Controllable reasoning "Llama-Nemotron models is their ability to toggle between standard chat mode and reasoning mode. This "reasoning toggle" allows users to dynamically control the level of reasoning performed during inference."
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what is Neural Architecture Search?
As opposed to manual, NAS employs algorithms to explore a vast search space of possible architectures and identify those that perform best on a given task.
- Puzzle Framework: The NAS framework used is called Puzzle (Bercovich et al., 2024), which transforms large language models into hardware-efficient variants under deployment constraints. (Page 3)
- Block-wise Local Distillation: Puzzle applies block-wise local distillation to build a library of alternative transformer blocks, each trained independently to improve computational properties. (Page 3)
- Mixed-Integer Programming (MIP): Puzzle uses a MIP solver to select the most efficient block configuration under given constraints like hardware compatibility, latency, memory budget, or desired throughput. (Page 4)
- Accuracy-Efficiency Tradeoff: Puzzle supports multiple block variants per layer with different accuracy-efficiency tradeoff profiles, enabling users to target specific points on the Pareto frontier. (Page 4)
- LN-Ultra Optimization: During Puzzle's architecture search phase, LN-Ultra is constrained to achieve at least a 1.5x latency reduction over Llama 3.1-405B-Instruct. (Page 5)
In essence, NAS, through the Puzzle framework, allows the researchers to automatically find efficient model architectures within the Llama 3 structure, optimizing for metrics like latency, memory usage, and throughput while maintaining a desired level of accuracy. This is a key step in creating the Llama-Nemotron models, particularly LN-Super and LN-Ultra.
Collective bias when individual LLM agents interact
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