Qwen2.5-1M Technical Report
Authors: name: An Yang, name: Bowen Yu, name: Chengyuan Li, name: Dayiheng Liu, name: Fei Huang, name: Haoyan Huang, name: Jiandong Jiang, name: Jianhong Tu, name: Jianwei Zhang, name: Jingren Zhou, name: Junyang Lin, name: Kai Dang, name: Kexin Yang, name: Le Yu, name: Mei Li, name: Minmin Sun, name: Qin Zhu, name: Rui Men, name: Tao He, name: Weijia Xu, name: Wenbiao Yin, name: Wenyuan Yu, name: Xiafei Qiu, name: Xingzhang Ren, name: Xinlong Yang, name: Yong Li, name: Zhiying Xu, name: Zipeng Zhang

We introduce Qwen2.5-1M, a series of models that extend the context length to
1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series
have significantly enhanced long-context capabilities through long-context
pre-training and post-training. Key techniques such as long data synthesis,
progressive pre-training, and multi-stage supervised fine-tuning are employed
to effectively enhance long-context performance while reducing training costs.
  To promote the use of long-context models among a broader user base, we
present and open-source our inference framework. This framework includes a
length extrapolation method that can expand the model context lengths by at
least four times, or even more, without additional training. To reduce
inference costs, we implement a sparse attention method along with chunked
prefill optimization for deployment scenarios and a sparsity refinement method
to improve precision. Additionally, we detail our optimizations in the
inference engine, including kernel optimization, pipeline parallelism, and
scheduling optimization, which significantly enhance overall inference
performance. By leveraging our inference framework, the Qwen2.5-1M models
achieve a remarkable 3x to 7x prefill speedup in scenarios with 1 million
tokens of context. This framework provides an efficient and powerful solution
for developing applications that require long-context processing using
open-source models.
  The Qwen2.5-1M series currently includes the open-source models
Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, as well as the API-accessed
model Qwen2.5-Turbo. Evaluations show that Qwen2.5-1M models have been greatly
improved in long-context tasks without compromising performance in
short-context scenarios. Specifically, the Qwen2.5-14B-Instruct-1M model
significantly outperforms GPT-4o-mini in long-context tasks and supports
contexts eight times longer.