nemo-evaluator-sdk

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

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

Performs high-performance vector similarity search and clustering using Facebook AI's efficient similarity search library for machine learning embeddings.

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

Implements and optimizes Mamba state space models for efficient sequence modeling and neural network architectures with advanced computational techniques.

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

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

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