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

Boost LLM reliability with dynamic sampling, retry logic, and a lightweight Go‑based proxy.

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.

Charla sobre determinismo y temperatura en modelos de lenguaje (temperature, top-p, top-k, muestreo) + Spec-Driven Development aplicado a productos institucionales. Deck reveal.js con demos interactivas.

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

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

Latency, diversity, and quality benchmark for autoregressive decoding strategies on RTX 2070: greedy, top-k, top-p, min-p, and beam search across GPT-2 models.