Project Overview
This project focuses on fine-tuning a Large Language Model (LLM) and evaluating its performance using key metrics such as training loss, validation loss, and other evaluation techniques. The project is organized into two main blocks:
1.Training:Covers the model training process with detailed steps, including data loading, preprocessing, model configuration, and training loops.
Tracks and visualizes the training loss and validation loss over time to assess the model's learning progress.
Key Steps:
Data Preparation:
Loading and preprocessing the dataset.
Model Configuration:
Setting up the model architecture and defining hyperparameters such as learning rate, batch size, optimizer, and loss function.
Training Loop:
Implementing the training process with steps for backpropagation and loss calculation.
Visualization:
Plotting training loss and validation loss to monitor the model's progress.
Important Hyperparameters:
Learning Rate: 0.001
Batch Size: 32
Optimizer: Adam
Dropout Rate: 0.3
Weight Decay: 0.01
Epochs/Steps: 60
2.Evaluation:Focuses on evaluating the trained model using different metrics.
Provides insights into the model's ability to generalize to unseen data and identifies potential areas for improvement.
Displays key metrics such as evaluation loss and discusses evaluation methods like BLEU scores and human evaluation for tasks like translation.
Evaluation Loss:
Final evaluation loss: 0.6981
BLEU Score:
Used to evaluate translation accuracy (if applicable).
Visualization:
Graphs showing training and validation loss trends.
Human Evaluation:
Suggested for tasks like translation to capture fluency, coherence, and relevance, which automated metrics may miss.
Training:
Run to train the model with the desired dataset and hyperparameters.
Evaluation:
After training, run to assess the model's performance and visualize the results.
Conclusion:
These training and evaluation metrics provide a comprehensive workflow for training and evaluating a large language model. By visualizing losses and using multiple evaluation metrics, you can effectively fine-tune and assess the model's performance.
