# Multilingual Translation

A translation model fine-tuned on mbart-large-50-many-to-many-mmt for English to Bhojpuri text translation

EraX-Translator-V1.0 is a multilingual translation model that supports translation tasks in various languages, including Vietnamese, English, Russian, French, Chinese, and more. It is particularly suitable for low-resource languages and the translation of ancient Chinese and Buddhist scriptures.

GemmaX2-28-9B-v0.1-Q2_K-GGUF is a GGUF format model converted from ModelSpace/GemmaX2-28-9B-v0.1, supporting multilingual translation tasks.

NLLB-200-3.3B is a multilingual neural machine translation model developed by Meta (formerly Facebook), supporting translation tasks between 200 languages.

The GemmaX2-28-2B GGUF quantized model is a series of quantized variants based on GemmaX2-28-2B-v0.1, designed for multilingual machine translation and supports 28 languages.

The GemmaX2-28-2B GGUF quantized model is a collection of quantized versions of the GemmaX2-28-2B-v0.1 translation large language model developed by Xiaomi, supporting machine translation tasks in 28 languages.

This model is a multilingual text generation model converted from bigscience/mt0-xxl-mt to GGUF format via llama.cpp, supporting various language tasks.

A universal Transformer model supporting over 100 languages, suitable for various natural language processing tasks

This is a Japanese speech-to-any-target-language text translation pipeline based on a cascaded approach, consisting of automatic speech recognition (ASR) and text translation components.

This model is a GGUF-converted version of google-t5/t5-large, supporting tasks like summarization and translation, and is applicable to multiple languages including English, French, Romanian, German, and more.

Ultravox is a multimodal speech large language model built upon Llama3.1-8B-Instruct and Whisper-small, capable of processing both speech and text inputs.

MADLAD-400 3B multilingual translation model, supporting translation tasks for over 400 languages, optimized in GGUF format.

This model is a GGUF-format version converted from Google's T5-base model, supporting multiple language tasks including summarization and translation.

MADLAD-400-10B-MT is a multilingual translation model that supports translation tasks between multiple languages.

This is an updated version of deberta-v2-xxlarge that implements the AutoModelForCausalLM class, enabling text generation. It fixes three issues in the original implementation, including the output embedding weight names, enhanced mask decoder implementation, and position embedding truncation handling.

Honyaku-7b-v2 is an improved version of its predecessor, with enhanced accuracy in multilingual tag adherence.

A 7-billion-parameter multilingual translation model based on the Mistral architecture, supporting translation-related tasks in 10 languages

TowerInstruct-7B-v0.2 is a multilingual instruction fine-tuned model developed by Unbabel, based on the Llama 2 architecture, supporting translation tasks in 10 languages.

A speech-to-text model fine-tuned based on openai/whisper-small, supporting audio translation and text generation tasks.

TowerInstruct-13B is a 13-billion-parameter language model obtained by fine-tuning TowerBase on the TowerBlocks supervised fine-tuning dataset, focusing on translation-related tasks

NLLB-200 Distilled 1.3B is a neural machine translation model developed by Meta, supporting translation between 200 languages, utilizing CTranslate2 for efficient inference.

IndicTrans2 is a high-quality machine translation model supporting mutual translation among 22 official Indian languages, based on a 320M-parameter distilled variant.

Gugugo is a model specialized in translation between Japanese and Korean.

A bidirectional translation model for English and Tamil based on the M2M100 architecture, released under the OpenRAIL license

NLLB is a multilingual machine translation model supporting 200 languages, designed to cover the majority of global languages, especially low-resource languages.

Insufficient model information available to provide a specific introduction.

NLLB-200-3.3B is a large-scale multilingual translation model supporting 200 languages, trained on the FLORES-200 dataset.

This repository contains a LLaMA-7b model internally fine-tuned on the Stanford Alpaca dataset, primarily for machine translation research.

FLAN-T5 is an instruction-fine-tuned language model based on T5, achieving superior performance through fine-tuning on over 1,000 multilingual tasks with the same parameter count

OpenVINO IR format version of the T5-small model, supporting text generation, translation, and other tasks

This is a neural machine translation model for translating from Catalan, Occitan, and Spanish to English, part of the OPUS-MT project.

This is a neural machine translation model from English to East Slavic languages (including Belarusian, Russian, Ukrainian), part of the OPUS-MT project.

This is a student project model supporting German, Icelandic, and multilingual translation.

mbart-large-cc25 is a multilingual pre-trained sequence-to-sequence model developed by Facebook, supporting translation tasks between 25 languages

This is a multilingual translation model based on the Transformer architecture, supporting translation from English to multiple Italic language family languages.

This is a multilingual translation model based on the Transformer architecture, supporting translation tasks from English to 18 Atlantic-Congo languages.

This is an Italian-to-Swedish machine translation model based on the Transformer-align architecture, developed by the Helsinki-NLP team.

A sequence-to-sequence language model based on the mbart50 architecture, supporting text generation tasks from Sinhala to English