Model reference · open weights

vjepa2.1-vitl-fpc64-384

Available as managed deployment Embeddings Dev-Jahn · community Embeddings 1 variants 1k dl/mo

vjepa2.1-vitl-fpc64-384 is an open-weight embedding model from Dev-Jahn. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byDev-Jahn
TypeEmbedding models
TaskEmbeddings
Parameters (lead)328M
Runs withtransformers
Released2026-06-23
Popularity1k downloads / month
LicenceOpen weights

About

What vjepa2.1-vitl-fpc64-384 is

HuggingFace-format weights for V-JEPA 2.1, ViT-L/16 @ 384px (distilled from ViT-G), for dense spatio-temporal feature extraction.

  • patch 16, tubelet 2 → a 64-frame 384² clip yields a 32 × 24 × 24 token grid (D=1024).
  • Converted from Meta's official checkpoint vjepa2_1_vitl_dist_vitG_384.pt using the HF port at github.com/Dev-Jahn/vjepa2-hf.
Read the full model card

Usage

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "Dev-Jahn/vjepa2.1-vitl-fpc64-384", trust_remote_code=True,
).eval().cuda()

# channels-first (B, C, T, H, W); H=W=384, ImageNet-normalized
pixel_values_videos = torch.randn(1, 3, 32, 384, 384, device="cuda")
with torch.no_grad():
    feats = model(pixel_values_videos, skip_predictor=True).last_hidden_state  # (B, N, 1024)

# dense grid: N = (T/2)*(384/16)*(384/16)
grid = feats.reshape(feats.shape[0], 16, 24, 24, -1)  # for T=32

Attribution & license

Derived from Meta's V-JEPA 2.1 (MIT/Apache-2.0). Port code and this conversion are Apache-2.0. See the original facebookresearch/vjepa2.

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys vjepa2-1-vitl-fpc64-384 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (vjepa2-1-vitl-fpc64-384 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":"vjepa2-1-vitl-fpc64-384","input":"text to embed"}'

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