Yuvrajxms09/Monad

Model Details

Model Description

The model has been trained on the WinoGrande dataset which tests the ability to resolve pronouns and make logical inferences in everyday scenarios.

Model Sources

• Base Model: https://huggingface.co/PleIAs/Monad

Training Data

Dataset: WinoGrande (allenai/winogrande)

• Size: 9,248 training examples, 1,267 validation examples
• Task: Commonsense reasoning with pronoun resolution
• Format: Multiple choice questions requiring logical reasoning

Training Hyperparameters

Epochs	Batch Size	Learning Rate	Warmup Ratio	Warmup Steps	Weight Decay	Max Gradient Norm	Evaluation Steps	Save Steps	Early Stopping Patience
5	16	1e-05	0.05	144	0.01	1.0	150	150	7

Training Results

Metric	Value
Final Training Loss	0.9143
Training Time	1,526.9s

Validation Performance

Validation loss stabilized between 0.83-0.86 throughout the training

Summary

The model achieved strong convergence during training:

• Final training loss: 0.9143
• Evaluation loss: ~0.834 (final checkpoint)
• Training completed: All 5 epochs with early stopping monitoring

Compute Infrastructure

Hardware

• GPU: Single NVIDIA A10G (24GB VRAM)
• Platform: Modal.com