REAP is Cerebras's one-shot method for pruning redundant Mixture-of-Experts. Learn the saliency math and how it shrinks GLM-4.6 and Qwen3-Coder by up to 40%.
Learn Constitutional AI (CAI) - Anthropic's technique for training safe AI without massive human labeling. Understand RLAIF, self-critique, and how it compares to RLHF.
Process 100K+ token contexts efficiently. Learn FlashAttention, linear attention, GLA, and how modern LLMs handle long documents without running out of memory.
Learn instruction tuning (SFT) - the technique that transforms base LLMs into assistants like ChatGPT. Covers dataset creation, Alpaca, FLAN, and quality vs quantity tradeoffs.
Understand how GitHub Copilot, Cursor, and Claude generate code. Learn pass@k evaluation, HumanEval benchmarks, and best practices for AI-assisted programming.
Discover how NASA and astronomers use AI to classify galaxies, detect exoplanets, and find gravitational waves. Includes JWST image processing and the future of autonomous discovery.
Run 70B parameter models on a single GPU. Learn LLM quantization from 8-bit to 2-bit precision - GPTQ, AWQ, GGUF, QuIP#, and when to use each method.
Understand RLHF - the technique behind ChatGPT and Claude. Learn reward modeling, PPO optimization, DPO, and how AI assistants become helpful and safe.
Understand how GPT-4V, LLaVA, and Claude see images. Learn VLM architecture - vision encoders, multimodal fusion, and how to reduce hallucination in production.
Learn RAG from scratch - chunking strategies, embeddings, vector databases, and fusion mechanisms. Build AI that cites sources and never hallucinates facts.
Understand MoE architecture - the technology behind Mixtral 8x7B, DeepSeek, and Grok. Learn expert routing, load balancing, and why sparse models beat dense ones.
Master chain-of-thought prompting - the technique that makes GPT-4 and Claude solve complex problems. Includes zero-shot CoT, self-consistency, and implementation examples.
