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In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network 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" (i.e., not changed during backpropagation). [2]
Tweaking refers to fine-tuning or adjusting a complex system, usually an electronic device. Tweaks are any small modifications intended to improve a system. In electronics, it is a synonym for "trimming."
Fine-tuning may refer to: Fine-tuning (deep learning) Fine-tuning (physics) Fine-tuned universe; See also. Tuning (disambiguation) This page was last edited on 24 ...
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.
An example of a fine-tuning problem considered by the scientific community to have a plausible "natural" solution is the cosmological flatness problem, which is solved if inflationary theory is correct: inflation forces the universe to become very flat, answering the question of why the universe is today observed to be flat to such a high degree.
This is an accepted version of this page This is the latest accepted revision, reviewed on 21 January 2025. Hypothesis about life in the universe For the concept of a fine-tuned Earth, see Rare Earth hypothesis. Part of a series on Physical cosmology Big Bang · Universe Age of the universe Chronology of the universe Early universe Inflation · Nucleosynthesis Backgrounds Gravitational wave ...
Generally, in order to get an LLM to use tools, one must fine-tune it for tool-use. If the number of tools is finite, then fine-tuning may be done just once. If the number of tools can grow arbitrarily, as with online API services, then the LLM can be fine-tuned to be able to read API documentation and call API correctly. [61] [62]
According to Google, it is possible to fine-tune models on CoT reasoning datasets to enhance this capability further and stimulate better interpretability. [19] [20] An example of a CoT prompting: [21] Q: {question} A: Let's think step by step. As originally proposed by Google, [11] each CoT prompt included a few Q&A examples.