Model reference · open weights
ijepa_vith14_1k is an open-weight embedding model from facebook, 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.
About
I-JEPA Model (Huge, fine-tuned on IN1K) I-JEPA is a method for self-supervised learning. At a high level, I-JEPA predicts the representations of part of an image from the representations of other parts of the same image: 1. without relying on pre-specified invariances to hand-crafted data transformations, which tend to be biased for particular downstream tasks, 2. and without having the model fill in pixel-level details, which tend to result in learning less semantically meaningful representations. How does it work? As opposed to generative methods that have a pixel decoder, I-JEPA has a predictor that makes predictions in latent space. The predictor in I-JEPA can be seen as a primitive (and restricted) world-model that is able to model spatial uncertainty in a static image from a partially observable context. This world model is semantic in the sense that it predicts high level information about unseen regions in the image, rather than pixel-level details. We trained a stochastic decoder that maps the I-JEPA predicted representations back in pixel space as sketches. The model correctly captures positional uncertainty and produces high-level object parts with the correct pose (e.g., dog’s head, wolf’s front legs). Intended uses & limitations I-JEPA can be used for image classification or feature extraction. This checkpoint in specific is intended for Feature Extraction. How to use Here is how to use this model for image feature extraction: BibTeX entry and citation info If you use I-JEPA or this code in your work, please cite:
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 631M |
| Variants | 1 |
| Runs with | transformers |
| Released | 2024-08-25 |
| Popularity | 37k downloads / month |
| Likes | 17 |
| Licence | Commercial licence needed |
How it works
Variants
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.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| ijepa_vith14_1k | 631M | BF16 | ~1.5 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys ijepa-vith14-1k for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ijepa-vith14-1k 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":"ijepa-vith14-1k","input":"text to embed"}'
Licence
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 ↗
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