Hyperparameter Search with Transformers and Ray Tune

Guiding Text Generation with Constrained Beam Search in 🤗 Transformers

Introducing Optimum: The Optimization Toolkit for Transformers at Scale

LiFT: Local Search via Linear Programming for Overfitting-Controlled Transformers

arXiv:2606. 16243v1 Announce Type: new Abstract: This paper proposes a Linear Programming (LP)-based local search framework for fine-tuning pretrained transformer models with explicit control against overfitting.

Accelerated Inference with Optimum and Transformers Pipelines

Retrieval Augmented Generation with Huggingface Transformers and Ray

Fine-Tune ViT for Image Classification with 🤗 Transformers

Train your first Decision Transformer

Statistically Valid Hyperparameter Selection: From Tuning to Guarantees

arXiv:2606. 25601v1 Announce Type: cross Abstract: Hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of freedom such as inference-time parameters, implementation-level settings, and thresholds driving decision rules.

Symmetry-Aware Transformer Training for Automated Planning

arXiv:2508. 07743v2 Announce Type: replace Abstract: While transformers excel in many settings, their application in the field of automated planning is limited.

The Transformer as a Polar State Estimator

arXiv:2605. 11007v2 Announce Type: replace-cross Abstract: We show that the core components of the Transformer -- attention, residual connections, and normalization -- arise naturally from a single geometric state estimation problem.

Parameter-Efficient Fine-Tuning using 🤗 PEFT

Generating Human-level Text with Contrastive Search in Transformers 🤗