Models & Labs
vLLM Boosts Transformers Backend for Native-Speed Inference
Hugging Face Bloghigh confidence
Why it matters
- →The update allows for high-performance inference without custom code.
- →It simplifies the deployment of Hugging Face models on vLLM.
- →The integration supports both training and inference, enhancing versatility.
Hugging Face has upgraded the transformers modeling backend for vLLM, enabling it to achieve native-speed inference comparable to vLLM's custom implementations. This enhancement uses torch.fx for static analysis and runtime layer fusions, allowing models to run efficiently without custom code. Developers can now use Hugging Face models on vLLM with a simple flag, maintaining parallelism options. This development simplifies the process for model authors, providing high performance for both training and inference without additional coding.
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