Categories: AI/ML Research

Understanding Text Generation Parameters in Transformers

This post is divided into seven parts; they are: – Core Text Generation Parameters – Experimenting with Temperature – Top-K and Top-P Sampling – Controlling Repetition – Greedy Decoding and Sampling – Parameters for Specific Applications – Beam Search and Multiple Sequences Generation Let’s pick the GPT-2 model as an example.
AI Generated Robotic Content

Recent Posts

Are there any open source alternatives to this?

I know there are models available that can fill in or edit parts, but I'm…

7 hours ago

The future of engineering belongs to those who build with AI, not without it

As we look ahead, the relationship between engineers and AI systems will likely evolve from…

8 hours ago

The 8 Best Handheld Vacuums, Tested and Reviewed (2025)

Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.

8 hours ago

I really miss the SD 1.5 days

submitted by /u/Dwanvea [link] [comments]

1 day ago

Latent Bridge Matching: Jasper’s Game-Changing Approach to Image Translation

Discover how latent bridge matching, pioneered by the Jasper research team, transforms image-to-image translation with…

1 day ago

A Gentle Introduction to SHAP for Tree-Based Models

Machine learning models have become increasingly sophisticated, but this complexity often comes at the cost…

1 day ago