| --- |
| library_name: transformers |
| license: apache-2.0 |
| license_link: https://huggingface.co/Qwen/Qwen3.5-4B-Base/blob/main/LICENSE |
| pipeline_tag: image-text-to-text |
| --- |
| |
| # Qwen3.5-4B-Base |
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| <img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png"> |
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| [](https://chat.qwen.ai) |
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| > [!Note] |
| > This repository contains model weights and configuration files for the pre-trained only model in the Hugging Face Transformers format. |
| > |
| > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. |
| > |
| > The intended use cases are fine-tuning, in-context learning experiments, and other research or development purposes, not direct interaction. |
| > However, the control tokens, e.g., `<|im_start|>` and `<|im_end|>` were trained to allow efficient LoRA-style PEFT with the official chat template, mitigating the need to finetune embeddings, a significant optimization given Qwen3.5's larger vocabulary. |
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| Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. |
|
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| ## Qwen3.5 Highlights |
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| Qwen3.5 features the following enhancement: |
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| - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks. |
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| - **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead. |
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| - **Scalable RL Generalization**: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability. |
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| - **Global Linguistic Coverage**: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding. |
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| - **Next-Generation Training Infrastructure**: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration. |
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| For more details, please refer to our blog post [Qwen3.5](https://qwen.ai/blog?id=qwen3.5). |
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| ## Model Overview |
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| - Type: Causal Language Model with Vision Encoder |
| - Training Stage: Pre-training & Post-training |
| - Language Model |
| - Number of Parameters: 4B |
| - Hidden Dimension: 2560 |
| - Token Embedding: 248320 (Padded) |
| - Number of Layers: 32 |
| - Hidden Layout: 8 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN)) |
| - Gated DeltaNet: |
| - Number of Linear Attention Heads: 32 for V and 16 for QK |
| - Head Dimension: 128 |
| - Gated Attention: |
| - Number of Attention Heads: 16 for Q and 4 for KV |
| - Head Dimension: 256 |
| - Rotary Position Embedding Dimension: 64 |
| - Feed Forward Network: |
| - Intermediate Dimension: 9216 |
| - LM Output: 248320 (Tied to token embedding) |
| - MTP: trained with multi-steps |
| - Context Length: 262,144 natively and extensible up to 1,010,000 tokens. |
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| ### Citation |
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| If you find our work helpful, feel free to give us a cite. |
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|
| ```bibtex |
| @misc{qwen3.5, |
| title = {{Qwen3.5}: Towards Native Multimodal Agents}, |
| author = {{Qwen Team}}, |
| month = {February}, |
| year = {2026}, |
| url = {https://qwen.ai/blog?id=qwen3.5} |
| } |
| ``` |
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|
