Siglip Large 256 GGUF

--- license: apache-2.0 tags: - siglip - vision - gguf - crispembed - image-embedding pipeline_tag: image-feature-extraction library_name: ggml --- # SigLIP ViT-L/16 Vision Encoder 256px (GGUF) GGUF conversion of [google/siglip-large-patch16-256](https://huggingface.co/google/siglip-large-patch16-256) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed). - **Architecture:** SigLIP ViT-L/16 vision encoder - **Parameters:** 304M - **Output:** 1024-dimensional L2-normalized embeddings - **Input:** 256x256 RGB image with SigLIP normalization (mean=0.5, std=0.5) - **Size:** ~1.2 GB - **Source:** google/siglip-large-patch16-256 ## Usage ```bash # Embed a single image crispembed -m siglip-large-256 --image photo.jpg # Batch processing crispembed -m siglip-large-256 --image-dir ./photos/ --output embeddings.bin ``` ## About SigLIP SigLIP replaces CLIP's softmax contrastive loss with a sigmoid loss, enabling better scaling and stronger performance on retrieval benchmarks. It uses a simpler normalization scheme (mean=0.5, std=0.5) compared to CLIP's ImageNet statistics. ## 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-large-patch16-256](https://huggingface.co/google/siglip-large-patch16-256) — 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.

Source context: 27 downloads · 1 likes · Pipeline image-feature-extraction · Library ggml · Repo cstr/siglip-large-256-GGUF

Source context: 63 downloads · 0 likes · Pipeline image-feature-extraction · Library ggml · Repo cstr/siglip-large-256-GGUF