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Escarda-VE
Quazim0t0/Escarda-VE
Escarda-VE is a machine learning model from Quazim0t0. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Vision encoder for the Escarda family (~39.6M, 448px / patch16 / 784 tokens). Byrne-VE plus a JEPA head next to the HRM refine block - that's the Escarda trait.
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Public
Updated Sep 20, 2026
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158 MB
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jul 6, 2026
- Updated
- Sep 20, 2026
- SHA
- f959a22e
Model card
From the Hugging Face model README
Escarda-VE
Vision encoder for the Escarda family (~39.6M, 448px / patch16 / 784 tokens). Byrne-VE plus a JEPA head next to the HRM refine block - that's the Escarda trait.
- ViT-style: RMSNorm, 2D-axial RoPE, QK-Norm, SwiGLU, HRM refine.
- JEPA head (auxiliary): from a patch token, predict the raster-order neighbour patch's representation (stop-gradient target, 1-cosine loss). Zero inference cost.
- Distilled from frozen DINOv2-base (50k steps, CLS + patch), then 20k steps of DINO-style self-distillation (EMA teacher, cosine prototypes, no collapse).
Escarda-VE vs Byrne-VE (DINOv2 teacher-alignment, n=1024 held-out)
| Byrne-VE | Escarda-VE | |
|---|---|---|
| Params | 39.34M | 39.60M (+JEPA head) |
| CLS cosine | 0.776 | 0.771 |
| PATCH cosine | 0.600 | 0.584 |
| JEPA self-consistency | - | 0.040 |
Escarda trades ~1-3% teacher-alignment (JEPA pulls a bit of capacity off pure mimicry) for a self-supervised spatial neighbour-prediction signal that costs nothing at inference. Same size class as Byrne-VE.
Related
- Byrne-VE - HRM-only sibling
- Escarda-Docling-126M - document VLM on this encoder
- Escarda-86M-Base
- License
- apache-2.0
- Created
- Jul 6, 2026
- Updated
- Sep 20, 2026