# xlm-roberta-large by Facebook AI community: Open Model Source: https://savrn.com/models/xlm-roberta-large Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve xlm-roberta-large (561M 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 | 1.3 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 | 0.6 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 24, 2026. [xlm-roberta-large on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/xlm-roberta-large/gpus) ## SAVRN's Notes on xlm-roberta-large Fitting this one is never the problem. At 16-bit the weights are 1.1 GB and the run needs 1.3 GB, so the cheapest setup on the page, one 192 GB MI300X at $1.85 an hour on-demand, has room for more than a hundred copies; put it on a GPU already serving something else. What the footprint buys is a fill-mask model trained on 2.5 TB of filtered CommonCrawl in 100 languages with a 250,002-entry vocabulary: a starting layer for multilingual text work you tune yourself, not a finished application. MIT asks only that the copyright and permission notice travel with what you ship; commercial use, modification and redistribution are allowed. Note the 514-token context, sentences and short passages rather than documents, and the 17 files totaling 11.2 GB across five formats, of which your runtime needs one. The releasing team did not write the card; start from arXiv 1911.02116. SAVRN Research, 2026-09-18 ## Model Card By Facebook AI community, published under mit, revision c23d21b0620b. ### XLM-RoBERTa (large-sized model) XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://savrn.com/papers/unsupervised-cross-lingual-representation-learning-at-scale) by Conneau et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/xlmr). Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. ### Model description XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. RoBERTa is a transformers model pretrained on a large corpus 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 those texts. [Read the full model card (579 words)](https://savrn.com/models/xlm-roberta-large/card) ## Configuration Architecture XLMRobertaForMaskedLM Context length (tokens) 514 Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 250,002 Model type xlm-roberta ## Identity and Version Repository FacebookAI/xlm-roberta-large Publisher Facebook AI community Task Fill mask Modality Text Library transformers Parameters 561M parameters Languages af, am, ar, as, az, be, bg, bn Revision c23d21b0620b635a76227c604d44e43a9f0ee389 First published 2022-03-02 Last updated 2024-02-19 ## Files and Weights 17 files, 11.2 GB in total. The weights are 5 files totalling 9.0 GB in bin, h5, msgpack, onnx, safetensors. Weights5 files · 9.0 GB Configuration3 files · 1.6 KB Tokenizer4 files · 26.2 MB Documentation1 file · 5.2 KB Other3 files · 2.2 GB Repository1 file · 623 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 2.2 GB | 96d19a73ca04 | | model.safetensors | Weights | 2.2 GB | 2dfa19f17241 | | onnx/model.onnx | Weights | 545.9 KB | bb5a52503a3e | | pytorch_model.bin | Weights | 2.2 GB | 01e55aa45dbb | | tf_model.h5 | Weights | 2.2 GB | a465c8d459fe | | config.json | Configuration | 616 B | — | | onnx/config.json | Configuration | 681 B | — | | onnx/special_tokens_map.json | Configuration | 280 B | — | | README.md | Documentation | 5.2 KB | — | | onnx/model.onnx_data | Other | 2.2 GB | 1798dab29db9 | | onnx/sentencepiece.bpe.model | Other | 5.1 MB | cfc8146abe2a | | sentencepiece.bpe.model | Other | 5.1 MB | — | | .gitattributes | Repository | 623 B | — | | onnx/tokenizer.json | Tokenizer | 17.1 MB | 62c24cdc13d4 | | onnx/tokenizer_config.json | Tokenizer | 418 B | — | | tokenizer.json | Tokenizer | 9.1 MB | — | | tokenizer_config.json | Tokenizer | 25 B | — | ## License and Download License mit Access Open weights, no gate Download size 9.0 GB [Download from Facebook AI community](https://huggingface.co/FacebookAI/xlm-roberta-large) Released by Facebook AI community through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by [arXiv:1911.02116](https://savrn.com/papers/unsupervised-cross-lingual-representation-learning-at-scale) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 9.0 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. ## Built on This Model - Derived from[ViDense](https://savrn.com/models/vidense) - Derived from[2026-10-13-xlmr-large-baseline_2_4](https://savrn.com/models/2026-10-13-xlmr-large-baseline-2-4) - Derived from[2026-10-13-xlmr-large-baseline_2_2](https://savrn.com/models/2026-10-13-xlmr-large-baseline-2-2) - Derived from[2026-10-13-xlmr-large-baseline_2_3](https://savrn.com/models/2026-10-13-xlmr-large-baseline-2-3) - Derived from[actividad10-tass](https://savrn.com/models/actividad10-tass) - Derived from[actividad10-sarcasmo](https://savrn.com/models/actividad10-sarcasmo) - Derived from[chitragupta-pii-indic](https://savrn.com/models/chitragupta-pii-indic) ## Compare xlm-roberta-large - [bert-large-uncased vs xlm-roberta-large](https://savrn.com/models/compare/bert-large-uncased-vs-xlm-roberta-large) - [esm2_t33_650M_UR50D vs xlm-roberta-large](https://savrn.com/models/compare/esm2-t33-650m-ur50d-vs-xlm-roberta-large) - [ModernBERT-large vs xlm-roberta-large](https://savrn.com/models/compare/modernbert-large-vs-xlm-roberta-large) ## Questions About xlm-roberta-large ### How much GPU memory does xlm-roberta-large need? About 1.3 GB at 16-bit and 0.3 GB at 4-bit: the weights (561M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run xlm-roberta-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 xlm-roberta-large commercially? Yes. xlm-roberta-large 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 xlm-roberta-large's context length? 514 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Fill mask ### [esm2_t33_650M_UR50D](https://savrn.com/models/esm2-t33-650m-ur50d) [AI at Meta](https://savrn.com/model-publishers/facebook) ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper. You may also be interested in some demo notebooks (PyTorch, TensorFlow) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train Open weights mit 652M parameters 1,026 tokens transformers [View model](https://savrn.com/models/esm2-t33-650m-ur50d) Model · Fill mask ### [minerva-mlm](https://savrn.com/models/minerva-mlm) [Garyk Brixi](https://savrn.com/model-publishers/gbrixi) Minerva-MLM is a genome language model for coevolutionary mining. The checkpoint includes the masked language model head and three interaction heads for base-pairing, repeat, and structure signals. Full documentation, finetuning scripts, notebooks, and setup are on the github. Try it in the browser here. Inference from this repo needs only Transformers and PyTorch: flash-attn is picked up automatically when present, otherwise Minerva falls back to PyTorch SDPA. The optional minerva-dna package adds plotting helpers, GenBank utilities and finetuning wrappers, and lets you load the model without trustremotecode: With minerva-dna installed, import the class directly and drop trustremotecode… Open weights apache-2.0 671M parameters transformers [View model](https://savrn.com/models/minerva-mlm) Model · Fill mask ### [minerva-mlm-8k](https://savrn.com/models/minerva-mlm-8k) [Garyk Brixi](https://savrn.com/model-publishers/gbrixi) Minerva-MLM is a genome language model for coevolutionary mining. The checkpoint includes the masked language model head and three interaction heads for base-pairing, repeat, and structure signals. Full documentation, finetuning scripts, notebooks, and setup are on the github. Try it in the browser here. Inference from this repo needs only Transformers and PyTorch: flash-attn is picked up automatically when present, otherwise Minerva falls back to PyTorch SDPA. The optional minerva-dna package adds plotting helpers, GenBank utilities and finetuning wrappers, and lets you load the model without trustremotecode: With minerva-dna installed, import the class directly and drop trustremotecode… Open weights apache-2.0 671M parameters transformers [View model](https://savrn.com/models/minerva-mlm-8k) Model · Fill mask ### [ModernBERT-large](https://savrn.com/models/modernbert-large) [Answer.AI](https://savrn.com/model-publishers/answerdotai) 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… Open weights apache-2.0 396M parameters 8,192 tokens transformers [View model](https://savrn.com/models/modernbert-large) 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) ## Facebook AI community [All models and datasets](https://savrn.com/model-publishers/facebookai) ## Versions - [c23d21b0620b](https://savrn.com/models/xlm-roberta-large/versions/c23d21b0620b) · current 2026-10-24 ## Explore More - [All fill mask models](https://savrn.com/models/tasks/fill-mask) - [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 2026-10-19. - [Hugging Face record](https://huggingface.co/FacebookAI/xlm-roberta-large) - [How the hub is built](https://savrn.com/model-hub/methodology)