Generating Human-level Text with Contrastive Search in Transformers 🤗

Guiding Text Generation with Constrained Beam Search in 🤗 Transformers

How to generate text: using different decoding methods for language generation with Transformers

Train and Fine-Tune Sentence Transformers Models

Welcome aMUSEd: Efficient Text-to-Image Generation

Sentence Transformers is joining Hugging Face!

Introduction to Transformers: an NLP Perspective

arXiv:2311. 17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing.

Sentence Transformers in the Hugging Face Hub

Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers

Little Brains, Big Feats: Exploring Compact Language Models

While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention. In this study, we investigate how smaller language models perform during the generation stage within a Retrieval-Augmented Generation (RAG) system.

DiffusionGemma: 4x faster text generation

Training Design for Text-to-Image Models: Lessons from Ablations

Fine-tuning GPT-2 from human preferences

We’ve fine-tuned the 774M parameter GPT-2 language model using human feedback for various tasks, successfully matching the preferences of the external human labelers, though those preferences did not always match our own. Specifically, for summarization tasks the labelers preferred sentences copied wholesale from the input (we’d only asked them to ensure accuracy), so our models learned to copy.