model-merging

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.

Streamlines distributed data processing and machine learning workflows using Ray's scalable data loading and transformation capabilities.

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

Systematically assess machine learning code generation models by benchmarking performance, identifying strengths/weaknesses, and generating comparative metrics.

Streamlines supervised fine-tuning of language models using Simple, Interpretable, Modular Policy Optimization (SIMPO) techniques

Enables remote neural network interpretation and analysis through advanced visualization, layer probing, and activation tracking techniques.

Optimizes large language model fine-tuning with memory-efficient techniques, reducing computational overhead and accelerating training processes.

Trains reinforcement learning models for robotic manipulation using advanced policy optimization techniques in a modular, reproducible research environment.