# ModernBERT-large by Answer.AI: Open-Weight Model Source: https://savrn.com/models/modernbert-large Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve ModernBERT-large (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 24, 2026. [ModernBERT-large on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/modernbert-large/gpus) ## SAVRN's Notes on ModernBERT-large Nothing here generates text. Answer.AI built ModernBERT-large as an encoder, trained on 2 trillion tokens of English and code, and our reading is retrieval and classification over long documents, up to its 8,192-token context. Memory is the smallest line item in the rack: 1.0 GB at 16-bit, 0.2 GB at 4-bit, against a cheapest slot of one MI300X with 192 GB at $1.85 an hour, so it rides alongside a generation model rather than earning its own card. The license is Apache 2.0: commercial use, modification and redistribution are allowed, notices stay, significant changes get stated, and contributors grant patents. Access is open. Check the disk footprint first, 16 files and 7.86 GB stored in float32, several times what loads into memory at 16-bit. It ships in safetensors, onnx and pytorch. The design paper is arXiv:2412.13663; read it for the local-global alternating attention behind the 8,192-token window. SAVRN Research, 2026-09-18 ## Model Card By Answer.AI, published under apache-2.0, revision 45bb4654a4d5. ### ModernBERT ### Table of Contents 1. Model Summary 2. Usage 3. Evaluation 4. Limitations 5. Training 6. License 7. Citation ### Model Summary ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as: - Rotary Positional Embeddings (RoPE) for long-context support. - Local-Global Alternating Attention for efficiency on long inputs. - Unpadding and Flash Attention for efficient inference. ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search. It is available in the following sizes: - [ModernBERT-base](https://savrn.com/models/modernbert-base) - 22 layers, 149 million parameters - [ModernBERT-large](https://savrn.com/models/modernbert-large) - 28 layers, 395 million parameters [Read the full model card (899 words)](https://savrn.com/models/modernbert-large/card) ## Configuration Architecture ModernBertForMaskedLM 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 float32 Model type modernbert ## Identity and Version Repository answerdotai/ModernBERT-large Publisher Answer.AI Task Fill mask Modality Text Library transformers Parameters 396M parameters Languages en Revision 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 First published 2024-12-11 Last updated 2025-01-15 ## Files and Weights 16 files, 7.9 GB in total. The weights are 10 files totalling 7.9 GB in bin, onnx, safetensors. Weights10 files · 7.9 GB Configuration2 files · 1.9 KB Tokenizer2 files · 2.2 MB Documentation1 file · 8.4 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.6 GB | 44510fec5d3a | | onnx/model.onnx | Weights | 1.6 GB | 6bab50009674 | | onnx/model_bnb4.onnx | Weights | 400.9 MB | 8b179381ce3c | | onnx/model_fp16.onnx | Weights | 792.2 MB | 29cef3547cd4 | | onnx/model_int8.onnx | Weights | 398.3 MB | 84baefd65fac | | onnx/model_q4.onnx | Weights | 422.4 MB | b480f884c31d | | onnx/model_q4f16.onnx | Weights | 297.5 MB | 0a12b6229a1f | | onnx/model_quantized.onnx | Weights | 398.3 MB | 84baefd65fac | | onnx/model_uint8.onnx | Weights | 398.3 MB | 9020c46e799c | | pytorch_model.bin | Weights | 1.6 GB | fabb014a0b62 | | config.json | Configuration | 1.2 KB | — | | special_tokens_map.json | Configuration | 694 B | — | | README.md | Documentation | 8.4 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 2.1 MB | — | | tokenizer_config.json | Tokenizer | 20.8 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 7.9 GB [Download from Answer.AI](https://huggingface.co/answerdotai/ModernBERT-large) Released by Answer.AI through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2412.13663](https://savrn.com/papers/smarter-better-faster-longer-a-modern-bidirectional-encoder-for-fast-memory-efficient-and) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 7.9 GB | | 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. ## Built on This Model - Derived from[ModernBERT-large-nli](https://savrn.com/models/modernbert-large-nli) - Derived from[finecat-nli-l](https://savrn.com/models/finecat-nli-l) - Derived from[Beveridge-Large-C](https://savrn.com/models/beveridge-large-c) - Derived from[IdeaLens-ModernBERT-L-NoParaphrase](https://savrn.com/models/idealens-modernbert-l-noparaphrase) - Derived from[ProseLens-ModernBERT-L](https://savrn.com/models/proselens-modernbert-l) - Derived from[IdeaLens-ModernBERT-L](https://savrn.com/models/idealens-modernbert-l) - Derived from[IdeaLens-ModernBERT-L-RolesOnly](https://savrn.com/models/idealens-modernbert-l-rolesonly) - Derived from[IdeaLens-ModernBERT-L-PerItem](https://savrn.com/models/idealens-modernbert-l-peritem) - Derived from[kodiak-large-v2-preview](https://savrn.com/models/kodiak-large-v2-preview) - Derived from[modernbert-large-pan2020-authorship-verification](https://savrn.com/models/modernbert-large-pan2020-authorship-verification) - Derived from[ProKope-421M](https://savrn.com/models/prokope-421m) - Derived from[laya-nextflow-nfcore](https://savrn.com/models/laya-nextflow-nfcore) - Derived from[disaster-interpretation-modernbert-large](https://savrn.com/models/disaster-interpretation-modernbert-large) ## Compare ModernBERT-large - [ModernBERT-large vs xlm-roberta-large](https://savrn.com/models/compare/modernbert-large-vs-xlm-roberta-large) ## Questions About ModernBERT-large ### How much GPU memory does ModernBERT-large 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 ModernBERT-large 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 ModernBERT-large commercially? Yes. ModernBERT-large 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 ModernBERT-large's context length? 8,192 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Fill mask ### [ModernBERT-Large-Instruct-Logician-v0.5](https://savrn.com/models/modernbert-large-instruct-logician-v0-5) [John Gorriceta](https://savrn.com/model-publishers/johngorri) This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask). - As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at predicting masked tokens inside structured prompts. Open weights apache-2.0 396M parameters 8,192 tokens [View model](https://savrn.com/models/modernbert-large-instruct-logician-v0-5) Model · Fill mask ### [roberta-large](https://savrn.com/models/roberta-large) [Facebook AI community](https://savrn.com/model-publishers/facebookai) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels… Open weights mit 355M parameters 514 tokens transformers [View model](https://savrn.com/models/roberta-large) Model · Fill mask ### [bert-large-uncased](https://savrn.com/models/bert-large-uncased) [BERT community](https://savrn.com/model-publishers/google-bert) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from… Open weights apache-2.0 336M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-large-uncased) Model · Fill mask ### [deberta-v2-large-japanese-char-wwm](https://savrn.com/models/deberta-v2-large-japanese-char-wwm) [Language Media Processing Lab at Kyoto University](https://savrn.com/model-publishers/ku-nlp) This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. This model is trained with character-level tokenization and whole word masking. You can use this model for masked language modeling as follows: You can also fine-tune this model on downstream tasks. There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer. The texts are tokenized into character-level tokens by sentencepiece. We used the following corpora for pre-training: - Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) - Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)… Open weights cc-by-sa-4.0 330M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v2-large-japanese-char-wwm) Model · Fill mask ### [hEVO-large-o](https://savrn.com/models/hevo-large-o) [Mulin Lab](https://savrn.com/model-publishers/mulinlab) hEVO-large-o is the large checkpoint in the hEvo public-data model family. It was independently pretrained on 1000 Genomes Project and Human Genome Diversity Project sequences using self-supervised masked language modeling. The checkpoint provenance records no target benchmark label supervision. Use a Python environment with PyTorch and the checkpoint's tested Transformers version: Access is gated for peer reviewers and requires manual approval by MulinLab. Sign in to Hugging Face, submit an access request on this page, and wait for approval. After approval, run hf auth login with your own read token; token=True below uses your locally stored credential. The model and tokenizer load… Access requested at publisher 319M parameters transformers [View model](https://savrn.com/models/hevo-large-o) Model · Fill mask ### [mmbert-small-vi-exam-seq-labeling](https://savrn.com/models/mmbert-small-vi-exam-seq-labeling) [Dao Minh](https://savrn.com/model-publishers/daominhwysi) This model is a fine-tuned version of jhu-clsp/mmBERT-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 4 - distributedtype: multi-GPU - numdevices: 2 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - totalevalbatchsize: 8 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 4 - mixedprecisiontraining: Native AMP - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 5.0.0 - Tokenizers 0.22.2 Open weights mit 308M parameters 8,192 tokens transformers [View model](https://savrn.com/models/mmbert-small-vi-exam-seq-labeling) ## Answer.AI [All models and datasets](https://savrn.com/model-publishers/answerdotai) ## Versions - [45bb4654a4d5](https://savrn.com/models/modernbert-large/versions/45bb4654a4d5) · current 2026-09-18 ## Explore More - [All fill mask models](https://savrn.com/models/tasks/fill-mask) - [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 2026-10-19. - [Answer.AI website](https://www.answer.ai/) - [Hugging Face record](https://huggingface.co/answerdotai/ModernBERT-large) - [How the hub is built](https://savrn.com/model-hub/methodology)