# siglip2-x256-explicit-content by Prithiv Sakthi: Open Model Source: https://savrn.com/models/siglip2-x256-explicit-content Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve siglip2-x256-explicit-content (93M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026. [siglip2-x256-explicit-content on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/siglip2-x256-explicit-content/gpus) ## SAVRN's Notes on siglip2-x256-explicit-content This classifier sorts images into content categories for moderation, built on Google's SigLIP 2 at 93M parameters. It is fast enough to screen uploads in real time. Apache 2.0 allows commercial use. Moderation models encode someone else's definition of explicit; test it against your own policy and a labeled sample, and keep a human review path for borderline content. SAVRN Research, 2026-09-18 ## Model Card By Prithiv Sakthi, published under apache-2.0, revision 143a4508c50d. siglip2-x256-explicit-content is a vision-language encoder model fine-tuned from siglip2-base-patch16-256 for multi-class image classification. Built on the SiglipForImageClassification architecture, the model is trained to identify and categorize content types in images, especially for explicit, suggestive, or safe media filtering. [!note] SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 ``` Classification Report: precision recall f1-score support Anime Picture 0.8940 0.8718 0.8827 5600 Hentai 0.8961 0.8935 0.8948 4180 Normal 0.9100 0.8895 0.8997 5503 Pornography 0.9496 0.9654 0.9574 5600 Enticing or Sensual 0.9132 0.9429 0.9278 5600 accuracy 0.9137 26483 macro avg 0.9126 0.9126 0.9125 26483 weighted avg 0.9135 0.9137 0.9135 26483 ``` ### Label Space: 5 Classes The model classifies each image into one of the following content categories: ``` Class 0: "Anime Picture" Class 1: "Hentai" Class 2: "Normal" Class 3: "Pornography" Class 4: "Enticing or Sensual" ``` ### Install Dependencies ``` pip install -q transformers torch pillow gradio ``` ### Inference Code [Read the full model card (326 words)](https://savrn.com/models/siglip2-x256-explicit-content/card) ## Configuration Architecture SiglipForImageClassification Context length (tokens) 64 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 256,000 Stored precision float32 Model type siglip ## Identity and Version Repository prithivMLmods/siglip2-x256-explicit-content Publisher Prithiv Sakthi Task Image classification Modality Image Library transformers Parameters 93M parameters Languages en Revision 143a4508c50d75299159a410d4649afe5b7e38fb First published 2025-04-29 Last updated 2025-04-30 ## Files and Weights 30 files, 3.5 GB in total. The weights are 17 files totalling 3.5 GB in bin, pt, pth, safetensors. Weights17 files · 3.5 GB Configuration11 files · 12.3 KB Documentation1 file · 3.8 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | checkpoint-1656/model.safetensors | Weights | 371.8 MB | 886acfda2690 | | checkpoint-1656/optimizer.pt | Weights | 686.9 MB | 9a80622a0edf | | checkpoint-1656/rng_state.pth | Weights | 14.2 KB | 66b395f57c7d | | checkpoint-1656/scheduler.pt | Weights | 1.1 KB | d36413dbf261 | | checkpoint-1656/training_args.bin | Weights | 5.3 KB | 77f0ca87b399 | | checkpoint-2484/model.safetensors | Weights | 371.8 MB | 846e9b76948a | | checkpoint-2484/optimizer.pt | Weights | 686.9 MB | 754fddb5628d | | checkpoint-2484/rng_state.pth | Weights | 14.2 KB | bb29b7c6a866 | | checkpoint-2484/scheduler.pt | Weights | 1.1 KB | a31cd561974c | | checkpoint-2484/training_args.bin | Weights | 5.3 KB | 77f0ca87b399 | | checkpoint-828/model.safetensors | Weights | 371.8 MB | c82837ce983c | | checkpoint-828/optimizer.pt | Weights | 686.9 MB | 04edb7400da1 | | checkpoint-828/rng_state.pth | Weights | 14.2 KB | 8ef17702a072 | | checkpoint-828/scheduler.pt | Weights | 1.1 KB | 317a7aa06c6e | | checkpoint-828/training_args.bin | Weights | 5.3 KB | 77f0ca87b399 | | model.safetensors | Weights | 371.8 MB | 846e9b76948a | | training_args.bin | Weights | 5.3 KB | 77f0ca87b399 | | checkpoint-1656/config.json | Configuration | 1.3 KB | — | | checkpoint-1656/preprocessor_config.json | Configuration | 394 B | — | | checkpoint-1656/trainer_state.json | Configuration | 1.9 KB | — | | checkpoint-2484/config.json | Configuration | 1.3 KB | — | | checkpoint-2484/preprocessor_config.json | Configuration | 394 B | — | | checkpoint-2484/trainer_state.json | Configuration | 2.4 KB | — | | checkpoint-828/config.json | Configuration | 1.3 KB | — | | checkpoint-828/preprocessor_config.json | Configuration | 394 B | — | | checkpoint-828/trainer_state.json | Configuration | 1.3 KB | — | | config.json | Configuration | 1.3 KB | — | | preprocessor_config.json | Configuration | 394 B | — | | README.md | Documentation | 3.8 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 3.5 GB [Download from Prithiv Sakthi](https://huggingface.co/prithivMLmods/siglip2-x256-explicit-content) Released by Prithiv Sakthi through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [google/siglip2-base-patch16-256](https://savrn.com/models/siglip2-base-patch16-256) - Described by [arXiv:2502.14786](https://savrn.com/papers/siglip-2-multilingual-vision-language-encoders-with-improved-semantic-understanding-locali) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 3.5 GB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About siglip2-x256-explicit-content ### How much GPU memory does siglip2-x256-explicit-content need? About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (93M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run siglip2-x256-explicit-content on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use siglip2-x256-explicit-content commercially? Yes. siglip2-x256-explicit-content is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is siglip2-x256-explicit-content's context length? 64 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Image classification ### [Gender-Classifier-Mini](https://savrn.com/models/gender-classifier-mini) [Prithiv Sakthi](https://savrn.com/model-publishers/prithivmlmods) The model categorizes images into two classes: The Gender-Classifier-Mini model is designed to classify images into gender categories. 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