# dark-pattern-distilbert by C P M Ashir: Open-Weight Model Source: https://savrn.com/models/dark-pattern-distilbert Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve dark-pattern-distilbert (67M 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 | 0.1 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 | 0.0 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 9, 2026. [dark-pattern-distilbert on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/dark-pattern-distilbert/gpus) ## Model Card This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Excerpt from the card by C P M Ashir. ## Configuration Architecture DistilBertForSequenceClassification Context length (tokens) 512 Vocabulary size 30,522 Model type distilbert ## Identity and Version Repository cpmashir/dark-pattern-distilbert Publisher C P M Ashir Task Text classification Modality Text Library transformers Parameters 67M parameters Languages Not stated by the source Revision 1f739c8f045407f44ef79ec3b3a342828e1ff19e First published 2026-09-22 Last updated 2026-10-07 ## Files and Weights 6 files, 268.6 MB in total. The weights are 1 file totalling 267.9 MB in safetensors. Weights1 file · 267.9 MB Configuration1 file · 1.1 KB Tokenizer2 files · 712.0 KB Documentation1 file · 5.2 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 267.9 MB | 92b74dac17e0 | | config.json | Configuration | 1.1 KB | — | | README.md | Documentation | 5.2 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 711.7 KB | — | | tokenizer_config.json | Tokenizer | 351 B | — | ## License and Download License Not stated by the source Access Open weights, no gate Download size 267.9 MB [Download from C P M Ashir](https://huggingface.co/cpmashir/dark-pattern-distilbert) Released by C P M Ashir through its official repository on Hugging Face. ## Built From - Described by [arXiv:1910.09700](https://savrn.com/papers/quantifying-the-carbon-emissions-of-machine-learning) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 267.9 MB | | 16-bit | 0.1 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 dark-pattern-distilbert ### How much GPU memory does dark-pattern-distilbert need? About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (67M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run dark-pattern-distilbert 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. ### What is dark-pattern-distilbert's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text classification ### [distilbert-base-uncased-finetuned-emotion](https://savrn.com/models/distilbert-base-uncased-finetuned-emotion) [Vinay](https://savrn.com/model-publishers/vinaypoli) This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. 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Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/phishing-email-detection-distilbert-v2-4-1) Model · Text classification ### [distilbert-base-uncased-finetuned-sst-2-english](https://savrn.com/models/distilbert-base-uncased-finetuned-sst-2-english) [DistilBERT community](https://savrn.com/model-publishers/distilbert) This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events… Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-base-uncased-finetuned-sst-2-english) Model · Text classification ### [food_not_food_text_classifier](https://savrn.com/models/food-not-food-text-classifier) [Harsh Deep Pandey](https://savrn.com/model-publishers/harshdpandey) This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. 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The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.1 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/food-not-food-classificer-distilbert-base-uncased) ## C P M Ashir [All models and datasets](https://savrn.com/model-publishers/cpmashir) ## Versions - [1f739c8f0454](https://savrn.com/models/dark-pattern-distilbert/versions/1f739c8f0454) · current 2026-10-07 ## Explore More - [All text classification models](https://savrn.com/models/tasks/text-classification) - [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 2026-10-07. - [Hugging Face record](https://huggingface.co/cpmashir/dark-pattern-distilbert) - [How the hub is built](https://savrn.com/model-hub/methodology)