dp-data-pipeline
♥Cherished
Experiments for filtering and evaluating Triton kernel datasets for SFT
Clauded With Love Rating
7.1 / 10
This project creates a comprehensive data pipeline for filtering and preparing PyTorch-to-Triton kernel datasets for supervised fine-tuning. It processes 18k+ samples through multiple filtering, evaluation, and synthetic data generation stages to create high-quality training datasets.
Code Quality5.5
Usefulness8.0
Claude Usage6.0
Documentation8.5
Originality7.5
Highlights
- ✓Comprehensive pipeline with 6 distinct processing steps including difficulty rating and deduplication
- ✓Well-organized dataset hierarchy with multiple variants (filtered, unique, synthetic tasks, no-reasoning) hosted on HuggingFace
- ✓Excellent documentation with clear dataset descriptions, sample counts, and step-by-step usage instructions
To Improve
- →Add actual implementation code and tests - the repository appears to contain only documentation without the Python scripts referenced
- →Include data validation and error handling details, plus examples of input/output formats for each pipeline stage