From 104c08fb272623b614bb1d054e48e4fb56681f34 Mon Sep 17 00:00:00 2001 From: ethan Date: Mon, 2 Dec 2024 01:33:58 -0800 Subject: [PATCH] solve ci issue --- .ci/skipped_notebooks.yml | 6 +++++- notebooks/glm-edge-v/README.md | 2 +- notebooks/glm-edge-v/glm-edge-v.ipynb | 4 ++-- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/.ci/skipped_notebooks.yml b/.ci/skipped_notebooks.yml index 2566487502d..d39c82e314b 100644 --- a/.ci/skipped_notebooks.yml +++ b/.ci/skipped_notebooks.yml @@ -562,4 +562,8 @@ - macos-13 - ubuntu-20.04 - ubuntu-22.04 - - windows-2019 \ No newline at end of file + - windows-2019 +- notebook: notebooks/glm-edge-v/glm-edge-v.ipynb + skips: + - os: + - macos-13 \ No newline at end of file diff --git a/notebooks/glm-edge-v/README.md b/notebooks/glm-edge-v/README.md index f0b11a89b23..56834bc4c23 100644 --- a/notebooks/glm-edge-v/README.md +++ b/notebooks/glm-edge-v/README.md @@ -1,6 +1,6 @@ ## Visual-language assistant with GLM-Edge-V and OpenVINO -The [GLM-Edge](https://huggingface.co/collections/THUDM/glm-edge-6743283c5809de4a7b9e0b8b) series is [Zhipu's](https://huggingface.co/THUDM) attempt to meet real-world deployment scenarios for edge devices. It consists of two sizes of large language dialogue models and multimodal understanding models (GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, GLM-Edge-V-5B). Among them, the 1.5B / 2B models are mainly targeted at platforms like mobile phones and car machines, while the 4B / 5B models are aimed at platforms like PCs. Based on the technological advancements of the GLM-4 series, some targeted adjustments have been made to the model structure and size, balancing model performance, real-world inference efficiency, and deployment convenience. Through deep collaboration with partner enterprises and relentless efforts in inference optimization, the GLM-Edge series models can run at extremely high speeds on some edge platforms. +The [GLM-Edge](https://huggingface.co/collections/THUDM/glm-edge-6743283c5809de4a7b9e0b8b) series is [Zhipu](https://huggingface.co/THUDM)'s attempt to meet real-world deployment scenarios for edge devices. It consists of two sizes of large language dialogue models and multimodal understanding models (GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, GLM-Edge-V-5B). Among them, the 1.5B / 2B models are mainly targeted at platforms like mobile phones and car machines, while the 4B / 5B models are aimed at platforms like PCs. Based on the technological advancements of the GLM-4 series, some targeted adjustments have been made to the model structure and size, balancing model performance, real-world inference efficiency, and deployment convenience. Through deep collaboration with partner enterprises and relentless efforts in inference optimization, the GLM-Edge series models can run at extremely high speeds on some edge platforms. In this tutorial we consider how to launch multimodal model GLM-Edge-V using OpenVINO for creation multimodal chatbot. Additionally, we optimize model to low precision using [NNCF](https://github.com/openvinotoolkit/nncf) diff --git a/notebooks/glm-edge-v/glm-edge-v.ipynb b/notebooks/glm-edge-v/glm-edge-v.ipynb index 11eb789f4df..ab53367879f 100644 --- a/notebooks/glm-edge-v/glm-edge-v.ipynb +++ b/notebooks/glm-edge-v/glm-edge-v.ipynb @@ -8,7 +8,7 @@ "source": [ "## Visual-language assistant with GLM-Edge-V and OpenVINO\n", "\n", - "The [GLM-Edge](https://huggingface.co/collections/THUDM/glm-edge-6743283c5809de4a7b9e0b8b) series is [Zhipu's](https://huggingface.co/THUDM) attempt to meet real-world deployment scenarios for edge devices. It consists of two sizes of large language dialogue models and multimodal understanding models (GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, GLM-Edge-V-5B). Among them, the 1.5B / 2B models are mainly targeted at platforms like mobile phones and car machines, while the 4B / 5B models are aimed at platforms like PCs. Based on the technological advancements of the GLM-4 series, some targeted adjustments have been made to the model structure and size, balancing model performance, real-world inference efficiency, and deployment convenience. Through deep collaboration with partner enterprises and relentless efforts in inference optimization, the GLM-Edge series models can run at extremely high speeds on some edge platforms.\n", + "The [GLM-Edge](https://huggingface.co/collections/THUDM/glm-edge-6743283c5809de4a7b9e0b8b) series is [Zhipu](https://huggingface.co/THUDM)'s attempt to meet real-world deployment scenarios for edge devices. It consists of two sizes of large language dialogue models and multimodal understanding models (GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, GLM-Edge-V-5B). Among them, the 1.5B / 2B models are mainly targeted at platforms like mobile phones and car machines, while the 4B / 5B models are aimed at platforms like PCs. Based on the technological advancements of the GLM-4 series, some targeted adjustments have been made to the model structure and size, balancing model performance, real-world inference efficiency, and deployment convenience. Through deep collaboration with partner enterprises and relentless efforts in inference optimization, the GLM-Edge series models can run at extremely high speeds on some edge platforms.\n", "\n", "In this tutorial we consider how to launch multimodal model GLM-Edge-V using OpenVINO for creation multimodal chatbot. Additionally, we optimize model to low precision using [NNCF](https://github.com/openvinotoolkit/nncf)\n", "#### Table of contents:\n", @@ -441,7 +441,7 @@ "version": "3.10.7" }, "openvino_notebooks": { - "imageUrl": "https://github.com/user-attachments/assets/06c51867-0580-4434-962e-31b6068c2001", + "imageUrl": "https://github.com/user-attachments/assets/a0c07db9-69d4-4dea-a8fc-424c02ccebf4", "tags": { "categories": [ "Model Demos",