Qwen2.5-7B-Instruct-1M
| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| Context Length | 1,010,000 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Number of Layers | 28 |
| Attention Heads | 28 for Q, 4 for KV (GQA) |
| Model Link | Hugging Face |
What is Qwen2.5-7B-Instruct-1M?
Qwen2.5-7B-Instruct-1M is a groundbreaking long-context language model that pushes the boundaries of context length handling in AI systems. With the ability to process up to 1 million tokens while maintaining high performance on shorter tasks, it represents a significant advancement in language model capabilities.
Implementation Details
The model leverages advanced architectural components including RoPE (Rotary Position Embedding), SwiGLU activation functions, and RMSNorm. It implements sparse attention and length extrapolation methods through a custom vLLM framework, achieving 3-7x speedup for long sequences.
- Custom vLLM implementation for optimal performance
- Supports both offline inference and OpenAI-like server deployment
- Requires CUDA 12.1/12.3 and Python 3.9-3.12
- Minimum 120GB VRAM for million-token sequences
Core Capabilities
- Process sequences up to 1,010,000 tokens
- Generate responses up to 8,192 tokens
- Maintains performance across both short and long-context tasks
- Efficient processing through sparse attention mechanisms
- Supports both chat and instruction-following tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle extremely long contexts (up to 1M tokens) while maintaining performance on shorter tasks sets it apart. It achieves this through innovative sparse attention mechanisms and custom optimization techniques.
Q: What are the recommended use cases?
The model excels in tasks requiring long-context understanding such as document analysis, extended conversations, and complex instruction following. It's particularly suitable for applications needing to process large amounts of context while maintaining coherent outputs.
