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+<!--Copyright 2024 The Qwen Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+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
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2025-05-14 and contributed to Hugging Face Transformers on 2025-03-31.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# Qwen3MoE
+
+[Qwen3MoE](https://huggingface.co/papers/2505.09388) is the mixture-of-experts variant in the Qwen3 family, with 30.5B total parameters and 3.3B active parameters per token. It uses 128 routed experts with 8 activated per token across 48 layers, and supports up to 131K context with YaRN. See also the dense variant [Qwen3](qwen3).
+
+The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModelForCausalLM`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipe = pipeline(
+    task="text-generation",
+    model="Qwen/Qwen3-30B-A3B",
+)
+pipe("The key to effective reasoning is")
+```
+
+</hfoption>
+<hfoption id="AutoModelForCausalLM">
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B")
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-30B-A3B",
+    device_map="auto",
+)
+input_ids = tokenizer("The key to effective reasoning is", return_tensors="pt").to(model.device)
+
+output = model.generate(**input_ids, max_new_tokens=50)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+</hfoptions>
+
+## Qwen3MoeConfig
+
+[[autodoc]] Qwen3MoeConfig
+
+## Qwen3_5MoeVisionConfig
+
+[[autodoc]] Qwen3_5MoeVisionConfig
+
+## Qwen3MoeModel
+
+[[autodoc]] Qwen3MoeModel
+    - forward
+
+## Qwen3MoeForCausalLM
+
+[[autodoc]] Qwen3MoeForCausalLM
+    - forward
+
+## Qwen3MoeForSequenceClassification
+
+[[autodoc]] Qwen3MoeForSequenceClassification
+    - forward
+
+## Qwen3MoeForTokenClassification
+
+[[autodoc]] Qwen3MoeForTokenClassification
+    - forward
+
+## Qwen3MoeForQuestionAnswering
+
+[[autodoc]] Qwen3MoeForQuestionAnswering
+    - forward