ray-data

Streamlines AI research workflows by providing curated Claude skills for data analysis, literature review, experiment design, and research paper generation.

Assists AI researchers in drafting, structuring, and generating machine learning research papers with academic writing best practices and technical precision.

Streamlines distributed machine learning training using Ray, optimizing hyperparameter tuning and parallel model execution across compute clusters.

Automates scientific literature curation by extracting, summarizing, and organizing research papers from marine biology and oceanography domains

Quantizes large language models using Activation-aware Weight Quantization (AWQ) to reduce model size and improve inference efficiency.

Automates complex AI prompt engineering and optimization using DSPy's programmatic framework for building reliable language model pipelines.

Streamlines large-scale distributed machine learning training for transformer models using PyTorch Titan, optimizing GPU utilization and model performance

Streamline machine learning experiment tracking, model versioning, and deployment management with comprehensive MLflow integration and best practices.

Trains large language models using open-source reinforcement learning from human feedback (RLHF) techniques with advanced alignment and reward modeling

Streamlines fine-tuning and deployment of Llama language models with automated configuration, dataset processing, and model optimization workflows.