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Qwen2.5-1M: Deploy Your Own Qwen with Context Length up to 1M Tokens Two months after upgrading Qwen2.5-Turbo to support context length up to one million tokens, we are back with the open-source Qwen2.5-1M models and the corresponding inference framework support. Here’s what you can expect from this release: Opensource Models: We’re releasing two new checkpoints, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, marking the first time we’ve upgraded our opensource Qwen models to handle 1M-token contexts. Inference Framework: To help developers deploy the Qwen2.5-1M series models more efficiently, we’ve fully open-sourced our inference framework based on vLLM. With integration with sparse attention methods, our framework can process 1M-token inputs 3x to 7x faster. Technical Report: We’re also sharing the technical details behind the Qwen2.5-1M series, including design insights for training and inference frameworks, as well as ablation experiments. You can experience Qwen2.5-1M models online by visiting our demo on Huggingface and Modelscope. Additionally, we recently introduced Qwen Chat, an advanced AI assistant from the Qwen series. With Qwen Chat, you can engage in conversations, write code, perform searches, generate images and videos, and utilize various tools. Notably, Qwen Chat also features the Qwen2.5-Turbo model, which supports long-context processing with a context length of up to 1M tokens.