clem 🤗 (@ClementDelangue): Big unlock for open-source AI inference: Hugging Face Transformers models can now run in vLLM at native speed, often matching or beating hand-written implementations. Until now, every new architectur
Major technical advancement that simplifies ML infrastructure by unifying training and inference codebases, directly impacting deployment workflows.
AI Summary
Hugging Face and vLLM have integrated to allow Transformers models to run at native speed in vLLM's optimized stack, eliminating the need for separate implementations. This unification means developers can write a model once in Transformers and immediately get production-ready performance without the maintenance burden of duplicate codebases. Benchmarks show the integrated approach matches or beats hand-written vLLM implementations across models from 4B to 235B parameters.
Excerpt
Big unlock for open-source AI inference: Hugging Face Transformers models can now run in vLLM at native speed, often matching or beating hand-written implementations. Until now, every new architecture often needed to be built twice: - Once in Transformers for training and https://t.co/DEw0WvnS1r
