Siglip So400m Patch14 384 GGUF
GGUF conversion of google/siglip-so400m-patch14-384 for use with CrispEmbed.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 1,60 GB
- Versão
- vRevision 3f5bc6c
- Atualizado
- 22 de out. de 2026
- Downloads
- 92
- Fonte
- Hugging Face
Descrição da fonte
SigLIP SoViT-400M/14 Vision Encoder 384px (GGUF)
GGUF conversion of google/siglip-so400m-patch14-384 for use with CrispEmbed.
- Architecture: SigLIP SoViT-400M/14 vision encoder (shape-optimized)
- Parameters: 428M
- Output: 1152-dimensional L2-normalized embeddings
- Input: 384x384 RGB image with SigLIP normalization (mean=0.5, std=0.5)
- Patch size: 14
- Size: ~1.6 GB
- Source: google/siglip-so400m-patch14-384
Usage
# Embed a single image
crispembed -m siglip-so400m-patch14-384 --image photo.jpg
# Batch processing
crispembed -m siglip-so400m-patch14-384 --image-dir ./photos/ --output embeddings.bin
About SigLIP SoViT-400M
SoViT-400M is a shape-optimized SigLIP variant that redistributes compute across width, depth, and MLP ratio for better efficiency. Combined with 384px input and patch size 14, it provides high-quality vision embeddings.
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: google/siglip-so400m-patch14-384 — published by
google. - Upstream licence:
apache-2.0. 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
Fontes
1 fonteVerificado 22 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatosiglip-so400m-patch14-384.gguf
gguf · 1,60 GB · SHA-256 4cf3cb855beb…8a90 · Hugging Face
Download fileTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- license: apache-2.0 tags: - siglip - vision - gguf - crispembed - image-embedding pipeline_tag: image-feature-extraction library_name: ggml --- # SigLIP SoViT-400M/14 Vision Encoder 384px (GGUF) GGUF conversion of [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed). - **Architecture:** SigLIP SoViT-400M/14 vision encoder (shape-optimized) - **Parameters:** 428M - **Output:** 1152-dimensional L2-normalized embeddings - **Input:** 384x384 RGB image with SigLIP normalization (mean=0.5, std=0.5) - **Patch size:** 14 - **Size:** ~1.6 GB - **Source:** google/siglip-so400m-patch14-384 ## Usage ```bash # Embed a single image crispembed -m siglip-so400m-patch14-384 --image photo.jpg # Batch processing crispembed -m siglip-so400m-patch14-384 --image-dir ./photos/ --output embeddings.bin ``` ## About SigLIP SoViT-400M SoViT-400M is a shape-optimized SigLIP variant that redistributes compute across width, depth, and MLP ratio for better efficiency. Combined with 384px input and patch size 14, it provides high-quality vision embeddings. ## 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:** [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) — published by `google`. - **Upstream licence:** `apache-2.0`. 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...
Source context: 92 downloads · 1 likes · Pipeline image-feature-extraction · Library ggml · Repo cstr/siglip-so400m-patch14-384-GGUF
Source context: 88 downloads · 0 likes · Pipeline image-feature-extraction · Library ggml · Repo cstr/siglip-so400m-patch14-384-GGUF
