OptILLM ModernBERT Large
| Property | Value |
|---|---|
| Base Model | ModernBERT-large |
| Author | codelion |
| Model Hub | Hugging Face |
| License | Not Specified |
What is optillm-modernbert-large?
OptILLM ModernBERT Large is a specialized routing model designed to intelligently select between various approaches for optimizing Large Language Model (LLM) inference. Built upon the ModernBERT-large architecture, this model demonstrates superior performance compared to its predecessor, achieving a 13.33% pass@1 rate on AIME 2024 benchmarks.
Implementation Details
The model implements a sophisticated architecture that combines the power of ModernBERT-large with additional components, including an effort encoder that considers token consumption. The classifier processes both the text embeddings and effort metrics to make routing decisions across 13 different approaches including MCTS, BON, MOA, and others.
- Custom OptILMClassifier architecture with effort encoding
- Maximum sequence length of 1024 tokens
- Integration with various optimization approaches (MCTS, BON, MOA, etc.)
- Efficient state management using safetensors
Core Capabilities
- Intelligent routing between multiple optimization approaches
- Context-aware decision making with effort consideration
- Superior performance compared to previous router models
- Seamless integration with the OptILLM framework
Frequently Asked Questions
Q: What makes this model unique?
The model's unique feature is its ability to combine both text understanding and computational effort estimation in making routing decisions, significantly outperforming previous routing models with a 2x improvement in pass@1 scores.
Q: What are the recommended use cases?
This model is specifically designed for optimizing LLM inference by selecting the most appropriate approach from multiple optimization strategies. It's particularly useful in systems that need to balance between different processing methods based on input complexity and resource constraints.
