sglang

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.

Generates comprehensive observability insights for Phoenix web applications, tracking performance metrics, tracing requests, and diagnosing system bottlenecks

Automates multi-agent AI task workflows with dynamic role assignment, collaborative problem-solving, and intelligent task delegation across complex research scenarios.

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

Trains sparse autoencoders on neural network activations to discover interpretable features and understand internal representations

Implements and evaluates RWKV language model architectures, providing tools for training, fine-tuning, and performance analysis of linear attention transformer alternatives.

Generates structured document outlines and hierarchical content maps with customizable depth and formatting for research and writing workflows