Explores how advanced AIs use 'chains of thought' reasoning to break complex problems into simpler steps, improving accuracy and performance.
A guide to benchmarking language models using a Jupyter Notebook that supports any OpenAI-compatible API, including Ollama and Foundry Local.
Explains why standard language model benchmarks are insufficient and how to build custom benchmarks for specific application needs.
Learn how to accurately calculate token counts for strings using language models with a provided Jupyter Notebook tool.
Analyzes the latest pre-training and post-training methodologies used in state-of-the-art LLMs like Qwen 2, Apple's models, Gemma 2, and Llama 3.1.
