How we sped up transformer inference 100x for 🤗 API customers

Accelerated Inference with Optimum and Transformers Pipelines

Our Transformers Code Agent beats the GAIA benchmark 🏅

License to Call: Introducing Transformers Agents 2.0

Overview of natively supported quantization schemes in 🤗 Transformers

Small-Scale Experiments: Are We There Yet?

arXiv:2608. 11859v1 Announce Type: new Abstract: Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver.

At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization

arXiv:2606. 26396v1 Announce Type: new Abstract: Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data.

Introducing Optimum: The Optimization Toolkit for Transformers at Scale

Tokenization in Transformers v5: Simpler, Clearer, and More Modular

Experimenting with the proposed Cross-Origin Storage API in Transformers.js

On the Expressive Power of Transformers

arXiv:2608. 12671v1 Announce Type: new Abstract: Multi-layer transformers form the critical component of essentially all large language models (LLMs) in use today.

Small-Scale Experiments: Are We There Yet?

Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver. Instead, researchers have found them unreliable at small scales (starting at 4M parameters) and concluded that sizable models cannot be avoided.

My Journey to a serverless transformers pipeline on Google Cloud