youyou125709/Qwen2.5-7B-Instruct-1M
Qwen2.5-7B-Instruct-1M is a 7.61 billion parameter causal language model developed by Qwen, featuring a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. This model is specifically optimized for ultra-long context tasks, supporting an impressive context length of up to 1 million tokens. It maintains strong performance on short tasks while significantly enhancing capabilities for processing extensive text sequences, making it ideal for applications requiring deep contextual understanding over very long documents.
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What is Qwen2.5-7B-Instruct-1M?
Qwen2.5-7B-Instruct-1M is a 7.61 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. Its primary distinguishing feature is its ultra-long context window, supporting up to 1 million tokens, a significant improvement over previous 128K versions. This model is built on a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Key Capabilities & Features
- Exceptional Long-Context Handling: Designed to process and generate text over extremely long sequences, up to 1,010,000 tokens for full context and 8192 tokens for generation.
- Optimized Inference Framework: Utilizes a custom vLLM implementation with sparse attention and length extrapolation, enabling 3 to 7 times speedup for 1M token sequences and improved accuracy for texts exceeding 256K tokens.
- Maintains Short-Context Performance: Despite its long-context focus, the model retains strong capabilities for shorter tasks.
- Robust Architecture: Features 28 layers and 28 attention heads (with 4 for KV in GQA).
When to Use This Model
This model is particularly well-suited for use cases demanding extensive contextual understanding and generation over very long documents or conversations. Consider Qwen2.5-7B-Instruct-1M if your application involves:
- Summarization of lengthy reports or books.
- Question answering over large knowledge bases.
- Deep analysis of legal documents or scientific papers.
- Maintaining coherent dialogue over extended chat histories.
For optimal performance with ultra-long texts, deployment with the custom vLLM framework is recommended, requiring specific hardware (e.g., 120GB VRAM for the 7B model for 1M tokens) and CUDA versions (12.1 or 12.3).