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94 lines
2.8 KiB
Markdown
94 lines
2.8 KiB
Markdown
<!--Copyright 2024 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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the License. You may obtain a copy of the License at
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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
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2025-05-14 and contributed to Hugging Face Transformers on 2025-03-31.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
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</div>
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</div>
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# Qwen3
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[Qwen3](https://huggingface.co/papers/2505.09388) is the dense model architecture in the Qwen3 family, available in sizes from 0.6B to 32B parameters. It supports both thinking mode (multi-step reasoning) and non-thinking mode, with seamless switching between the two. Qwen3 was trained on approximately 36T tokens covering 119 languages. See also the MoE variant [Qwen3MoE](qwen3_moe).
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The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModelForCausalLM`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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from transformers import pipeline
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pipe = pipeline(
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task="text-generation",
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model="Qwen/Qwen3-0.6B",
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)
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pipe("The key to effective reasoning is")
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```
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</hfoption>
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<hfoption id="AutoModelForCausalLM">
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
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model = AutoModelForCausalLM.from_pretrained(
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"Qwen/Qwen3-0.6B",
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device_map="auto",
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)
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input_ids = tokenizer("The key to effective reasoning is", return_tensors="pt").to(model.device)
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output = model.generate(**input_ids, max_new_tokens=50)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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</hfoption>
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</hfoptions>
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## Qwen3Config
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[[autodoc]] Qwen3Config
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## Qwen3Model
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[[autodoc]] Qwen3Model
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- forward
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## Qwen3ForCausalLM
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[[autodoc]] Qwen3ForCausalLM
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- forward
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## Qwen3ForSequenceClassification
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[[autodoc]] Qwen3ForSequenceClassification
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- forward
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## Qwen3ForTokenClassification
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[[autodoc]] Qwen3ForTokenClassification
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- forward
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## Qwen3ForQuestionAnswering
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[[autodoc]] Qwen3ForQuestionAnswering
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- forward
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