githubnemo
daf335f503
The current state of the PEFT docs is not one of structure and I was constantly annoyed that whenever I wanted to change something there were several places that needed touching and they all felt disconnected. So this is my attempt at structuring the docs. Some of these ideas are quite old (discussed in 01/2025) but are still valid. I've removed most of the code guides without replacement. That's not ideal, I think we should have code examples but I'm think they should be method-focused. Maybe one general example of a training workflow is sufficient because most methods follow the same scheme. All details from the method guides (prompting, lora, oft/boft, etc.) are now integrated into the respective method pages instead. I would have hesitated to do this if these guides would have integrated information about the adapters but they didn't. I think it makes a lot more sense to have one place for each method to gather examples/tips/recommendations and that is now the `package_refernce/<method>` page. This page now also hosts a small space that shows the MetaMathQA (and potentially other) benchmark results highlighted for that method. I've moved the LoRA initializations to `package_reference/lora#Initialization` and converted the init methods to `<hfoption>`-tags. This collapses them to a list but may reduce searchability through the document - at least firefox is not able to search 'through' the option tabs. This also doesn't make them appear in the ToC and people specifically searching for, say, PiSSA won't find it directly. I think that's OK though, since the search is able to locate it. The quicktour is a bit more detailed about what happens under the hood (quick doesn't have to mean simplistic) and includes some new visualizations. I hope that we can integrate more visualizations in the future where it makes sense. * Remove PEFT method space + front page buttons The space was not that useful anymore since most methods are compatible with most models. The front page buttons are, at least temporarily, with the exception of the quicktour and method overview buttons. I like the visuals but there should only be elements that are useful. --------- Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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WaveFT: Wavelet Fine-Tuning
WaveFT is a novel parameter-efficient fine-tuning (PEFT) method that introduces sparse updates in the wavelet domain of residual matrices. Unlike LoRA, which is constrained by discrete low-rank choices, WaveFT enables fine-grained control over the number of trainable parameters by directly learning a sparse set of coefficients in the transformed space. These coefficients are then mapped back to the weight domain via the Inverse Discrete Wavelet Transform (IDWT), producing high-rank updates without incurring inference overhead.
WaveFT currently has the following constraint:
- Only
nn.Linearlayers are supported.
The abstract from the paper is:
Efficiently adapting large foundation models is critical, especially with tight compute and memory budgets. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA offer limited granularity and effectiveness in few-parameter regimes. We propose Wavelet Fine-Tuning (WaveFT), a novel PEFT method that learns highly sparse updates in the wavelet domain of residual matrices. WaveFT allows precise control of trainable parameters, offering fine-grained capacity adjustment and excelling with remarkably low parameter count, potentially far fewer than LoRA’s minimum—ideal for extreme parameter-efficient scenarios. Evaluated on personalized text-to-image generation using Stable Diffusion XL as baseline, WaveFT significantly outperforms LoRA and other PEFT methods, especially at low parameter counts; achieving superior subject fidelity, prompt alignment, and image diversity.
Benchmark overview
API
WaveFTConfig
autodoc tuners.waveft.config.WaveFTConfig
WaveFTModel
autodoc tuners.waveft.model.WaveFTModel