Hy-MT2-30B-A3B

Model · Translation

twi-translator-gemma4-31b

Ghana NLP Community

An English-to-Twi (Akan) translation model fine-tuned on google/gemma-4-31B-it to translate English like an expert human translator. 1. Tool Calling: The model inspects the English source text, extracts key nominal terms, and emits a lookuptwitranslation tool call. 2. Offline Dictionary Lookup: The runtime looks up candidate Twi translations in an offline dictionary (478,822 English→Twi terms from SHOLA Gemini machine translations). 3. Linguistic Reasoning: The tool results are fed back into the model's thought channel (reasoningcontent), where the model reasons about morphology, syntax, and idioms. 4. Final Translation: The model outputs the accurate Twi translation, preserving the…

Model · Translation

ChindaMT-4B

iApp Technology

Paper: Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation, AACL-IJCNLP 2026 Main Conference ChindaMT-4B is an open-weight Thai-English machine translation model fine-tuned from Qwen/Qwen3.5-4B on Grounded, a 1.97M-record dataset built by Reference-Grounded Data Curation (RGDC). It translates in both directions and follows auxiliary rules given in the prompt, such as terminology, register, length, and output format. It is one of three sizes in the ChindaMT family (4B, 2B, 0.8B). Plain translation. Same template for both directions; swap the language line and the source tag: With rules. Add a Rules: block between the language line and the source line.…

Model · Translation

madlad400-3b-mt

Google

MADLAD-400-3B-MT is a multilingual machine translation model based on the T5 architecture that was trained on 1 trillion tokens covering over 450 languages using publicly available data. It is competitive with models that are significantly larger. Disclaimer: Juarez Bochi, who was not involved in this research, converted the original weights and wrote the contents of this model card based on the original paper and Flan-T5. Find below some example scripts on how to use the model: First, install the Python packages that are required: pip install transformers accelerate sentencepiece Usage with candle: We also provide a quantized model (1.65 GB vs the original 11.8 GB file): See the research…

Model · Translation

t5-3b

T5 community

The developers of the Text-To-Text Transfer Transformer (T5) write: T5-3B is the checkpoint with 3 billion 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 et…

Model · Translation

Hy-MT2-1.8B

Tencent

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…

Model · Translation

HY-MT1.5-1.8B-FP8

Tencent

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.…