Model reference · open weights

bartpho-syllable

Embeddings vinai Embeddings 1 build Open weights 92k dl/mo

bartpho-syllable is an open-weight embedding model from vinai. bartpho-syllable (BF16) weighs 1.6 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • BARTpho-syllable is a large-scale monolingual sequence-to-sequence model pre-trained for Vietnamese, developed by vinai.
  • It utilizes the BART large architecture and is designed for generative natural language processing tasks.
  • The model supports a context length of 1024 tokens and is released under the MIT license.

Summary of the vinai/bartpho-syllable model card, 2026-10-01

What it is

Released byvinai
Released2022-03-02
VRAM1.6 GB for the weights

What it runs on

Memory and cards for bartpho-syllable (BF16)

1.6 GBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What vinai says about bartpho-syllable

Read the model card

Two BARTpho versions BARTpho-syllable and BARTpho-word are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art.

The general architecture and experimental results of BARTpho can be found in our paper:

@article{bartpho,
title     = {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}},
author    = {Nguyen Luong Tran and Duong Minh Le and Dat Quoc Nguyen},
journal   = {arXiv preprint},
volume    = {arXiv:2109.09701},
year      = {2021}
}

Please CITE our paper when BARTpho is used to help produce published results or incorporated into other software.

For further information or requests, please go to BARTpho's homepage!

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms