Dive into the world of text embeddings. This course will guide you through leveraging text embeddings to enhance various natural language processing (NLP) tasks.

Implementation demonstrating how temperature, top-p (nucleus sampling), and top-k sampling parameters transform raw logits into probability distributions for text generation. Includes mathematical explanations and visual examples of each sampling strategy.

A short TeX paper formalizing the “Anthem” decoding recipe (temp=0.75, top_k=50, top_p=0.95, min_p=0.05) and explaining why pairing it with a strong persona/system prompt produces coherent, agentic “thinking-being” outputs.

phi3mini 🧊🖥️🛝 : Microsoft Phi 3 Mini Model # Generative AI # Chat Playground # Microsoft Foundry

sdkgenai 🛠️🔃📦 : Gen AI SDK # Model Parameters # Safety Filters # Multi-turn Chat # Content Streaming # Asynchronous Requests # Token Counting # Context Caching # Function Calling # Batch Prediction # Text Embeddings