# invictus717/MetaTransformer

Meta-Transformer for Unified Multimodal Learning

Repository: https://github.com/invictus717/MetaTransformer
Canonical: https://ross.abutalabs.com/products/metatransformer
Homepage: https://arxiv.org/abs/2307.10802
Language: Python
License: Apache-2.0
License Family: permissive
Topics: artificial-intelligence, computer-vision, machine-learning, multimedia, multimodal, transformers, foundationmodel
Last push: 2023-12-05T07:36:11+00:00

## Health v2 (maintenance only)
Score: 19/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 82
- inputs: {"age_days": 1152, "days_push": 1002, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1647, forks 116 (observed 2026-08-28T04:05:16.433760+00:00)

## What it is
Meta-Transformer is a research framework for unified multimodal learning that maps inputs from 12 modalities (text, images, point clouds, audio, video, time series, tabular data, etc.) into a shared token space processed by a frozen encoder. It accompanies an arXiv paper and provides pretrained models and code for multimodal perception without paired multimodal training data.

## Use cases
- process images, text, audio, and point clouds with one unified model
- multimodal learning without paired training data
- run perception across 12 modalities with a frozen transformer encoder
- benchmark a foundation model on multimodal tasks
- extract features from tabular, time-series, and graph data with a transformer
- research unified multimodal tokenization

## When to choose
- you need a single model to handle many data modalities
- you lack paired multimodal training data
- you want to reproduce or extend the Meta-Transformer paper
- you need modality-agnostic feature extraction

## When to avoid
- you need a production-ready, actively maintained multimodal API
- you only work with a single modality like text or images
- you need fine-grained state-of-the-art results on one specific task
- you require frequent updates or commercial support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, transformers, image-processing, audio-processing, video-processing, nlp
- domain: artificial-intelligence, machine-learning, computer-vision, deep-learning
- platform: python, cross-platform
- tags: multimodal, foundation-model, transformer, research-code, unified-tokenizer, frozen-encoder, natural-language-processing, gpu

## Member repositories
- invictus717/MetaTransformer (main) score 19

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.433760+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:18.752177+00:00, confidence not recorded.
  - readme: https://github.com/invictus717/MetaTransformer (fetched 2026-08-28T04:05:16.433760+00:00, sha d6106294cd79)
  - homepage: https://arxiv.org/abs/2307.10802 (fetched 2026-08-29T11:18:45.709076+00:00, sha fbc903a2fa70)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:18:45.711859+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:18:45.715561+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:18:45.717713+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:18:45.713759+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.