# rorshark-vit-base by Chester Enright: Open-Weight Model Source: https://savrn.com/models/rorshark-vit-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve rorshark-vit-base (86M 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. [rorshark-vit-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/rorshark-vit-base/gpus) ## SAVRN's Notes on rorshark-vit-base This is a ViT base model fine-tuned on a private image folder dataset, and the card does not say what the classes are. It still draws about 789,000 downloads a month, most likely from automated pipelines rather than people choosing it. Apache 2.0 allows commercial use. Without published labels, training data or intended use, we cannot recommend it for anything you need to explain. Inspect the label map in its configuration before using it, and prefer models with documented training data. SAVRN Research, 2026-09-18 ## Model Card By Chester Enright, published under apache-2.0, revision 85b973ba04fa. This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 5.0 - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0 Read Chester Enright's full model card This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://savrn.com/models/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0393 - Accuracy: 0.9923 ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data More information needed ### Training procedure #### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 #### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | | --- | --- | --- | --- | --- | | 0.0597 | 1.0 | 368 | 0.0546 | 0.9865 | | 0.2009 | 2.0 | 736 | 0.0531 | 0.9865 | | 0.0114 | 3.0 | 1104 | 0.0418 | 0.9904 | | 0.0998 | 4.0 | 1472 | 0.0425 | 0.9904 | | 0.1244 | 5.0 | 1840 | 0.0393 | 0.9923 | #### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0 ## Configuration Architecture ViTForImageClassification Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Stored precision float32 Model type vit ## Identity and Version Repository amunchet/rorshark-vit-base Publisher Chester Enright Task Image classification Modality Image Library transformers Parameters 86M parameters Languages vit Revision 85b973ba04fa78630ebffce57bb1b784128eb9fc First published 2023-11-18 Last updated 2023-11-18 ## Files and Weights 12 files, 343.3 MB in total. The weights are 2 files totalling 343.2 MB in bin, safetensors. Weights2 files · 343.2 MB Configuration6 files · 25.9 KB Documentation1 file · 2.0 KB Other2 files · 35.6 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 343.2 MB | 3eec7fd63f2d | | training_args.bin | Weights | 4.7 KB | ffcc3f59e708 | | all_results.json | Configuration | 350 B | — | | config.json | Configuration | 767 B | — | | eval_results.json | Configuration | 203 B | — | | preprocessor_config.json | Configuration | 325 B | — | | train_results.json | Configuration | 167 B | — | | trainer_state.json | Configuration | 24.1 KB | — | | README.md | Documentation | 2.0 KB | — | | runs/Nov18_20-49-17_gpu2/events.out.tfevents.1700340562.gpu2.60464.0 | Other | 35.2 KB | 33e3163f81d6 | | runs/Nov18_20-49-17_gpu2/events.out.tfevents.1700341120.gpu2.60464.1 | Other | 411 B | 1790db96b3f6 | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 343.2 MB [Download from Chester Enright](https://huggingface.co/amunchet/rorshark-vit-base) Released by Chester Enright through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [google/vit-base-patch16-224-in21k](https://savrn.com/models/vit-base-patch16-224-in21k) - Trained on (disclosed) imagefolder ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | imagefolder | Configuration defaultTask Image ClassificationMetric AccuracyComparison conditions not established | 0.992293 | [amunchet](https://huggingface.co/amunchet/rorshark-vit-base) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 343.2 MB | | 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. ## Compare rorshark-vit-base - [nsfw_image_detection vs rorshark-vit-base](https://savrn.com/models/compare/nsfw-image-detection-vs-rorshark-vit-base) ## Questions About rorshark-vit-base ### How much GPU memory does rorshark-vit-base need? About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (86M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run rorshark-vit-base 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 rorshark-vit-base commercially? Yes. rorshark-vit-base 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. ## Similar Models Model · Image classification ### [nsfw_image_detection](https://savrn.com/models/nsfw-image-detection) [Falcons.ai](https://savrn.com/model-publishers/falconsai) The Fine-Tuned Vision Transformer (ViT) is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks. During the training phase, meticulous attention was given to hyperparameter settings to ensure optimal model performance. 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Open weights 86M parameters transformers [View model](https://savrn.com/models/vit-age-classifier) ## Chester Enright [All models and datasets](https://savrn.com/model-publishers/amunchet) ## Versions - [85b973ba04fa](https://savrn.com/models/rorshark-vit-base/versions/85b973ba04fa) · current 2026-09-18 ## Explore More - [All image classification models](https://savrn.com/models/tasks/image-classification) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/amunchet/rorshark-vit-base) - [How the hub is built](https://savrn.com/model-hub/methodology)