# muril-cyberbullying-detection by Suyash Sahu: Open Model Source: https://savrn.com/models/muril-cyberbullying-detection Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve muril-cyberbullying-detection (238M 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 | 0.5 GB | 0.6 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 | 0.2 GB | 0.3 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 | 0.1 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 | 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 7, 2026. [muril-cyberbullying-detection on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/muril-cyberbullying-detection/gpus) ## Model Card By Suyash Sahu, published under mit, revision e8a34004c520. ### MuRIL Multilingual Cyberbullying Detection (v2) This model is a fine-tuned version of Google's MuRIL (Multilingual Representations for Indian Languages) BERT architecture, specifically adapted for multi-class Cyberbullying & Hate Speech Detection. It categorizes social media text and online commentary across 6 categories: 1. age: Cyberbullying targeting an individual's age. 2. ethnicity: Bullying or hate speech targeting ethnicity, race, or caste. 3. gender: Misogyny, sexism, or gender-based harassment. 4. religion: Hate speech or insults targeting religious beliefs. 5. other_cyberbullying: General toxic harassment, insults, or threats. 6. not_cyberbullying: Benign, safe, neutral, or positive text. ### Model Performance Evaluated on the held-out multi-class test benchmark: | Metric | Score | | --- | --- | | Overall Accuracy | 81.97% | | Macro Precision | 83.21% | | Macro Recall | 83.41% | | Macro F1-Score | 83.29% | #### Per-Class F1 Breakdown: - Age: 97.76% F1 - Ethnicity: 95.86% F1 - Religion: 95.03% F1 - Gender: 86.32% F1 - Not Cyberbullying: 64.63% F1 - Other Cyberbullying: 60.11% F1 ### Quickstart & Inference [Read the full model card (242 words)](https://savrn.com/models/muril-cyberbullying-detection/card) ## Configuration Architecture BertForSequenceClassification Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 197,285 Model type bert ## Identity and Version Repository suyashsahu00/muril-cyberbullying-detection Publisher Suyash Sahu Task Text classification Modality Text Library Not stated by the source Parameters 238M parameters Languages en, hi Revision e8a34004c520b7363f70e19e6e4a0aae1b51072f First published 2026-09-03 Last updated 2026-09-23 ## Files and Weights 7 files, 956.7 MB in total. The weights are 1 file totalling 950.3 MB in safetensors. Weights1 file · 950.3 MB Configuration2 files · 1.4 KB Tokenizer2 files · 6.4 MB Documentation1 file · 3.1 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 950.3 MB | 0b671e02716a | | config.json | Configuration | 1.1 KB | — | | label_map.json | Configuration | 331 B | — | | README.md | Documentation | 3.1 KB | — | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 6.4 MB | — | | tokenizer_config.json | Tokenizer | 390 B | — | ## License and Download License mit Access Open weights, no gate Download size 950.3 MB [Download from Suyash Sahu](https://huggingface.co/suyashsahu00/muril-cyberbullying-detection) Released by Suyash Sahu through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 950.3 MB | | 16-bit | 0.5 GB | | 8-bit | 0.2 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About muril-cyberbullying-detection ### How much GPU memory does muril-cyberbullying-detection need? About 0.6 GB at 16-bit and 0.1 GB at 4-bit: the weights (238M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run muril-cyberbullying-detection 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 muril-cyberbullying-detection commercially? Yes. muril-cyberbullying-detection is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is muril-cyberbullying-detection's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text classification ### [SciFive-base-Pubmed_PMC](https://savrn.com/models/scifive-base-pubmed-pmc) [Razent](https://savrn.com/model-publishers/razent) Paper: SciFive: a text-to-text transformer model for biomedical literature Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet For more details, do check out our Github repo. 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The saved model artifacts in this repository report the… Open weights mit 278M parameters 514 tokens transformers [View model](https://savrn.com/models/snd-sentiment-analysis) ## Suyash Sahu [All models and datasets](https://savrn.com/model-publishers/suyashsahu00) ## Versions - [e8a34004c520](https://savrn.com/models/muril-cyberbullying-detection/versions/e8a34004c520) · current 2026-09-23 ## Explore More - [All text classification models](https://savrn.com/models/tasks/text-classification) - [All models under mit](https://savrn.com/models/licenses/mit) - [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 2026-09-23. - [Hugging Face record](https://huggingface.co/suyashsahu00/muril-cyberbullying-detection) - [How the hub is built](https://savrn.com/model-hub/methodology)