qwen2.5-7b-instruct-1m

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. The model has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 1,010,000 tokens and generation 8192 tokens - 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. - You can also use the previous framework that supports Qwen2.5 for inference, but accuracy degradation may occur for sequences exceeding 262,144 tokens. For more details, please refer to our blog, GitHub, Technical Report, and Documentation.

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