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@@ -15,7 +15,7 @@ Qwen2 is the new series of Qwen large language models. For Qwen2, we release a n
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  Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
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- Qwen2-MoE-57B-A14B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.
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  For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/) and [GitHub](https://github.com/QwenLM/Qwen2).
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  <br>
@@ -73,7 +73,7 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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  ### Processing Long Texts
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- To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
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  For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps:
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@@ -90,7 +90,7 @@ For deployment, we recommend using vLLM. You can enable the long-context capabil
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  // adding the following snippets
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  "rope_scaling": {
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- "factor": 4.0,
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  "original_max_position_embeddings": 32768,
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  "type": "yarn"
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  }
 
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  Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
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+ Qwen2-MoE-57B-A14B-Instruct supports a context length of up to 65,536 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.
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  For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/) and [GitHub](https://github.com/QwenLM/Qwen2).
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  <br>
 
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  ### Processing Long Texts
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+ To handle extensive inputs exceeding 65,536 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
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  For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps:
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  // adding the following snippets
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  "rope_scaling": {
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+ "factor": 2.0,
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  "original_max_position_embeddings": 32768,
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  "type": "yarn"
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  }