Hunyuan Translation Model Version 1.5 includes a 1.8B translation model, HY-MT1.5-1.8B, and a 7B translation model, HY-MT1.5-7B. Both models focus on supporting mutual translation across 33 languages and incorporating 5 ethnic and dialect variations. Among them, HY-MT1.5-7B is an upgraded version of our WMT25 championship model, optimized for explanatory translation and mixed-language scenarios, with newly added support for terminology intervention, contextual translation, and formatted translation. Despite having less than one-third the parameters of HY-MT1.5-7B, HY-MT1.5-1.8B delivers translation performance comparable to its larger counterpart, achieving both high speed and high quality.…
Open weights
1.8B parameters
262,144 tokens
transformers
The developers of the Text-To-Text Transfer Transformer (T5) write: T5-Large is the checkpoint with 770 million parameters. The developers write in a blog post that the model: See the blog post and research paper for further details. The model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5. The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.). Thereby, the following datasets were being used for (1.) and (2.): 1. Datasets used for Unsupervised denoising objective: 2. Datasets used for Supervised text-to-text language modeling objective - CoLA Warstadt…
Open weights
apache-2.0
738M parameters
transformers
English ｜ 中文 Hy-MT2 is a family of “fast-thinking” multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of which support translation among 33 languages and effectively follow translation instructions in multiple languages. For on-device deployment, AngelSlim 1.25-bit extreme quantization reduces the storage requirement of the 1.8B model to only 440 MB and improves inference speed by 1.5x. Multi-dimensional evaluations show that Hy-MT2 delivers outstanding performance across general, real-world business, domain-specific, and instruction-following translation tasks. The 7B and 30B-A3B models outperform…
Open weights
apache-2.0
2B parameters
262,144 tokens
transformers
This model is a fine-tuned checkpoint of mBART-large-50. mbart-large-50-many-to-many-mmt is fine-tuned for multilingual machine translation. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper. The model can translate directly between any pair of 50 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the forcedbostokenid parameter to the generate method. See the model hub to look for more fine-tuned versions. Arabic (arAR), Czech (csCZ), German (deDE), English (enXX), Spanish (esXX), Estonian (etEE), Finnish…
Open weights
611M parameters
1,024 tokens
transformers
This is mbart-large-cc25, finetuned on wmtenro. It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in romanianpostprocessing.md Original Code: https://github.com/pytorch/fairseq/tree/master/examples/mbart Docs: https://huggingface.co/transformers/master/modeldoc/mbart.html
Open weights
mit
611M parameters
1,024 tokens
transformers
This is the model card of IndicTrans2 En-Indic Distilled 200M variant. Please refer to section 7.6: Distilled Models in the TMLR submission for further details on model training, data and metrics. Please refer to the github repository for a detail description on how to use HF compatible IndicTrans2 models for inference. - New RoPE based IndicTrans2 models which are capable of handling sequence lengths upto 2048 tokens are available here - These models can be used by just changing the modelname parameter. Please read the model card of the RoPE-IT2 models for more information about the generation. - It is recommended to run these models with flashattention2 for efficient generation. If you…
Access requested at publisher
mit
275M parameters
transformers
