Ollama model card for obsidian web clipper

ollama-model-card-clipper.json

{
	"schemaVersion": "0.1.0",
	"name": "Ollama Model Card",
	"behavior": "create",
	"noteContentFormat": "# {{title}}\n\n**Model Summary**\n\n{{selectorHtml:#summary-content|markdown|callout}}\n\n**{{selectorHtml:div.use-panel?data-panel|first|markdown}} usage**\n\n```sh\n{{selectorHtml:div.use-panel > pre|first|markdown}}\n```\n\n**Models**\n\n{{selector:div.hidden.group a.font-medium, div.hidden.group p.col-span-2.text-neutral-500|map:v => v.trim()|table:(\"Name\",\"Size\",\"Context\",\"Input\")}}\n\n{{selectorHtml:section.flex-col > div > a|markdown}}\n\n## Description\n\n{{selectorHtml:div#readme > div > div#display|markdown}}\n",
	"properties": [
		{
			"name": "title",
			"value": "{{title}}",
			"type": "text"
		},
		{
			"name": "clipped_date",
			"value": "{{time}}",
			"type": "date"
		},
		{
			"name": "source",
			"value": "{{url}}",
			"type": "text"
		},
		{
			"name": "description",
			"value": "{{description}}",
			"type": "text"
		},
		{
			"name": "model_tags",
			"value": "{{selector:.inline-flex.items-center.rounded-md}}",
			"type": "multitext"
		}
	],
	"triggers": [
		"https://ollama.com/library"
	],
	"noteNameFormat": "{{title}}",
	"path": "Imports/ollama"
}

example output:

---

title: "glm-ocr"
clipped_date: 2026-02-25
source: "https://ollama.com/library/glm-ocr"
description: "GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture."
cover_img: "https://ollama.com/public/og.png"
skills: "["vision","tools"]"

glm-ocr

Model Summary

[!info]
GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture.

cli usage

ollama run glm-ocr

Models

Name	Size	Context	Input
glm-ocr:latest	2.2GB	128K	Text, Image
glm-ocr:q8_0	1.6GB	128K	Text, Image
glm-ocr:bf16	2.2GB	128K	Text, Image

View all →

Description

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder.

Usage

Text recognition

ollama run glm-ocr Text Recognition: ./image.png

Table recognition

ollama run glm-ocr Table Recognition: ./image.png

Figure recognition

ollama run glm-ocr Figure Recognition: ./image.png

Key features

• State-of-the-Art Performance: Achieves a score of 94.62 on OmniDocBench V1.5, ranking #1 overall, and delivers state-of-the-art results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction.
• Optimized for Real-World Scenarios: Designed and optimized for practical business use cases, maintaining robust performance on complex tables, code-heavy documents, seals, and other challenging real-world layouts.
• Efficient Inference: With only 0.9B parameters, GLM-OCR supports deployment via vLLM, SGLang, and Ollama, significantly reducing inference latency and compute cost, making it ideal for high-concurrency services and edge deployments.
• Easy to Use: Fully open-sourced and equipped with a comprehensive SDK and inference toolchain, offering simple installation, one-line invocation, and smooth integration into existing production pipelines.