Chapter 12: Inference - Generating New Text
The article explains how to implement text generation using a trained GPT model in C#. It details the inference process, where the model samples tokens sequentially based on learned probabilities until a stopping condition is met. Temperature is introduced as a parameter to control the randomness and creativity of the generated output.
- ▪The text generation process starts with a BOS token and repeatedly feeds predicted tokens back into the model.
- ▪Temperature scaling adjusts the probability distribution, influencing the diversity and conservatism of the output.
- ▪The model generates new, plausible names not present in the training data by learning underlying statistical patterns.
- ▪A key component of the inference loop is the use of a key-value cache to improve efficiency during autoregressive generation.
- ▪The same sampling logic applies to large language models like ChatGPT during conversation generation.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 675295) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Gary Jackson Posted on May 2 • Originally published at garyjackson.dev Chapter 12: Inference - Generating New Text #csharp #machinelearning #transformers #tutorial Building a GPT From Scratch in C# (14 Part Series) 1 Building a GPT From Scratch in C# - Introduction 2 Chapter 0: Project Setup ... 10 more parts...
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