rwkv-architecture

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

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

Streamlines machine learning experiment tracking, visualization, and hyperparameter optimization using Weights & Biases platform integration

Detect and filter potentially harmful or inappropriate content in AI conversations using advanced safety classification models.

Perform efficient semantic vector search and similarity matching using the Qdrant vector database for advanced AI-powered information retrieval.

Enables high-performance local inference and quantization for large language models using efficient C/C++ implementations and GGML formats.

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

Streamlines parameter-efficient fine-tuning of large language models using Transformers Reinforcement Learning (TRL) techniques and best practices.