# Multilingual Large Model

Falcon-H1 is an efficient hybrid architecture language model developed by TII, combining the advantages of Transformers and Mamba architectures, supporting English and multilingual tasks.

Qwen3-14B-Base is the latest generation large language model in the Qwen series, offering a dense model with 14.8 billion parameters, supporting a context length of 32k and covering 119 languages.

Qwen3-4B-Base is the latest generation of the Qwen series with 4 billion parameters, supporting 32k context length and multilingual processing.

Qwen3-1.7B-Base is the latest generation large language model in the Qwen series, providing high-quality pretrained language modeling capabilities.

The latest generation large language model in the Qwen series, offering a 14.8 billion parameter pre-trained base model with support for 32k ultra-long context understanding

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete dense model and mixture-of-experts (MoE) model system, covering 36 trillion tokens of pre-training data in 119 languages.

EMMA-500 is a multilingual large language model based on the Llama 2 7B architecture, supporting over 500 languages and excelling in tasks such as commonsense reasoning and machine translation.

360 Zhinao 3-7B-O1.5 is a long chain-of-thought model open-sourced by Qihoo 360, fine-tuned based on 360 Zhinao 3-7B-Instruct, supporting complex reasoning tasks.

The GGUF quantized version of Qwen2.5-72B-Instruct, supporting multiple precision formats for efficient inference across different hardware environments.

LLaMA is an open and efficient foundational language model developed by Meta AI, offering a 7B-parameter version that supports 20 languages, focusing on natural language processing research.