# OpenMed-NER-ChemicalDetect-MultiMed-568M by OpenMed Source: https://savrn.com/models/openmed-ner-chemicaldetect-multimed-568m Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve OpenMed-NER-ChemicalDetect-MultiMed-568M (567M 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 | 1.1 GB | 1.4 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 | 0.6 GB | 0.7 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 | 0.3 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 | 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-ChemicalDetect-MultiMed-568M on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/openmed-ner-chemicaldetect-multimed-568m/gpus) ## Model Card By OpenMed, published under apache-2.0, revision 2a1e30bdff5f. Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature ### Model Overview 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. 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 BC4CHEMD 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-CHEM - I-CHEM ### Dataset BC4CHEMD is a biomedical NER corpus for chemical entity recognition from the BioCreative IV challenge. [Read the full model card (1,084 words)](https://savrn.com/models/openmed-ner-chemicaldetect-multimed-568m/card) ## Configuration Architecture XLMRobertaForTokenClassification Context length (tokens) 8,194 Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 250,002 Stored precision bfloat16 Model type xlm-roberta ## Identity and Version Repository OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M Publisher OpenMed Task Token classification Modality Text Library transformers Parameters 567M parameters Languages en Revision 2a1e30bdff5f1158588fa2fe278634788c3d41da First published 2025-07-18 Last updated 2025-08-05 ## Files and Weights 10 files, 1.2 GB in total. The weights are 1 file totalling 1.1 GB in safetensors. Weights1 file · 1.1 GB Configuration3 files · 2.0 KB Tokenizer2 files · 17.1 MB Documentation1 file · 11.7 KB Other2 files · 5.6 MB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.1 GB | 8de9711a14de | | config.json | Configuration | 819 B | — | | special_tokens_map.json | Configuration | 964 B | — | | test_results.json | Configuration | 195 B | — | | README.md | Documentation | 11.7 KB | — | | openmed_vs_sota_grouped_bars.png | Other | 497.0 KB | 626b37d9b20c | | sentencepiece.bpe.model | Other | 5.1 MB | cfc8146abe2a | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 17.1 MB | abf1c7593e60 | | tokenizer_config.json | Tokenizer | 1.2 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 1.1 GB [Download from OpenMed](https://huggingface.co/OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M) 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 | 1.1 GB | | 16-bit | 1.1 GB | | 8-bit | 0.6 GB | | 4-bit | 0.3 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About OpenMed-NER-ChemicalDetect-MultiMed-568M ### How much GPU memory does OpenMed-NER-ChemicalDetect-MultiMed-568M need? About 1.4 GB at 16-bit and 0.3 GB at 4-bit: the weights (567M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run OpenMed-NER-ChemicalDetect-MultiMed-568M 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-ChemicalDetect-MultiMed-568M commercially? Yes. OpenMed-NER-ChemicalDetect-MultiMed-568M 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-ChemicalDetect-MultiMed-568M's context length? 8,194 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [OpenMed-NER-OncologyDetect-MultiMed-568M](https://savrn.com/models/openmed-ner-oncologydetect-multimed-568m) [OpenMed](https://savrn.com/model-publishers/openmed) Specialized model for Cancer Genetics - Cancer-related genetic entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for cancer genetics - cancer-related genetic entities. 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It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.22.2 Open weights mit 559M parameters 514 tokens transformers [View model](https://savrn.com/models/2026-10-13-xlmr-large-baseline-2-3) ## OpenMed [All models and datasets](https://savrn.com/model-publishers/openmed) ## Versions - [2a1e30bdff5f](https://savrn.com/models/openmed-ner-chemicaldetect-multimed-568m/versions/2a1e30bdff5f) · 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-ChemicalDetect-MultiMed-568M) - [How the hub is built](https://savrn.com/model-hub/methodology)