Model reference · open weights

vit_large_patch16_224.mae

vit_large_patch16_224.mae is an open-weight embedding model from timm, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Licence fee required Embeddings timm 1 variants 42k downloads/mo
Request a licence + hosting quote All served models Not on the shared API today — deployed on request.

About

What vit_large_patch16_224.mae is

Model card for vitlargepatch16224.mae A Vision Transformer (ViT) image feature model. Pretrained on ImageNet-1k with Self-Supervised Masked Autoencoder (MAE) method. Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 303.3 - GMACs: 61.6 - Activations (M): 63.5 - Image size: 224 x 224 - Papers: - Masked Autoencoders Are Scalable Vision Learners: https://arxiv.org/abs/2111.06377 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - Pretrain Dataset: ImageNet-1k - Original: https://github.com/facebookresearch/mae Model Usage Image Classification Image Embeddings Model Comparison Explore the dataset and runtime metrics of this model in timm model results. Citation

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

Makertimm
TypeEmbedding models
Parameters (lead)303M
Variants1
Runs withtimm
Released2023-05-09
Popularity42k downloads / month
Likes2
LicenceCommercial licence needed

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
vit_large_patch16_224.mae303MBF16~0.7 GBWeights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys vit-large-patch16-224-mae for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (vit-large-patch16-224-mae below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"vit-large-patch16-224-mae","input":"text to embed"}'

Details

Languages, data & research

Tags

timm pytorch safetensors image-feature-extraction transformers

Papers

Licence

Commercial licence needed

The weights are open but cc-by-nc-4.0 needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗

Sources

Weights & code

Want vit_large_patch16_224.mae on EU-owned hardware?

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Explore

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