# OpenMed-NER-SpeciesDetect-ModernClinical-395M by OpenMed Source: https://savrn.com/models/openmed-ner-speciesdetect-modernclinical-395m Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve OpenMed-NER-SpeciesDetect-ModernClinical-395M (396M 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 | 0.8 GB | 1.0 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 | | 8-bit | 0.4 GB | 0.5 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 | 0.2 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 | 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 25, 2026. [OpenMed-NER-SpeciesDetect-ModernClinical-395M on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/openmed-ner-speciesdetect-modernclinical-395m/gpus) ## Model Card By OpenMed, published under apache-2.0, revision 01ad8e2c2d50. Specialized model for Species Entity Recognition - Species and organism names ### Model Overview This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for species entity recognition - species and organism names. 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 LINNAEUS 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-SPECIES - I-SPECIES ### Dataset Linnaeus corpus is designed for species name identification and taxonomic entity recognition in biomedical literature. [Read the full model card (1,080 words)](https://savrn.com/models/openmed-ner-speciesdetect-modernclinical-395m/card) ## Configuration Architecture ModernBertForTokenClassification Context length (tokens) 8,192 Layers 28 Hidden size 1,024 Feed-forward size 2,624 Attention heads 16 Vocabulary size 50,368 Stored precision bfloat16 Model type modernbert ## Identity and Version Repository OpenMed/OpenMed-NER-SpeciesDetect-ModernClinical-395M Publisher OpenMed Task Token classification Modality Text Library transformers Parameters 396M parameters Languages en Revision 01ad8e2c2d503d40840a3c96e5d7ba4dffa1902b First published 2025-07-18 Last updated 2025-08-05 ## Files and Weights 9 files, 795.8 MB in total. The weights are 1 file totalling 791.7 MB in safetensors. Weights1 file · 791.7 MB Configuration3 files · 2.3 KB Tokenizer2 files · 3.6 MB Documentation1 file · 11.6 KB Other1 file · 497.0 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 791.7 MB | cb0bce0aa76f | | config.json | Configuration | 1.4 KB | — | | special_tokens_map.json | Configuration | 694 B | — | | test_results.json | Configuration | 196 B | — | | README.md | Documentation | 11.6 KB | — | | openmed_vs_sota_grouped_bars.png | Other | 497.0 KB | 626b37d9b20c | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 3.6 MB | — | | tokenizer_config.json | Tokenizer | 20.8 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 791.7 MB [Download from OpenMed](https://huggingface.co/OpenMed/OpenMed-NER-SpeciesDetect-ModernClinical-395M) 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 | 791.7 MB | | 16-bit | 0.8 GB | | 8-bit | 0.4 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About OpenMed-NER-SpeciesDetect-ModernClinical-395M ### How much GPU memory does OpenMed-NER-SpeciesDetect-ModernClinical-395M need? About 1 GB at 16-bit and 0.2 GB at 4-bit: the weights (396M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run OpenMed-NER-SpeciesDetect-ModernClinical-395M 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-SpeciesDetect-ModernClinical-395M commercially? Yes. OpenMed-NER-SpeciesDetect-ModernClinical-395M 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-SpeciesDetect-ModernClinical-395M's context length? 8,192 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [OpenMed-NER-ChemicalDetect-ModernMed-395M](https://savrn.com/models/openmed-ner-chemicaldetect-modernmed-395m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. 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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… Open weights apache-2.0 434M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-pharmadetect-superclinical-434m) Model · Token classification ### [OpenMed-NER-DiseaseDetect-SuperClinical-434M](https://savrn.com/models/openmed-ner-diseasedetect-superclinical-434m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the bc5cdr dataset. 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.… Open weights apache-2.0 434M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-diseasedetect-superclinical-434m) Model · Token classification ### [deid_roberta_i2b2](https://savrn.com/models/deid-roberta-i2b2) [One Brave Idea](https://savrn.com/model-publishers/obi) A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes. A token can either be classified as non-PHI or as one of the 11 PHI types. 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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.… Open weights apache-2.0 334M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-diseasedetect-biomed-335m) ## OpenMed [All models and datasets](https://savrn.com/model-publishers/openmed) ## Versions - [01ad8e2c2d50](https://savrn.com/models/openmed-ner-speciesdetect-modernclinical-395m/versions/01ad8e2c2d50) · current 2026-09-18 ## Explore More - [All token classification models](https://savrn.com/models/tasks/token-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/OpenMed/OpenMed-NER-SpeciesDetect-ModernClinical-395M) - [How the hub is built](https://savrn.com/model-hub/methodology)