unsloth/Qwen2.5-14B-Instruct-1M
unsloth/Qwen2.5-14B-Instruct-1M is a 14.7 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen Team. This model is specifically optimized for ultra-long context tasks, supporting an impressive context length of up to 1,010,000 tokens. It maintains strong performance on shorter tasks while significantly enhancing capabilities for processing and generating content over extended sequences.
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Qwen2.5-14B-Instruct-1M Overview
This model is a 14.7 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by the Qwen Team. Its primary distinguishing feature is its ultra-long context capability, supporting up to 1,010,000 tokens, making it suitable for tasks requiring extensive contextual understanding.
Key Capabilities
- Extended Context Handling: Designed to process and generate content over sequences up to 1 million tokens, significantly outperforming the 128K version in long-context scenarios.
- Architecture: Built on transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
- Optimized Inference: Recommends deployment with a custom vLLM framework that incorporates sparse attention and length extrapolation for improved efficiency and accuracy with long sequences, offering 3-7x speedup for 1M token tasks.
- Short Task Performance: Maintains strong performance on conventional short-context tasks.
Good For
- Ultra-long document analysis: Summarization, question answering, and information extraction from very large texts.
- Complex codebases: Understanding and generating code within extensive projects.
- Conversational AI: Maintaining coherence and context over extremely long dialogues.
For more technical details, refer to the official blog and GitHub repository.