Description of the feature request:

Description:
Create an interactive Streamlit-based web application designed to simplify fine-tuning of Google's Gemma open models on custom datasets. The application allows users to easily upload and preprocess datasets (supporting CSV, JSONL, and TXT formats), configure essential hyperparameters such as learning rate, batch size, and epochs, and monitor training progress through real-time visualizations including loss curves, evaluation metrics, and live-generated text examples.

Key features include:
Real-Time Training Visualization: Dynamic updates of training loss, accuracy, F1-scores, and generated text examples.
LoRA Integration: Efficient fine-tuning using Low-Rank Adaptation (LoRA) with customizable parameters.
Model Export: Convenient options to download the trained model in PyTorch format, with placeholder functionality for TensorFlow SavedModel and GGUF conversions.
Cloud Integration (Optional): Basic framework for integration with Google Cloud Storage and Vertex AI for scalable training (currently placeholder implementation).
Clear Documentation & Examples: Embedded tooltips, step-by-step guidance, and a straightforward UI ensure accessibility for all users.

Demo:

What problem are you trying to solve with this feature?

No response

Any other information you'd like to share?

No response