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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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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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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 MoE](https://qwen.ai/blog?id=qwen3.5) is the sparse-expert variant of Qwen3.5. It keeps the same natively multimodal decoder and 3:1 Gated DeltaNet/Gated Attention backbone, but replaces dense FFNs with a 256-expert sparse mixture — 8 routed experts are activated per token, plus 1 shared expert — so total parameters scale well past the dense checkpoints while active compute per token stays much smaller.
+
+Notable checkpoints include Qwen/Qwen3.5-35B-A3B (35B total/3B active), Qwen/Qwen3.5-122B-A10B, Qwen/Qwen3.5-397B-A17B, and Qwen/Qwen3.6-35B-A3B. Qwen3.6 checkpoints share the same architecture and `model_type` as Qwen3.5 and are loaded with the same classes. The text tower reuses `Qwen3NextSparseMoeBlock` and expert kernels from Qwen3-Next; the vision tower is inherited from Qwen3-VL.
+
+You can find all the official Qwen3.5 MoE 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/Qwen3.5-35B-A3B",
+    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_5MoeForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-35B-A3B")
+model = Qwen3_5MoeForCausalLM.from_pretrained(
+    "Qwen/Qwen3.5-35B-A3B",
+    device_map="auto",
+)
+
+inputs = tokenizer("Explain mixture-of-experts in one paragraph.", return_tensors="pt").to(model.device)
+generated_ids = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+</hfoption>
+</hfoptions>
+
+## Usage tips and notes
+
+- When training or fine-tuning, set `output_router_logits=True` so the forward returns router logits and the load-balancing auxiliary loss is added to the total loss (scaled by `router_aux_loss_coef`, default `0.001`). Without it, experts can collapse to a few popular slots.
+- [`Qwen3_5MoeCausalLMOutputWithPast`] includes a `router_logits` field. Downstream code that destructures model outputs by position needs to account for it or switch to keyword access.
+- For Qwen3.5-35B-A3B, the text config uses `hidden_size=2048` across 40 layers, 256 experts with 8 routed + 1 shared per token, and `moe_intermediate_size=512` — very different shapes from the dense Qwen3.5 checkpoints, so weights are not interchangeable.
+- Native context is 262,144 tokens. To reach the advertised ~1M context, enable YaRN rope scaling via the config's `rope_scaling` field — plain loading gives you the native window only.
+- As with Qwen3.5, linear-attention layers depend on optional `causal_conv1d` (from [Dao-AILab](https://github.com/Dao-AILab/causal-conv1d)). Without it, the model silently falls back to slower and more memory hungry PyTorch ops.
+
+## Qwen3_5MoeConfig
+
+[[autodoc]] Qwen3_5MoeConfig
+
+## Qwen3_5MoeTextConfig
+
+[[autodoc]] Qwen3_5MoeTextConfig
+
+## Qwen3_5MoeVisionConfig
+
+[[autodoc]] Qwen3_5MoeVisionConfig
+
+## Qwen3_5MoeVisionModel
+
+[[autodoc]] Qwen3_5MoeVisionModel
+    - forward
+
+## Qwen3_5MoeTextModel
+
+[[autodoc]] Qwen3_5MoeTextModel
+    - forward
+
+## Qwen3_5MoeModel
+
+[[autodoc]] Qwen3_5MoeModel
+    - forward
+
+## Qwen3_5MoeForCausalLM
+
+[[autodoc]] Qwen3_5MoeForCausalLM
+    - forward
+
+## Qwen3_5MoeForConditionalGeneration
+
+[[autodoc]] Qwen3_5MoeForConditionalGeneration
+    - forward