[QEff Finetune]: Adding steps about how to fine tune on any custom dataset. #381
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quic-swatia
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Apr 28, 2025
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quic-swatia
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- Added steps on how to create the custom_dataset.py to run fine-tuning through QEfficient pipeline on any custom dataset. Also, added a detailed template for the user which covers how to create custom_dataset.py
- Added the argument 'context_length' in the existing APIs which helps run fine tuning with padding for custom dataset.
Signed-off-by: Swati Allabadi <[email protected]>
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Signed-off-by: Swati Allabadi <[email protected]>
quic-meetkuma
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May 12, 2025
| To run fine tuning for any user specific dataset, prepare the dataset using the following steps: | ||
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| 1) Create a directory named 'dataset' inside efficient-transformers. |
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double space between "a" and "directory"
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| def tokenize(): | ||
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Add some comment as "Implement tokenization and prepare inputs for the training."
| # load dataset | ||
| # based on split, retrieve only the specific portion of the dataset (train or eval) either here or at the last | ||
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Add one more comment as "Define a prompt template"
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| def apply_prompt_template(): | ||
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Add some comment as "Convert the raw input into format as per the template defined earlier."
| 5) Inside get_custom_dataset(), dataset needs to prepared for fine tuning. So, the user needs to apply prompt and tokenize the dataset accordingly. Please refer the below template on how to define get_custom_dataset(). | ||
| 6) For examples, please refer python files present in efficient-transformers/QEfficient/finetune/dataset. In case of Samsum dataset, get_preprocessed_samsum() of efficient-transformers/QEfficient/finetune/dataset/samsum_dataset.py is called. | ||
| 7) In efficient-transformers/QEfficient/finetune/configs/dataset_config.py, for custom_dataset class, pass the appropriate value for train_split and test_split according to the dataset keys corresponding to train and test data points. |
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I think this is no longer needed after PR#289. We can directly pass --train_split and --test_split from the CLI.
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