No Code Tuning / Fine-Tuning Models – Qubrid AI Platform
The No Code Tuning feature allows users to fine-tune pre-deployed Text Generation and Code Generation models on the Qubrid AI platform—without writing a single line of code. Using an intuitive interface and user credits, users can train models with their own dataset and deploy them seamlessly on GPU instances.
Key Benefits
- Improved Performance: Gain higher accuracy and better predictive power by customizing models to your data.
- Reduced Training Time: Efficient hyperparameter optimization techniques save compute resources and time.
- Flexible Approaches: Supports fine-tuning pre-trained models or training from scratch.
- Automated Hyperparameter Search: Integration with tools that support grid search, random search, and advanced optimization (e.g., Bayesian optimization, Hyperopt).
- Scalable & Repeatable: Easily reproduce tuning experiments and scale across GPU instances.
Typical Use Cases
| Use Case | Description |
|---|---|
| Fine-Tuning Pre-Trained Models | Adapt general models like Hugging Face transformers to domain-specific data |
| Hyperparameter Optimization | Systematically explore learning rates, batch sizes, optimizers, etc. |
| Transfer Learning | Leverage knowledge from one task to improve performance on another |
| Model Compression & Pruning | Tune for efficient deployment on edge or limited compute resources |
| Custom Architecture Search | Explore variations on existing architectures for better task fit |
How It Works
- Select Model and Dataset: Choose the base model to tune and prepare your dataset.
- Define Tuning Parameters: Specify hyperparameters or layers to adjust.
- Choose Optimization Strategy: Pick from manual tuning, grid/random search, or automated search methods.
- Run Training Jobs: Deploy tuning jobs on GPU instances for faster experimentation.
- Evaluate & Compare Results: Analyze model metrics, select the best-performing model.
- Deploy Tuned Model: Integrate into inference pipelines or production environments seamlessly.
Why Use Model Tuning?
- Maximize your model’s potential tailored to your specific application.
- Avoid costly trial-and-error with guided or automated tuning workflows.
- Leverage GPU-accelerated infrastructure to speed up experimentation.
- Easily integrate with AI/ML templates and open source inferencing frameworks.
Get Started Now
Optimize your AI projects through intelligent model tuning. Drive better insights, predictions, and operational efficiency by refining your models with ease and confidence!
Empower your machine learning workflows with efficient, scalable, and reproducible model tuning capabilities.