Model reference · open weights

fastvit_mci0.apple_mclip2_dfndr2b

Embeddings timm Image embed 1 build Its own licence terms 887 dl/mo

fastvit_mci0.apple_mclip2_dfndr2b is an open-weight embedding model from timm. fastvit_mci0.apple_mclip2_dfndr2b (FP32) weighs 23 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • fastvit_mci0.apple_mclip2_dfndr2b is an image-feature-extraction model developed by timm.
  • It functions as the image encoder for MobileCLIP v2 and was trained on the DFNDR-2B dataset.
  • The model contains 11M parameters and is distributed under the apple-amlr licence.

Summary of the timm/fastvit_mci0.apple_mclip2_dfndr2b model card, 2026-10-01

What it is

Released bytimm
Released2025-09-10
Parameters11M
VRAM23 MB for the weights

What it runs on

Memory and cards for fastvit_mci0.apple_mclip2_dfndr2b (FP32)

23 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 fastvit_mci0.apple_mclip2_dfndr2b

Read the model card

A MobileCLIP v2 (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/MobileCLIP2-S0-OpenCLIP.

Model Details

  • Dataset: DFNDR-2B
  • Papers:
    • MobileCLIP2: Improving Multi-Modal Reinforced Training: https://arxiv.org/abs/2508.20691

Citation

@article{faghri2025mobileclip2,
          title={MobileCLIP2: Improving Multi-Modal Reinforced Training},
          author={Faghri, Fartash and Vasu, Pavan Kumar Anasosalu and Koc, Cem and Shankar, Vaishaal and Toshev, Alexander and Tuzel, Oncel and Pouransari, Hadi},
          journal={arXiv preprint arXiv:2508.20691},
          year={2025}
        }

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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