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clip-vit-base-patch32-lora-mnist – AI Model by Selsabeel | AlphaNeural AI
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clip-vit-base-patch32-lora-mnist
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clip-vit-base-patch32-lora-mnist
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: 1.1071
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
23.4592
1.0
59
4.4694
14.9199
2.0
118
3.2267
11.5701
3.0
177
1.7225
9.1476
4.0
236
1.2955
8.2926
5.0
295
1.1071
Framework versions
PEFT 0.18.1
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 2.19.2
Tokenizers 0.22.2
