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marker

Sat, 16 Aug 2025 15:27:34 +0800

datalab-to/marker

Marker

Marker converts documents to markdown, JSON, chunks, and HTML quickly and accurately.

Converts PDF, image, PPTX, DOCX, XLSX, HTML, EPUB files in all languages
Formats tables, forms, equations, inline math, links, references, and code blocks
Extracts and saves images
Removes headers/footers/other artifacts
Extensible with your own formatting and logic
Does structured extraction, given a JSON schema (beta)
Optionally boost accuracy with LLMs (and your own prompt)
Works on GPU, CPU, or MPS

Performance

Marker benchmarks favorably compared to cloud services like Llamaparse and Mathpix, as well as other open source tools.

The above results are running single PDF pages serially. Marker is significantly faster when running in batch mode, with a projected throughput of 25 pages/second on an H100.

See below for detailed speed and accuracy benchmarks, and instructions on how to run your own benchmarks.

Hybrid Mode

For the highest accuracy, pass the --use_llm flag to use an LLM alongside marker. This will do things like merge tables across pages, handle inline math, format tables properly, and extract values from forms. It can use any gemini or ollama model. By default, it uses gemini-2.0-flash. See below for details.

Here is a table benchmark comparing marker, gemini flash alone, and marker with use_llm:

As you can see, the use_llm mode offers higher accuracy than marker or gemini alone.

Examples

PDF	File type	Markdown	JSON
Think Python	Textbook	View	View
Switch Transformers	arXiv paper	View	View
Multi-column CNN	arXiv paper	View	View

Commercial usage

I want marker to be as widely accessible as possible, while still funding my development/training costs. Research and personal usage is always okay, but there are some restrictions on commercial usage.

The weights for the models are licensed cc-by-nc-sa-4.0, but I will waive that for any organization under $2M USD in gross revenue in the most recent 12-month period AND under $2M in lifetime VC/angel funding raised. You also must not be competitive with the Datalab API. If you want to remove the GPL license requirements (dual-license) and/or use the weights commercially over the revenue limit, check out the options here.

Hosted API

There’s a hosted API for marker available here:

Supports PDF, image, PPT, PPTX, DOC, DOCX, XLS, XLSX, HTML, EPUB files
1/4th the price of leading cloud-based competitors
Fast - ~15s for a 250 page PDF
Supports LLM mode
High uptime (99.99%)

Community

Discord is where we discuss future development.

Installation

You’ll need python 3.10+ and PyTorch.

Install with:

`1`	`pip install marker-pdf`

If you want to use marker on documents other than PDFs, you will need to install additional dependencies with:

`1`	`pip install marker-pdf[full]`

Usage

First, some configuration:

Your torch device will be automatically detected, but you can override this. For example, TORCH_DEVICE=cuda.
Some PDFs, even digital ones, have bad text in them. Set --force_ocr to force OCR on all lines, or the strip_existing_ocr to keep all digital text, and strip out any existing OCR text.
If you care about inline math, set force_ocr to convert inline math to LaTeX.

Interactive App

I’ve included a streamlit app that lets you interactively try marker with some basic options. Run it with:

1
2

pip install streamlit streamlit-ace
marker_gui

Convert a single file

`1`	`marker_single /path/to/file.pdf`

You can pass in PDFs or images.

Options:

--page_range TEXT: Specify which pages to process. Accepts comma-separated page numbers and ranges. Example: --page_range "0,5-10,20" will process pages 0, 5 through 10, and page 20.
--output_format [markdown|json|html|chunks]: Specify the format for the output results.
--output_dir PATH: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR.
--paginate_output: Paginates the output, using \n\n{PAGE_NUMBER} followed by - * 48, then \n\n
--use_llm: Uses an LLM to improve accuracy. You will need to configure the LLM backend - see below.
--force_ocr: Force OCR processing on the entire document, even for pages that might contain extractable text. This will also format inline math properly.
--block_correction_prompt: if LLM mode is active, an optional prompt that will be used to correct the output of marker. This is useful for custom formatting or logic that you want to apply to the output.
--strip_existing_ocr: Remove all existing OCR text in the document and re-OCR with surya.
--redo_inline_math: If you want the absolute highest quality inline math conversion, use this along with --use_llm.
--disable_image_extraction: Don’t extract images from the PDF. If you also specify --use_llm, then images will be replaced with a description.
--debug: Enable debug mode for additional logging and diagnostic information.
--processors TEXT: Override the default processors by providing their full module paths, separated by commas. Example: --processors "module1.processor1,module2.processor2"
--config_json PATH: Path to a JSON configuration file containing additional settings.
config --help: List all available builders, processors, and converters, and their associated configuration. These values can be used to build a JSON configuration file for additional tweaking of marker defaults.
--converter_cls: One of marker.converters.pdf.PdfConverter (default) or marker.converters.table.TableConverter. The PdfConverter will convert the whole PDF, the TableConverter will only extract and convert tables.
--llm_service: Which llm service to use if --use_llm is passed. This defaults to marker.services.gemini.GoogleGeminiService.
--help: see all of the flags that can be passed into marker. (it supports many more options then are listed above)

The list of supported languages for surya OCR is here. If you don’t need OCR, marker can work with any language.

Convert multiple files

`1`	`marker /path/to/input/folder`

marker supports all the same options from marker_single above.
--workers is the number of conversion workers to run simultaneously. This is automatically set by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage. Marker will use 5GB of VRAM per worker at the peak, and 3.5GB average.

Convert multiple files on multiple GPUs

`1`	`NUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out`

NUM_DEVICES is the number of GPUs to use. Should be 2 or greater.
NUM_WORKERS is the number of parallel processes to run on each GPU.

Use from python

See the PdfConverter class at marker/converters/pdf.py function for additional arguments that can be passed.

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.output import text_from_rendered

converter = PdfConverter(
    artifact_dict=create_model_dict(),
)
rendered = converter("FILEPATH")
text, _, images = text_from_rendered(rendered)

rendered will be a pydantic basemodel with different properties depending on the output type requested. With markdown output (default), you’ll have the properties markdown, metadata, and images. For json output, you’ll have children, block_type, and metadata.

Custom configuration

You can pass configuration using the ConfigParser. To see all available options, do marker_single --help.

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.config.parser import ConfigParser

config = {
    "output_format": "json",
    "ADDITIONAL_KEY": "VALUE"
}
config_parser = ConfigParser(config)

converter = PdfConverter(
    config=config_parser.generate_config_dict(),
    artifact_dict=create_model_dict(),
    processor_list=config_parser.get_processors(),
    renderer=config_parser.get_renderer(),
    llm_service=config_parser.get_llm_service()
)
rendered = converter("FILEPATH")

Extract blocks

Each document consists of one or more pages. Pages contain blocks, which can themselves contain other blocks. It’s possible to programmatically manipulate these blocks.

Here’s an example of extracting all forms from a document:

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.schema import BlockTypes

converter = PdfConverter(
    artifact_dict=create_model_dict(),
)
document = converter.build_document("FILEPATH")
forms = document.contained_blocks((BlockTypes.Form,))

Look at the processors for more examples of extracting and manipulating blocks.

Other converters

You can also use other converters that define different conversion pipelines:

Extract tables

The TableConverter will only convert and extract tables:

from marker.converters.table import TableConverter
from marker.models import create_model_dict
from marker.output import text_from_rendered

converter = TableConverter(
    artifact_dict=create_model_dict(),
)
rendered = converter("FILEPATH")
text, _, images = text_from_rendered(rendered)

This takes all the same configuration as the PdfConverter. You can specify the configuration force_layout_block=Table to avoid layout detection and instead assume every page is a table. Set output_format=json to also get cell bounding boxes.

You can also run this via the CLI with

`1`	`marker_single FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter --output_format json`

OCR Only

If you only want to run OCR, you can also do that through the OCRConverter. Set --keep_chars to keep individual characters and bounding boxes.

from marker.converters.ocr import OCRConverter
from marker.models import create_model_dict

converter = OCRConverter(
    artifact_dict=create_model_dict(),
)
rendered = converter("FILEPATH")

This takes all the same configuration as the PdfConverter.

You can also run this via the CLI with

`1`	`marker_single FILENAME --converter_cls marker.converters.ocr.OCRConverter`

Structured Extraction (beta)

You can run structured extraction via the ExtractionConverter. This requires an llm service to be setup first (see here for details). You’ll get a JSON output with the extracted values.

from marker.converters.extraction import ExtractionConverter
from marker.models import create_model_dict
from marker.config.parser import ConfigParser
from pydantic import BaseModel

class Links(BaseModel):
    links: list[str]
    
schema = Links.model_json_schema()
config_parser = ConfigParser({
    "page_schema": schema
})

converter = ExtractionConverter(
    artifact_dict=create_model_dict(),
    config=config_parser.generate_config_dict(),
    llm_service=config_parser.get_llm_service(),
)
rendered = converter("FILEPATH")

Rendered will have an original_markdown field. If you pass this back in next time you run the converter, as the existing_markdown config key, you can skip re-parsing the document.

Output Formats

Markdown

Markdown output will include:

image links (images will be saved in the same folder)
formatted tables
embedded LaTeX equations (fenced with $$)
Code is fenced with triple backticks
Superscripts for footnotes

HTML

HTML output is similar to markdown output:

Images are included via img tags
equations are fenced with <math> tags
code is in pre tags

JSON

JSON output will be organized in a tree-like structure, with the leaf nodes being blocks. Examples of leaf nodes are a single list item, a paragraph of text, or an image.

The output will be a list, with each list item representing a page. Each page is considered a block in the internal marker schema. There are different types of blocks to represent different elements.

Pages have the keys:

id - unique id for the block.
block_type - the type of block. The possible block types can be seen in marker/schema/__init__.py. As of this writing, they are [“Line”, “Span”, “FigureGroup”, “TableGroup”, “ListGroup”, “PictureGroup”, “Page”, “Caption”, “Code”, “Figure”, “Footnote”, “Form”, “Equation”, “Handwriting”, “TextInlineMath”, “ListItem”, “PageFooter”, “PageHeader”, “Picture”, “SectionHeader”, “Table”, “Text”, “TableOfContents”, “Document”]
html - the HTML for the page. Note that this will have recursive references to children. The content-ref tags must be replaced with the child content if you want the full html. You can see an example of this at marker/output.py:json_to_html. That function will take in a single block from the json output, and turn it into HTML.
polygon - the 4-corner polygon of the page, in (x1,y1), (x2,y2), (x3, y3), (x4, y4) format. (x1,y1) is the top left, and coordinates go clockwise.
children - the child blocks.

The child blocks have two additional keys:

section_hierarchy - indicates the sections that the block is part of. 1 indicates an h1 tag, 2 an h2, and so on.
images - base64 encoded images. The key will be the block id, and the data will be the encoded image.

Note that child blocks of pages can have their own children as well (a tree structure).

{
      "id": "/page/10/Page/366",
      "block_type": "Page",
      "html": "<content-ref src='/page/10/SectionHeader/0'></content-ref><content-ref src='/page/10/SectionHeader/1'></content-ref><content-ref src='/page/10/Text/2'></content-ref><content-ref src='/page/10/Text/3'></content-ref><content-ref src='/page/10/Figure/4'></content-ref><content-ref src='/page/10/SectionHeader/5'></content-ref><content-ref src='/page/10/SectionHeader/6'></content-ref><content-ref src='/page/10/TextInlineMath/7'></content-ref><content-ref src='/page/10/TextInlineMath/8'></content-ref><content-ref src='/page/10/Table/9'></content-ref><content-ref src='/page/10/SectionHeader/10'></content-ref><content-ref src='/page/10/Text/11'></content-ref>",
      "polygon": [[0.0, 0.0], [612.0, 0.0], [612.0, 792.0], [0.0, 792.0]],
      "children": [
        {
          "id": "/page/10/SectionHeader/0",
          "block_type": "SectionHeader",
          "html": "<h1>Supplementary Material for <i>Subspace Adversarial Training</i> </h1>",
          "polygon": [
            [217.845703125, 80.630859375], [374.73046875, 80.630859375],
            [374.73046875, 107.0],
            [217.845703125, 107.0]
          ],
          "children": null,
          "section_hierarchy": {
            "1": "/page/10/SectionHeader/1"
          },
          "images": {}
        },
        ...
        ]
    }

Chunks

Chunks format is similar to JSON, but flattens everything into a single list instead of a tree. Only the top level blocks from each page show up. It also has the full HTML of each block inside, so you don’t need to crawl the tree to reconstruct it. This enable flexible and easy chunking for RAG.

Metadata

All output formats will return a metadata dictionary, with the following fields:

{
    "table_of_contents": [
      {
        "title": "Introduction",
        "heading_level": 1,
        "page_id": 0,
        "polygon": [...]
      }
    ], // computed PDF table of contents
    "page_stats": [
      {
        "page_id":  0, 
        "text_extraction_method": "pdftext",
        "block_counts": [("Span", 200), ...]
      },
      ...
    ]
}

LLM Services

When running with the --use_llm flag, you have a choice of services you can use:

Gemini - this will use the Gemini developer API by default. You’ll need to pass --gemini_api_key to configuration.
Google Vertex - this will use vertex, which can be more reliable. You’ll need to pass --vertex_project_id. To use it, set --llm_service=marker.services.vertex.GoogleVertexService.
Ollama - this will use local models. You can configure --ollama_base_url and --ollama_model. To use it, set --llm_service=marker.services.ollama.OllamaService.
Claude - this will use the anthropic API. You can configure --claude_api_key, and --claude_model_name. To use it, set --llm_service=marker.services.claude.ClaudeService.
OpenAI - this supports any openai-like endpoint. You can configure --openai_api_key, --openai_model, and --openai_base_url. To use it, set --llm_service=marker.services.openai.OpenAIService.
Azure OpenAI - this uses the Azure OpenAI service. You can configure --azure_endpoint, --azure_api_key, and --deployment_name. To use it, set --llm_service=marker.services.azure_openai.AzureOpenAIService.

These services may have additional optional configuration as well - you can see it by viewing the classes.

Internals

Marker is easy to extend. The core units of marker are:

Providers, at marker/providers. These provide information from a source file, like a PDF.
Builders, at marker/builders. These generate the initial document blocks and fill in text, using info from the providers.
Processors, at marker/processors. These process specific blocks, for example the table formatter is a processor.
Renderers, at marker/renderers. These use the blocks to render output.
Schema, at marker/schema. The classes for all the block types.
Converters, at marker/converters. They run the whole end to end pipeline.

To customize processing behavior, override the processors. To add new output formats, write a new renderer. For additional input formats, write a new provider.

Processors and renderers can be directly passed into the base PDFConverter, so you can specify your own custom processing easily.

API server

There is a very simple API server you can run like this:

1
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pip install -U uvicorn fastapi python-multipart
marker_server --port 8001

This will start a fastapi server that you can access at localhost:8001. You can go to localhost:8001/docs to see the endpoint options.

You can send requests like this:

import requests
import json

post_data = {
    'filepath': 'FILEPATH',
    # Add other params here
}

requests.post("http://localhost:8001/marker", data=json.dumps(post_data)).json()

Note that this is not a very robust API, and is only intended for small-scale use. If you want to use this server, but want a more robust conversion option, you can use the hosted Datalab API.

Troubleshooting

There are some settings that you may find useful if things aren’t working the way you expect:

If you have issues with accuracy, try setting --use_llm to use an LLM to improve quality. You must set GOOGLE_API_KEY to a Gemini API key for this to work.
Make sure to set force_ocr if you see garbled text - this will re-OCR the document.
TORCH_DEVICE - set this to force marker to use a given torch device for inference.
If you’re getting out of memory errors, decrease worker count. You can also try splitting up long PDFs into multiple files.

Debugging

Pass the debug option to activate debug mode. This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information.

Benchmarks

Overall PDF Conversion

We created a benchmark set by extracting single PDF pages from common crawl. We scored based on a heuristic that aligns text with ground truth text segments, and an LLM as a judge scoring method.

Method	Avg Time	Heuristic Score	LLM Score
marker	2.83837	95.6709	4.23916
llamaparse	23.348	84.2442	3.97619
mathpix	6.36223	86.4281	4.15626
docling	3.69949	86.7073	3.70429

Benchmarks were run on an H100 for markjer and docling - llamaparse and mathpix used their cloud services. We can also look at it by document type:

Document Type	Marker heuristic	Marker LLM	Llamaparse Heuristic	Llamaparse LLM	Mathpix Heuristic	Mathpix LLM	Docling Heuristic	Docling LLM
Scientific paper	96.6737	4.34899	87.1651	3.96421	91.2267	4.46861	92.135	3.72422
Book page	97.1846	4.16168	90.9532	4.07186	93.8886	4.35329	90.0556	3.64671
Other	95.1632	4.25076	81.1385	4.01835	79.6231	4.00306	83.8223	3.76147
Form	88.0147	3.84663	66.3081	3.68712	64.7512	3.33129	68.3857	3.40491
Presentation	95.1562	4.13669	81.2261	4	83.6737	3.95683	84.8405	3.86331
Financial document	95.3697	4.39106	82.5812	4.16111	81.3115	4.05556	86.3882	3.8
Letter	98.4021	4.5	93.4477	4.28125	96.0383	4.45312	92.0952	4.09375
Engineering document	93.9244	4.04412	77.4854	3.72059	80.3319	3.88235	79.6807	3.42647
Legal document	96.689	4.27759	86.9769	3.87584	91.601	4.20805	87.8383	3.65552
Newspaper page	98.8733	4.25806	84.7492	3.90323	96.9963	4.45161	92.6496	3.51613
Magazine page	98.2145	4.38776	87.2902	3.97959	93.5934	4.16327	93.0892	4.02041

Throughput

We benchmarked throughput using a single long PDF.

Method	Time per page	Time per document	VRAM used
marker	0.18	43.42	3.17GB

The projected throughput is 122 pages per second on an H100 - we can run 22 individual processes given the VRAM used.

Table Conversion

Marker can extract tables from PDFs using marker.converters.table.TableConverter. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of FinTabNet. The HTML representations are compared using a tree edit distance based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves these scores:

Method	Avg score	Total tables
marker	0.816	99
marker w/use_llm	0.907	99
gemini	0.829	99

The --use_llm flag can significantly improve table recognition performance, as you can see.

We filter out tables that we cannot align with the ground truth, since fintabnet and our layout model have slightly different detection methods (this results in some tables being split/merged).

Running your own benchmarks

You can benchmark the performance of marker on your machine. Install marker manually with:

1
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git clone https://github.com/VikParuchuri/marker.git
poetry install

Overall PDF Conversion

Download the benchmark data here and unzip. Then run the overall benchmark like this:

`1`	`python benchmarks/overall.py --methods marker --scores heuristic,llm`

Options:

--use_llm use an llm to improve the marker results.
--max_rows how many rows to process for the benchmark.
--methods can be llamaparse, mathpix, docling, marker. Comma separated.
--scores which scoring functions to use, can be llm, heuristic. Comma separated.

Table Conversion

The processed FinTabNet dataset is hosted here and is automatically downloaded. Run the benchmark with:

`1`	`python benchmarks/table/table.py --max_rows 100`

Options:

--use_llm uses an llm with marker to improve accuracy.
--use_gemini also benchmarks gemini 2.0 flash.

How it works

Marker is a pipeline of deep learning models:

Extract text, OCR if necessary (heuristics, surya)
Detect page layout and find reading order (surya)
Clean and format each block (heuristics, texify, surya)
Optionally use an LLM to improve quality
Combine blocks and postprocess complete text

It only uses models where necessary, which improves speed and accuracy.

Limitations

PDF is a tricky format, so marker will not always work perfectly. Here are some known limitations that are on the roadmap to address:

Very complex layouts, with nested tables and forms, may not work
Forms may not be rendered well

Note: Passing the --use_llm and --force_ocr flags will mostly solve these issues.

swagger-ui

Sat, 05 Jul 2025 15:27:27 +0800

swagger-api/swagger-ui

Introduction

Swagger UI allows anyone — be it your development team or your end consumers — to visualize and interact with the API’s resources without having any of the implementation logic in place. It’s automatically generated from your OpenAPI (formerly known as Swagger) Specification, with the visual documentation making it easy for back end implementation and client side consumption.

General

👉🏼 Want to score an easy open-source contribution? Check out our Good first issue label.

🕰️ Looking for the older version of Swagger UI? Refer to the 2.x branch.

This repository publishes three different NPM modules:

swagger-ui is a traditional npm module intended for use in single-page applications that are capable of resolving dependencies (via Webpack, Browserify, etc.).
swagger-ui-dist is a dependency-free module that includes everything you need to serve Swagger UI in a server-side project, or a single-page application that can’t resolve npm module dependencies.
swagger-ui-react is Swagger UI packaged as a React component for use in React applications.

We strongly suggest that you use swagger-ui instead of swagger-ui-dist if you’re building a single-page application, since swagger-ui-dist is significantly larger.

If you are looking for plain ol’ HTML/JS/CSS, download the latest release and copy the contents of the /dist folder to your server.

Compatibility

The OpenAPI Specification has undergone 5 revisions since initial creation in 2010. Compatibility between Swagger UI and the OpenAPI Specification is as follows:

Swagger UI Version	Release Date	OpenAPI Spec compatibility	Notes
5.19.0	2025-02-17	2.0, 3.0.0, 3.0.1, 3.0.2, 3.0.3, 3.0.4, 3.1.0, 3.1.1	tag v5.19.0
5.0.0	2023-06-12	2.0, 3.0.0, 3.0.1, 3.0.2, 3.0.3, 3.1.0	tag v5.0.0
4.0.0	2021-11-03	2.0, 3.0.0, 3.0.1, 3.0.2, 3.0.3	tag v4.0.0
3.18.3	2018-08-03	2.0, 3.0.0, 3.0.1, 3.0.2, 3.0.3	tag v3.18.3
3.0.21	2017-07-26	2.0	tag v3.0.21
2.2.10	2017-01-04	1.1, 1.2, 2.0	tag v2.2.10
2.1.5	2016-07-20	1.1, 1.2, 2.0	tag v2.1.5
2.0.24	2014-09-12	1.1, 1.2	tag v2.0.24
1.0.13	2013-03-08	1.1, 1.2	tag v1.0.13
1.0.1	2011-10-11	1.0, 1.1	tag v1.0.1

Anonymized analytics

SwaggerUI uses Scarf to collect anonymized installation analytics. These analytics help support the maintainers of this library and ONLY run during installation. To opt out, you can set the scarfSettings.enabled field to false in your project’s package.json:

// package.json
{
  // ...
  "scarfSettings": {
    "enabled": false
  }
  // ...
}

Alternatively, you can set the environment variable SCARF_ANALYTICS to false as part of the environment that installs your npm packages, e.g., SCARF_ANALYTICS=false npm install.

Documentation

Usage

Customization

Development

Contributing

Contributing

Integration Tests

You will need JDK of version 7 or higher as instructed here https://nightwatchjs.org/guide/getting-started/installation.html#install-selenium-server

Integration tests can be run locally with npm run e2e - be sure you aren’t running a dev server when testing!

Browser support

Swagger UI works in the latest versions of Chrome, Safari, Firefox, and Edge.

Known Issues

To help with the migration, here are the currently known issues with 3.X. This list will update regularly, and will not include features that were not implemented in previous versions.

Only part of the parameters previously supported are available.
The JSON Form Editor is not implemented.
Support for collectionFormat is partial.
l10n (translations) is not implemented.
Relative path support for external files is not implemented.

Security contact

Please disclose any security-related issues or vulnerabilities by emailing security@swagger.io, instead of using the public issue tracker.

License

SwaggerUI is licensed under Apache 2.0 license. SwaggerUI comes with an explicit NOTICE file containing additional legal notices and information.

AdminLTE

Wed, 02 Jul 2025 15:29:57 +0800

ColorlibHQ/AdminLTE

AdminLTE - Bootstrap 5 Admin Dashboard

AdminLTE is a fully responsive administration template. Based on Bootstrap 5 framework and also the JavaScript plugins. Highly customizable and easy to use. Fits many screen resolutions from small mobile devices to large desktops.

What’s New in v4.0.0-rc3

Production Deployment & Cross-Platform Compatibility - This release resolves critical production deployment issues:

Fixed Production Builds - Resolved CSS/JS path issues, sidebar navigation, and image loading in all deployment scenarios
Smart Path Resolution - Automatic relative path calculation works for root deployment, sub-folders, and CDN hosting
RTL CSS Fix - Eliminated rtlcss interference with standard LTR production builds
Updated Dependencies - Bootstrap 5.3.7, Bootstrap Icons 1.13.1, OverlayScrollbars 2.11.0
Zero Console Errors - Fixed all CDN integrity mismatches and runtime issues
FTP/Static Host Ready - Perfect compatibility with traditional hosting and modern static platforms

Key Improvements:

✅ Development and production environments now behave identically
✅ Images, CSS, and JavaScript load correctly in any deployment structure
✅ Sidebar navigation displays properly with badges and arrow indicators
✅ All CDN resources load without console errors
✅ Complete production build included in repository for easy deployment

See the CHANGELOG.md for complete details.

Looking for Premium Templates?

AdminLTE.io just opened a new premium templates page. Hand picked to ensure the best quality and the most affordable prices. Visit https://adminlte.io/premium for more information.

AdminLTE has been carefully coded with clear comments in all of its JS, SCSS and HTML files. SCSS has been used to increase code customizability.

Quick start

Development

To start developing with AdminLTE:

Install dependencies: npm install
Start development server: npm start (opens browser at http://localhost:3000)
Start coding! Files auto-compile and refresh on changes

Production Build

To build for production:

Full production build: npm run production (includes linting and optimization)
Quick build: npm run build (faster for development/testing)

Available Scripts

npm start - Start development server with file watching
npm run build - Build all assets for development
npm run production - Full production build with linting and bundlewatch
npm run lint - Run all linters (JS, CSS, docs, lockfile)
npm run css - Build CSS only
npm run js - Build JavaScript only

Browser Support

AdminLTE supports all modern browsers with the latest Bootstrap 5.3.7:

Chrome (latest)
Firefox (latest)
Safari (latest)
Edge (latest)

Contributing

Highly welcome.
For your extra reference check AdminLTE v4 Contribution Guide
First thing first, you should have bit knowledge about NodeJS.
Github Knowledge.
Install NodeJS LTS version.
Clone this Repository to your machine and change to master branch.
Go to Cloned Folder.
In cli/bash run npm install it will install dependency from package.json.
After installation completes, run npm start
Cool, Send your changes in PR to master branch.

Sponsorship

Support AdminLTE development by becoming a sponsor. Github Sponsors or PayPal

License

AdminLTE is an open source project by AdminLTE.io that is licensed under MIT. AdminLTE.io reserves the right to change the license of future releases.

Image Credits

quarkdown

Fri, 06 Jun 2025 15:28:54 +0800

iamgio/quarkdown

Download
Latest | Stable

About

Quarkdown is a modern Markdown-based typesetting system, designed around the key concept of versatility, by seamlessly compiling a project into a print-ready book or an interactive presentation. All through an incredibly powerful Turing-complete extension of Markdown, ensuring your ideas flow automatically into paper.

Original credits: Attention Is All You Need

Born as an extension of CommonMark and GFM, the Quarkdown Flavor brings functions to Markdown, along with many other syntax extensions.

This is a function call:

1
2

.somefunction {arg1} {arg2}
    Body argument

Possibilities are unlimited thanks to an ever-expanding standard library, which offers layout builders, I/O, math, conditional statements and loops.

Not enough? You can still define your own functions and variables — all within Markdown. You can even create awesome libraries for everyone to use.

.function {greet}
    to from:
    **Hello, .to** from .from!

.greet {world} from:{iamgio}

Result: Hello, world from iamgio!

This out-of-the-box scripting support opens doors to complex and dynamic content that would be otherwise impossible to achieve with vanilla Markdown.

Combined with live preview and :zap: fast compilation speed, Quarkdown simply gets the work done.

Check the wiki to learn more about the language and its features.

Check out the demo presentation here

Built with Quarkdown itself — source code

(Desktop view is suggested)

As simple as you expect…

Inspired by: X-ray flashes from a nearby supermassive black hole accelerate mysteriously

...as complex as you need.

Targets

HTML
- :white_check_mark: Plain output (default)
- :white_check_mark: Slides ^{via reveal.js}
- :white_check_mark: Paged (books, articles) ^{via paged.js}
  Paged documents require a webserver to render in the browser. See the -p option below.
PDF
- :white_check_mark: All document types and features supported by HTML are also supported when exporting to PDF.
- Check the wiki’s PDF export page to learn more.

The desired document type can be set by calling the .doctype function within the source itself:

.doctype {slides}
.doctype {paged}

Comparison

	Quarkdown	Markdown	LaTeX	Typst	AsciiDoc	MDX
Concise and readable	:white_check_mark:	:white_check_mark:	:x:	:white_check_mark:	:white_check_mark:	:white_check_mark:
Full document control	:white_check_mark:	:x:	:white_check_mark:	:white_check_mark:	:x:	:x:
Scripting	:white_check_mark:	:x:	Partial	:white_check_mark:	:x:	:white_check_mark:
Book/article export	:white_check_mark:	:x:	:white_check_mark:	:white_check_mark:	:white_check_mark:	Third-party
Presentation export	:white_check_mark:	:x:	:white_check_mark:	:white_check_mark:	:white_check_mark:	Third-party
Learning curve	:green_circle:	:green_circle:	:red_circle:	:orange_circle:	:green_circle:	:green_circle:
Targets	HTML, PDF	HTML	PDF, PostScript	PDF	HTML, PDF, ePub	HTML

LaTeX Quarkdown

\tableofcontents

\section{Section}

\subsection{Subsection}

\begin{enumerate}
    \item \textbf{First} item
    \item \textbf{Second} item
\end{itemize}

\begin{center}
    This text is \textit{centered}.
\end{center}

\begin{figure}[!h]
    \centering
    \begin{subfigure}[b]
        \includegraphics[width=0.3\linewidth]{img1.png}
    \end{subfigure}
    \begin{subfigure}[b]
        \includegraphics[width=0.3\linewidth]{img2.png}
    \end{subfigure}
    \begin{subfigure}[b]
        \includegraphics[width=0.3\linewidth]{img3.png}
    \end{subfigure}
\end{figure}

.tableofcontents

# Section

## Subsection

1. **First** item
2. **Second** item

.center
    This text is _centered_.

.row alignment:{spacebetween}
    ![Image 1](img1.png)

    ![Image 2](img2.png)
    
    ![Image 3](img3.png)

Getting started

Installation

Download quarkdown.zip from the latest stable release or build it with gradlew distZip, and unzip it.

The bin directory contains the executable scripts. Optionally, adding it to your PATH allows you to access Quarkdown more easily.
The lib/qmd directory contains .qmd libraries that can be imported into a project.

Java 17 or higher is required. All major operating systems are supported.

Creating a project

Running quarkdown create [directory] will launch the prompt-based project wizard, making it quicker than ever to set up a new Quarkdown project, with all metadata and initial content already present.

For more information about the project creator, check out its wiki page.

Alternatively, you may manually create a .qmd source file and start from there.

Compiling

Running quarkdown c file.qmd will compile the given file and save the output to file.

If the project is composed by multiple source files, the target file must be the root one, i.e. the one that includes the other files.

How to include other files?

If you would like to familiarize yourself with Quarkdown instead, quarkdown repl lets you play with an interactive REPL mode.

Options

-p or --preview: enables automatic content reloading after compiling.
If a webserver is not running yet, it is started and the document is opened in the default browser.
This is required in order to render paged documents in the browser.
-w or --watch: recompiles the source everytime a file from the source directory is changed.

[!TIP] Combine -p -w to achieve live preview!

--pdf: produces a PDF file. Learn more in the wiki’s PDF export page.
-o <dir> or --output <dir>: sets the directory of the output files. Defaults to ./output.
-l <dir> or --libs <dir>: sets the directory where external libraries can be loaded from. Defaults to <install dir>/lib/qmd. (?)
-r <renderer> or --render <renderer>: sets the target renderer. Defaults to html. Accepted values:
- html
- html-pdf (equivalent to -r html --pdf)
--server-port <port>: optional customization of the local webserver’s port. Defaults to 8089.
--pretty: produces pretty output code. This is useful for debugging or to read the output code more easily, but it should be disabled in production as the results might be visually affected.
--clean: deletes the content of the output directory before producing new files. Destructive operation.
--strict: forces the program to exit if an error occurs. When not in strict mode, errors are shown as boxes in the document.
--no-media-storage: turns the media storage system off. (?)
-Dloglevel=<level> (JVM property): sets the log level. If set to warning or higher, the output content is not printed out.

Mock document

Mock, written in Quarkdown, is a comprehensive collection of visual elements offered by the language, making it ideal for exploring and understanding its key features — all while playing and experimenting hands-on with a concrete outcome in the form of pages or slides.

The document’s source files are available in the mock directory, and can be compiled via quarkdown c mock/main.qmd -p.
The PDF artifacts generated for all possible theme combinations are available and can be viewed in the generated branch.

Contributing

Contributions are welcome! Please check CONTRIBUTING.md to know how contribute via issues or pull requests.

Concept

The logo resembles the original Markdown icon, with focus on Quarkdown’s completeness, richness of features and customization options, emphasized by the revolving arrow all around the sphere.

What could be mistaken for a planet is actually a quark or, more specifically, a down quark, an elementary particle that is a major constituent of matter: they give life to every complex structure we know of, while also being one of the lightest objects in existence.

This is, indeed, the concept Quarkdown is built upon.

HTML on Producthunt daily

marker

datalab-to/marker

Marker

Performance

Hybrid Mode

Examples

Commercial usage

Hosted API

Community

Installation

Usage

Interactive App

Convert a single file

Convert multiple files

Convert multiple files on multiple GPUs

Use from python

Custom configuration

Extract blocks

Other converters

Extract tables

OCR Only

Structured Extraction (beta)

Output Formats

Markdown

HTML

JSON

Chunks

Metadata

LLM Services

Internals

API server

Troubleshooting

Debugging

Benchmarks

Overall PDF Conversion

Throughput

Table Conversion

Running your own benchmarks

Overall PDF Conversion

Table Conversion

How it works

Limitations

swagger-ui

swagger-api/swagger-ui

Introduction

General

Compatibility

Anonymized analytics

Documentation

Usage

Customization

Development

Contributing

Integration Tests

Browser support

Known Issues

Security contact

License

AdminLTE

ColorlibHQ/AdminLTE

AdminLTE - Bootstrap 5 Admin Dashboard

What’s New in v4.0.0-rc3

Looking for Premium Templates?

Quick start

Development

Production Build

Available Scripts

Browser Support

Contributing

Sponsorship

License

Image Credits

quarkdown

iamgio/quarkdown

Table of contents

About

Check out the demo presentation here

As simple as you expect…

...as complex as you need.