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clip-vit-base-patch32-lora-stanford_cars – AI Model by Selsabeel | AlphaNeural AI
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clip-vit-base-patch32-lora-stanford_cars
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clip-vit-base-patch32-lora-stanford_cars
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: 2.9357
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
No log
1.0
8
9.1044
41.2705
2.0
16
5.7771
27.9521
3.0
24
4.0577
20.0191
4.0
32
3.2174
16.6518
5.0
40
2.9357
Framework versions
PEFT 0.18.1
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 2.19.2
Tokenizers 0.22.2
