Instructions to use MLap/paligemma_intersections with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MLap/paligemma_intersections with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/paligemma-3b-pt-224") model = PeftModel.from_pretrained(base_model, "MLap/paligemma_intersections") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: mit | |
| base_model: google/paligemma-3b-pt-224 | |
| tags: | |
| - generated_from_trainer | |
| - vlm | |
| - PaliGemma | |
| - LoRA | |
| - PEFT | |
| model-index: | |
| - name: paligemma_intersections | |
| results: [] | |
| datasets: | |
| - ariG23498/intersection-dataset | |
| language: | |
| - en | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| ## Model description | |
| This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on [ariG23498/intersection-dataset](https://huggingface.co/datasets/ariG23498/intersection-dataset). | |
| ## Training procedure | |
| Finetuning done using (LoRA) PEFT method. Rank = 8 choosen. | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 4 | |
| - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 2 | |
| - num_epochs: 2 | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.50.2 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 |