Instructions to use devkyle/base-lora-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use devkyle/base-lora-v1 with PEFT:
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- Notebooks
- Google Colab
- Kaggle
| base_model: openai/whisper-base | |
| library_name: peft | |
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: whisper-base-akan-v1 | |
| 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. --> | |
| # whisper-base-akan-v1 | |
| This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - eval_loss: 1.0085 | |
| - eval_runtime: 32.8168 | |
| - eval_samples_per_second: 6.094 | |
| - eval_steps_per_second: 0.762 | |
| - epoch: 20.0 | |
| - step: 1000 | |
| ## 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: 0.0001 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 50 | |
| - training_steps: 2000 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - PEFT 0.13.3.dev0 | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.19.1 |