unsloth/Qwen3.5-9B-Base
The unsloth/Qwen3.5-9B-Base is a 9 billion parameter causal language model developed by Qwen, featuring a unified vision-language foundation and an efficient hybrid architecture. It integrates multimodal learning and architectural efficiency, excelling in reasoning, coding, agents, and visual understanding benchmarks. With a native context length of 262,144 tokens, extensible up to 1,010,000, it is designed for fine-tuning, in-context learning, and research purposes.
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Qwen3.5-9B-Base Overview
Qwen3.5-9B-Base is a 9 billion parameter causal language model developed by Qwen, distinguished by its unified vision-language foundation and efficient hybrid architecture. This model integrates significant advancements in multimodal learning, architectural efficiency, and reinforcement learning, making it a powerful tool for various AI applications.
Key Capabilities & Features
- Unified Vision-Language Foundation: Achieves cross-generational parity with Qwen3 and surpasses Qwen3-VL models across reasoning, coding, agent 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 to deliver high-throughput inference with minimal latency and cost.
- Scalable RL Generalization: Features reinforcement learning scaled across millions of agent environments with progressively complex task distributions, enhancing real-world adaptability.
- Global Linguistic Coverage: Supports 201 languages and dialects, enabling broad deployment with nuanced cultural and regional understanding.
- Extended Context Length: Natively supports 262,144 tokens, extensible up to 1,010,000 tokens.
Intended Use Cases
This pre-trained model is primarily intended for:
- Fine-tuning for specific downstream tasks.
- In-context learning experiments.
- General research and development purposes.
It is compatible with Hugging Face Transformers, vLLM, and SGLang, and its control tokens are optimized for efficient LoRA-style PEFT, mitigating the need for embedding fine-tuning despite a larger vocabulary.