---
library_name: peft
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- base_model:adapter:google/vit-base-patch16-224-in21k
- lora
- transformers
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-lora
  results: []
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sajjadi/Fast-PEFT/runs/xnaua1w4)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sajjadi/Fast-PEFT/runs/xnaua1w4)
# vit-base-patch16-224-in21k-lora

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0513
- Accuracy: 0.9849
- Solar Loss: 0.0526
- Solar Accuracy: 0.9853

## 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.16.0
- Transformers 4.53.2
- Pytorch 2.5.1+cu121
- Datasets 3.0.1
- Tokenizers 0.21.0