opt125m-lora-pruned-wanda-unstructured

This model is a fine-tuned version of saresri/pruned_opt 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: 0.0002
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Evaluation Results (Validation): {'rouge1': 0.2448, 'rouge2': 0.0663, 'rougeL': 0.1595, 'rougeLsum': 0.1847}

Evaluation Results (Test): {'rouge1': 0.3729, 'rouge2': 0.2203, 'rougeL': 0.3078, 'rougeLsum': 0.3259}

Framework versions

• PEFT 0.14.0
• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.21.0