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README.md
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print("content:", content)
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```
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For deployment, you can use `
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```shell
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```
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```shell
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```
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## Note on FP8
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For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`.
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## Switching Between Thinking and Non-Thinking Mode
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> [!TIP]
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> The `enable_thinking` switch is also available in APIs created by
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> Please refer to our documentation for [
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### `enable_thinking=True`
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print("content:", content)
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```
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For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or to create an OpenAI-compatible API endpoint:
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- SGLang:
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```shell
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python -m sglang.launch_server --model-path Qwen/Qwen3-4B-FP8 --reasoning-parser qwen3
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```
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- vLLM:
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```shell
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vllm serve Qwen/Qwen3-4B-FP8 --enable-reasoning --reasoning-parser deepseek_r1
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```
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For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3.
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## Note on FP8
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For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`.
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## Switching Between Thinking and Non-Thinking Mode
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> [!TIP]
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> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
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> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
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### `enable_thinking=True`
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