Principle:Hpcaitech ColossalAI Evaluation Configuration
| Knowledge Sources | |
|---|---|
| Domains | Evaluation, Infrastructure |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A configuration pattern using JSON files to define model, dataset, and metric specifications for multi-benchmark language model evaluation.
Description
Evaluation Configuration defines the evaluation pipeline through two JSON config files: an inference config (specifying models and datasets) and an evaluation config (specifying metrics per dataset). This decoupled design allows reusing the same inference results across different metric configurations.
Usage
Use this before running distributed inference to specify which models to evaluate, which benchmarks to use, and which metrics to compute.
Theoretical Basis
The two-stage config pattern:
- Inference config: Defines models (class, path, kwargs) and datasets (class, path, few_shot, save_path)
- Evaluation config: Defines metrics per dataset (accuracy, perplexity, BLEU, ROUGE, etc.)
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Principle
Implementation
Heuristic
Environment