CLIP ViT-B/16 Vision Encoder (GGUF)

• Architecture: CLIP ViT-B/16 vision encoder
• Parameters: 86M
• Output: 512-dimensional L2-normalized embeddings (768d internal, projected to 512d via visual_projection)
• Input: 224x224 RGB image with CLIP normalization
• Size: ~329 MB
• Source: openai/clip-vit-base-patch16

Usage

Cross-modal pairing

Notes

• All output embeddings are L2-normalized.
• This is a GGUF conversion; weights are numerically equivalent to the original HuggingFace model.

Provenance and EU AI Act Art. 53 note

• Upstream model: openai/clip-vit-base-patch16 — published by openai.
• Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.