metadata
license: mit
tags:
- mirror
- xai
- multimodal
- clip
- zero-shot-image-classification
CLIP ViT-B/32 (OpenAI) — xaitalk mirror
This is a bit-identical mirror of the canonical artifact from OpenAI.
The mirror exists only as a resilience fallback for the xaitalk library — the upstream remains authoritative. All credit and licensing for the model belong to the original authors.
Attribution
| Field | Value |
|---|---|
| Original authors | OpenAI |
| Upstream (authoritative) | https://huggingface.co/openai/clip-vit-base-patch32 |
| Source repo | https://github.com/openai/CLIP |
| Paper | https://arxiv.org/abs/2103.00020 (Radford et al. 2021) |
| License | mit (inherited from upstream — please respect upstream's terms) |
| Mirror file | pytorch_model.bin |
| SHA-256 | a63082132ba4f97a80bea76823f544493bffa8082296d62d71581a4feff1576f |
| Size | 605,247,071 bytes (577.2 MB) |
How xaitalk loads this file
from xaitalk.hub import ensure_model
weights_path = ensure_model("clip-vit-b32")
# Tries the canonical upstream first; falls back to this xaitalk mirror
# automatically if upstream is unreachable.
Why mirror?
xaitalk's research-grade reproducibility claim relies on every weight file
being recoverable years from now. We mirror artifacts ≤ 2.5 GB under
xaitalk/*-mirror so the pipeline survives upstream URL changes, repo
renames, or deletions. Bit-level parity with the canonical is asserted in
CI via python -m xaitalk.hub verify-mirrors.
Citation
If you use this model, please cite the original paper (not the mirror):
https://arxiv.org/abs/2103.00020 (Radford et al. 2021)