#fine-tuning
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Customizing Flux: From Generative Media to Robotics
Black Forest Labs demonstrates how to extend foundational video models like Flux beyond creative media into action prediction and robotics through prompt upsampling, modular moderation, and weight-based fine-tuning.
AI EngineerATOD: Hybrid Distillation for Autonomous Agent Training
ATOD combines on-policy distillation with reinforcement learning using an annealed schedule and turn-level reweighting to train small agent models that outperform their larger teacher models.
Fine-Tuning Tiny LLMs for On-Device AI Agents
Developers can achieve production-grade performance on-device by choosing between system-level models (Gemini Nano) for general tasks or fine-tuning tiny LLMs (<1B parameters) via LiteRT-LM for specialized, high-accuracy agentic workflows.
AI EngineerAccelerating MoE Fine-Tuning with NVIDIA NeMo AutoModel
NVIDIA NeMo AutoModel extends Hugging Face Transformers v5 to provide 3.4-3.7x higher training throughput and 29-32% lower memory usage for MoE models by integrating Expert Parallelism, DeepEP, and TransformerEngine kernels.
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