Instructions to use lyleokoth/code-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lyleokoth/code-extraction 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, "lyleokoth/code-extraction") - Notebooks
- Google Colab
- Kaggle
| base_model: google/paligemma-3b-pt-224 | |
| datasets: | |
| - imagefolder | |
| library_name: peft | |
| license: gemma | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: code-extraction | |
| results: [] | |
| <!-- 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. --> | |
| # code-extraction | |
| This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2910 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 2 | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 1.1102 | 0.1064 | 10 | 0.9836 | | |
| | 0.9563 | 0.2128 | 20 | 0.8361 | | |
| | 0.8725 | 0.3191 | 30 | 0.7021 | | |
| | 0.8441 | 0.4255 | 40 | 0.5871 | | |
| | 0.6958 | 0.5319 | 50 | 0.5101 | | |
| | 0.6931 | 0.6383 | 60 | 0.4598 | | |
| | 0.5352 | 0.7447 | 70 | 0.4224 | | |
| | 0.4966 | 0.8511 | 80 | 0.3931 | | |
| | 0.6237 | 0.9574 | 90 | 0.3646 | | |
| | 0.4289 | 1.0638 | 100 | 0.3423 | | |
| | 0.5224 | 1.1702 | 110 | 0.3226 | | |
| | 0.5532 | 1.2766 | 120 | 0.3140 | | |
| | 0.3561 | 1.3830 | 130 | 0.3053 | | |
| | 0.3985 | 1.4894 | 140 | 0.3027 | | |
| | 0.39 | 1.5957 | 150 | 0.2992 | | |
| | 0.3741 | 1.7021 | 160 | 0.2943 | | |
| | 0.2028 | 1.8085 | 170 | 0.2898 | | |
| | 0.3935 | 1.9149 | 180 | 0.2910 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.42.3 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 |