---
title: Qwen3.5-9B-Base
canonical_url: "https://www.modelscope.cn/models/Qwen/Qwen3.5-9B-Base"
md_url: "https://www.modelscope.cn/models/Qwen/Qwen3.5-9B-Base.md"
repository: Qwen/Qwen3.5-9B-Base
chinese_name: "千问3.5-9B-Base"
last_updated: 2026-04-23
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen3_5
architectures:
  - Qwen3_5ForConditionalGeneration
parameters: 9.7B
tensor_type:
  - F32
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
downloads: 19532
stars: 15
---

# Qwen3.5-9B-Base

> Qwen3.5-9B-Base - Qwen 在 ModelScope 开源的模型。[!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,…

Qwen/Qwen3.5-9B-Base 是 ModelScope 魔搭社区上的 9.7B 参数image-text-to-text模型，采用 apache-2.0 许可。

- **Repository**: Qwen/Qwen3.5-9B-Base
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 9.7B
- **Downloads**: 19532
- **Stars**: 15
- **Last updated**: 2026-04-23

Source: https://www.modelscope.cn/models/Qwen/Qwen3.5-9B-Base

---

# Qwen3.5-9B-Base

<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png">

[![Qwen Chat](https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5)](https://chat.qwen.ai)

> [!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.


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.

## Qwen3.5 Highlights

Qwen3.5 features the following enhancement:

- **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.

- **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

- **Scalable RL Generalization**: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

- **Global Linguistic Coverage**: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

- **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.


For more details, please refer to our blog post [Qwen3.5](https://qwen.ai/blog?id=qwen3.5).


## Model Overview

- Type: Causal Language Model with Vision Encoder
- Training Stage: Pre-training & Post-training
- Language Model
    - Number of Parameters: 9B
    - Hidden Dimension: 4096
    - 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: 12288
    - LM Output: 248320 (Padded)
    - MTP: trained with multi-steps  
- Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

### Citation

If you find our work helpful, feel free to give us a cite.

```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}
}
```