Skip to content
#

nucleus-sampling

Here are 9 public repositories matching this topic...

Language: All
Filter by language

An experiment-driven laboratory for understanding LLM sampling strategies. Explore how Temperature, Top-k, and Top-p influence token probabilities and generation diversity through reproducible simulations, visualizations, and real Groq API experiments.

  • Updated Oct 20, 2026
  • Python

The LLM Defense Framework enhances large language model security through post-processing defenses and statistical guarantees based on one-class SVM. It combines advanced sampling methods with adaptive policy updates and comprehensive evaluation metrics, providing researchers and practitioners with tools to build more secure AI systems.

  • Updated Feb 6, 2025
  • Python

Orchestrator-worker multi-agent pattern: one planner, three independent workers with identical prompts but different sampling configs (temperature/top_p). Isolates what sampling alone changes in model behavior — conservative vs balanced vs creative brainstorming.

  • Updated Aug 27, 2026
  • Python

Add this topic to your repo

To associate your repository with the nucleus-sampling topic, visit your repo's landing page and select "manage topics."

Learn more