Parameter-Efficient Fine-Tuning using 🤗 PEFT

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Motivation Large Language Models (LLMs) based on the transformer architecture, like GPT, T5, and BERT have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. They have also started foraying into other domains, such as Computer Vision (CV) (VIT, Stable Diffusion, LayoutLM) and Audio (Whisper, XLS-R). The conventional paradigm is large-scale pretraining on generic web-scale data, followed by fine-tuning to downstream tasks. Fine-tuning these pretrained LLMs on downstream datasets results in huge performance gains when compared to using the pretrained LLMs…

related reading

• Recent Advances in Language Model Fine-tuningruder.io
• [2106.09685] LoRA: Low-Rank Adaptation of Large Language Modelsarxiv.org
• The Llama Hitchiking Guide to Local LLMs – hackerllamaosanseviero.github.io
• [2005.14165] Language Models are Few-Shot Learnersarxiv.org
• Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem – PyTorchpytorch.org
• Efficient LLM Finetuning with Unsloth | Modal Docsmodal.com
• Getting Caught Up to Modern LLM Research | Samarth Goeldev.samarthgoel.com
• GitHub - NVIDIA-NeMo/Automodel: 🚀 Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face supportgithub.com
• Fine-tuning LLMs Guide | Unsloth Documentationdocs.unsloth.ai
• Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrouai.googleblog.com
• Tinkerthinkingmachines.ai
• Hugging Face – The AI community building the future.huggingface.co