LLM Inference Hyperparameters: Temperature, Top-P Nucleus, Top-K, Repetition Penalty & Context Windows
Controlling text generation probability distributions, logits scaling, nucleus sampling, stop sequences, and context length extension.
⚡ Executive Summary
Master decoding parameters: Temperature logits scaling, Top-P nucleus sampling, Top-K truncation, Repetition Penalties, and system prompt engineering.
Key Takeaways
- ✓Sampling Parameters — covered in depth with practical examples, formulas, and code.
- ✓Context Window & System Prompts — covered in depth with practical examples, formulas, and code.
LLM Inference & Sampling Hyperparameters
#1. Sampling Parameters
1.1 Temperature
Scales logits prior to Softmax:
1.2 Top-P (Nucleus Sampling)
Samples from the smallest set of top tokens whose cumulative probability exceeds P (e.g. P = 0.90).
1.3 Top-K & Repetition Penalty
#2. Context Window & System Prompts
Surya N
Full Stack Developer
Surya N is part of the Junglans Solutions engineering team, specializing in llm engineering. Junglans builds a 20-product ecosystem of local-first enterprise software — AI developer tools, encrypted communication, and data infrastructure with zero cloud telemetry.
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