OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Awesome Vlm Architectures

Awesome Vlm Architectures is a visual, citation-linked catalog of vision-language and multimodal model architectures. Entries summarize design choices such as modality alignment or fusion, training stages and datasets, and link to primary sources; architecture figures are included when available.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Awesome Vlm Architectures — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the catalog during literature review, early model selection or architecture research when you need a structured starting point and links to original sources.

Who it is for

  • Researchers comparing vision-language model families and design approaches.
  • AI practitioners looking for primary papers and architecture references before selecting a model.
  • Readers tracing multimodal architecture releases or exploring areas such as OCR, video, GUI agents and embodied AI.

Documented uses

  • Compare architectures and modality-fusion approaches across model families.
  • Find primary papers, diagrams, training information and datasets associated with a model.
  • Trace model releases through the catalog’s timeline.
  • Discover architectures organized around application areas such as OCR, video and GUI agents.
Before you choose

Requirements & limits

  • The resource is a Markdown repository and curated index, not a model execution or hosting service.
  • Entries summarize primary research; consult and cite the original paper for claims about an individual model.
  • Architecture figures are credited individually and subject to a separate figure notice.
  • The repository identifies its license as CC0-1.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Compare vision-language model architectures using the catalog

Explore model families and retrieve architecture summaries and primary research references.

Before you start

  • Access to the public GitHub repository.

Steps

  1. Open the Models section of the repository README.

  2. Browse the chronological model index or entries grouped by architecture area to find models of interest.

  3. Expand a model panel to read its architecture summary, including details such as modality alignment or fusion, training stages, and datasets.

  4. Follow the entry’s primary-source link to consult the original paper; cite that paper for claims about an individual model.

Expected outcome

A set of model architecture summaries and linked papers for comparison or research.

Guide limitations

  • The catalog is a guide to the literature, not a substitute for original papers.
  • Architecture figures are included when available and may be subject to rights described in the figure notice.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
gokayfem/awesome-vlm-architectures · Official homepage title · retained in its source language
How we review sourcesSuggest a correction