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LinkedIn has introduced the Liger Kernel, a collection of Triton kernels designed to enhance the efficiency of large language model (LLM) training. This new technology promises to increase multi-GPU training throughput by 20% and reduce memory usage by 60%. The Liger Kernel can be integrated into existing systems with a single line of code, using the command `apply_liger_kernel_to_llama()`, making it a convenient solution for optimizing LLM training workflows. The kernel is compatible with PyTorch models and has been demonstrated to work effectively with PyTorch Lightning and huggingface Transformers. This development represents a significant advancement in resource optimization for LLM training.
That's impressive! Liger-Kernel could be a significant step forward in optimizing resource usage and improving efficiency in LLM training. How does it compare to existing solutions in terms of ease of integration and compatibility with different GPU architectures? https://t.co/28oeIU9m6d
Speed up LLM training throughput by 20% and reduce memory usage by 60% with the new Liger Kernel by LinkedIn 🤯🤯. Patch PyTorch models with one-line to get the speed up. The repo has examples with PyTorch Lightning. Link to the repo in the replies. https://t.co/U0I91HfqrU
Big Update! 20% higher throughput and 60% memory reduction for multi-GPU fine-tuning with @huggingface Transformers! 🤯 The LLM research team from LinkedIn released new efficient GPU Kernels (Liger Kernels) to speed up and reduce memory when fine-tuning LLMs! 🚀 TL;DR: 🚀 Boost… https://t.co/iRAEIbHkUv
