Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer | NVIDIA Technical Blog
… The approach is simple to implement, requires no access to the teacher’s internal features, and is highly effective for classification tasks. …
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What is response-based knowledge distillation?
Response-based knowledge distillation transfers a teacher model’s knowledge to a student by training the student to match the teacher’s soft output probabilities rather than only hard labels. These soft targets convey inter-class similarities, for example that “cat” is closer to “tiger” than to “car,” and the student is optimized to align with them using KL divergence. The approach is simple to implement, requires no access to the teacher’s internal features, and is highly effective for classification tasks. In practice, it’s common to combine the distillation loss with standard cross-entropy
Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer | NVIDIA Technical BlogAutomating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy | NVIDIA Technical Blog
… AutoDeploy shifts this workflow toward a compiler-driven approach. …
Build a Retrieval-Augmented Generation (RAG) Agent with NVIDIA Nemotron | NVIDIA Technical Blog
… Your agent will automatically reload as you make changes and save your code. langraph dev To chat with your agent, a simple Streamlit app has been included in the Simple Agents Client . …
LLM Inference Benchmarking: How Much Does Your LLM Inference Cost? | NVIDIA Technical Blog
… This methodological approach will prepare users to build LLM applications and roll them out at scale. …
MLOps – NVIDIA Technical Blog
… Approaches... …
Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron | NVIDIA Technical Blog
… Mathematically, it is conceptually a block orthogonalization hence the name blockwise , similar to the blocking approach first introduced in Scalable Second Order Optimization for Deep Learning . …