finetuned-gemma-lora

Fine-Tuned Gemma Model using LoRA

This repository contains the fine-tuned Gemma model, using LoRA (Low-Rank Adaptation), a method for efficient fine-tuning of large-scale models. The model is designed for NLP tasks and is fine-tuned to handle sequence lengths of up to 256 tokens.

Model Details

• Base Model: Gemma
• Fine-tuning Technique: LoRA (Low-Rank Adaptation)
• Framework: Keras
• Optimizer: AdamW
• Sequence Length: 256 tokens
• Epochs: 1
• Batch Size: 1

Usage

To use the model for inference, you can load it using Keras or TensorFlow. Here's an example of how to load the model and use it for predictions:

import tensorflow as tf

# Load the fine-tuned model
model = tf.keras.models.load_model("path_to_finetuned_gemma.h5")

# Example input (adjust based on your actual input format)
input_data = ["Sample input text for prediction"]

# Preprocess input data if necessary

# Get predictions
predictions = model.predict(input_data)
print(predictions)

Fine-Tuning Process

The model was fine-tuned using the LoRA method, which allows us to adapt a pre-trained model efficiently without needing to update all of its parameters. The fine-tuning setup included:

• Optimizer: AdamW with a learning rate of 5e-5.
• Weight Decay: 0.01, with bias and layer normalization terms excluded from decay.
• Loss Function: Sparse Categorical Crossentropy.
• Training Data: Custom dataset for NLP tasks (details can be added here).

Performance

This model is expected to perform well on tasks such as text classification, text generation, and other sequence-related NLP tasks. However, you may further fine-tune it for your specific use case.

How to Fine-Tune Further

If you want to fine-tune this model further on your own dataset, you can do so by loading the model and running additional training steps.

License

This model is released under the Apache-2.0 license.