Qwen2.5-14B-Instruct-1M
| Property | Value |
|---|---|
| Parameter Count | 14.7B (13.1B Non-Embedding) |
| Context Length | 1,010,000 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm, and GQA |
| Model Type | Causal Language Model |
| Paper | arXiv:2501.15383 |
What is Qwen2.5-14B-Instruct-1M?
Qwen2.5-14B-Instruct-1M is an advanced language model designed to handle extremely long contexts of up to 1 million tokens. It represents a significant advancement in context length handling while maintaining strong performance on shorter tasks. The model features 48 layers and uses specialized attention mechanisms with 40 heads for queries and 8 for key-values.
Implementation Details
The model leverages custom vLLM implementation with sparse attention and length extrapolation methods for efficient processing of long sequences. It requires substantial computational resources, with recommended VRAM requirements of 320GB for handling million-token sequences.
- Advanced architecture combining RoPE, SwiGLU, and RMSNorm
- Supports generation of up to 8,192 tokens
- Optimized for both Ampere and Hopper GPU architectures
- Implements Group Query Attention (GQA) for improved efficiency
Core Capabilities
- Processing ultra-long sequences up to 1M tokens
- Maintained performance on shorter context tasks
- 3-7x speedup for long sequence processing
- Efficient handling of complex instruction-following tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 1M token contexts while maintaining performance on shorter tasks sets it apart. Its custom vLLM implementation with sparse attention makes it particularly efficient for long-context processing.
Q: What are the recommended use cases?
The model excels at tasks requiring analysis of very long documents, complex reasoning across extensive contexts, and generation of detailed responses. It's particularly suited for applications needing to process large amounts of context information.
