Instructions to use TienAnh/Finetune_lora_OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TienAnh/Finetune_lora_OCR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TienAnh/Finetune_lora_OCR", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TienAnh/Finetune_lora_OCR", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Finetune_lora_OCR
This model was trained from scratch on an unknown dataset.
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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_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: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
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