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README.md
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<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
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This repository contains the model of the paper [Qwen2.5-1M Technical Report](https://huggingface.co/papers/2501.15383).
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## Introduction
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Qwen2.5-1M is the long-context version of the Qwen2.5 series models, supporting a context length of up to 1M tokens. Compared to the Qwen2.5 128K version, Qwen2.5-1M demonstrates significantly improved performance in handling long-context tasks while maintaining its capability in short tasks.
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- We recommend deploying with our custom vLLM, which introduces sparse attention and length extrapolation methods to ensure efficiency and accuracy for long-context tasks. For specific guidance, refer to [this section](#processing-ultra-long-texts).
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- You can also use the previous framework that supports Qwen2.5 for inference, but accuracy degradation may occur for sequences exceeding 262,144 tokens.
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For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-1m/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
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## Requirements
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The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
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## Evaluation & Performance
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Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-1m/) and our [technical report](https://
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## Citation
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<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
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</a>
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## Introduction
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Qwen2.5-1M is the long-context version of the Qwen2.5 series models, supporting a context length of up to 1M tokens. Compared to the Qwen2.5 128K version, Qwen2.5-1M demonstrates significantly improved performance in handling long-context tasks while maintaining its capability in short tasks.
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- We recommend deploying with our custom vLLM, which introduces sparse attention and length extrapolation methods to ensure efficiency and accuracy for long-context tasks. For specific guidance, refer to [this section](#processing-ultra-long-texts).
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- You can also use the previous framework that supports Qwen2.5 for inference, but accuracy degradation may occur for sequences exceeding 262,144 tokens.
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For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-1m/), [GitHub](https://github.com/QwenLM/Qwen2.5), [Technical Report](https://huggingface.co/papers/2501.15383), and [Documentation](https://qwen.readthedocs.io/en/latest/).
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## Requirements
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The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
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## Evaluation & Performance
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Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-1m/) and our [technical report](https://arxiv.org/abs/2501.15383).
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## Citation
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