# OpenMed-NER-OrganismDetect-BioPatient-108M by OpenMed Source: https://savrn.com/models/openmed-ner-organismdetect-biopatient-108m Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve OpenMed-NER-OrganismDetect-BioPatient-108M (108M 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 23, 2026. [OpenMed-NER-OrganismDetect-BioPatient-108M on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/openmed-ner-organismdetect-biopatient-108m/gpus) ## Model Card By OpenMed, published under apache-2.0, revision bc18d6d72f86. Specialized model for Species Entity Recognition - Species names from the Species-800 dataset ### Model Overview This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for species entity recognition - species names from the species-800 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. #### Key Features - High Precision: Optimized for biomedical entity recognition - Domain-Specific: Trained on curated SPECIES800 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 Species800 is a corpus for species recognition and taxonomy classification in biomedical texts. [Read the full model card (1,084 words)](https://savrn.com/models/openmed-ner-organismdetect-biopatient-108m/card) ## Configuration Architecture BertForTokenClassification Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 28,996 Stored precision bfloat16 Model type bert ## Identity and Version Repository OpenMed/OpenMed-NER-OrganismDetect-BioPatient-108M Publisher OpenMed Task Token classification Modality Text Library transformers Parameters 108M parameters Languages en Revision bc18d6d72f8687d0a52290ef45f31d25ee471438 First published 2025-07-16 Last updated 2025-08-05 ## Files and Weights 10 files, 216.9 MB in total. The weights are 1 file totalling 215.5 MB in safetensors. Weights1 file · 215.5 MB Configuration3 files · 1.1 KB Tokenizer3 files · 883.9 KB 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 | 215.5 MB | 52786260f40d | | config.json | Configuration | 750 B | — | | special_tokens_map.json | Configuration | 125 B | — | | test_results.json | Configuration | 197 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 | 669.2 KB | — | | tokenizer_config.json | Tokenizer | 1.3 KB | — | | vocab.txt | Tokenizer | 213.4 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 215.5 MB [Download from OpenMed](https://huggingface.co/OpenMed/OpenMed-NER-OrganismDetect-BioPatient-108M) 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 | 215.5 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 OpenMed-NER-OrganismDetect-BioPatient-108M ### How much GPU memory does OpenMed-NER-OrganismDetect-BioPatient-108M need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (108M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run OpenMed-NER-OrganismDetect-BioPatient-108M 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-OrganismDetect-BioPatient-108M commercially? Yes. OpenMed-NER-OrganismDetect-BioPatient-108M 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-OrganismDetect-BioPatient-108M's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [bert-base-cased-pos-tagging-ewt](https://savrn.com/models/bert-base-cased-pos-tagging-ewt) [Angel Rivaldo Canche Chuc](https://savrn.com/model-publishers/rivaldo2309030) Modelo bert-base-cased ajustado finamente (Fine-tuned) para la tarea de Part-of-Speech (POS) Tagging utilizando las 17 etiquetas universales (UPOS) del dataset universal-dependencies/universaldependencies (configuración enewt). Etiquetado Morfosintáctico Automático: Clasificación de categorías gramaticales (Part-of-Speech Tagging) en oraciones en idioma inglés. Análisis Lingüístico: Extracción de características sintácticas para sistemas downstream de NLP, desambiguación léxica y procesamiento del lenguaje. Parameter Groups (Método 1 - Partial): classifier: Learning Rate 1e-3 bert.encoder.layer.10 y layer.11: Learning Rate 2e-5 Parameter Groups (Método 2 - Full Ganador): classifier… Open weights apache-2.0 108M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-base-cased-pos-tagging-ewt) Model · Token classification ### [bert-base-NER](https://savrn.com/models/bert-base-ner) [D](https://savrn.com/model-publishers/dslim) If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. 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It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 64 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 3 - totaltrainbatchsize: 192 - totalevalbatchsize: 24 - lrschedulertype: linear - numepochs: 2 - mixedprecisiontraining: Native AMP - Transformers 5.17.0 - Pytorch 2.14.0+cu130 - Datasets 5.0.1 - Tokenizers 0.23.2 Open weights 109M parameters 1,026 tokens transformers [View model](https://savrn.com/models/py314-pylingual-v11-segmenter) Model · Token classification ### [OpenMed-NER-AnatomyDetect-ElectraMed-109M](https://savrn.com/models/openmed-ner-anatomydetect-electramed-109m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for anatomical entity recognition - anatomical structures and body parts. 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 109M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-anatomydetect-electramed-109m) Model · Token classification ### [OpenMed-NER-SpeciesDetect-ElectraMed-109M](https://savrn.com/models/openmed-ner-speciesdetect-electramed-109m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Species Entity Recognition - Species and organism names 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. 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This model can identify and classify the… Open weights apache-2.0 109M parameters 512 tokens transformers [View model](https://savrn.com/models/openmed-ner-genomicdetect-pubmed-109m) ## OpenMed [All models and datasets](https://savrn.com/model-publishers/openmed) ## Versions - [bc18d6d72f86](https://savrn.com/models/openmed-ner-organismdetect-biopatient-108m/versions/bc18d6d72f86) · 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-OrganismDetect-BioPatient-108M) - [How the hub is built](https://savrn.com/model-hub/methodology)