性能。除了能进行多轮对话，GLM-4-9B-Chat\n还具备网页浏览、代码执行、自定义工具调用（Function Call）和长文本推理（支持最大 128K 上下文）等高级功能。本代模型增加了多语言支持，支持包括日语，韩语，德语在内的\n26 种语言。我们还推出了支持 1M 上下文长度（约 200 万中文字符）的 \u003cstrong\u003eGLM-4-9B-Chat-1M\u003c/strong\u003e 模型和基于 GLM-4-9B 的多模态模型\nGLM-4V-9B。\u003cstrong\u003eGLM-4V-9B\u003c/strong\u003e 具备 1120 * 1120 高分辨率下的中英双语多轮对话能力，在中英文综合能力、感知推理、文字识别、图表理解等多方面多模态评测中，GLM-4V-9B\n表现出超越 GPT-4-turbo-2024-04-09、Gemini 1.0 Pro、Qwen-VL-Max 和 Claude 3 Opus 的卓越性能。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel List\u003c/h2\u003e\u003ca id=\"user-content-model-list\" class=\"anchor\" aria-label=\"Permalink: Model List\" href=\"#model-list\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eType\u003c/th\u003e\n\u003cth align=\"center\"\u003eSeq Length\u003c/th\u003e\n\u003cth align=\"center\"\u003eTransformers Version\u003c/th\u003e\n\u003cth align=\"center\"\u003eDownload\u003c/th\u003e\n\u003cth align=\"center\"\u003eOnline Demo\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGLM-4-9B\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBase\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8K\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e4.44.0 - 4.45.0\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b\" rel=\"nofollow\"\u003e🤗 Huggingface\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4-9b\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://wisemodel.cn/models/ZhipuAI/glm-4-9b\" rel=\"nofollow\"\u003e🟣 WiseModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGLM-4-9B-Chat\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e128K\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026gt;= 4.44.0\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b-chat\" rel=\"nofollow\"\u003e🤗 Huggingface\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://wisemodel.cn/models/ZhipuAI/GLM-4-9B-Chat\" rel=\"nofollow\"\u003e🟣 WiseModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://modelscope.cn/studios/dash-infer/GLM-4-Chat-DashInfer-Demo/summary\" rel=\"nofollow\"\u003e🤖 ModelScope CPU\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/studios/ZhipuAI/glm-4-9b-chat-vllm/summary\" rel=\"nofollow\"\u003e🤖 ModelScope vLLM\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGLM-4-9B-Chat-HF\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e128K\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026gt;= 4.46.0\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b-chat-hf\" rel=\"nofollow\"\u003e🤗 Huggingface\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-hf\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://modelscope.cn/studios/dash-infer/GLM-4-Chat-DashInfer-Demo/summary\" rel=\"nofollow\"\u003e🤖 ModelScope CPU\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/studios/ZhipuAI/glm-4-9b-chat-vllm/summary\" rel=\"nofollow\"\u003e🤖 ModelScope vLLM\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGLM-4-9B-Chat-1M\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026gt;= 4.44.0\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b-chat-1m\" rel=\"nofollow\"\u003e🤗 Huggingface\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-1m\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://wisemodel.cn/models/ZhipuAI/GLM-4-9B-Chat-1M\" rel=\"nofollow\"\u003e🟣 WiseModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGLM-4-9B-Chat-1M-HF\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026gt;= 4.46.0\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/THUDM/glm-4-9b-chat-1m-hf\" rel=\"nofollow\"\u003e🤗 Huggingface\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-1m-hf\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGLM-4V-9B\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8K\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026gt;= 4.46.0\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/THUDM/glm-4v-9b\" rel=\"nofollow\"\u003e🤗 Huggingface\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://modelscope.cn/models/ZhipuAI/glm-4v-9b\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003cbr\u003e \u003ca href=\"https://wisemodel.cn/models/ZhipuAI/GLM-4V-9B\" rel=\"nofollow\"\u003e🟣 WiseModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://modelscope.cn/studios/ZhipuAI/glm-4v-9b-Demo/summary\" rel=\"nofollow\"\u003e🤖 ModelScope\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e评测结果\u003c/h2\u003e\u003ca id=\"user-content-评测结果\" class=\"anchor\" aria-label=\"Permalink: 评测结果\" href=\"#评测结果\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e对话模型典型任务\u003c/h3\u003e\u003ca id=\"user-content-对话模型典型任务\" class=\"anchor\" aria-label=\"Permalink: 对话模型典型任务\" href=\"#对话模型典型任务\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eAlignBench\u003c/th\u003e\n\u003cth align=\"center\"\u003eMT-Bench\u003c/th\u003e\n\u003cth align=\"center\"\u003eIFEval\u003c/th\u003e\n\u003cth align=\"center\"\u003eMMLU\u003c/th\u003e\n\u003cth align=\"center\"\u003eC-Eval\u003c/th\u003e\n\u003cth align=\"center\"\u003eGSM8K\u003c/th\u003e\n\u003cth align=\"center\"\u003eMATH\u003c/th\u003e\n\u003cth align=\"center\"\u003eHumanEval\u003c/th\u003e\n\u003cth align=\"center\"\u003eNaturalCodeBench\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eLlama-3-8B-Instruct\u003c/td\u003e\n\u003ctd align=\"center\"\u003e6.40\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8.00\u003c/td\u003e\n\u003ctd align=\"center\"\u003e68.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e68.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e51.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e79.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e30.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e62.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e24.7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eChatGLM3-6B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5.18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5.50\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e61.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e72.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e58.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e11.3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eGLM-4-9B-Chat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e7.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8.35\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e72.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e75.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e79.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e71.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e32.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e基座模型典型任务\u003c/h3\u003e\u003ca id=\"user-content-基座模型典型任务\" class=\"anchor\" aria-label=\"Permalink: 基座模型典型任务\" href=\"#基座模型典型任务\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eMMLU\u003c/th\u003e\n\u003cth align=\"center\"\u003eC-Eval\u003c/th\u003e\n\u003cth align=\"center\"\u003eGPQA\u003c/th\u003e\n\u003cth align=\"center\"\u003eGSM8K\u003c/th\u003e\n\u003cth align=\"center\"\u003eMATH\u003c/th\u003e\n\u003cth align=\"center\"\u003eHumanEval\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eLlama-3-8B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e66.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e51.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eLlama-3-8B-Instruct\u003c/td\u003e\n\u003ctd align=\"center\"\u003e68.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e51.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e79.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e30.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e62.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eChatGLM3-6B-Base\u003c/td\u003e\n\u003ctd align=\"center\"\u003e61.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e26.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e72.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e58.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eGLM-4-9B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e74.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e77.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e84.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e30.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e70.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e由于 \u003ccode\u003eGLM-4-9B\u003c/code\u003e 在预训练过程中加入了部分数学、推理、代码相关的 instruction 数据，所以将 Llama-3-8B-Instruct 也列入比较范围。\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e长文本\u003c/h3\u003e\u003ca id=\"user-content-长文本\" class=\"anchor\" aria-label=\"Permalink: 长文本\" href=\"#长文本\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e在 1M 的上下文长度下进行\u003ca href=\"https://github.com/LargeWorldModel/LWM/blob/main/scripts/eval_needle.py\"\u003e大海捞针实验\u003c/a\u003e，结果如下：\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/THUDM/GLM-4/blob/main/resources/eval_needle.jpeg\"\u003e\u003cimg src=\"/THUDM/GLM-4/raw/main/resources/eval_needle.jpeg\" alt=\"needle\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e在 LongBench-Chat 上对长文本能力进行了进一步评测，结果如下:\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/THUDM/GLM-4/blob/main/resources/longbench.png\"\u003e\u003cimg src=\"/THUDM/GLM-4/raw/main/resources/longbench.png\" alt=\"描述文字\" style=\"width: 65%; max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e多语言能力\u003c/h3\u003e\u003ca id=\"user-content-多语言能力\" class=\"anchor\" aria-label=\"Permalink: 多语言能力\" href=\"#多语言能力\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e在六个多语言数据集上对 GLM-4-9B-Chat 和 Llama-3-8B-Instruct 进行了测试，测试结果及数据集对应选取语言如下表\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eDataset\u003c/th\u003e\n\u003cth align=\"center\"\u003eLlama-3-8B-Instruct\u003c/th\u003e\n\u003cth align=\"center\"\u003eGLM-4-9B-Chat\u003c/th\u003e\n\u003cth align=\"center\"\u003eLanguages\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eM-MMLU\u003c/td\u003e\n\u003ctd align=\"center\"\u003e49.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e56.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003eall\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eFLORES\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003eru, es, de, fr, it, pt, pl, ja, nl, ar, tr, cs, vi, fa, hu, el, ro, sv, uk, fi, ko, da, bg, no\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eMGSM\u003c/td\u003e\n\u003ctd align=\"center\"\u003e54.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e65.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003ezh, en, bn, de, es, fr, ja, ru, sw, te, th\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eXWinograd\u003c/td\u003e\n\u003ctd align=\"center\"\u003e61.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e73.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003ezh, en, fr, jp, ru, pt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eXStoryCloze\u003c/td\u003e\n\u003ctd align=\"center\"\u003e84.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e90.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003ezh, en, ar, es, eu, hi, id, my, ru, sw, te\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eXCOPA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e73.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e80.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003ezh, et, ht, id, it, qu, sw, ta, th, tr, vi\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e工具调用能力\u003c/h3\u003e\u003ca id=\"user-content-工具调用能力\" class=\"anchor\" aria-label=\"Permalink: 工具调用能力\" href=\"#工具调用能力\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e我们在 \u003ca href=\"https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard\"\u003eBerkeley Function Calling Leaderboard\u003c/a\u003e\n上进行了测试并得到了以下结果：\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eOverall Acc.\u003c/th\u003e\n\u003cth align=\"center\"\u003eAST Summary\u003c/th\u003e\n\u003cth align=\"center\"\u003eExec Summary\u003c/th\u003e\n\u003cth align=\"center\"\u003eRelevance\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eLlama-3-8B-Instruct\u003c/td\u003e\n\u003ctd align=\"center\"\u003e58.88\u003c/td\u003e\n\u003ctd align=\"center\"\u003e59.25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e70.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.83\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003egpt-4-turbo-2024-04-09\u003c/td\u003e\n\u003ctd align=\"center\"\u003e81.24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e82.14\u003c/td\u003e\n\u003ctd align=\"center\"\u003e78.61\u003c/td\u003e\n\u003ctd align=\"center\"\u003e88.75\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eChatGLM3-6B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e57.88\u003c/td\u003e\n\u003ctd align=\"center\"\u003e62.18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69.78\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5.42\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eGLM-4-9B-Chat\u003c/td\u003e\n\u003ctd align=\"center\"\u003e81.00\u003c/td\u003e\n\u003ctd align=\"center\"\u003e80.26\u003c/td\u003e\n\u003ctd align=\"center\"\u003e84.40\u003c/td\u003e\n\u003ctd align=\"center\"\u003e87.92\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e多模态能力\u003c/h3\u003e\u003ca id=\"user-content-多模态能力\" class=\"anchor\" aria-label=\"Permalink: 多模态能力\" href=\"#多模态能力\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eGLM-4V-9B 是一个多模态语言模型，具备视觉理解能力，其相关经典任务的评测结果如下：\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMMBench-EN-Test\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMMBench-CN-Test\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eSEEDBench_IMG\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMMStar\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMMMU\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMME\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eHallusionBench\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eAI2D\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eOCRBench\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003egpt-4o-2024-05-13\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e83.4\u003c/td\u003e\n\u003ctd\u003e82.1\u003c/td\u003e\n\u003ctd\u003e77.1\u003c/td\u003e\n\u003ctd\u003e63.9\u003c/td\u003e\n\u003ctd\u003e69.2\u003c/td\u003e\n\u003ctd\u003e2310.3\u003c/td\u003e\n\u003ctd\u003e55.0\u003c/td\u003e\n\u003ctd\u003e84.6\u003c/td\u003e\n\u003ctd\u003e736\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003egpt-4-turbo-2024-04-09\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e81.0\u003c/td\u003e\n\u003ctd\u003e80.2\u003c/td\u003e\n\u003ctd\u003e73.0\u003c/td\u003e\n\u003ctd\u003e56.0\u003c/td\u003e\n\u003ctd\u003e61.7\u003c/td\u003e\n\u003ctd\u003e2070.2\u003c/td\u003e\n\u003ctd\u003e43.9\u003c/td\u003e\n\u003ctd\u003e78.6\u003c/td\u003e\n\u003ctd\u003e656\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003egpt-4-1106-preview\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e77.0\u003c/td\u003e\n\u003ctd\u003e74.4\u003c/td\u003e\n\u003ctd\u003e72.3\u003c/td\u003e\n\u003ctd\u003e49.7\u003c/td\u003e\n\u003ctd\u003e53.8\u003c/td\u003e\n\u003ctd\u003e1771.5\u003c/td\u003e\n\u003ctd\u003e46.5\u003c/td\u003e\n\u003ctd\u003e75.9\u003c/td\u003e\n\u003ctd\u003e516\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eInternVL-Chat-V1.5\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e82.3\u003c/td\u003e\n\u003ctd\u003e80.7\u003c/td\u003e\n\u003ctd\u003e75.2\u003c/td\u003e\n\u003ctd\u003e57.1\u003c/td\u003e\n\u003ctd\u003e46.8\u003c/td\u003e\n\u003ctd\u003e2189.6\u003c/td\u003e\n\u003ctd\u003e47.4\u003c/td\u003e\n\u003ctd\u003e80.6\u003c/td\u003e\n\u003ctd\u003e720\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eLLaVA-Next-Yi-34B\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e81.1\u003c/td\u003e\n\u003ctd\u003e79.0\u003c/td\u003e\n\u003ctd\u003e75.7\u003c/td\u003e\n\u003ctd\u003e51.6\u003c/td\u003e\n\u003ctd\u003e48.8\u003c/td\u003e\n\u003ctd\u003e2050.2\u003c/td\u003e\n\u003ctd\u003e34.8\u003c/td\u003e\n\u003ctd\u003e78.9\u003c/td\u003e\n\u003ctd\u003e574\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStep-1V\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e80.7\u003c/td\u003e\n\u003ctd\u003e79.9\u003c/td\u003e\n\u003ctd\u003e70.3\u003c/td\u003e\n\u003ctd\u003e50.0\u003c/td\u003e\n\u003ctd\u003e49.9\u003c/td\u003e\n\u003ctd\u003e2206.4\u003c/td\u003e\n\u003ctd\u003e48.4\u003c/td\u003e\n\u003ctd\u003e79.2\u003c/td\u003e\n\u003ctd\u003e625\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMiniCPM-Llama3-V2.5\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e77.6\u003c/td\u003e\n\u003ctd\u003e73.8\u003c/td\u003e\n\u003ctd\u003e72.3\u003c/td\u003e\n\u003ctd\u003e51.8\u003c/td\u003e\n\u003ctd\u003e45.8\u003c/td\u003e\n\u003ctd\u003e2024.6\u003c/td\u003e\n\u003ctd\u003e42.4\u003c/td\u003e\n\u003ctd\u003e78.4\u003c/td\u003e\n\u003ctd\u003e725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQwen-VL-Max\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e77.6\u003c/td\u003e\n\u003ctd\u003e75.7\u003c/td\u003e\n\u003ctd\u003e72.7\u003c/td\u003e\n\u003ctd\u003e49.5\u003c/td\u003e\n\u003ctd\u003e52.0\u003c/td\u003e\n\u003ctd\u003e2281.7\u003c/td\u003e\n\u003ctd\u003e41.2\u003c/td\u003e\n\u003ctd\u003e75.7\u003c/td\u003e\n\u003ctd\u003e684\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGemini 1.0 Pro\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e73.6\u003c/td\u003e\n\u003ctd\u003e74.3\u003c/td\u003e\n\u003ctd\u003e70.7\u003c/td\u003e\n\u003ctd\u003e38.6\u003c/td\u003e\n\u003ctd\u003e49.0\u003c/td\u003e\n\u003ctd\u003e2148.9\u003c/td\u003e\n\u003ctd\u003e45.7\u003c/td\u003e\n\u003ctd\u003e72.9\u003c/td\u003e\n\u003ctd\u003e680\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eClaude 3 Opus\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e63.3\u003c/td\u003e\n\u003ctd\u003e59.2\u003c/td\u003e\n\u003ctd\u003e64.0\u003c/td\u003e\n\u003ctd\u003e45.7\u003c/td\u003e\n\u003ctd\u003e54.9\u003c/td\u003e\n\u003ctd\u003e1586.8\u003c/td\u003e\n\u003ctd\u003e37.8\u003c/td\u003e\n\u003ctd\u003e70.6\u003c/td\u003e\n\u003ctd\u003e694\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGLM-4V-9B\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e81.1\u003c/td\u003e\n\u003ctd\u003e79.4\u003c/td\u003e\n\u003ctd\u003e76.8\u003c/td\u003e\n\u003ctd\u003e58.7\u003c/td\u003e\n\u003ctd\u003e47.2\u003c/td\u003e\n\u003ctd\u003e2163.8\u003c/td\u003e\n\u003ctd\u003e46.6\u003c/td\u003e\n\u003ctd\u003e81.1\u003c/td\u003e\n\u003ctd\u003e786\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e快速调用\u003c/h2\u003e\u003ca id=\"user-content-快速调用\" class=\"anchor\" aria-label=\"Permalink: 快速调用\" href=\"#快速调用\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e硬件配置和系统要求，请查看\u003ca href=\"/THUDM/GLM-4/blob/main/basic_demo/README.md\"\u003e这里\u003c/a\u003e。\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e使用以下方法快速调用 GLM-4-9B-Chat 语言模型\u003c/h3\u003e\u003ca id=\"user-content-使用以下方法快速调用-glm-4-9b-chat-语言模型\" class=\"anchor\" aria-label=\"Permalink: 使用以下方法快速调用 GLM-4-9B-Chat 语言模型\" href=\"#使用以下方法快速调用-glm-4-9b-chat-语言模型\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e使用 transformers 后端进行推理:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport os\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '0' # 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号\nMODEL_PATH = \u0026quot;THUDM/glm-4-9b-chat-hf\u0026quot;\n\ndevice = \u0026quot;cuda\u0026quot; if torch.cuda.is_available() else \u0026quot;cpu\u0026quot;\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)\n\nquery = \u0026quot;你好\u0026quot;\n\ninputs = tokenizer.apply_chat_template([{\u0026quot;role\u0026quot;: \u0026quot;user\u0026quot;, \u0026quot;content\u0026quot;: query}],\n add_generation_prompt=True,\n tokenize=True,\n return_tensors=\u0026quot;pt\u0026quot;,\n return_dict=True\n )\n\ninputs = inputs.to(device)\nmodel = AutoModelForCausalLM.from_pretrained(\n MODEL_PATH,\n torch_dtype=torch.bfloat16,\n low_cpu_mem_usage=True,\n trust_remote_code=True,\n device_map=\u0026quot;auto\u0026quot;\n).eval()\n\ngen_kwargs = {\u0026quot;max_length\u0026quot;: 2500, \u0026quot;do_sample\u0026quot;: True, \u0026quot;top_k\u0026quot;: 1}\nwith torch.no_grad():\n outputs = model.generate(**inputs, **gen_kwargs)\n outputs = outputs[:, inputs['input_ids'].shape[1]:]\n print(tokenizer.decode(outputs[0], skip_special_tokens=True))\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModelForCausalLM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eenviron\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'CUDA_VISIBLE_DEVICES'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'0'\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"THUDM/glm-4-9b-chat-hf\"\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"cuda\"\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecuda\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eis_available\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"cpu\"\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003equery\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"你好\"\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eapply_chat_template\u003c/span\u003e([{\u003cspan class=\"pl-s\"\u003e\"role\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"user\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"content\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003equery\u003c/span\u003e}],\n \u003cspan class=\"pl-s1\"\u003eadd_generation_prompt\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etokenize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ereturn_tensors\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"pt\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ereturn_dict\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e\n )\n\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModelForCausalLM\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\n \u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etorch_dtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebfloat16\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elow_cpu_mem_usage\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003edevice_map\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"auto\"\u003c/span\u003e\n).\u003cspan class=\"pl-c1\"\u003eeval\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003egen_kwargs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\u003cspan class=\"pl-s\"\u003e\"max_length\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e2500\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"do_sample\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"top_k\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e}\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eno_grad\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egenerate\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egen_kwargs\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[:, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'input_ids'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eshape\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]:]\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edecode\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eskip_special_tokens\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e使用 vLLM 后端进行推理:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from transformers import AutoTokenizer\nfrom vllm import LLM, SamplingParams\n\n# GLM-4-9B-Chat-1M\n# max_model_len, tp_size = 1048576, 4\n# 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_size\nmax_model_len, tp_size = 131072, 1\nmodel_name = \u0026quot;THUDM/glm-4-9b-chat-hf\u0026quot;\nprompt = [{\u0026quot;role\u0026quot;: \u0026quot;user\u0026quot;, \u0026quot;content\u0026quot;: \u0026quot;你好\u0026quot;}]\n\ntokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\nllm = LLM(\n model=model_name,\n tensor_parallel_size=tp_size,\n max_model_len=max_model_len,\n trust_remote_code=True,\n enforce_eager=True,\n # GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数\n # enable_chunked_prefill=True,\n # max_num_batched_tokens=8192\n)\nstop_token_ids = [151329, 151336, 151338]\nsampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)\n\ninputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)\noutputs = llm.generate(prompts=inputs, sampling_params=sampling_params)\n\nprint(outputs[0].outputs[0].text)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evllm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eLLM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eSamplingParams\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# GLM-4-9B-Chat-1M\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# max_model_len, tp_size = 1048576, 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_size\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emax_model_len\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etp_size\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e131072\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel_name\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"THUDM/glm-4-9b-chat-hf\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eprompt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [{\u003cspan class=\"pl-s\"\u003e\"role\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"user\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"content\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"你好\"\u003c/span\u003e}]\n\n\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ellm\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eLLM\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel_name\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etensor_parallel_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etp_size\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_model_len\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emax_model_len\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eenforce_eager\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-c\"\u003e# GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# enable_chunked_prefill=True,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# max_num_batched_tokens=8192\u003c/span\u003e\n)\n\u003cspan class=\"pl-s1\"\u003estop_token_ids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-c1\"\u003e151329\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e151336\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e151338\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003esampling_params\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSamplingParams\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etemperature\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.95\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_tokens\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estop_token_ids\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003estop_token_ids\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eapply_chat_template\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eprompt\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etokenize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eadd_generation_prompt\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ellm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egenerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eprompts\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esampling_params\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esampling_params\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003etext\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e使用以下方法快速调用 GLM-4V-9B 多模态模型\u003c/h3\u003e\u003ca id=\"user-content-使用以下方法快速调用-glm-4v-9b-多模态模型\" class=\"anchor\" aria-label=\"Permalink: 使用以下方法快速调用 GLM-4V-9B 多模态模型\" href=\"#使用以下方法快速调用-glm-4v-9b-多模态模型\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e使用 transformers 后端进行推理:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import torch\nfrom PIL import Image\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport os\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '0' # 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号\nMODEL_PATH = \u0026quot;THUDM/glm-4v-9b\u0026quot;\n\ndevice = \u0026quot;cuda\u0026quot; if torch.cuda.is_available() else \u0026quot;cpu\u0026quot;\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)\n\nquery = '描述这张图片'\nimage = Image.open(\u0026quot;your image\u0026quot;).convert('RGB')\ninputs = tokenizer.apply_chat_template([{\u0026quot;role\u0026quot;: \u0026quot;user\u0026quot;, \u0026quot;image\u0026quot;: image, \u0026quot;content\u0026quot;: query}],\n add_generation_prompt=True, tokenize=True, return_tensors=\u0026quot;pt\u0026quot;,\n return_dict=True) # chat mode\n\ninputs = inputs.to(device)\nmodel = AutoModelForCausalLM.from_pretrained(\n MODEL_PATH,\n torch_dtype=torch.bfloat16,\n low_cpu_mem_usage=True,\n trust_remote_code=True,\n device_map=\u0026quot;auto\u0026quot;\n).eval()\n\ngen_kwargs = {\u0026quot;max_length\u0026quot;: 2500, \u0026quot;do_sample\u0026quot;: True, \u0026quot;top_k\u0026quot;: 1}\nwith torch.no_grad():\n outputs = model.generate(**inputs, **gen_kwargs)\n outputs = outputs[:, inputs['input_ids'].shape[1]:]\n print(tokenizer.decode(outputs[0]))\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ePIL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eImage\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModelForCausalLM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eenviron\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'CUDA_VISIBLE_DEVICES'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'0'\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"THUDM/glm-4v-9b\"\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"cuda\"\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecuda\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eis_available\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"cpu\"\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003equery\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'描述这张图片'\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eImage\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"your image\"\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003econvert\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'RGB'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eapply_chat_template\u003c/span\u003e([{\u003cspan class=\"pl-s\"\u003e\"role\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"user\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"image\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"content\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003equery\u003c/span\u003e}],\n \u003cspan class=\"pl-s1\"\u003eadd_generation_prompt\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etokenize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ereturn_tensors\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"pt\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ereturn_dict\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# chat mode\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModelForCausalLM\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_pretrained\u003c/span\u003e(\n \u003cspan class=\"pl-c1\"\u003eMODEL_PATH\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etorch_dtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebfloat16\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elow_cpu_mem_usage\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003edevice_map\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"auto\"\u003c/span\u003e\n).\u003cspan class=\"pl-c1\"\u003eeval\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003egen_kwargs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\u003cspan class=\"pl-s\"\u003e\"max_length\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e2500\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"do_sample\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"top_k\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e}\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eno_grad\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egenerate\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egen_kwargs\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[:, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'input_ids'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eshape\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]:]\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edecode\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e]))\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e使用 vLLM 后端进行推理:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from PIL import Image\nfrom vllm import LLM, SamplingParams\n\nmodel_name = \u0026quot;THUDM/glm-4v-9b\u0026quot;\n\nllm = LLM(model=model_name,\n tensor_parallel_size=1,\n max_model_len=8192,\n trust_remote_code=True,\n enforce_eager=True)\nstop_token_ids = [151329, 151336, 151338]\nsampling_params = SamplingParams(temperature=0.2,\n max_tokens=1024,\n stop_token_ids=stop_token_ids)\n\nprompt = \u0026quot;What's the content of the image?\u0026quot;\nimage = Image.open(\u0026quot;your image\u0026quot;).convert('RGB')\ninputs = {\n \u0026quot;prompt\u0026quot;: prompt,\n \u0026quot;multi_modal_data\u0026quot;: {\n \u0026quot;image\u0026quot;: image\n },\n }\noutputs = llm.generate(inputs, sampling_params=sampling_params)\n\nfor o in outputs:\n generated_text = o.outputs[0].text\n print(generated_text)\n\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ePIL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eImage\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evllm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eLLM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eSamplingParams\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003emodel_name\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"THUDM/glm-4v-9b\"\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ellm\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eLLM\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel_name\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etensor_parallel_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_model_len\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8192\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etrust_remote_code\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eenforce_eager\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003estop_token_ids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-c1\"\u003e151329\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e151336\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e151338\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003esampling_params\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSamplingParams\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etemperature\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_tokens\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003estop_token_ids\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003estop_token_ids\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eprompt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"What's the content of the image?\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eImage\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"your image\"\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003econvert\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'RGB'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\n \u003cspan class=\"pl-s\"\u003e\"prompt\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eprompt\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"multi_modal_data\"\u003c/span\u003e: {\n \u003cspan class=\"pl-s\"\u003e\"image\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e\n },\n }\n\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ellm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egenerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esampling_params\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esampling_params\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003egenerated_text\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eo\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003etext\u003c/span\u003e\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egenerated_text\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e完整项目列表\u003c/h2\u003e\u003ca id=\"user-content-完整项目列表\" class=\"anchor\" aria-label=\"Permalink: 完整项目列表\" href=\"#完整项目列表\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e如果你想更进一步了解 GLM-4-9B 系列开源模型，本开源仓库通过以下内容为开发者提供基础的 GLM-4-9B 的使用和开发代码\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"/THUDM/GLM-4/blob/main/basic_demo/README.md\"\u003ebasic_demo\u003c/a\u003e: 在这里包含了\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e使用 transformers 和 vLLM 后端的交互代码\u003c/li\u003e\n\u003cli\u003eOpenAI API 后端交互代码\u003c/li\u003e\n\u003cli\u003eBatch 推理代码\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"/THUDM/GLM-4/blob/main/composite_demo/README.md\"\u003ecomposite_demo\u003c/a\u003e: 在这里包含了\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGLM-4-9B-Chat 以及 GLM-4V-9B 开源模型的完整功能演示代码，包含了 All Tools 能力、长文档解读和多模态能力的展示。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"/THUDM/GLM-4/blob/main/finetune_demo/README.md\"\u003efintune_demo\u003c/a\u003e: 在这里包含了\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePEFT (LORA, P-Tuning) 微调代码\u003c/li\u003e\n\u003cli\u003eSFT 微调代码\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"/THUDM/GLM-4/blob/main/intel_device_demo\"\u003eintel_device_demo\u003c/a\u003e: 在这里包含了\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e使用 OpenVINO 部署模型代码\u003c/li\u003e\n\u003cli\u003e使用 Intel® Extension for Transformers 部署模型代码\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e友情链接\u003c/h2\u003e\u003ca id=\"user-content-友情链接\" class=\"anchor\" aria-label=\"Permalink: 友情链接\" href=\"#友情链接\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hiyouga/LLaMA-Factory\"\u003eLLaMA-Factory\u003c/a\u003e: 高效开源微调框架，已支持 GLM-4-9B-Chat 语言模型微调。\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/modelscope/swift\"\u003eSWIFT\u003c/a\u003e: 魔搭社区的大模型/多模态大模型训练框架，已支持 GLM-4-9B-Chat / GLM-4V-9B\n模型微调。\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/xorbitsai/inference\"\u003eXorbits Inference\u003c/a\u003e: 性能强大且功能全面的分布式推理框架，轻松一键部署你自己的模型或内置的前沿开源模型。\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chatchat-space/Langchain-Chatchat\"\u003eLangChain-ChatChat\u003c/a\u003e: 基于 Langchain 与 ChatGLM 等语言模型的 RAG\n与 Agent 应用\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datawhalechina/self-llm/tree/master/models/GLM-4\"\u003eself-llm\u003c/a\u003e: Datawhale 团队的提供的 GLM-4-9B\n系列模型使用教程。\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/li-plus/chatglm.cpp\"\u003echatglm.cpp\u003c/a\u003e: 类似 llama.cpp 的量化加速推理方案，实现笔记本上实时对话\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/openvinotoolkit\"\u003eOpenVINO\u003c/a\u003e:\nIntel 开发的高性能 CPU,GPU及NPU 加速推理方案，可以参考此 \u003ca href=\"https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot-generate-api.ipynb\"\u003e步骤\u003c/a\u003e 部署 glm-4-9b-chat 模型。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e协议\u003c/h2\u003e\u003ca id=\"user-content-协议\" class=\"anchor\" aria-label=\"Permalink: 协议\" href=\"#协议\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGLM-4 模型的权重的使用则需要遵循 \u003ca href=\"https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE\" rel=\"nofollow\"\u003e模型协议\u003c/a\u003e。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e本开源仓库的代码则遵循 \u003ca href=\"/THUDM/GLM-4/blob/main/LICENSE\"\u003eApache 2.0\u003c/a\u003e 协议。\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e请您严格遵循开源协议。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e引用\u003c/h2\u003e\u003ca id=\"user-content-引用\" class=\"anchor\" aria-label=\"Permalink: 引用\" href=\"#引用\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e如果你觉得我们的工作有帮助的话，请考虑引用下列论文。\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{glm2024chatglm,\n title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, \n author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},\n year={2024},\n eprint={2406.12793},\n archivePrefix={arXiv},\n primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{glm2024chatglm,\n title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, \n author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},\n year={2024},\n eprint={2406.12793},\n archivePrefix={arXiv},\n primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{wang2023cogvlm,\n title={CogVLM: Visual Expert for Pretrained Language Models}, \n author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},\n year={2023},\n eprint={2311.03079},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{wang2023cogvlm,\n title={CogVLM: Visual Expert for Pretrained Language Models}, \n author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},\n year={2023},\n eprint={2311.03079},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"GLM-4","anchor":"glm-4","htmlText":"GLM-4"},{"level":2,"text":"项目更新","anchor":"项目更新","htmlText":"项目更新"},{"level":2,"text":"模型介绍","anchor":"模型介绍","htmlText":"模型介绍"},{"level":2,"text":"Model List","anchor":"model-list","htmlText":"Model List"},{"level":2,"text":"评测结果","anchor":"评测结果","htmlText":"评测结果"},{"level":3,"text":"对话模型典型任务","anchor":"对话模型典型任务","htmlText":"对话模型典型任务"},{"level":3,"text":"基座模型典型任务","anchor":"基座模型典型任务","htmlText":"基座模型典型任务"},{"level":3,"text":"长文本","anchor":"长文本","htmlText":"长文本"},{"level":3,"text":"多语言能力","anchor":"多语言能力","htmlText":"多语言能力"},{"level":3,"text":"工具调用能力","anchor":"工具调用能力","htmlText":"工具调用能力"},{"level":3,"text":"多模态能力","anchor":"多模态能力","htmlText":"多模态能力"},{"level":2,"text":"快速调用","anchor":"快速调用","htmlText":"快速调用"},{"level":3,"text":"使用以下方法快速调用 GLM-4-9B-Chat 语言模型","anchor":"使用以下方法快速调用-glm-4-9b-chat-语言模型","htmlText":"使用以下方法快速调用 GLM-4-9B-Chat 语言模型"},{"level":3,"text":"使用以下方法快速调用 GLM-4V-9B 多模态模型","anchor":"使用以下方法快速调用-glm-4v-9b-多模态模型","htmlText":"使用以下方法快速调用 GLM-4V-9B 多模态模型"},{"level":2,"text":"完整项目列表","anchor":"完整项目列表","htmlText":"完整项目列表"},{"level":2,"text":"友情链接","anchor":"友情链接","htmlText":"友情链接"},{"level":2,"text":"协议","anchor":"协议","htmlText":"协议"},{"level":2,"text":"引用","anchor":"引用","htmlText":"引用"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FTHUDM%2FGLM-4"}},{"displayName":"LICENSE","repoName":"GLM-4","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FTHUDM%2FGLM-4"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> 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0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p align="center" dir="auto"> 📄<a href="https://arxiv.org/pdf/2406.12793" rel="nofollow"> Report </a> • 🤗 <a href="https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7" rel="nofollow">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" rel="nofollow">ModelScope</a> • 🟣 <a href="https://wisemodel.cn/models/ZhipuAI/glm-4-9b-chat" rel="nofollow">WiseModel</a> • 🐦 <a href="https://twitter.com/thukeg" rel="nofollow">Twitter</a> • 👋 加入我们的 <a href="https://discord.gg/8cnQKdAprg" rel="nofollow">Discord</a> 和 <a href="/THUDM/GLM-4/blob/main/resources/WECHAT.md">微信</a> </p> <p align="center" dir="auto"> 📍在 <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9" rel="nofollow">智谱AI开放平台</a> 体验和使用更大规模的 GLM 商业模型。 </p> <p dir="auto">Read this in <a href="/THUDM/GLM-4/blob/main/README_en.md">English</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">项目更新</h2><a id="user-content-项目更新" class="anchor" aria-label="Permalink: 项目更新" href="#项目更新"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>🔥🔥 <strong>News</strong>: <code>2024/12/10</code>: 本仓库微调代码支持使用<code>Ascend NPU</code>进行微调。请更新微调代码并查看代码内注释。</li> <li>🔥 <strong>News</strong>: <code>2024/11/01</code>: 本仓库依赖进行升级，请更新<code>requirements.txt</code>中的依赖以保证正常运行模型。<a href="https://huggingface.co/THUDM/glm-4-9b-chat-hf" rel="nofollow">glm-4-9b-chat-hf</a> 是适配 <code>transformers>=4.46.2</code> 的模型权重，使用 <code>transformers</code> 库中的 <code>GlmModel</code> 类实现。同时，<a href="https://huggingface.co/THUDM/glm-4-9b-chat" rel="nofollow">glm-4-9b-chat</a>, <a href="https://huggingface.co/THUDM/glm-4v-9b" rel="nofollow">glm-4v-9b</a> 中的 <code>tokenzier_chatglm.py</code> 已经更新以适配最新版本的 <code>transformers</code>库。请前往 HuggingFace 更新文件。</li> <li>🔥 <strong>News</strong>: <code>2024/10/27</code>: 我们开源了 <a href="https://github.com/THUDM/LongReward">LongReward</a>，这是一个使用 AI 反馈改进长上下文大型语言模型。</li> <li>🔥 <strong>News</strong>: <code>2024/10/25</code>: 我们开源了端到端中英语音对话模型 <a href="https://github.com/THUDM/GLM-4-Voice">GLM-4-Voice</a>。</li> <li>🔥 <strong>News</strong>: <code>2024/09/05</code> 我们开源了使LLMs能够在长上下文问答中生成细粒度引用的模型 <a href="https://huggingface.co/THUDM/LongCite-glm4-9b" rel="nofollow">longcite-glm4-9b</a> 以及数据集 <a href="https://huggingface.co/datasets/THUDM/LongCite-45k" rel="nofollow">LongCite-45k</a>, 欢迎在 <a href="https://huggingface.co/spaces/THUDM/LongCite" rel="nofollow">Huggingface Space</a> 在线体验。</li> <li>🔥<strong>News</strong>: <code>2024/08/15</code>: 我们开源具备长文本输出能力(单轮对话大模型输出可超过1万token) 的模型 <a href="https://huggingface.co/THUDM/LongWriter-glm4-9b" rel="nofollow">longwriter-glm4-9b</a> 以及数据集 <a href="https://huggingface.co/datasets/THUDM/LongWriter-6k" rel="nofollow">LongWriter-6k</a>, 欢迎在 <a href="https://huggingface.co/spaces/THUDM/LongWriter" rel="nofollow">Huggingface Space</a> 或 <a href="https://modelscope.cn/studios/ZhipuAI/LongWriter-glm4-9b-demo" rel="nofollow">魔搭社区空间</a> 在线体验。</li> <li>🔥 <strong>News</strong>: <code>2024/07/24</code>: 我们发布了与长文本相关的最新技术解读，关注 <a href="https://medium.com/@ChatGLM/glm-long-scaling-pre-trained-model-contexts-to-millions-caa3c48dea85" rel="nofollow">这里</a> 查看我们在训练 GLM-4-9B 开源模型中关于长文本技术的技术报告。</li> <li>🔥 <strong>News</strong>: <code>2024/07/09</code>: GLM-4-9B-Chat 模型已适配 <a href="https://github.com/ollama/ollama">Ollama</a>, <a href="https://github.com/ggerganov/llama.cpp">Llama.cpp</a>，您可以在 <a href="https://github.com/ggerganov/llama.cpp/pull/8031" data-hovercard-type="pull_request" data-hovercard-url="/ggml-org/llama.cpp/pull/8031/hovercard">PR</a> 查看具体的细节。</li> <li>🔥 <strong>News</strong>: <code>2024/06/18</code>: 我们发布 <a href="https://arxiv.org/pdf/2406.12793" rel="nofollow">技术报告</a>, 欢迎查看。</li> <li>🔥 <strong>News</strong>: <code>2024/06/05</code>: 我们发布 GLM-4-9B 系列开源模型。</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">模型介绍</h2><a id="user-content-模型介绍" class="anchor" aria-label="Permalink: 模型介绍" href="#模型介绍"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">GLM-4-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源版本。在语义、数学、推理、代码和知识等多方面的数据集测评中， <strong>GLM-4-9B</strong> 及其人类偏好对齐的版本 <strong>GLM-4-9B-Chat</strong> 均表现出超越 Llama-3-8B 的卓越性能。除了能进行多轮对话，GLM-4-9B-Chat 还具备网页浏览、代码执行、自定义工具调用（Function Call）和长文本推理（支持最大 128K 上下文）等高级功能。本代模型增加了多语言支持，支持包括日语，韩语，德语在内的 26 种语言。我们还推出了支持 1M 上下文长度（约 200 万中文字符）的 <strong>GLM-4-9B-Chat-1M</strong> 模型和基于 GLM-4-9B 的多模态模型 GLM-4V-9B。<strong>GLM-4V-9B</strong> 具备 1120 * 1120 高分辨率下的中英双语多轮对话能力，在中英文综合能力、感知推理、文字识别、图表理解等多方面多模态评测中，GLM-4V-9B 表现出超越 GPT-4-turbo-2024-04-09、Gemini 1.0 Pro、Qwen-VL-Max 和 Claude 3 Opus 的卓越性能。</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model List</h2><a id="user-content-model-list" class="anchor" aria-label="Permalink: Model List" href="#model-list"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Model</th> <th align="center">Type</th> <th align="center">Seq Length</th> <th align="center">Transformers Version</th> <th align="center">Download</th> <th align="center">Online Demo</th> </tr> </thead> <tbody> <tr> <td align="center">GLM-4-9B</td> <td align="center">Base</td> <td align="center">8K</td> <td align="center"><code>4.44.0 - 4.45.0</code></td> <td align="center"><a href="https://huggingface.co/THUDM/glm-4-9b" rel="nofollow">🤗 Huggingface</a><br> <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b" rel="nofollow">🤖 ModelScope</a><br> <a href="https://wisemodel.cn/models/ZhipuAI/glm-4-9b" rel="nofollow">🟣 WiseModel</a></td> <td align="center">/</td> </tr> <tr> <td align="center">GLM-4-9B-Chat</td> <td align="center">Chat</td> <td align="center">128K</td> <td align="center"><code>>= 4.44.0</code></td> <td align="center"><a href="https://huggingface.co/THUDM/glm-4-9b-chat" rel="nofollow">🤗 Huggingface</a><br> <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" rel="nofollow">🤖 ModelScope</a><br> <a href="https://wisemodel.cn/models/ZhipuAI/GLM-4-9B-Chat" rel="nofollow">🟣 WiseModel</a></td> <td align="center"><a href="https://modelscope.cn/studios/dash-infer/GLM-4-Chat-DashInfer-Demo/summary" rel="nofollow">🤖 ModelScope CPU</a><br> <a href="https://modelscope.cn/studios/ZhipuAI/glm-4-9b-chat-vllm/summary" rel="nofollow">🤖 ModelScope vLLM</a></td> </tr> <tr> <td align="center">GLM-4-9B-Chat-HF</td> <td align="center">Chat</td> <td align="center">128K</td> <td align="center"><code>>= 4.46.0</code></td> <td align="center"><a href="https://huggingface.co/THUDM/glm-4-9b-chat-hf" rel="nofollow">🤗 Huggingface</a><br> <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-hf" rel="nofollow">🤖 ModelScope</a></td> <td align="center"><a href="https://modelscope.cn/studios/dash-infer/GLM-4-Chat-DashInfer-Demo/summary" rel="nofollow">🤖 ModelScope CPU</a><br> <a href="https://modelscope.cn/studios/ZhipuAI/glm-4-9b-chat-vllm/summary" rel="nofollow">🤖 ModelScope vLLM</a></td> </tr> <tr> <td align="center">GLM-4-9B-Chat-1M</td> <td align="center">Chat</td> <td align="center">1M</td> <td align="center"><code>>= 4.44.0</code></td> <td align="center"><a href="https://huggingface.co/THUDM/glm-4-9b-chat-1m" rel="nofollow">🤗 Huggingface</a><br> <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-1m" rel="nofollow">🤖 ModelScope</a><br> <a href="https://wisemodel.cn/models/ZhipuAI/GLM-4-9B-Chat-1M" rel="nofollow">🟣 WiseModel</a></td> <td align="center">/</td> </tr> <tr> <td align="center">GLM-4-9B-Chat-1M-HF</td> <td align="center">Chat</td> <td align="center">1M</td> <td align="center"><code>>= 4.46.0</code></td> <td align="center"><a href="https://huggingface.co/THUDM/glm-4-9b-chat-1m-hf" rel="nofollow">🤗 Huggingface</a><br> <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-1m-hf" rel="nofollow">🤖 ModelScope</a></td> <td align="center">/</td> </tr> <tr> <td align="center">GLM-4V-9B</td> <td align="center">Chat</td> <td align="center">8K</td> <td align="center"><code>>= 4.46.0</code></td> <td align="center"><a href="https://huggingface.co/THUDM/glm-4v-9b" rel="nofollow">🤗 Huggingface</a><br> <a href="https://modelscope.cn/models/ZhipuAI/glm-4v-9b" rel="nofollow">🤖 ModelScope</a><br> <a href="https://wisemodel.cn/models/ZhipuAI/GLM-4V-9B" rel="nofollow">🟣 WiseModel</a></td> <td align="center"><a href="https://modelscope.cn/studios/ZhipuAI/glm-4v-9b-Demo/summary" rel="nofollow">🤖 ModelScope</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">评测结果</h2><a id="user-content-评测结果" class="anchor" aria-label="Permalink: 评测结果" href="#评测结果"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">对话模型典型任务</h3><a id="user-content-对话模型典型任务" class="anchor" aria-label="Permalink: 对话模型典型任务" href="#对话模型典型任务"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">Model</th> <th align="center">AlignBench</th> <th align="center">MT-Bench</th> <th align="center">IFEval</th> <th align="center">MMLU</th> <th align="center">C-Eval</th> <th align="center">GSM8K</th> <th align="center">MATH</th> <th align="center">HumanEval</th> <th align="center">NaturalCodeBench</th> </tr> </thead> <tbody> <tr> <td align="left">Llama-3-8B-Instruct</td> <td align="center">6.40</td> <td align="center">8.00</td> <td align="center">68.6</td> <td align="center">68.4</td> <td align="center">51.3</td> <td align="center">79.6</td> <td align="center">30.0</td> <td align="center">62.2</td> <td align="center">24.7</td> </tr> <tr> <td align="left">ChatGLM3-6B</td> <td align="center">5.18</td> <td align="center">5.50</td> <td align="center">28.1</td> <td align="center">61.4</td> <td align="center">69.0</td> <td align="center">72.3</td> <td align="center">25.7</td> <td align="center">58.5</td> <td align="center">11.3</td> </tr> <tr> <td align="left">GLM-4-9B-Chat</td> <td align="center">7.01</td> <td align="center">8.35</td> <td align="center">69.0</td> <td align="center">72.4</td> <td align="center">75.6</td> <td align="center">79.6</td> <td align="center">50.6</td> <td align="center">71.8</td> <td align="center">32.2</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">基座模型典型任务</h3><a id="user-content-基座模型典型任务" class="anchor" aria-label="Permalink: 基座模型典型任务" href="#基座模型典型任务"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">Model</th> <th align="center">MMLU</th> <th align="center">C-Eval</th> <th align="center">GPQA</th> <th align="center">GSM8K</th> <th align="center">MATH</th> <th align="center">HumanEval</th> </tr> </thead> <tbody> <tr> <td align="left">Llama-3-8B</td> <td align="center">66.6</td> <td align="center">51.2</td> <td align="center">-</td> <td align="center">45.8</td> <td align="center">-</td> <td align="center">33.5</td> </tr> <tr> <td align="left">Llama-3-8B-Instruct</td> <td align="center">68.4</td> <td align="center">51.3</td> <td align="center">34.2</td> <td align="center">79.6</td> <td align="center">30.0</td> <td align="center">62.2</td> </tr> <tr> <td align="left">ChatGLM3-6B-Base</td> <td align="center">61.4</td> <td align="center">69.0</td> <td align="center">26.8</td> <td align="center">72.3</td> <td align="center">25.7</td> <td align="center">58.5</td> </tr> <tr> <td align="left">GLM-4-9B</td> <td align="center">74.7</td> <td align="center">77.1</td> <td align="center">34.3</td> <td align="center">84.0</td> <td align="center">30.4</td> <td align="center">70.1</td> </tr> </tbody> </table></markdown-accessiblity-table> <blockquote> <p dir="auto">由于 <code>GLM-4-9B</code> 在预训练过程中加入了部分数学、推理、代码相关的 instruction 数据，所以将 Llama-3-8B-Instruct 也列入比较范围。</p> </blockquote> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">长文本</h3><a id="user-content-长文本" class="anchor" aria-label="Permalink: 长文本" href="#长文本"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">在 1M 的上下文长度下进行<a href="https://github.com/LargeWorldModel/LWM/blob/main/scripts/eval_needle.py">大海捞针实验</a>，结果如下：</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/THUDM/GLM-4/blob/main/resources/eval_needle.jpeg"><img src="/THUDM/GLM-4/raw/main/resources/eval_needle.jpeg" alt="needle" style="max-width: 100%;"></a></p> <p dir="auto">在 LongBench-Chat 上对长文本能力进行了进一步评测，结果如下:</p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/THUDM/GLM-4/blob/main/resources/longbench.png"><img src="/THUDM/GLM-4/raw/main/resources/longbench.png" alt="描述文字" style="width: 65%; max-width: 100%;"></a> </p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">多语言能力</h3><a id="user-content-多语言能力" class="anchor" aria-label="Permalink: 多语言能力" href="#多语言能力"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">在六个多语言数据集上对 GLM-4-9B-Chat 和 Llama-3-8B-Instruct 进行了测试，测试结果及数据集对应选取语言如下表</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">Dataset</th> <th align="center">Llama-3-8B-Instruct</th> <th align="center">GLM-4-9B-Chat</th> <th align="center">Languages</th> </tr> </thead> <tbody> <tr> <td align="left">M-MMLU</td> <td align="center">49.6</td> <td align="center">56.6</td> <td align="center">all</td> </tr> <tr> <td align="left">FLORES</td> <td align="center">25.0</td> <td align="center">28.8</td> <td align="center">ru, es, de, fr, it, pt, pl, ja, nl, ar, tr, cs, vi, fa, hu, el, ro, sv, uk, fi, ko, da, bg, no</td> </tr> <tr> <td align="left">MGSM</td> <td align="center">54.0</td> <td align="center">65.3</td> <td align="center">zh, en, bn, de, es, fr, ja, ru, sw, te, th</td> </tr> <tr> <td align="left">XWinograd</td> <td align="center">61.7</td> <td align="center">73.1</td> <td align="center">zh, en, fr, jp, ru, pt</td> </tr> <tr> <td align="left">XStoryCloze</td> <td align="center">84.7</td> <td align="center">90.7</td> <td align="center">zh, en, ar, es, eu, hi, id, my, ru, sw, te</td> </tr> <tr> <td align="left">XCOPA</td> <td align="center">73.3</td> <td align="center">80.1</td> <td align="center">zh, et, ht, id, it, qu, sw, ta, th, tr, vi</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">工具调用能力</h3><a id="user-content-工具调用能力" class="anchor" aria-label="Permalink: 工具调用能力" href="#工具调用能力"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">我们在 <a href="https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard">Berkeley Function Calling Leaderboard</a> 上进行了测试并得到了以下结果：</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">Model</th> <th align="center">Overall Acc.</th> <th align="center">AST Summary</th> <th align="center">Exec Summary</th> <th align="center">Relevance</th> </tr> </thead> <tbody> <tr> <td align="left">Llama-3-8B-Instruct</td> <td align="center">58.88</td> <td align="center">59.25</td> <td align="center">70.01</td> <td align="center">45.83</td> </tr> <tr> <td align="left">gpt-4-turbo-2024-04-09</td> <td align="center">81.24</td> <td align="center">82.14</td> <td align="center">78.61</td> <td align="center">88.75</td> </tr> <tr> <td align="left">ChatGLM3-6B</td> <td align="center">57.88</td> <td align="center">62.18</td> <td align="center">69.78</td> <td align="center">5.42</td> </tr> <tr> <td align="left">GLM-4-9B-Chat</td> <td align="center">81.00</td> <td align="center">80.26</td> <td align="center">84.40</td> <td align="center">87.92</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">多模态能力</h3><a id="user-content-多模态能力" class="anchor" aria-label="Permalink: 多模态能力" href="#多模态能力"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">GLM-4V-9B 是一个多模态语言模型，具备视觉理解能力，其相关经典任务的评测结果如下：</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th><strong>MMBench-EN-Test</strong></th> <th><strong>MMBench-CN-Test</strong></th> <th><strong>SEEDBench_IMG</strong></th> <th><strong>MMStar</strong></th> <th><strong>MMMU</strong></th> <th><strong>MME</strong></th> <th><strong>HallusionBench</strong></th> <th><strong>AI2D</strong></th> <th><strong>OCRBench</strong></th> </tr> </thead> <tbody> <tr> <td><strong>gpt-4o-2024-05-13</strong></td> <td>83.4</td> <td>82.1</td> <td>77.1</td> <td>63.9</td> <td>69.2</td> <td>2310.3</td> <td>55.0</td> <td>84.6</td> <td>736</td> </tr> <tr> <td><strong>gpt-4-turbo-2024-04-09</strong></td> <td>81.0</td> <td>80.2</td> <td>73.0</td> <td>56.0</td> <td>61.7</td> <td>2070.2</td> <td>43.9</td> <td>78.6</td> <td>656</td> </tr> <tr> <td><strong>gpt-4-1106-preview</strong></td> <td>77.0</td> <td>74.4</td> <td>72.3</td> <td>49.7</td> <td>53.8</td> <td>1771.5</td> <td>46.5</td> <td>75.9</td> <td>516</td> </tr> <tr> <td><strong>InternVL-Chat-V1.5</strong></td> <td>82.3</td> <td>80.7</td> <td>75.2</td> <td>57.1</td> <td>46.8</td> <td>2189.6</td> <td>47.4</td> <td>80.6</td> <td>720</td> </tr> <tr> <td><strong>LLaVA-Next-Yi-34B</strong></td> <td>81.1</td> <td>79.0</td> <td>75.7</td> <td>51.6</td> <td>48.8</td> <td>2050.2</td> <td>34.8</td> <td>78.9</td> <td>574</td> </tr> <tr> <td><strong>Step-1V</strong></td> <td>80.7</td> <td>79.9</td> <td>70.3</td> <td>50.0</td> <td>49.9</td> <td>2206.4</td> <td>48.4</td> <td>79.2</td> <td>625</td> </tr> <tr> <td><strong>MiniCPM-Llama3-V2.5</strong></td> <td>77.6</td> <td>73.8</td> <td>72.3</td> <td>51.8</td> <td>45.8</td> <td>2024.6</td> <td>42.4</td> <td>78.4</td> <td>725</td> </tr> <tr> <td><strong>Qwen-VL-Max</strong></td> <td>77.6</td> <td>75.7</td> <td>72.7</td> <td>49.5</td> <td>52.0</td> <td>2281.7</td> <td>41.2</td> <td>75.7</td> <td>684</td> </tr> <tr> <td><strong>Gemini 1.0 Pro</strong></td> <td>73.6</td> <td>74.3</td> <td>70.7</td> <td>38.6</td> <td>49.0</td> <td>2148.9</td> <td>45.7</td> <td>72.9</td> <td>680</td> </tr> <tr> <td><strong>Claude 3 Opus</strong></td> <td>63.3</td> <td>59.2</td> <td>64.0</td> <td>45.7</td> <td>54.9</td> <td>1586.8</td> <td>37.8</td> <td>70.6</td> <td>694</td> </tr> <tr> <td><strong>GLM-4V-9B</strong></td> <td>81.1</td> <td>79.4</td> <td>76.8</td> <td>58.7</td> <td>47.2</td> <td>2163.8</td> <td>46.6</td> <td>81.1</td> <td>786</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">快速调用</h2><a id="user-content-快速调用" class="anchor" aria-label="Permalink: 快速调用" href="#快速调用"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>硬件配置和系统要求，请查看<a href="/THUDM/GLM-4/blob/main/basic_demo/README.md">这里</a>。</strong></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">使用以下方法快速调用 GLM-4-9B-Chat 语言模型</h3><a id="user-content-使用以下方法快速调用-glm-4-9b-chat-语言模型" class="anchor" aria-label="Permalink: 使用以下方法快速调用 GLM-4-9B-Chat 语言模型" href="#使用以下方法快速调用-glm-4-9b-chat-语言模型"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">使用 transformers 后端进行推理:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import torch from transformers import AutoModelForCausalLM, AutoTokenizer import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' # 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号 MODEL_PATH = "THUDM/glm-4-9b-chat-hf" device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) query = "你好" inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True ) inputs = inputs.to(device) model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, device_map="auto" ).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0], skip_special_tokens=True))"><pre><span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">AutoModelForCausalLM</span>, <span class="pl-v">AutoTokenizer</span> <span class="pl-k">import</span> <span class="pl-s1">os</span> <span class="pl-s1">os</span>.<span class="pl-c1">environ</span>[<span class="pl-s">'CUDA_VISIBLE_DEVICES'</span>] <span class="pl-c1">=</span> <span class="pl-s">'0'</span> <span class="pl-c"># 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号</span> <span class="pl-c1">MODEL_PATH</span> <span class="pl-c1">=</span> <span class="pl-s">"THUDM/glm-4-9b-chat-hf"</span> <span class="pl-s1">device</span> <span class="pl-c1">=</span> <span class="pl-s">"cuda"</span> <span class="pl-k">if</span> <span class="pl-s1">torch</span>.<span class="pl-c1">cuda</span>.<span class="pl-c1">is_available</span>() <span class="pl-k">else</span> <span class="pl-s">"cpu"</span> <span class="pl-s1">tokenizer</span> <span class="pl-c1">=</span> <span class="pl-v">AutoTokenizer</span>.<span class="pl-c1">from_pretrained</span>(<span class="pl-c1">MODEL_PATH</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-s1">query</span> <span class="pl-c1">=</span> <span class="pl-s">"你好"</span> <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-s1">tokenizer</span>.<span class="pl-c1">apply_chat_template</span>([{<span class="pl-s">"role"</span>: <span class="pl-s">"user"</span>, <span class="pl-s">"content"</span>: <span class="pl-s1">query</span>}], <span class="pl-s1">add_generation_prompt</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">tokenize</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">return_tensors</span><span class="pl-c1">=</span><span class="pl-s">"pt"</span>, <span class="pl-s1">return_dict</span><span class="pl-c1">=</span><span class="pl-c1">True</span> ) <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-s1">inputs</span>.<span class="pl-c1">to</span>(<span class="pl-s1">device</span>) <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModelForCausalLM</span>.<span class="pl-c1">from_pretrained</span>( <span class="pl-c1">MODEL_PATH</span>, <span class="pl-s1">torch_dtype</span><span class="pl-c1">=</span><span class="pl-s1">torch</span>.<span class="pl-c1">bfloat16</span>, <span class="pl-s1">low_cpu_mem_usage</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">device_map</span><span class="pl-c1">=</span><span class="pl-s">"auto"</span> ).<span class="pl-c1">eval</span>() <span class="pl-s1">gen_kwargs</span> <span class="pl-c1">=</span> {<span class="pl-s">"max_length"</span>: <span class="pl-c1">2500</span>, <span class="pl-s">"do_sample"</span>: <span class="pl-c1">True</span>, <span class="pl-s">"top_k"</span>: <span class="pl-c1">1</span>} <span class="pl-k">with</span> <span class="pl-s1">torch</span>.<span class="pl-c1">no_grad</span>(): <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">model</span>.<span class="pl-c1">generate</span>(<span class="pl-c1">**</span><span class="pl-s1">inputs</span>, <span class="pl-c1">**</span><span class="pl-s1">gen_kwargs</span>) <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">outputs</span>[:, <span class="pl-s1">inputs</span>[<span class="pl-s">'input_ids'</span>].<span class="pl-c1">shape</span>[<span class="pl-c1">1</span>]:] <span class="pl-en">print</span>(<span class="pl-s1">tokenizer</span>.<span class="pl-c1">decode</span>(<span class="pl-s1">outputs</span>[<span class="pl-c1">0</span>], <span class="pl-s1">skip_special_tokens</span><span class="pl-c1">=</span><span class="pl-c1">True</span>))</pre></div> <p dir="auto">使用 vLLM 后端进行推理:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from transformers import AutoTokenizer from vllm import LLM, SamplingParams # GLM-4-9B-Chat-1M # max_model_len, tp_size = 1048576, 4 # 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_size max_model_len, tp_size = 131072, 1 model_name = "THUDM/glm-4-9b-chat-hf" prompt = [{"role": "user", "content": "你好"}] tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) llm = LLM( model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True, # GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数 # enable_chunked_prefill=True, # max_num_batched_tokens=8192 ) stop_token_ids = [151329, 151336, 151338] sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids) inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True) outputs = llm.generate(prompts=inputs, sampling_params=sampling_params) print(outputs[0].outputs[0].text)"><pre><span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">AutoTokenizer</span> <span class="pl-k">from</span> <span class="pl-s1">vllm</span> <span class="pl-k">import</span> <span class="pl-c1">LLM</span>, <span class="pl-v">SamplingParams</span> <span class="pl-c"># GLM-4-9B-Chat-1M</span> <span class="pl-c"># max_model_len, tp_size = 1048576, 4</span> <span class="pl-c"># 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_size</span> <span class="pl-s1">max_model_len</span>, <span class="pl-s1">tp_size</span> <span class="pl-c1">=</span> <span class="pl-c1">131072</span>, <span class="pl-c1">1</span> <span class="pl-s1">model_name</span> <span class="pl-c1">=</span> <span class="pl-s">"THUDM/glm-4-9b-chat-hf"</span> <span class="pl-s1">prompt</span> <span class="pl-c1">=</span> [{<span class="pl-s">"role"</span>: <span class="pl-s">"user"</span>, <span class="pl-s">"content"</span>: <span class="pl-s">"你好"</span>}] <span class="pl-s1">tokenizer</span> <span class="pl-c1">=</span> <span class="pl-v">AutoTokenizer</span>.<span class="pl-c1">from_pretrained</span>(<span class="pl-s1">model_name</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-s1">llm</span> <span class="pl-c1">=</span> <span class="pl-en">LLM</span>( <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">model_name</span>, <span class="pl-s1">tensor_parallel_size</span><span class="pl-c1">=</span><span class="pl-s1">tp_size</span>, <span class="pl-s1">max_model_len</span><span class="pl-c1">=</span><span class="pl-s1">max_model_len</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">enforce_eager</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-c"># GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数</span> <span class="pl-c"># enable_chunked_prefill=True,</span> <span class="pl-c"># max_num_batched_tokens=8192</span> ) <span class="pl-s1">stop_token_ids</span> <span class="pl-c1">=</span> [<span class="pl-c1">151329</span>, <span class="pl-c1">151336</span>, <span class="pl-c1">151338</span>] <span class="pl-s1">sampling_params</span> <span class="pl-c1">=</span> <span class="pl-en">SamplingParams</span>(<span class="pl-s1">temperature</span><span class="pl-c1">=</span><span class="pl-c1">0.95</span>, <span class="pl-s1">max_tokens</span><span class="pl-c1">=</span><span class="pl-c1">1024</span>, <span class="pl-s1">stop_token_ids</span><span class="pl-c1">=</span><span class="pl-s1">stop_token_ids</span>) <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-s1">tokenizer</span>.<span class="pl-c1">apply_chat_template</span>(<span class="pl-s1">prompt</span>, <span class="pl-s1">tokenize</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">add_generation_prompt</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">llm</span>.<span class="pl-c1">generate</span>(<span class="pl-s1">prompts</span><span class="pl-c1">=</span><span class="pl-s1">inputs</span>, <span class="pl-s1">sampling_params</span><span class="pl-c1">=</span><span class="pl-s1">sampling_params</span>) <span class="pl-en">print</span>(<span class="pl-s1">outputs</span>[<span class="pl-c1">0</span>].<span class="pl-c1">outputs</span>[<span class="pl-c1">0</span>].<span class="pl-c1">text</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">使用以下方法快速调用 GLM-4V-9B 多模态模型</h3><a id="user-content-使用以下方法快速调用-glm-4v-9b-多模态模型" class="anchor" aria-label="Permalink: 使用以下方法快速调用 GLM-4V-9B 多模态模型" href="#使用以下方法快速调用-glm-4v-9b-多模态模型"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">使用 transformers 后端进行推理:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import torch from PIL import Image from transformers import AutoModelForCausalLM, AutoTokenizer import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' # 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号 MODEL_PATH = "THUDM/glm-4v-9b" device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) query = '描述这张图片' image = Image.open("your image").convert('RGB') inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True) # chat mode inputs = inputs.to(device) model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, device_map="auto" ).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0]))"><pre><span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-k">from</span> <span class="pl-c1">PIL</span> <span class="pl-k">import</span> <span class="pl-v">Image</span> <span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">AutoModelForCausalLM</span>, <span class="pl-v">AutoTokenizer</span> <span class="pl-k">import</span> <span class="pl-s1">os</span> <span class="pl-s1">os</span>.<span class="pl-c1">environ</span>[<span class="pl-s">'CUDA_VISIBLE_DEVICES'</span>] <span class="pl-c1">=</span> <span class="pl-s">'0'</span> <span class="pl-c"># 设置 GPU 编号，如果单机单卡指定一个，单机多卡指定多个 GPU 编号</span> <span class="pl-c1">MODEL_PATH</span> <span class="pl-c1">=</span> <span class="pl-s">"THUDM/glm-4v-9b"</span> <span class="pl-s1">device</span> <span class="pl-c1">=</span> <span class="pl-s">"cuda"</span> <span class="pl-k">if</span> <span class="pl-s1">torch</span>.<span class="pl-c1">cuda</span>.<span class="pl-c1">is_available</span>() <span class="pl-k">else</span> <span class="pl-s">"cpu"</span> <span class="pl-s1">tokenizer</span> <span class="pl-c1">=</span> <span class="pl-v">AutoTokenizer</span>.<span class="pl-c1">from_pretrained</span>(<span class="pl-c1">MODEL_PATH</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-s1">query</span> <span class="pl-c1">=</span> <span class="pl-s">'描述这张图片'</span> <span class="pl-s1">image</span> <span class="pl-c1">=</span> <span class="pl-v">Image</span>.<span class="pl-c1">open</span>(<span class="pl-s">"your image"</span>).<span class="pl-c1">convert</span>(<span class="pl-s">'RGB'</span>) <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-s1">tokenizer</span>.<span class="pl-c1">apply_chat_template</span>([{<span class="pl-s">"role"</span>: <span class="pl-s">"user"</span>, <span class="pl-s">"image"</span>: <span class="pl-s1">image</span>, <span class="pl-s">"content"</span>: <span class="pl-s1">query</span>}], <span class="pl-s1">add_generation_prompt</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">tokenize</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">return_tensors</span><span class="pl-c1">=</span><span class="pl-s">"pt"</span>, <span class="pl-s1">return_dict</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c"># chat mode</span> <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-s1">inputs</span>.<span class="pl-c1">to</span>(<span class="pl-s1">device</span>) <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModelForCausalLM</span>.<span class="pl-c1">from_pretrained</span>( <span class="pl-c1">MODEL_PATH</span>, <span class="pl-s1">torch_dtype</span><span class="pl-c1">=</span><span class="pl-s1">torch</span>.<span class="pl-c1">bfloat16</span>, <span class="pl-s1">low_cpu_mem_usage</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">device_map</span><span class="pl-c1">=</span><span class="pl-s">"auto"</span> ).<span class="pl-c1">eval</span>() <span class="pl-s1">gen_kwargs</span> <span class="pl-c1">=</span> {<span class="pl-s">"max_length"</span>: <span class="pl-c1">2500</span>, <span class="pl-s">"do_sample"</span>: <span class="pl-c1">True</span>, <span class="pl-s">"top_k"</span>: <span class="pl-c1">1</span>} <span class="pl-k">with</span> <span class="pl-s1">torch</span>.<span class="pl-c1">no_grad</span>(): <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">model</span>.<span class="pl-c1">generate</span>(<span class="pl-c1">**</span><span class="pl-s1">inputs</span>, <span class="pl-c1">**</span><span class="pl-s1">gen_kwargs</span>) <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">outputs</span>[:, <span class="pl-s1">inputs</span>[<span class="pl-s">'input_ids'</span>].<span class="pl-c1">shape</span>[<span class="pl-c1">1</span>]:] <span class="pl-en">print</span>(<span class="pl-s1">tokenizer</span>.<span class="pl-c1">decode</span>(<span class="pl-s1">outputs</span>[<span class="pl-c1">0</span>]))</pre></div> <p dir="auto">使用 vLLM 后端进行推理:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from PIL import Image from vllm import LLM, SamplingParams model_name = "THUDM/glm-4v-9b" llm = LLM(model=model_name, tensor_parallel_size=1, max_model_len=8192, trust_remote_code=True, enforce_eager=True) stop_token_ids = [151329, 151336, 151338] sampling_params = SamplingParams(temperature=0.2, max_tokens=1024, stop_token_ids=stop_token_ids) prompt = "What's the content of the image?" image = Image.open("your image").convert('RGB') inputs = { "prompt": prompt, "multi_modal_data": { "image": image }, } outputs = llm.generate(inputs, sampling_params=sampling_params) for o in outputs: generated_text = o.outputs[0].text print(generated_text) "><pre><span class="pl-k">from</span> <span class="pl-c1">PIL</span> <span class="pl-k">import</span> <span class="pl-v">Image</span> <span class="pl-k">from</span> <span class="pl-s1">vllm</span> <span class="pl-k">import</span> <span class="pl-c1">LLM</span>, <span class="pl-v">SamplingParams</span> <span class="pl-s1">model_name</span> <span class="pl-c1">=</span> <span class="pl-s">"THUDM/glm-4v-9b"</span> <span class="pl-s1">llm</span> <span class="pl-c1">=</span> <span class="pl-en">LLM</span>(<span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">model_name</span>, <span class="pl-s1">tensor_parallel_size</span><span class="pl-c1">=</span><span class="pl-c1">1</span>, <span class="pl-s1">max_model_len</span><span class="pl-c1">=</span><span class="pl-c1">8192</span>, <span class="pl-s1">trust_remote_code</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">enforce_eager</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-s1">stop_token_ids</span> <span class="pl-c1">=</span> [<span class="pl-c1">151329</span>, <span class="pl-c1">151336</span>, <span class="pl-c1">151338</span>] <span class="pl-s1">sampling_params</span> <span class="pl-c1">=</span> <span class="pl-en">SamplingParams</span>(<span class="pl-s1">temperature</span><span class="pl-c1">=</span><span class="pl-c1">0.2</span>, <span class="pl-s1">max_tokens</span><span class="pl-c1">=</span><span class="pl-c1">1024</span>, <span class="pl-s1">stop_token_ids</span><span class="pl-c1">=</span><span class="pl-s1">stop_token_ids</span>) <span class="pl-s1">prompt</span> <span class="pl-c1">=</span> <span class="pl-s">"What's the content of the image?"</span> <span class="pl-s1">image</span> <span class="pl-c1">=</span> <span class="pl-v">Image</span>.<span class="pl-c1">open</span>(<span class="pl-s">"your image"</span>).<span class="pl-c1">convert</span>(<span class="pl-s">'RGB'</span>) <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> { <span class="pl-s">"prompt"</span>: <span class="pl-s1">prompt</span>, <span class="pl-s">"multi_modal_data"</span>: { <span class="pl-s">"image"</span>: <span class="pl-s1">image</span> }, } <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">llm</span>.<span class="pl-c1">generate</span>(<span class="pl-s1">inputs</span>, <span class="pl-s1">sampling_params</span><span class="pl-c1">=</span><span class="pl-s1">sampling_params</span>) <span class="pl-k">for</span> <span class="pl-s1">o</span> <span class="pl-c1">in</span> <span class="pl-s1">outputs</span>: <span class="pl-s1">generated_text</span> <span class="pl-c1">=</span> <span class="pl-s1">o</span>.<span class="pl-c1">outputs</span>[<span class="pl-c1">0</span>].<span class="pl-c1">text</span> <span class="pl-en">print</span>(<span class="pl-s1">generated_text</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">完整项目列表</h2><a id="user-content-完整项目列表" class="anchor" aria-label="Permalink: 完整项目列表" href="#完整项目列表"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">如果你想更进一步了解 GLM-4-9B 系列开源模型，本开源仓库通过以下内容为开发者提供基础的 GLM-4-9B 的使用和开发代码</p> <ul dir="auto"> <li> <p dir="auto"><a href="/THUDM/GLM-4/blob/main/basic_demo/README.md">basic_demo</a>: 在这里包含了</p> <ul dir="auto"> <li>使用 transformers 和 vLLM 后端的交互代码</li> <li>OpenAI API 后端交互代码</li> <li>Batch 推理代码</li> </ul> </li> <li> <p dir="auto"><a href="/THUDM/GLM-4/blob/main/composite_demo/README.md">composite_demo</a>: 在这里包含了</p> <ul dir="auto"> <li>GLM-4-9B-Chat 以及 GLM-4V-9B 开源模型的完整功能演示代码，包含了 All Tools 能力、长文档解读和多模态能力的展示。</li> </ul> </li> <li> <p dir="auto"><a href="/THUDM/GLM-4/blob/main/finetune_demo/README.md">fintune_demo</a>: 在这里包含了</p> <ul dir="auto"> <li>PEFT (LORA, P-Tuning) 微调代码</li> <li>SFT 微调代码</li> </ul> </li> <li> <p dir="auto"><a href="/THUDM/GLM-4/blob/main/intel_device_demo">intel_device_demo</a>: 在这里包含了</p> <ul dir="auto"> <li>使用 OpenVINO 部署模型代码</li> <li>使用 Intel® Extension for Transformers 部署模型代码</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">友情链接</h2><a id="user-content-友情链接" class="anchor" aria-label="Permalink: 友情链接" href="#友情链接"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/hiyouga/LLaMA-Factory">LLaMA-Factory</a>: 高效开源微调框架，已支持 GLM-4-9B-Chat 语言模型微调。</li> <li><a href="https://github.com/modelscope/swift">SWIFT</a>: 魔搭社区的大模型/多模态大模型训练框架，已支持 GLM-4-9B-Chat / GLM-4V-9B 模型微调。</li> <li><a href="https://github.com/xorbitsai/inference">Xorbits Inference</a>: 性能强大且功能全面的分布式推理框架，轻松一键部署你自己的模型或内置的前沿开源模型。</li> <li><a href="https://github.com/chatchat-space/Langchain-Chatchat">LangChain-ChatChat</a>: 基于 Langchain 与 ChatGLM 等语言模型的 RAG 与 Agent 应用</li> <li><a href="https://github.com/datawhalechina/self-llm/tree/master/models/GLM-4">self-llm</a>: Datawhale 团队的提供的 GLM-4-9B 系列模型使用教程。</li> <li><a href="https://github.com/li-plus/chatglm.cpp">chatglm.cpp</a>: 类似 llama.cpp 的量化加速推理方案，实现笔记本上实时对话</li> <li><a href="https://github.com/openvinotoolkit">OpenVINO</a>: Intel 开发的高性能 CPU,GPU及NPU 加速推理方案，可以参考此 <a href="https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot-generate-api.ipynb">步骤</a> 部署 glm-4-9b-chat 模型。</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">协议</h2><a id="user-content-协议" class="anchor" aria-label="Permalink: 协议" href="#协议"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto">GLM-4 模型的权重的使用则需要遵循 <a href="https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE" rel="nofollow">模型协议</a>。</p> </li> <li> <p dir="auto">本开源仓库的代码则遵循 <a href="/THUDM/GLM-4/blob/main/LICENSE">Apache 2.0</a> 协议。</p> </li> </ul> <p dir="auto">请您严格遵循开源协议。</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">引用</h2><a id="user-content-引用" class="anchor" aria-label="Permalink: 引用" href="#引用"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">如果你觉得我们的工作有帮助的话，请考虑引用下列论文。</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{glm2024chatglm, title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang}, year={2024}, eprint={2406.12793}, archivePrefix={arXiv}, primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'} }"><pre class="notranslate"><code>@misc{glm2024chatglm, title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang}, year={2024}, eprint={2406.12793}, archivePrefix={arXiv}, primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'} } </code></pre></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{wang2023cogvlm, title={CogVLM: Visual Expert for Pretrained Language Models}, author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang}, year={2023}, eprint={2311.03079}, archivePrefix={arXiv}, primaryClass={cs.CV} }"><pre class="notranslate"><code>@misc{wang2023cogvlm, title={CogVLM: Visual Expert for Pretrained Language Models}, author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang}, year={2023}, eprint={2311.03079}, archivePrefix={arXiv}, primaryClass={cs.CV} } </code></pre></div> </article></div></div></div></div></div>   <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="YQtJuh9aKBn9D9lbBGVzROz+oCm94yUHVaUX/WSY16bTxXoqZxSZqTDns9NEPlqOrAFoWWmVcmQSTTPh9euupQ==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型 </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/glm" 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