X-JEPA Model Checkpoints

Pretrained checkpoints for the paper:

Latent Prediction Needs Alignment: A Controlled Study of Joint-Embedding Predictive Vision-Language Learning Mohammad Kohankhaki, Daniel Kusuma, Shirin Salehi, Carsten Kamp, Sigrid Brell-Cokcan, and Anke Schmeink — to appear at AACL-IJCNLP 2026

Code: github.com/mohkoh19/x-jepa

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Checkpoints

Weights are stored as safetensors, so they load without executing arbitrary code (no pickle).

File	Model	Parameters	Size
clip.safetensors	CLIP baseline	196.5M	1.1 GB
siglip.safetensors	SigLIP baseline	196.5M	1.1 GB
xjepa_p.safetensors	X-JEPA [P] — prediction only	398.9M	1.6 GB
xjepa_tc.safetensors	X-JEPA [TC] — target-contrastive	400.1M	1.6 GB
xjepa_pa_lam003.safetensors	X-JEPA [P,A] λ=0.03	398.9M	1.6 GB
xjepa_pa_lam01.safetensors	X-JEPA [P,A] λ=0.10 (main model)	398.9M	1.6 GB
xjepa_pa_lam03.safetensors	X-JEPA [P,A] λ=0.30	398.9M	1.6 GB
xjepa_pa_lam10.safetensors	X-JEPA [P,A] λ=1.00	398.9M	1.6 GB

Download

git clone https://github.com/mohkoh19/x-jepa.git
cd x-jepa
bash scripts/download_checkpoints.sh        # -> checkpoints/*.safetensors

or a single file:

wget https://huggingface.co/mohkoh/x-jepa/resolve/main/xjepa_pa_lam01.safetensors

Evaluation

The code repository resolves each file's architecture from configs/checkpoints/<name>.yaml, so a downloaded checkpoint can be evaluated directly:

python src/eval.py experiment=eval/coco_karpathy_zeroshot \
  ckpt_path=checkpoints/xjepa_pa_lam01.safetensors \
  paths.data_dir=/path/to/data

scripts/quantitative_eval.py runs the full main-results suite.

Main Results (Paper Table 1)

All values are percentages; VSR is AUROC.

Model	COCO ZS MR	Flickr30k ZS MR	SugarCrepe++	SVO-Probes	VSR	NLVR2 token	NLVR2 global
CLIP	67.89	79.87	71.68	84.43	63.75	54.93	57.05
SigLIP	67.67	80.32	69.79	84.30	62.77	55.00	57.43
X-JEPA [P]	0.10	0.21	37.06	50.36	48.52	53.05	52.22
X-JEPA [TC]	44.60	48.30	44.17	80.73	57.26	56.42	51.08
X-JEPA [P,A] λ=0.10	69.39	81.53	73.30	85.13	63.91	60.11	58.38
X-JEPA [P,A] λ=0.30	69.47	81.66	72.59	85.19	64.58	59.67	58.32
X-JEPA [P,A] λ=1.00	69.05	81.35	72.21	84.75	63.86	57.84	57.64

Citation

@inproceedings{kohankhaki2026latent,
  title     = {Latent Prediction Needs Alignment: A Controlled Study of Joint-Embedding Predictive Vision-Language Learning},
  author    = {Kohankhaki, Mohammad and Kusuma, Daniel and Salehi, Shirin and Kamp, Carsten and Brell-Cokcan, Sigrid and Schmeink, Anke},
  booktitle = {Proceedings of AACL-IJCNLP 2026},
  year      = {2026},
  publisher = {Association for Computational Linguistics},
  note      = {To appear},
}

License

Released under CC BY-NC 4.0 (research and other non-commercial use, with attribution). Parts of the training code derive from the I-JEPA release, which is distributed under the same licence.