In deep learning, fine-tuning is an approach to transfer learning in which the weights of a pre-trained model are trained on new data.[1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (not updated during the backpropagation step).[2] A model may also be augmented with "adapters" that consist of far fewer parameters than the original model, and fine-tuned in a parameter–efficient way by tuning the weights of the adapters and leaving the rest of the model's weights frozen.[3]
For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen because they capture lower-level features, while later layers often discern high-level features that can be more related to the task that the model is trained on.[2][4]
Models that are pre-trained on large and general corpora are usually fine-tuned by reusing the model's parameters as a starting point and adding a task-specific layer trained from scratch.[5] Fine-tuning the full model is common as well and often yields better results, but it is more computationally expensive.[6]
Fine-tuning is typically accomplished with supervised learning, but there are also techniques to fine-tune a model using weak supervision.[7] Fine-tuning can be combined with a reinforcement learning from human feedback-based objective to produce language models like ChatGPT (a fine-tuned version of GPT-3) and Sparrow.[8][9]
Fine-tuning can degrade a model's robustness to distribution shifts.[10][11] One mitigation is to linearly interpolate a fine-tuned model's weights with the weights of the original model, which can greatly increase out-of-distribution performance while largely retaining the in-distribution performance of the fine-tuned model.[12]
Low-rank adaptation (LoRA) is an adapter-based technique for efficiently finetuning models. The basic idea is to design a low-rank matrix that is then added to the original matrix.[13] An "adapter" in this context is a collection of low-rank matrices, which when added to a base model, produces a finetuned model. It allows for performance that approaches full-model fine-tuning with less space requirement. A language model with billions of parameters may be LoRA fine-tuned with only several millions of parameters.
LoRA-based fine-tuning has become popular in the Stable Diffusion community.[14] Support for LoRA is being integrated into the Diffusers library from Hugging Face.[15] Support for LoRA and similar techniques is also available for a wide range of other models through Hugging Face's Parameter-Efficient Fine-Tuning (PEFT) package.[16]
Fine-tuning is common in natural language processing (NLP), especially in the domain of language modeling. Large language models like OpenAI's series of GPT foundation models can be fine-tuned on data for specific downstream NLP tasks (tasks that use a pre-trained model) to improve performance over the unmodified pre-trained model.[6]
Commercially-offered large language models can sometimes be fine-tuned if the provider offers a fine-tuning API. As of June 19, 2023, language model fine-tuning APIs are offered by OpenAI and Microsoft Azure's Azure OpenAI Service for a subset of their models, as well as by Google Cloud Platform for some of their PaLM models, and by others.[17][18][19] Not all commercial models currently support fine-tuning.