Qwen2.5-1M

An open-source Qwen model supporting a context of 1 million tokens, suitable for long sequence processing tasks.

CommonProductProductivityLong sequence processingOpen-source model

Qwen2.5-1M is an open-source AI language model designed for long sequence tasks, with support for context lengths of up to 1 million tokens. Through innovative training methods and technical optimizations, it significantly enhances the performance and efficiency of long sequence processing. This model excels in long context tasks while maintaining strong performance in short text scenarios, making it an excellent open-source alternative among existing long context models. It is suitable for applications involving extensive text data, such as document analysis and information retrieval, providing developers with robust language processing capabilities.

Supports a maximum context length of 1 million tokens
ideal for long sequence processing tasks.
Open-source model available in 7B and 14B versions for easy developer access.
Inference framework based on vLLM
integrating sparse attention methods
resulting in 3-7x speedup in inference.
Technical reports share insights on training and inference framework designs and results from ablation studies.
Online demos available on Hugging Face and Modelscope to experience the model's performance.

\This product is ideal for developers
researchers
and enterprises that handle long text data
particularly in fields such as natural language processing
text analysis

In long context tasks
such as needle-in-a-haystack scenarios
the model can accurately retrieve hidden information from 1 million token documents.
Demonstrates outstanding performance in complex long context understanding tasks like RULER
LV-Eval

1. Meet system requirements: Use a GPU with an optimized kernel
either from the Ampere or Hopper architecture
with CUDA version 12.1 or 12.3
and Python version >= 3.9 and <= 3.12.
2. Clone the vLLM repository and install the dependencies

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430139

Bounce Rate

52.10%

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1.6

Visit Duration

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