Cooolder/SCOPE is a 4 billion parameter model based on Qwen/Qwen3-4B-Instruct-2507, designed for pre-hoc performance estimation of large language models. It predicts an LLM's expected correctness and output token length for a given query by analyzing historical behaviors on similar questions. This framework enables scalable, explainable, and controllable LLM routing, allowing users to manage accuracy-cost trade-offs and generalize to unseen models without retraining.
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SCOPE: Scalable and Controllable Outcome Performance Estimator
SCOPE (Scalable and Controllable Outcome Performance Estimator) is a 4 billion parameter model, built on the Qwen/Qwen3-4B-Instruct-2507 base, that redefines LLM routing as a pre-hoc estimation problem. Instead of directly classifying and selecting a model, SCOPE predicts an LLM's expected performance (correctness) and inference cost (token length) for a given query. This prediction is based on the target model's historical behavior on similar questions, provided as 'anchor questions' in the input prompt.
Key Capabilities
- Performance Prediction: Estimates whether an LLM will answer a question correctly before expensive inference.
- Cost Estimation: Predicts the output token length for resource planning and budget management.
- Scalable Routing: Enables efficient LLM routing and selection across diverse model portfolios.
- Generalization: Can generalize to unseen LLMs without requiring specific training for each new model.
- Controllable Trade-offs: Allows users to dynamically control the balance between accuracy and cost using a budget-aware utility function.
- Explainable Decisions: Provides an analysis of the reasoning behind its performance predictions.
Good for
- Optimizing LLM inference costs by predicting outcomes before execution.
- Building dynamic LLM routing systems that adapt to different models and user budgets.
- Evaluating the potential performance of various LLMs on specific tasks without extensive testing.
- Applications requiring a balance between prediction accuracy and computational efficiency.
SCOPE is trained using Supervised Fine-Tuning (SFT) and Reinforcement Learning (GRPO) and utilizes a specific prompt format that includes a target question and several anchor questions with their known performance data. For optimal performance, it is recommended to use multiple samples (8+) and aggregate predictions, with a temperature of 0.6-0.7, and to use vLLM for batch inference.