Hugging Face Overview
Qwen3.5-397B-A17B: A Multimodal Agent Foundation Model
Qwen3.5-397B-A17B is a powerful 397 billion parameter multimodal causal language model developed by Qwen, designed for exceptional utility and performance across diverse tasks. It integrates advancements in multimodal learning, architectural efficiency, and scalable reinforcement learning to deliver robust real-world adaptability.
Key Capabilities
- Unified Vision-Language Foundation: Achieves strong performance across reasoning, coding, agentic tasks, and visual understanding benchmarks through early fusion training on multimodal tokens.
- Efficient Hybrid Architecture: Utilizes Gated Delta Networks combined with sparse Mixture-of-Experts for high-throughput inference with optimized latency and cost.
- Scalable RL Generalization: Enhanced real-world adaptability through reinforcement learning scaled across millions of agent environments.
- Global Linguistic Coverage: Supports 201 languages and dialects, enabling inclusive worldwide deployment with nuanced cultural and regional understanding.
- Ultra-Long Context: Natively supports 262,144 tokens, extensible up to 1,010,000 tokens with YaRN scaling, ideal for complex, long-horizon tasks.
- Strong Agentic Performance: Excels in general agent, search agent, and coding agent benchmarks, demonstrating advanced tool-calling capabilities.
Good for
- Developing multimodal AI applications requiring both visual and linguistic understanding.
- Building intelligent agents that can reason, code, and interact with tools.
- Applications demanding ultra-long context processing, such as document analysis or complex problem-solving.
- Deploying globally accessible AI solutions due to extensive multilingual support.
- Scenarios requiring efficient inference for large-scale models.