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Clip Vit Large Patch14 336 GGUF

GGUF conversion of openai/clip-vit-large-patch14-336 for use with CrispEmbed.

TipoOtherVersãoRevision a32f137Cnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
1,13 GB
Versão
vRevision a32f137
Atualizado
22 de out. de 2026
Downloads
17
Fonte
Hugging Face

Descrição da fonte

CLIP ViT-L/14@336 Vision Encoder (GGUF)

GGUF conversion of openai/clip-vit-large-patch14-336 for use with CrispEmbed.

  • Architecture: CLIP ViT-L/14 vision encoder (336px variant)
  • Parameters: 304M
  • Output: 768-dimensional L2-normalized embeddings (1024d internal, projected to 768d)
  • Input: 336x336 RGB image with CLIP normalization
  • Size: ~1.2 GB
  • Source: openai/clip-vit-large-patch14-336

Usage

# Embed a single image
crispembed -m clip-vit-large-patch14-336 --image photo.jpg

# Batch processing
crispembed -m clip-vit-large-patch14-336 --image-dir ./photos/ --output embeddings.bin

Higher input resolution (336x336 vs 224x224) captures finer spatial detail compared to the standard ViT-L/14.

Cross-modal pairing

Shares an embedding space with cstr/clip-text-large-GGUF for zero-shot image-text matching.

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-large-patch14-336 — 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.

Fontes

1 fonte
Hugging FacePrimárioVersão a32f13725e74d735efc5995263e11986f9287f8f
huggingface.co/cstr/clip-vit-large-patch14-336-GGUF/tree/a32f13725e74d735efc5995263e11986f9287f8f

Verificado 22 de out.

Artefatos de modelo

1 artefato

clip-vit-large-patch14-336.gguf

gguf · 1,13 GB · SHA-256 f96e54e463bb…ffec · Hugging Face

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Trechos de fonte

3 trechos

--- license: mit tags: - clip - vision - gguf - crispembed - image-embedding pipeline_tag: image-feature-extraction library_name: ggml --- # CLIP ViT-L/14@336 Vision Encoder (GGUF) GGUF conversion of [openai/clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed). - **Architecture:** CLIP ViT-L/14 vision encoder (336px variant) - **Parameters:** 304M - **Output:** 768-dimensional L2-normalized embeddings (1024d internal, projected to 768d) - **Input:** 336x336 RGB image with CLIP normalization - **Size:** ~1.2 GB - **Source:** openai/clip-vit-large-patch14-336 ## Usage ```bash # Embed a single image crispembed -m clip-vit-large-patch14-336 --image photo.jpg # Batch processing crispembed -m clip-vit-large-patch14-336 --image-dir ./photos/ --output embeddings.bin ``` Higher input resolution (336x336 vs 224x224) captures finer spatial detail compared to the standard ViT-L/14. ## Cross-modal pairing Shares an embedding space with [cstr/clip-text-large-GGUF](https://huggingface.co/cstr/clip-text-large-GGUF) for zero-shot image-text matching. ## 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-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) — 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 capa...

Source context: 17 downloads · 0 likes · Pipeline image-feature-extraction · Library ggml · Repo cstr/clip-vit-large-patch14-336-GGUF

Source context: 35 downloads · 0 likes · Pipeline image-feature-extraction · Library ggml · Repo cstr/clip-vit-large-patch14-336-GGUF