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clip-vit-base-patch32-lora-svhn
This model is a fine-tuned version of openai/clip-vit-base-patch32 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2464
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: 0.005
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 17.9752 | 1.0 | 72 | 4.4578 |
| 16.1884 | 2.0 | 144 | 3.8206 |
| 13.6996 | 3.0 | 216 | 2.6664 |
| 10.6310 | 4.0 | 288 | 1.5987 |
| 8.2592 | 5.0 | 360 | 1.2464 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 2.19.2
- Tokenizers 0.22.2
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Model tree for Selsabeel/clip-vit-base-patch32-lora-svhn
Base model
openai/clip-vit-base-patch32