# ptt5-base-summ by Recogna NLP: Open-Weight Model Source: https://savrn.com/models/ptt5-base-summ Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve ptt5-base-summ (223M 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 | 0.5 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 | | 8-bit | 0.2 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 | | 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 23, 2026. [ptt5-base-summ on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/ptt5-base-summ/gpus) ## Model Card By Recogna NLP, published under mit, revision 84509bf8ec61. ### Portuguese T5 for Abstractive Summarization (PTT5 Summ) ### Introduction PTT5 Summ is a fine-tuned [PTT5](https://github.com/unicamp-dl/PTT5) model to perform Abstractive Summarization in Brazilian Portuguese texts. This model was fine-tuned on the datasets: [RecognaSumm](https://huggingface.co/datasets/recogna-nlp/recognasumm), [WikiLingua](https://github.com/esdurmus/Wikilingua), [XL-Sum](https://github.com/csebuetnlp/xl-sum), TeMário and [CSTNews](http://nilc.icmc.usp.br/CSTNews/login/?next=/CSTNews/). For further information, please go to [PTT5 Summ repository](https://github.com/pedropaiola/ptt5-summ). ### Available models | Model | Dataset used in fine-tuning | | --- | --- | | [recogna-nlp/ptt5-base-summ](https://savrn.com/models/ptt5-base-summ) | [RecognaSumm](https://huggingface.co/datasets/recogna-nlp/recognasumm) | | [recogna-nlp/ptt5-base-summ-wikilingua](https://huggingface.co/recogna-nlp/ptt5-base-summ-wikilingua) | WikiLingua | | [recogna-nlp/ptt5-base-summ-xlsum](https://savrn.com/models/ptt5-base-summ-xlsum) | XL-Sum | | [recogna-nlp/ptt5-base-summ-temario](https://huggingface.co/recogna-nlp/ptt5-base-summ-temario) | 1st phase: WikiLingua. 2nd phase: TeMario | | [recogna-nlp/ptt5-base-summ-cstnews](https://huggingface.co/recogna-nlp/ptt5-base-summ-cstnews) | 1st phase: WikiLingua. 2nd phase: CSTNews | ### Usage example [Read the full model card (582 words)](https://savrn.com/models/ptt5-base-summ/card) ## Configuration Architecture T5ForConditionalGeneration Vocabulary size 32,128 Stored precision float32 Model type t5 ## Identity and Version Repository recogna-nlp/ptt5-base-summ Publisher Recogna NLP Task Summarization Modality Text Library transformers Parameters 223M parameters Languages pt Revision 84509bf8ec61f26f6f9403a8c3ab55f4cfe2cc66 First published 2023-12-22 Last updated 2024-10-31 ## Files and Weights 9 files, 1.8 GB in total. The weights are 2 files totalling 1.8 GB in bin, safetensors. Weights2 files · 1.8 GB Configuration3 files · 3.1 KB Tokenizer2 files · 758.0 KB Documentation1 file · 12.2 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 891.6 MB | 2e8d150b3b08 | | pytorch_model.bin | Weights | 891.7 MB | 603281b7ef3d | | config.json | Configuration | 780 B | — | | generation_config.json | Configuration | 142 B | — | | special_tokens_map.json | Configuration | 2.2 KB | — | | README.md | Documentation | 12.2 KB | — | | .gitattributes | Repository | 1.5 KB | — | | spiece.model | Tokenizer | 755.6 KB | fcb25b1d67f0 | | tokenizer_config.json | Tokenizer | 2.4 KB | — | ## License and Download License mit Access Open weights, no gate Download size 1.8 GB [Download from Recogna NLP](https://huggingface.co/recogna-nlp/ptt5-base-summ) Released by Recogna NLP through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Trained on (disclosed) recogna-nlp/recognasumm ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.8 GB | | 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 ptt5-base-summ ### How much GPU memory does ptt5-base-summ need? About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (223M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run ptt5-base-summ 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 ptt5-base-summ commercially? Yes. ptt5-base-summ 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. ## Similar Models Model · Summarization ### [rut5-base-summ](https://savrn.com/models/rut5-base-summ) [Dmitry Balobin](https://savrn.com/model-publishers/d0rj) Finetuned ai-forever/ruT5-base for text and dialogue summarization. All 'train' subsets was concatenated and shuffled with seed 1000 - 7. Train subset = 155678 rows. Evaluation on 10% of concatenated 'validation' subsets = 1458 rows. See WandB logs. See report at REPORT WIP. Open weights 223M parameters transformers [View model](https://savrn.com/models/rut5-base-summ) Model · Summarization ### [T5-base-10K-summarization](https://savrn.com/models/t5-base-10k-summarization) [Yatharth Sant](https://savrn.com/model-publishers/yatharth97) This model is a fine-tuned version of Google's T5-Base model tailored for summarizing financial 10K report sections. T5-Base-10K-Summarization is optimized to condense lengthy 10K reports into manageable summaries, enabling quick insights into financial data and trends. Ideal for use by financial analysts and regulatory agencies needing rapid insights from 10K reports. It may not be suited for summarizing non-financial documents or informal texts. Trained on a diverse collection of 10K reports from various industries, annotated for summarization to ensure broad applicability and accuracy. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8… Open weights 223M parameters transformers [View model](https://savrn.com/models/t5-base-10k-summarization) Model · Summarization ### [ptt5-base-summ-xlsum](https://savrn.com/models/ptt5-base-summ-xlsum) [Recogna NLP](https://savrn.com/model-publishers/recogna-nlp) PTT5 Summ is a fine-tuned PTT5 model to perform Abstractive Summarization in Brazilian Portuguese texts. This model was fine-tuned on the datasets: RecognaSumm, WikiLingua, XL-Sum, TeMário.pdf) and CSTNews. For further information, please go to PTT5 Summ repository. author="Paiola, Pedro H. and de Rosa, Gustavo H. and Papa, Jo{\~a}o P.", editor="Xavier-Junior, Jo{\~a}o Carlos and Rios, Ricardo Ara{\'u}jo", title="Deep Learning-Based Abstractive Summarization for Brazilian Portuguese Texts", booktitle="BRACIS 2022: Intelligent Systems", year="2022", publisher="Springer International Publishing", address="Cham", pages="479--493", isbn="978-3-031-21689-3"} This model was fine-tuned using the… Open weights cc-by-nc-sa-4.0 223M parameters transformers [View model](https://savrn.com/models/ptt5-base-summ-xlsum) Model · Summarization ### [led-base-book-summary](https://savrn.com/models/led-base-book-summary) [Peter Szemraj](https://savrn.com/model-publishers/pszemraj) The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned from allenai/led-base-16384 to condense extensive technical, academic, and narrative content in a fairly generalizable way. - Ideal for summarizing long narratives, articles, papers, textbooks, and other documents. - the sparknotes-esque style leads to 'explanations' in the summarized content, offering insightful output. The model was trained on the BookSum dataset released by SalesForce, which leads to the bsd-3-clause license. The training process involved 16 epochs with parameters tweaked to facilitate very fine-tuning-type training (super low learning rate). This model is the… Open weights apache-2.0 162M parameters transformers [View model](https://savrn.com/models/led-base-book-summary) Model · Summarization ### [distilbart-news-summarizer](https://savrn.com/models/distilbart-news-summarizer) [Sachin Rao](https://savrn.com/model-publishers/sachin21112004) Imagine you have a very long news article, and you want someone to read it and tell you the key points in just a few sentences. That's exactly what this model does! This model takes a long news article and turns it into a short, easy-to-read summary. - It writes back a 3-4 sentence summary that captures all the important information The special thing about this model is that it's: 1. Very accurate - It understands news writing style very well 2. Very fast - It works quickly even on regular computers (not just expensive AI servers) 3. Specialized in news - It was trained specifically on news articles, so it understands how journalists write 4. Good with financial news - It knows market… Open weights agpl-3.0 306M parameters 1,024 tokens transformers [View model](https://savrn.com/models/distilbart-news-summarizer) Model · Summarization ### [kobart-summary-v3](https://savrn.com/models/kobart-summary-v3) [EbanLee](https://savrn.com/model-publishers/ebanlee) 이 모델은 kobart모델을 문서요약, 도서자료요약, 요약문 및 레포트 생성 데이터로 fine-tuning한 모델입니다. Open weights 124M parameters 1,026 tokens transformers [View model](https://savrn.com/models/kobart-summary-v3) ## Recogna NLP [All models and datasets](https://savrn.com/model-publishers/recogna-nlp) ## Versions - [84509bf8ec61](https://savrn.com/models/ptt5-base-summ/versions/84509bf8ec61) · current 2026-09-18 ## Explore More - [All summarization models](https://savrn.com/models/tasks/summarization) - [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 2026-10-21. - [Hugging Face record](https://huggingface.co/recogna-nlp/ptt5-base-summ) - [How the hub is built](https://savrn.com/model-hub/methodology)