guidance

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.

Evaluates and benchmarks NVIDIA NeMo language models with comprehensive performance metrics, test suite generation, and model comparison tools.

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.

Streamlines distributed training and inference for machine learning models across multiple GPUs, TPUs, and hardware configurations using Hugging Face Accelerate.

Accelerates AI model inference by predicting and parallel processing multiple token candidates to reduce latency and improve generation speed.

Quantize large language models to GGUF format, reducing model size and improving inference performance across different hardware platforms.