Learn advanced techniques for overcoming GPU memory limitations when training large neural networks, including gradient accumulation and low memory optimization (LOMO) strategies.
Latest Posts
Demystifying Perplexity: A Core Measure of Language Model Performance
Explore perplexity as the fundamental metric for evaluating language models, from its mathematical foundations to practical implementation, and understand how this measure of prediction uncertainty drives AI development.
Running LLMs Locally: The Power of llama.cpp and Cosmopolitan libc
Discover how llama.cpp and Cosmopolitan libc enable efficient local deployment of large language models on consumer hardware, providing privacy, offline capabilities, and cross-platform compatibility.
Vector Databases: Powering Semantic Search and Modern AI Retrieval
Discover how vector databases enable semantic search and retrieval-augmented generation by efficiently storing, indexing, and querying high-dimensional embeddings for AI applications beyond traditional keyword matching.
AI Data and Legal Considerations: Navigating the Complex Landscape
Explore the critical intersection of AI development, data governance, and legal compliance, with practical guidance on navigating evolving regulations and implementing responsible data practices.
Energy-Based Models (EBMs): Unifying Self-Supervised Learning Through Scalar Energy Functions
Explore how Energy-Based Models provide a powerful unifying framework for machine learning, using scalar energy functions to revolutionize self-supervised learning, generative modeling, and representation learning.