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All digests tagged Supervised Fine-Tuning (SFT)
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The continual learning loop is unsolved. We must balance SFT (knowledge injection) and RL (behavior optimization) by carefully tuning the SFT/Distillation to RL ratio for optimal model performance.
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The key goal was within this three-week sprint being able to have a measurable lift in performance and accuracy on this benchmark using the Neotron model postrained with the core weave dedicated inference platforms and .com web search team.
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SmithTune provides a single, end-to-end workflow to convert agent traces into custom fine-tuned models. The process includes dataset creation, training (using providers like Fireworks or Baseten), and evaluation.
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NVIDIA experts detail the Nemotron post-training pipeline, explaining how structured data, NeMo Gym, and MOPD are used to build agentic LLMs. Learn about data bias mitigation and the evolution from pre-training.
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The lecture reviews advanced Transformer optimizations, covering Grouped Query Attention (GQA) to reduce KV cache memory and Mixture of Experts (MoE) for scaling parameters while controlling compute. It also details In-Context Learning and SFT.
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