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+<!--Copyright 2026 The Qwen Team and The HuggingFace Team. All rights reserved.
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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+*This model was contributed to Hugging Face Transformers on 2026-02-09.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# Qwen3.5
+
+[Qwen3.5](https://qwen.ai/blog?id=qwen3.5) is Qwen's natively multimodal foundation model family, trained from scratch on interleaved text, image, and video tokens. It uses a 3:1 hybrid attention stack — three Gated DeltaNet (linear attention) layers for every one Gated Attention (full attention) layer — so long context and vision tokens can be served without paying full quadratic cost on every block.
+
+This page covers the dense Qwen3.5 and Qwen3.6 variants (Qwen/Qwen3.5-9B, Qwen/Qwen3.5-27B, Qwen/Qwen3.6-27B). Qwen3.6 checkpoints share the same architecture and `model_type` as Qwen3.5 and are loaded with the same classes. For the sparse mixture-of-experts variants see [Qwen3.5 MoE](./qwen3_5_moe). The text backbone reuses Qwen3-Next's linear-attention decoder with a three-component multimodal RoPE; the vision tower reuses the Qwen3-VL encoder.
+
+You can find all the official Qwen3.5 checkpoints under the [Qwen](https://huggingface.co/Qwen) organization.
+
+## Quickstart
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    task="text-generation",
+    model="Qwen/Qwen3.5-9B",
+    device_map="auto",
+)
+print(pipe("The capital of France is", max_new_tokens=20)[0]["generated_text"])
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoTokenizer, Qwen3_5ForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
+model = Qwen3_5ForCausalLM.from_pretrained(
+    "Qwen/Qwen3.5-9B",
+    device_map="auto",
+)
+
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt").to(model.device)
+generated_ids = model.generate(**inputs, max_new_tokens=30)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+</hfoption>
+</hfoptions>
+
+## Usage tips and notes
+
+- Layers are hybrid: [`Qwen3_5TextConfig`]'s `layer_types` is a per-layer list of `"linear_attention"` or `"full_attention"` that encodes the 3:1 Gated DeltaNet / Gated Attention stack. The DeltaNet path (`Qwen3NextGatedDeltaNet`) needs the optional `causal_conv1d` (from [Dao-AILab](https://github.com/Dao-AILab/causal-conv1d)) and `fla` packages for its fast kernels — without them, the model silently falls back to slower and more memory hungry PyTorch ops.
+- Multimodal RoPE splits the head dimension into three components (temporal, height, width) via `mrope_section` on the text config. If you replace the rotary module, preserve this split or position encodings for image and video tokens will be misaligned.
+- Use [`Qwen3_5ForCausalLM`] for text-only generation with [`Qwen3_5TextConfig`]; use [`Qwen3_5ForConditionalGeneration`] with the full [`Qwen3_5Config`] and a processor ([`~AutoProcessor.from_pretrained`]) to feed interleaved image/video + text via [`~ProcessorMixin.apply_chat_template`].
+
+## Qwen3_5Config
+
+[[autodoc]] Qwen3_5Config
+
+## Qwen3_5TextConfig
+
+[[autodoc]] Qwen3_5TextConfig
+
+## Qwen3_5VisionConfig
+
+[[autodoc]] Qwen3_5VisionConfig
+
+## Qwen3_5Tokenizer
+
+[[autodoc]] Qwen3_5Tokenizer
+
+## Qwen3_5VisionModel
+
+[[autodoc]] Qwen3_5VisionModel
+    - forward
+
+## Qwen3_5TextModel
+
+[[autodoc]] Qwen3_5TextModel
+    - forward
+
+## Qwen3_5Model
+
+[[autodoc]] Qwen3_5Model
+    - forward
+
+## Qwen3_5ForCausalLM
+
+[[autodoc]] Qwen3_5ForCausalLM
+    - forward
+
+## Qwen3_5ForConditionalGeneration
+
+[[autodoc]] Qwen3_5ForConditionalGeneration
+    - forward
+<<<<<<< HEAD
+
+## Qwen3_5ForSequenceClassification
+
+[[autodoc]] Qwen3_5ForSequenceClassification
+    - forward
+
+## Qwen3_5TextForSequenceClassification
+
+[[autodoc]] Qwen3_5TextForSequenceClassification
+    - forward
+
+## Qwen3_5ForTokenClassification
+
+[[autodoc]] Qwen3_5ForTokenClassification
+    - forward
+
+## Qwen3_5Tokenizer
+
+[[autodoc]] Qwen3_5Tokenizer
+=======
+>>>>>>> 52b4732861 (docs)