Hyper3-CLIP beta
Hyper3-CLIP beta is the hyper³labs ViT-B hyperbolic checkpoint.
This repository publishes the raw PyTorch training checkpoint for the completed
500k-step paper-scratch run. It is not the older Hyper3-CLIP v0.5
SentenceTransformers package.
Artifact
- Checkpoint:
checkpoint_final.pt
- Config:
config.yaml
- Training metadata:
metadata.json
- Objective:
uncha (see config.yaml for the full loss configuration)
- Vision backbone:
vit_base_patch16_224
- Vision pretrained:
false
- Text model architecture/tokenizer:
openai/clip-vit-base-patch32
- Text pretrained:
false
- Embedding dimension: 512
- Training steps: 500,000
- Global batch size: 768
Evaluation
The eval/ directory includes the paper-comparable full benchmark table and the
raw wide summary row used for the current model comparison.
Headline row from the local full eval:
- ImageNet top-1: 46.984%
- COCO I2T/T2I R@10: 84.30 / 73.19
- Flickr I2T/T2I R@10: 97.60 / 91.44
- WordNet hierarchy: TIE 3.1597, LCA 2.0786, Jaccard 0.8179
- PEP AUC/AP: 96.07 / 69.36
The checkpoint is strong on retrieval in the paper-comparable table, but weak on
several flat/fine-grained zero-shot datasets such as Food101, CUB, Flowers102,
Cars, and Aircraft. Treat this release as a research checkpoint, not a polished
production model.
Loading
This is a raw training checkpoint. Use the hyper³labs hyper3-clip codebase and
the included config.yaml to instantiate the model, then load
checkpoint_final.pt.
1import torch
2
3checkpoint = torch.load("checkpoint_final.pt", map_location="cpu", weights_only=False)
4state_dict = checkpoint.get("model", checkpoint)
License And Attribution
The model materials in this repository are released under OpenMDW-1.0.
Redistributions should preserve NOTICE, LICENSE, and the model card when
practical.
Please cite and link to the original hyper³labs model repository when publishing
benchmarks, papers, derivative checkpoints, or public demos based on this model.
