Enterprise Service
Custom Fine-Tuning
Overview.
Transform TNSA's foundation models into domain-specific AI solutions tailored to your unique business needs. Our custom fine-tuning service enables you to adapt powerful AI models to your specific use cases, data, and requirements.
Fine-Tuning Methods.
Full Fine-Tuning
Update all model parameters for maximum adaptation. Best for large datasets and significant domain shifts. Provides highest accuracy but requires more computational resources.
LoRA (Low-Rank Adaptation)
Efficient fine-tuning by training small adapter layers. Reduces training time by 3x and memory usage by 70% while maintaining 95%+ of full fine-tuning performance.
QLoRA (Quantized LoRA)
Memory-efficient fine-tuning using 4-bit quantization. Enables fine-tuning of 70B+ parameter models on single GPUs while preserving quality.
Instruction Tuning
Specialized fine-tuning for following instructions and task completion. Improves zero-shot performance on new tasks by 40-60%.
Training Pipeline.
Data Preparation
Clean, format, and validate training data
Hyperparameter Optimization
Learning rate, batch size, epochs tuning
Training & Monitoring
Distributed training with real-time metrics
Evaluation & Validation
Benchmark testing and quality assurance
Performance Metrics.
Applications.
Industry-specific chatbots, specialized content generation, custom classification systems, domain-specific question answering, enterprise knowledge management, legal document analysis, medical diagnosis assistance, and financial forecasting.