wehub-resource-sync
caf324b09d
tests / check_code_quality (push) Waiting to run
tests / tests (ubuntu-latest, 3.10) (push) Blocked by required conditions
tests / tests (ubuntu-latest, 3.11) (push) Blocked by required conditions
Deploy "method_comparison" Gradio to Spaces / deploy (push) Waiting to run
Deploy "PEFT shop" Gradio app to Spaces / deploy (push) Waiting to run
tests on transformers main / tests (push) Waiting to run
tests / tests (ubuntu-latest, 3.12) (push) Blocked by required conditions
tests / tests (ubuntu-latest, 3.13) (push) Blocked by required conditions
tests / tests (windows-latest, 3.10) (push) Blocked by required conditions
tests / tests (windows-latest, 3.11) (push) Blocked by required conditions
tests / tests (windows-latest, 3.12) (push) Blocked by required conditions
tests / tests (windows-latest, 3.13) (push) Blocked by required conditions
Secret Leaks / trufflehog (push) Waiting to run
CI security linting / zizmor latest via Cargo (push) Waiting to run
Build documentation / build (push) Failing after 0s
Fine-tuning for image classification using LoRA and 🤗 PEFT
Vision Transformer model from transformers
We provide a notebook (image_classification_peft_lora.ipynb) where we learn how to use LoRA from 🤗 PEFT to fine-tune an image classification model by ONLY using 0.7% of the original trainable parameters of the model.
LoRA adds low-rank "update matrices" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are merged with the original model parameters. For more details, check out the original LoRA paper.
PoolFormer model from timm
The notebook image_classification_timm_peft_lora.ipynb showcases fine-tuning an image classification model using from the timm library. Again, LoRA is used to reduce the numberof trainable parameters to a fraction of the total.