# OpenMed-PII-SuperClinical-Small-44M-v1 by OpenMed Source: https://savrn.com/models/openmed-pii-superclinical-small-44m-v1 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve OpenMed-PII-SuperClinical-Small-44M-v1 (141M 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 | 0.3 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 | 0.2 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 | | 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 24, 2026. [OpenMed-PII-SuperClinical-Small-44M-v1 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/openmed-pii-superclinical-small-44m-v1/gpus) ## SAVRN's Notes on OpenMed-PII-SuperClinical-Small-44M-v1 This model detects 54 types of personal information in text, including names, addresses, Social Security numbers and medical record numbers, so records can be de-identified before sharing. Despite the 44M in its name, our record counts 141M parameters on a DeBERTa-v2 base. Apache 2.0 allows commercial use. De-identification is a compliance control, so measure recall on your own records and keep a human review step for anything you release. It was evaluated on NVIDIA's Nemotron-PII test set. SAVRN Research, 2026-09-18 ## Model Card By OpenMed, published under apache-2.0, revision a2360d3f4252. PII Detection Model | 44M Parameters | Open Source ### Model Description OpenMed-PII-SuperClinical-Small-44M-v1 is a transformer-based token classification model fine-tuned for Personally Identifiable Information (PII) detection in text. This model identifies and classifies 54 types of sensitive information including names, addresses, SSNs, medical record numbers, and more. #### Key Features - High Accuracy: Achieves strong F1 scores across diverse PII categories - Comprehensive Coverage: Detects 50+ entity types spanning personal, financial, medical, and contact information - Privacy-Focused: Designed for de-identification and compliance with HIPAA, GDPR, and other privacy regulations - Production-Ready: Optimized for real-world text processing pipelines ### Performance Evaluated on a stratified 2,000-sample test set from NVIDIA Nemotron-PII: | Metric | Score | | --- | --- | | Micro F1 | 0.9539 | | Precision | 0.9549 | | Recall | 0.9529 | | Macro F1 | 0.9542 | | Weighted F1 | 0.9532 | | Accuracy | 0.9935 | #### Top 10 PII Models [Read the full model card (914 words)](https://savrn.com/models/openmed-pii-superclinical-small-44m-v1/card) ## Configuration Architecture DebertaV2ForTokenClassification Context length (tokens) 512 Layers 6 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 128,100 Model type deberta-v2 ## Identity and Version Repository OpenMed/OpenMed-PII-SuperClinical-Small-44M-v1 Publisher OpenMed Task Token classification Modality Text Library transformers Parameters 141M parameters Languages en Revision a2360d3f42526fc660ac3b2b2301e1c2d94eba61 First published 2026-01-13 Last updated 2026-01-13 ## Files and Weights 13 files, 576.7 MB in total. The weights are 1 file totalling 565.6 MB in safetensors. Weights1 file · 565.6 MB Configuration7 files · 8.3 KB Tokenizer2 files · 8.7 MB Documentation1 file · 10.2 KB Other1 file · 2.5 MB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 565.6 MB | bf896f393c7c | | added_tokens.json | Configuration | 23 B | — | | all_results.json | Configuration | 808 B | — | | config.json | Configuration | 6.3 KB | — | | eval_results.json | Configuration | 320 B | — | | special_tokens_map.json | Configuration | 286 B | — | | test_results.json | Configuration | 304 B | — | | train_results.json | Configuration | 206 B | — | | README.md | Documentation | 10.2 KB | — | | spm.model | Other | 2.5 MB | c679fbf93643 | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 8.7 MB | — | | tokenizer_config.json | Tokenizer | 1.3 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 565.6 MB [Download from OpenMed](https://huggingface.co/OpenMed/OpenMed-PII-SuperClinical-Small-44M-v1) Released by OpenMed through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [microsoft/deberta-v3-small](https://savrn.com/models/deberta-v3-small) - Trained on (disclosed) nvidia/Nemotron-PII ## 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 | | --- | --- | --- | --- | --- | --- | | nvidia/Nemotron-PII (test_strat) | Task Named Entity RecognitionMetric F1 (micro)Comparison conditions not established | 0.9539 | [OpenMed](https://huggingface.co/OpenMed/OpenMed-PII-SuperClinical-Small-44M-v1) Publisher reported | Evaluated revision not stated | — | | nvidia/Nemotron-PII (test_strat) | Task Named Entity RecognitionMetric PrecisionComparison conditions not established | 0.9549 | [OpenMed](https://huggingface.co/OpenMed/OpenMed-PII-SuperClinical-Small-44M-v1) Publisher reported | Evaluated revision not stated | — | | nvidia/Nemotron-PII (test_strat) | Task Named Entity RecognitionMetric RecallComparison conditions not established | 0.9529 | [OpenMed](https://huggingface.co/OpenMed/OpenMed-PII-SuperClinical-Small-44M-v1) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 565.6 MB | | 16-bit | 0.3 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 OpenMed-PII-SuperClinical-Small-44M-v1 ### How much GPU memory does OpenMed-PII-SuperClinical-Small-44M-v1 need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (141M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run OpenMed-PII-SuperClinical-Small-44M-v1 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 OpenMed-PII-SuperClinical-Small-44M-v1 commercially? Yes. OpenMed-PII-SuperClinical-Small-44M-v1 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 OpenMed-PII-SuperClinical-Small-44M-v1's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [OpenMed-NER-ProteinDetect-SuperClinical-141M](https://savrn.com/models/openmed-ner-proteindetect-superclinical-141m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Biomedical Entity Recognition - Various biomedical entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for biomedical entity recognition - various biomedical entities. 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