Update README.md

README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3.5-9B-Base/blob/main/LICENSE
+pipeline_tag: image-text-to-text
+---
+
+# 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}
+}
+```