# LondonLB by J: Open-Weight Model Source: https://savrn.com/models/londonlb Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve LondonLB (184M 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 | 0.4 GB | 0.4 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 | 0.2 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 | | 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 8, 2026. [LondonLB on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/londonlb/gpus) ## Model Card By J, published under mit, revision cc021e06f33e. ### LondonLB: personal-name recognition for the London Letter-Books LondonLB is a fine-tuned version of [microsoft/deberta-v3-base](https://savrn.com/models/deberta-v3-base) that identifies personal names in Reginald R. Sharpe's Calendar of Letter-Books of the City of London, volumes A to I (c. 1275–1422). It was built to support research on medieval London naming practices, in particular the transition from by-names to hereditary family names, by making it possible to extract every named individual from roughly 14,800 calendar entries. On a held-out gold-standard set of 200 entries containing 1,136 names, the model reaches an exact-match F1 of 0.977, and finds 1,135 of the 1,136 names at least partially. ### Model details [Read the full model card (1,524 words)](https://savrn.com/models/londonlb/card) ## Configuration Architecture DebertaV2ForTokenClassification Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 128,100 Model type deberta-v2 ## Identity and Version Repository jmcewan3/LondonLB Publisher J Task Token classification Modality Text Library transformers Parameters 184M parameters Languages en Revision cc021e06f33e6df4fc4d48b995f0bfc8210fe8b9 First published 2026-09-26 Last updated 2026-10-08 ## Files and Weights 10 files, 746.5 MB in total. The weights are 1 file totalling 735.4 MB in safetensors. Weights1 file · 735.4 MB Configuration3 files · 1.4 KB Tokenizer2 files · 8.7 MB Documentation1 file · 11.1 KB Other2 files · 2.5 MB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 735.4 MB | eeb6ea2a4c97 | | added_tokens.json | Configuration | 26 B | — | | config.json | Configuration | 1.1 KB | — | | special_tokens_map.json | Configuration | 301 B | — | | README.md | Documentation | 11.1 KB | — | | 2026_oct2_convertandtrain.ipynb | Other | 25.8 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 mit Access Open weights, no gate Download size 735.4 MB [Download from J](https://huggingface.co/jmcewan3/LondonLB) Released by J through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Derived from [microsoft/deberta-v3-base](https://savrn.com/models/deberta-v3-base) - Described by [arXiv:2111.09543](https://savrn.com/papers/debertav3-improving-deberta-using-electra-style-pre-training-with-gradient-disentangled-em) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 735.4 MB | | 16-bit | 0.4 GB | | 8-bit | 0.2 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About LondonLB ### How much GPU memory does LondonLB need? About 0.4 GB at 16-bit and 0.1 GB at 4-bit: the weights (184M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run LondonLB 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 LondonLB commercially? Yes. LondonLB is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is LondonLB's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [llmlingua-2-bert-base-multilingual-cased-meetingbank](https://savrn.com/models/llmlingua-2-bert-base-multilingual-cased-meetingbank) [Microsoft](https://savrn.com/model-publishers/microsoft) This model was introduced in the paper LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (Pan et al, 2024). It is a BERT multilingual base model (cased) finetuned to perform token classification for task agnostic prompt compression. The probability $p{preserve}$ of each token $xi$ is used as the metric for compression. 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The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. If you use the model, please reference this work in your paper: The original repository for the paper can be found at https://github.com/Babelscape/wikineural. You can use this model with Transformers pipeline for NER. 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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… Open weights apache-2.0 150M parameters 8,192 tokens transformers [View model](https://savrn.com/models/openmed-ner-chemicaldetect-modernmed-149m) ## J [All models and datasets](https://savrn.com/model-publishers/jmcewan3) ## Versions - [cc021e06f33e](https://savrn.com/models/londonlb/versions/cc021e06f33e) · current 2026-10-08 ## Explore More - [All token classification models](https://savrn.com/models/tasks/token-classification) - [All models under mit](https://savrn.com/models/licenses/mit) - [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 2026-10-08. - [Hugging Face record](https://huggingface.co/jmcewan3/LondonLB) - [How the hub is built](https://savrn.com/model-hub/methodology)