sparse-autoencoder-training

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.

Trains compact language models with minimal compute, enabling efficient text generation and fine-tuning on small datasets using PyTorch and transformer architectures.

Compress and transfer complex machine learning model knowledge into smaller, more efficient neural networks with minimal performance loss

Efficiently deploy and serve large language models using vLLM for high-performance inference with optimized GPU utilization and low-latency responses

Automatically segment and extract precise object masks from images using Meta AI's advanced computer vision model with high accuracy and flexibility.

Tokenize and encode text using advanced subword segmentation for multilingual NLP tasks and machine translation models

Validates and sanitizes AI prompts to prevent injection attacks, filter sensitive content, and ensure safe, controlled interactions with language models.