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Qwen3.5 122B-A10B is a multimodal vision-language model built on a hybrid architecture that combines Gated Delta Networks with a sparse Mixture-of-Experts design. By utilizing a 3:1 ratio of linear attention to full attention, the model achieves efficient long-context processing while maintaining high-throughput inference. With 122 billion total parameters and 10 billion active parameters per forward pass, it is engineered to serve as a versatile foundation for native multimodal agent applications, supporting text, image, and video inputs with significant performance gains in coding, reasoning, and visual analysis.
The model benefits from a training lineage that emphasizes reinforcement learning at scale, utilizing million-agent environments to improve adaptability across complex task distributions. Its development included early fusion training on multimodal tokens, which allows it to outperform previous generations in cross-modal benchmarks. With expanded support for 201 languages and dialects, the model is designed for global deployment. Its architecture and training infrastructure enable high-efficiency multimodal processing, making it a robust choice for developers seeking a balance between the performance of flagship models and the operational efficiency required for diverse enterprise tasks.
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... Qwen3.5-122B-A10B #알리바바 #Alibaba #LLM #대형언어모델 #deepseek #chatGPT #gemini #Grok #Qwen #coding #Huggingface #토질및기초기술사 #토목 ...
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