# bert-log-anomaly-detection by Aungruk Vanichanai: Open Model Source: https://savrn.com/models/bert-log-anomaly-detection Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve bert-log-anomaly-detection (109M 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 | 0.3 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 | | 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 | 0.1 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 22, 2026. [bert-log-anomaly-detection on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bert-log-anomaly-detection/gpus) ## SAVRN's Notes on bert-log-anomaly-detection This model does one narrow job: it reads a single SQL transaction log line and labels it Normal or Anomaly. It is a fine-tuned BERT at 109M parameters, released in January 2026, and it has drawn roughly 241,000 downloads a month despite almost no likes, which suggests automated pulls rather than a broad community. Apache 2.0 allows commercial use. We would treat it as a starting point, not a control. Anomaly labels learned from someone else's databases rarely transfer cleanly, so fine-tune on your own logs and measure false positives before it feeds any alerting or security workflow. SAVRN Research, 2026-09-18 ## Model Card By Aungruk Vanichanai, published under apache-2.0, revision 9ece304f330c. ### Model Card for Model ID Model Summary 1. bert-log-anomaly-detection is a BERT-based NLP model fine-tuned for single SQL transaction log anomaly detection. 2. The model classifies each database transaction log as either Normal or Anomaly, with the goal of supporting AI-powered fraud detection and cybersecurity monitoring systems. 3. This model was developed as part of the Samsung × KBTG Digital Fraud Cybersecurity Hackathon (Thailand) under the AI-Powered Fraud Detection & Prevention track. #### Model Description This model analyzes individual SQL database transaction logs and detects abnormal patterns that may indicate fraudulent, malicious, or suspicious behavior. Demo: Hackathon prototype - Developed by: Aungruk Vanichanai, Napat Wanitwatthakorn, Thanakrit Sriphiphattana - Shared by: Aungruk Vanichanai - Model type: Transformer-based binary text classifier - Language(s) (NLP): English (SQL logs in text format) - License: Apache 2.0 - Finetuned from model: google-bert/bert-base-uncased #### Model Sources - GitHub Repository: https://github.com/AungMoonLord/AI-Cybersecurity-Hackathon/tree/main/New%20Finetune%20Hackathon #### How to Get Started with the Model ### Step 1 (Setup) ``` import torch from transformers import BertForSequenceClassification, BertTokenizer MODEL_PATH = "AungMoonLord/bert-log-anomaly-detection" model = BertForSequenceClassification.from_pretrained(MODEL_PATH) tokenizer = BertTokenizer.from_pretrained(MODEL_PATH) model.eval() ``` [Read the full model card (496 words)](https://savrn.com/models/bert-log-anomaly-detection/card) ## Configuration Architecture BertForSequenceClassification Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 30,522 Model type bert ## Identity and Version Repository AungMoonLord/bert-log-anomaly-detection Publisher Aungruk Vanichanai Task Text classification Modality Text Library transformers Parameters 109M parameters Languages Not stated by the source Revision 9ece304f330c8fa08a9ba825fd3d1adcf746aaf6 First published 2026-01-06 Last updated 2026-01-10 ## Files and Weights 8 files, 438.9 MB in total. The weights are 1 file totalling 438.0 MB in safetensors. Weights1 file · 438.0 MB Configuration2 files · 1.4 KB Tokenizer3 files · 944.6 KB Documentation1 file · 5.1 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 438.0 MB | 9cd32047860f | | config.json | Configuration | 707 B | — | | special_tokens_map.json | Configuration | 732 B | — | | README.md | Documentation | 5.1 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 711.7 KB | — | | tokenizer_config.json | Tokenizer | 1.5 KB | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 438.0 MB [Download from Aungruk Vanichanai](https://huggingface.co/AungMoonLord/bert-log-anomaly-detection) Released by Aungruk Vanichanai through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [google-bert/bert-base-uncased](https://savrn.com/models/bert-base-uncased) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 438.0 MB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About bert-log-anomaly-detection ### How much GPU memory does bert-log-anomaly-detection need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run bert-log-anomaly-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 bert-log-anomaly-detection commercially? Yes. bert-log-anomaly-detection 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 bert-log-anomaly-detection's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text classification ### [electra-base-discriminator-nvidia-aegis-v2-augmented](https://savrn.com/models/electra-base-discriminator-nvidia-aegis-v2-augmented) [Leonardo Mauro](https://savrn.com/model-publishers/leomaurodesenv) This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset. 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Performance on real-world data may differ from the reported… Open weights 109M parameters 512 tokens transformers [View model](https://savrn.com/models/topic-classification-bert) ## Aungruk Vanichanai [All models and datasets](https://savrn.com/model-publishers/aungmoonlord) ## Versions - [9ece304f330c](https://savrn.com/models/bert-log-anomaly-detection/versions/9ece304f330c) · current 2026-09-18 ## Explore More - [All text classification models](https://savrn.com/models/tasks/text-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 2026-10-20. - [Hugging Face record](https://huggingface.co/AungMoonLord/bert-log-anomaly-detection) - [How the hub is built](https://savrn.com/model-hub/methodology)