Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
Paper • 2603.01759 • Published • 1
Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 78, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 54, in _get_pipeline_from_tar
current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 332, in torch_loads
return torch.load(io.BytesIO(data), weights_only=True)
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 1601, in load
return _load(
opened_zipfile,
...<3 lines>...
**pickle_load_args,
)
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 2221, in _load
result = unpickler.load()
File "/usr/local/lib/python3.14/site-packages/torch/_weights_only_unpickler.py", line 541, in load
self.append(self.persistent_load(pid))
~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 2185, in persistent_load
typed_storage = load_tensor(
dtype, nbytes, key, _maybe_decode_ascii(location)
)
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 2147, in load_tensor
wrap_storage = restore_location(storage, location)
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 734, in default_restore_location
result = fn(storage, location)
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 667, in _deserialize
device = _validate_device(location, backend_name)
File "/usr/local/lib/python3.14/site-packages/torch/serialization.py", line 634, in _validate_device
raise RuntimeError(
...<5 lines>...
)
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RS-LT — dataset for MetaPEFT (CVPR 2025 Highlight)
This dataset accompanies the CVPR 2025 Highlight paper "Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning" by Zichen Tian, Yaoyao Liu and Qianru Sun.
Paper
- Title: Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
- Venue: CVPR 2025 (Highlight), pp. 23037-23047
- arXiv: https://arxiv.org/abs/2603.01759
- CVF Open Access: https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Meta-Learning_Hyperparameters_for_Parameter_Efficient_Fine-Tuning_CVPR_2025_paper.html
- Project page: https://www.doem1997.com/metapeft/
- Code: https://github.com/doem97/metalora
- Video: https://www.youtube.com/watch?v=3_DaZLZBGD4
Abstract
Training large foundation models from scratch for domain-specific applications is almost impossible due to data limits and long-tailed distributions -- taking remote sensing (RS) as an example. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module influence during fine-tuning. MetaPEFT dynamically adjusts three key factors of PEFT on RS images: module insertion, layer selection, and module-wise learning rates, which collectively control the influence of PEFT modules across the network. We conduct extensive experiments on three transfer-learning scenarios and five datasets in both RS and natural image domains. The results show that MetaPEFT achieves state-of-the-art performance in cross-spectral adaptation, requiring only a small amount of trainable parameters and improving tail-class accuracy significantly.
Naming
The paper is called MetaPEFT; the code release is called MetaLoRA / Meta LoRA. They are the same CVPR 2025 Highlight paper by Tian, Liu and Sun.
Citation
@InProceedings{Tian_2025_CVPR,
author = {Tian, Zichen and Liu, Yaoyao and Sun, Qianru},
title = {Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2025},
pages = {23037-23047}
}
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