LLM Training

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ChatGPT Gemini Copilot Claude Perplexity DeepSeek Grok Qwen Studio

Mixed precision training Distributed training Scaling laws Training data curation Continual pretraining Data preparation Learning rate scheduling Pretraining Checkpointing Tokenizer training

Pre-training then fine-tuning: the original recipe Training on synthetic data and model collapse worries Distilling a reasoning model into a smaller one Reproducing a training run is basically impossible Instruction tuning after pre-training Pre-training data: where it comes from and what is missing How much compute goes into training a frontier model Post-training stages explained end to end Copyright questions around training corpora Scaling a training cluster without wasting money Data quality filtering at scale Distributed training and what breaks at scale Curriculum and data ordering in training runs

LangChain Generative pre-trained transformers Open-Source LLMs Prompt Engineering LLM Fine-Tuning Multimodal Models Reasoning Models LLM Applications LLM Safety & Alignment Retrieval-Augmented Generation