A technical guide on training AI models with your own data, the fine-tuning process, and infrastructure requirements.
Why Train Your Own Model?
General-purpose AI models work well for many tasks, but when it comes to industry-specific terminology, business logic, and data patterns, customized models deliver significantly better results.
What Is Fine-Tuning?
Fine-tuning is the process of retraining a pre-trained large language model (LLM) with your own dataset to specialize it for your industry. It requires far less data and computing resources compared to training from scratch.
- Supervised Fine-Tuning (SFT): Guiding model behavior with labeled question-answer pairs
- LoRA/QLoRA: Efficient fine-tuning with low resource consumption — possible even with a single GPU
- RLHF: Improving response quality through human feedback
- DPO: Direct Preference Optimization — a simpler alternative to RLHF
Data Preparation
Data Collection: Gather customer support records, technical documents, emails, and industry-specific materials.
Data Cleaning: Remove noisy data, duplicates, and personal information.
Data Format: Structure as instruction-response pairs or conversation format.
Data Volume: 500-5,000 examples for LoRA; 10,000+ recommended for full fine-tuning.
Infrastructure Requirements
- GPU: Minimum 1x A100 40GB for 7B models, 4x A100 80GB for 70B models
- RAM: 2-3x the model size in system memory
- Storage: NVMe SSD for training data + checkpoints (minimum 1TB)
- Software: Hugging Face Transformers, Axolotl, LLaMA-Factory
Evaluation and Testing
Measure trained model performance with benchmark datasets, A/B tests, and human evaluation. Use metrics like perplexity, BLEU, and ROUGE alongside industry-specific evaluation criteria.
Model Training with CloudRUX
Size the GPU requirements of your model training project together with our engineers. Details of our GPU service will be announced at launch.