# distilroberta-base by DistilBERT community: Open Model Source: https://savrn.com/models/distilroberta-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve distilroberta-base (83M 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 | 0.2 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 | | 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 | 0.0 GB | 0.0 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 | 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. [distilroberta-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/distilroberta-base/gpus) ## SAVRN's Notes on distilroberta-base Two tenths of a gigabyte. That is the memory this masked-language model needs at 16-bit, and it settles the hardware question. The cheapest setup on our Index is one MI300X with 192 GB at $1.85 an hour on-demand, which leaves most of that card idle, so it belongs on a GPU you already run, beside other small models, or as the base for a fine-tuned classifier, which the publisher says is its main purpose. At 6 layers and 83M parameters against 125M for RoBERTa-base, a fine-tuning run is a smaller job too. Apache 2.0 allows commercial use, modification and redistribution and carries a patent grant, so a fine-tuned derivative can ship in a product with the notices attached. Check the 514-token context, which means long documents get chunked, and note the openwebtext training corpus and the 2024-02-19 last update. Weights come as safetensors, pytorch, jax, rust and tf. SAVRN Research, 2026-09-18 ## Model Card By DistilBERT community, published under apache-2.0, revision fb53ab880285. ### Model Card for DistilRoBERTa base ### Table of Contents 1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. How To Get Started With the Model ### Model Details ### Model Description This model is a distilled version of the [RoBERTa-base model](https://huggingface.co/roberta-base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert-base-uncased). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is case-sensitive: it makes a difference between english and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base. We encourage users of this model card to check out the [RoBERTa-base model card](https://huggingface.co/roberta-base) to learn more about usage, limitations and potential biases. [Read the full model card (804 words)](https://savrn.com/models/distilroberta-base/card) ## Configuration Architecture RobertaForMaskedLM Context length (tokens) 514 Layers 6 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 50,265 Model type roberta ## Identity and Version Repository distilbert/distilroberta-base Publisher DistilBERT community Task Fill mask Modality Text Library transformers Parameters 83M parameters Languages en Revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b First published 2022-03-02 Last updated 2024-02-19 ## Files and Weights 13 files, 2.0 GB in total. The weights are 5 files totalling 2.0 GB in bin, h5, msgpack, ot, safetensors. Weights5 files · 2.0 GB Configuration1 file · 480 B Tokenizer4 files · 2.7 MB Documentation1 file · 7.5 KB Other1 file · 603.3 KB Repository1 file · 445 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 328.7 MB | 4da82a2cd90e | | model.safetensors | Weights | 331.1 MB | 2b11ca9cf3d2 | | pytorch_model.bin | Weights | 331.1 MB | ec1ed6ff4c97 | | rust_model.ot | Weights | 485.5 MB | a5bc30eee4d3 | | tf_model.h5 | Weights | 487.2 MB | 92d6207eedd0 | | config.json | Configuration | 480 B | — | | README.md | Documentation | 7.5 KB | — | | dict.txt | Other | 603.3 KB | — | | .gitattributes | Repository | 445 B | — | | merges.txt | Tokenizer | 456.3 KB | — | | tokenizer.json | Tokenizer | 1.4 MB | — | | tokenizer_config.json | Tokenizer | 25 B | — | | vocab.json | Tokenizer | 898.8 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 2.0 GB [Download from DistilBERT community](https://huggingface.co/distilbert/distilroberta-base) Released by DistilBERT community through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:1910.01108](https://savrn.com/papers/distilbert-a-distilled-version-of-bert-smaller-faster-cheaper-and-lighter) - Described by [arXiv:1910.09700](https://savrn.com/papers/quantifying-the-carbon-emissions-of-machine-learning) - Trained on (disclosed) openwebtext ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.0 GB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Quantized from[nli-distilroberta-base](https://savrn.com/models/nli-distilroberta-base) - Derived from[nli-distilroberta-base](https://savrn.com/models/nli-distilroberta-base) - Derived from[my_awesome_eli5_mlm_model](https://savrn.com/models/my-awesome-eli5-mlm-model) - Derived from[Toxic-Not-Toxic](https://savrn.com/models/toxic-not-toxic) - Derived from[distilroberta-base-finetuned-wikitext2](https://savrn.com/models/distilroberta-base-finetuned-wikitext2) ## Compare distilroberta-base - [distilbert-base-uncased vs distilroberta-base](https://savrn.com/models/compare/distilbert-base-uncased-vs-distilroberta-base) ## Questions About distilroberta-base ### How much GPU memory does distilroberta-base need? About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (83M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run distilroberta-base 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 distilroberta-base commercially? Yes. distilroberta-base 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 distilroberta-base's context length? 514 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Fill mask ### [my_awesome_eli5_mlm_model](https://savrn.com/models/my-awesome-eli5-mlm-model) [Ibrahim Samir Elmansoury](https://savrn.com/model-publishers/ibrahimmansoury) This model is a fine-tuned version of distilbert/distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 82M parameters 514 tokens transformers [View model](https://savrn.com/models/my-awesome-eli5-mlm-model) Model · Fill mask ### [distilroberta-base-finetuned-wikitext2](https://savrn.com/models/distilroberta-base-finetuned-wikitext2) [Pufhv98w](https://savrn.com/model-publishers/3809ksdfh) This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3.0 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 82M parameters 514 tokens transformers [View model](https://savrn.com/models/distilroberta-base-finetuned-wikitext2) Model · Fill mask ### [SecBERT](https://savrn.com/models/secbert) [Kun](https://savrn.com/model-publishers/jackaduma) This is the pretrained model presented in SecBERT: A Pretrained Language Model for Cyber Security Text, which is a BERT model trained on cyber security text. The training corpus was papers taken from SecBERT has its own wordpiece vocabulary (secvocab) that's built to best match the training corpus. We trained SecBERT and SecRoBERTa versions. We proposed to build language model which work on cyber security text, as result, it can improve downstream tasks (NER, Text Classification, Semantic Understand, Q&A) in Cyber Security Domain. First, as below shows Fill-Mask pipeline in Google Bert, AllenAI SciBert and our SecBERT. The original repo can be found here. Open weights apache-2.0 84M parameters 514 tokens transformers [View model](https://savrn.com/models/secbert) Model · Fill mask ### [hEVO-base-o](https://savrn.com/models/hevo-base-o) [Mulin Lab](https://savrn.com/model-publishers/mulinlab) hEVO-base-o is the base 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 87M parameters transformers [View model](https://savrn.com/models/hevo-base-o) Model · Fill mask ### [distilbert-base-uncased](https://savrn.com/models/distilbert-base-uncased) [DistilBERT community](https://savrn.com/model-publishers/distilbert) This model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English. DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. 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 using the BERT base model. More precisely, it was pretrained… Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-base-uncased) Model · Fill mask ### [bert-base-chinese](https://savrn.com/models/bert-base-chinese) [BERT community](https://savrn.com/model-publishers/google-bert) This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper). This model can be used for masked language modeling CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). typevocabsize: 2 vocabsize: 21128 numhiddenlayers: 12 Open weights apache-2.0 103M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-base-chinese) ## DistilBERT community [All models and datasets](https://savrn.com/model-publishers/distilbert) ## Versions - [fb53ab880285](https://savrn.com/models/distilroberta-base/versions/fb53ab880285) · current 2026-10-23 ## 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. - [Hugging Face record](https://huggingface.co/distilbert/distilroberta-base) - [How the hub is built](https://savrn.com/model-hub/methodology)