What is Fine-Tuning?
Fine-tuning is the process of adapting a pre-trained language model to perform specific tasks or understand domain-specific knowledge. It's like teaching a brilliant student to specialize in a particular field while retaining their general knowledge.
Model Accuracy
+35% improvement
Understanding Fine-Tuning
Fine-tuning in machine learning is the process of adapting a pre-trained model to perform specific tasks or understand domain-specific knowledge. Unlike training a model from scratch, fine-tuning leverages the existing knowledge of a foundation model and refines it for particular use cases.
Key Concept
Think of fine-tuning like teaching a university graduate to specialize in a specific profession. They already have foundational knowledge, but you're helping them excel in a particular domain.
Fine-Tuning vs. Training from Scratch
Fine-Tuning
- Faster training time
- Less data required
- Cost-effective
- Retains general knowledge
Training from Scratch
- Longer training time
- Massive datasets needed
- Expensive compute costs
- Full control over architecture
Quick Navigation
Triangle OS Advantage
How Fine-Tuning Works
1. Data Preparation
Prepare and format your training dataset
- • JSONL format required
- • Minimum 100 examples
- • Quality over quantity
- • Data validation included
2. Configuration
Set training parameters and hyperparameters
- • Learning rate optimization
- • Batch size selection
- • Epoch configuration
- • Validation split
3. Training
Automated training on secure UK infrastructure
- • Real-time monitoring
- • Progress tracking
- • Early stopping
- • Automatic checkpointing
4. Deployment
Deploy and use your custom model
- • Instant deployment
- • Same API interface
- • Performance metrics
- • Version management
Technical Deep Dive
Understanding the technical aspects of fine-tuning Triangle LLM models
Common Fine-Tuning Use Cases
Brand Voice Consistency
Train models to match your company's tone and communication style
Before Fine-Tuning
"Thank you for contacting us. We will help you with your issue."
After Fine-Tuning
"Hi there! We're absolutely delighted to help you today. Let's get this sorted for you right away! 😊"
Performance Improvements
Measurable improvements in customer service metrics
Fine-Tuning Best Practices
Data Quality
- • Ensure data diversity and representation
- • Remove duplicates and inconsistencies
- • Validate data format and structure
- • Balance dataset across different scenarios
Hyperparameter Tuning
- • Start with recommended learning rates
- • Monitor training and validation loss
- • Use early stopping to prevent overfitting
- • Experiment with batch sizes
Evaluation & Testing
- • Use held-out test sets for evaluation
- • Test on real-world scenarios
- • Monitor performance metrics continuously
- • Compare against baseline models
Fine-Tuning Pricing
Triangle-Fast
7B parameter model - Perfect for simple tasks
- Fast training (2-4 hours)
- Cost-effective inference
- Up to 10K training examples
- Basic support included
Triangle-Pro
175B parameter model - Advanced capabilities
- Advanced reasoning capabilities
- Complex task handling
- Up to 50K training examples
- Priority support included
Triangle-Vision
90B parameter multi-modal model
- Multi-modal training support
- Image + text datasets
- Up to 25K training examples
- Specialized support included
All plans include free validation dataset analysis, performance metrics, and model versioning
Ready to Fine-Tune Your Model?
Get started with fine-tuning in minutes. Our platform handles the complexity while you focus on your data and use case.
Need Help Getting Started?
Our team of AI specialists can help you design the perfect fine-tuning strategy for your use case.