Liger Kernel

A single line of code, AutoLigerKernelForCausalLM, patches optimized Triton kernels into a Hugging Face model, which is how LinkedIn's Liger Kernel raises multi-GPU training throughput by about 20 percent while cutting memory use around 60 percent. The library swaps RMSNorm, RoPE, SwiGLU, cross entropy, and fused linear cross entropy operations for exact fused implementations with no approximations, and adds chunked post-training loss kernels for DPO, ORPO, SimPO, and KTO that reduce alignment and distillation memory by up to 80 percent. It works alongside PyTorch FSDP, DeepSpeed, and Flash Attention, covers more than 30 architectures including Llama, Mistral, Gemma, and Qwen, and runs on NVIDIA CUDA, AMD ROCm, and Ascend NPU hardware. Installation is pip install liger-kernel with torch 2.1.2 and triton 2.3.0 or newer on the CUDA path. Axolotl, LLaMA-Factory, and Hugging Face TRL have adopted it as a standard efficiency layer, the BSD-2-Clause license permits commercial use, and the project holds around 6,500 GitHub stars.