# OpenMed-NER-ProteinDetect-SuperClinical-141M by OpenMed Source: https://savrn.com/models/openmed-ner-proteindetect-superclinical-141m Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve OpenMed-NER-ProteinDetect-SuperClinical-141M (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-NER-ProteinDetect-SuperClinical-141M on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/openmed-ner-proteindetect-superclinical-141m/gpus) ## Model Card By OpenMed, published under apache-2.0, revision 032c6664562e. Specialized model for Biomedical Entity Recognition - Various biomedical entities ### Model Overview This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for biomedical entity recognition - various biomedical entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. #### Key Features - High Precision: Optimized for biomedical entity recognition - Domain-Specific: Trained on curated FSU dataset - Production-Ready: Validated on clinical benchmarks - Easy Integration: Compatible with Hugging Face Transformers ecosystem #### Supported Entity Types This model can identify and classify the following biomedical entities: - B-protein - B-protein_complex - B-protein_enum - B-protein_familiy_or_group - B-protein_variant See 5 more entity types... - `I-protein` - `I-protein_complex` - `I-protein_enum` - `I-protein_familiy_or_group` - `I-protein_variant` ### Dataset [Read the full model card (1,099 words)](https://savrn.com/models/openmed-ner-proteindetect-superclinical-141m/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 Stored precision bfloat16 Model type deberta-v2 ## Identity and Version Repository OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-141M Publisher OpenMed Task Token classification Modality Text Library transformers Parameters 141M parameters Languages en Revision 032c6664562eaead89decba331e23313bcdbe2c9 First published 2025-07-18 Last updated 2025-08-05 ## Files and Weights 11 files, 294.3 MB in total. The weights are 1 file totalling 282.6 MB in safetensors. Weights1 file · 282.6 MB Configuration4 files · 2.0 KB Tokenizer2 files · 8.7 MB Documentation1 file · 11.9 KB Other2 files · 3.0 MB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 282.6 MB | d6c86653ba88 | | added_tokens.json | Configuration | 23 B | — | | config.json | Configuration | 1.5 KB | — | | special_tokens_map.json | Configuration | 286 B | — | | test_results.json | Configuration | 196 B | — | | README.md | Documentation | 11.9 KB | — | | openmed_vs_sota_grouped_bars.png | Other | 497.0 KB | 626b37d9b20c | | spm.model | Other | 2.5 MB | c679fbf93643 | | .gitattributes | Repository | 1.6 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 282.6 MB [Download from OpenMed](https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-141M) Released by OpenMed through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2508.01630](https://savrn.com/papers/openmed-ner-open-source-domain-adapted-state-of-the-art-transformers-for-biomedical-ner-ac) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 282.6 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-NER-ProteinDetect-SuperClinical-141M ### How much GPU memory does OpenMed-NER-ProteinDetect-SuperClinical-141M 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-NER-ProteinDetect-SuperClinical-141M 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-NER-ProteinDetect-SuperClinical-141M commercially? Yes. OpenMed-NER-ProteinDetect-SuperClinical-141M 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-NER-ProteinDetect-SuperClinical-141M's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [OpenMed-PII-SuperClinical-Small-44M-v1](https://savrn.com/models/openmed-pii-superclinical-small-44m-v1) [OpenMed](https://savrn.com/model-publishers/openmed) 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. 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