weights-and-biases

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 reinforcement learning model training in PyTorch with automated hyperparameter tuning, environment setup, and advanced policy optimization techniques.

Performs hardware-aware quantization of neural networks using HQQ (Highly Quantized Quantization) to reduce model size and improve inference efficiency.

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

Quantize large language models to reduce memory footprint and accelerate inference using efficient 8-bit and 4-bit compression techniques with bitsandbytes.

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

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

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

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