Fine-tuning (deep learning)

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In deep learning, fine-tuning is an approach to transfer learning in which the parameters 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

Sparrow.[8][9]

Robustness

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]

Variants

Low-rank adaptation

Low-rank adaptation (LoRA) is an adapter-based technique for efficiently fine-tuning 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 fine-tuned 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 was 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]

Applications

Natural language processing

Fine-tuning is common in

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]

Commercial models

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.

Open-source models

Other companies such as Meta (Llama family), Alibaba (Qwen family) and Mixtral.AI (Mixtral) published open source models with different sizes on GitHub, which can be fine-tuned. This offers the advantage of true data security for companies, as they can control where the model is hosted.

See also

References

  1. ISBN 978-1-5443-6137-6. Archived from the original on January 10, 2023. Retrieved January 10, 2023.{{cite book}}: CS1 maint: location missing publisher (link
    )
  2. ^ a b "CS231n Convolutional Neural Networks for Visual Recognition". cs231n.github.io. Retrieved 9 March 2023.
  3. ^ Liu, Haokun; Tam, Derek; Muqeeth, Mohammed; Mohta, Jay; Huang, Tenghao; Bansal, Mohit; Raffel, Colin A (2022). Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; Oh, A. (eds.). Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (PDF). Advances in Neural Information Processing Systems. Vol. 35. Curran Associates, Inc. pp. 1950–1965.
  4. arXiv:1311.2901
    .
  5. arXiv:2002.06305. {{cite journal}}: Cite journal requires |journal= (help
    )
  6. ^
    arXiv:2112.08718
    .
  7. arXiv:2010.07835
    .
  8. ^ "Introducing ChatGPT". openai.com. Retrieved 9 March 2023.
  9. arXiv:2209.14375. {{cite journal}}: Cite journal requires |journal= (help
    )
  10. arXiv:2103.00020 [cs.CV
    ].
  11. arXiv:2202.10054
    .
  12. arXiv:2109.01903 [cs.CV
    ].
  13. arXiv:2106.09685
    .
  14. ^ Ryu, Simo (February 13, 2023). "Using Low-rank adaptation to quickly fine-tune diffusion models". GitHub. Retrieved June 19, 2023.
  15. ^ Cuenca, Pedro; Paul, Sayak (January 26, 2023). "Using LoRA for Efficient Stable Diffusion Fine-Tuning". Hugging Face. Retrieved June 19, 2023.
  16. ^ "Parameter-Efficient Fine-Tuning using 🤗 PEFT". huggingface.co. Retrieved 2023-06-20.
  17. ^ "Fine-tuning". OpenAI. Retrieved 2023-06-19.
  18. ^ "Learn how to customize a model for your application". Microsoft. Retrieved 2023-06-19.
  19. ^ "Tune text foundation models". Retrieved 2023-06-19.
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