Qwen3-4B-MLX-4bit is the MLX-quantized version of Qwen3-4B, the latest generation of large language models from the Qwen series. It is a 4 billion parameter causal language model that supports seamless switching between thinking mode for complex logical reasoning, mathematics, and coding tasks, and non-thinking mode for efficient general-purpose dialogue. The model excels in reasoning capabilities, human preference alignment for creative writing and role-playing, agent capabilities with tool integration, and supports over 100 languages with strong multilingual instruction following and translation abilities. It natively supports context lengths of up to 32,768 tokens and can be extended to 131,072 tokens using YaRN scaling techniques.
Qwen3-4B-MLX-4bit is the MLX-quantized version of Qwen3-4B, the latest generation of large language models from the Qwen series. It is a 4 billion parameter causal language model that supports seamless switching between thinking mode for complex logical reasoning, mathematics, and coding tasks, and non-thinking mode for efficient general-purpose dialogue. The model excels in reasoning capabilities, human preference alignment for creative writing and role-playing, agent capabilities with tool integration, and supports over 100 languages with strong multilingual instruction following and translation abilities. It natively supports context lengths of up to 32,768 tokens and can be extended to 131,072 tokens using YaRN scaling techniques.
Qwen3-4B-MLX-4bit is the MLX-quantized version of Qwen3-4B, the latest generation of large language models from the Qwen series. It is a 4 billion parameter causal language model that supports seamless switching between thinking mode for complex logical reasoning, mathematics, and coding tasks, and non-thinking mode for efficient general-purpose dialogue. The model excels in reasoning capabilities, human preference alignment for creative writing and role-playing, agent capabilities with tool integration, and supports over 100 languages with strong multilingual instruction following and translation abilities. It natively supports context lengths of up to 32,768 tokens and can be extended to 131,072 tokens using YaRN scaling techniques.