The name is the button.

An agent writes the code now, and a human thumb hovers over Accept all a hundred times a day. The honest question of this decade is what, exactly, you just accepted. So I read the diff.

Eight years as a backend engineer in Stockholm, back in Tokyo since 2023, and I still ship something small most nights, usually badly. My one rule: the only real review of a model is the thing you built with it. That is why there is no leaderboard on this site.

Around a hundred and forty sources come through — the labs' own posts, the papers, the tools, the runtimes and languages nobody is watching yet, the chip under the demo, and the newsletters people actually forward. What is moving gets counted so the week reads at a glance, and every few days I explain one mechanism in plain words.

A story here is a headline, a short excerpt, one image and the link to whoever wrote it. We never reproduce the post; the images belong to their authors. A post written in Japanese appears in Japanese, exactly as it was published, and the link goes to the original.

— Yuki Halvorsen, Tokyo

← Accept All Archive

Hugging Face BlogLabs

tokenizers v1: encode, decode and scaling, measured

September 21

The tokenizer has not historically been the bottleneck within ML workflows. Compute-wise, tokenization is light compared to the heavy modeling happening in the rest of the pipeline.

Research

Read at Hugging Face Blog ↗

One letter a week, in your inbox.

Latest

tokenizers v1: encode, decode and scaling, measured

Related

LabsBenchMIRT: What are LLM benchmarks actually measuring? Hugging Face Blog

LabsTraining and Finetuning Multi-Vector Embedding Models with Sentence Transformers Hugging Face Blog

LabsHow Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code Hugging Face Blog

LabsMeasuring benchmark optimization in speech recognition Hugging Face Blog

LabsMulti-Vector (Late Interaction) Embedding Models with Sentence Transformers Hugging Face Blog

SiliconBenchmarking LLM Inference at Scale with AIPerf NVIDIA Technical Blog