Model reference · open weights

aimv2_large_patch14_224.apple_pt_dist

Embeddings timm Image embed 1 build Its own licence terms 3k dl/mo

aimv2_large_patch14_224.apple_pt_dist is an open-weight embedding model from timm. aimv2_large_patch14_224.apple_pt_dist (FP32) weighs 618 MB; the smallest configuration that runs it is RTX 3060 12 GB.

What it is

Released bytimm
Released2024-12-31
Parameters309M
VRAM618 MB for the weights

What it runs on

Memory and cards for aimv2_large_patch14_224.apple_pt_dist (FP32)

618 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What timm says about aimv2_large_patch14_224.apple_pt_dist

timm compatible AIM-v2 (https://huggingface.co/papers/2411.14402) image encoder weights from https://huggingface.co/apple/aimv2-large-patch14-224-distilled

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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, which is the basis of search and RAG.
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