Native-speed vLLM transformers modeling backend
The transformers vLLM backend now matches or exceeds the performance of custom vLLM implementations for multiple LLM architectures, enabling model authors to use their existing transformers code for ultra-fast inference without porting anything. It integrates seamlessly with vLLM's optimized inference techniques, such as continuous batching and custom attention kernels, and supports a wide range of models through consistent APIs. The backend dynamically applies inference-specific layer fusions at runtime, matching the speed of custom code, and allows for efficient deployment with minimal changes to serving configurations.
More from Huggingface
LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge
Thinking of ACE? We Can Do It with Fewer Tokens
Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
