Fine-Tuning LLMs on Proprietary Data: Step-by-Step Guide

Neural network visualisation representing large language model fine-tuning

General-purpose LLMs don't know your products, customers, terminology, or business context. Fine-tuning changes that — it adapts a foundation model's weights specifically to your data, dramatically improving performance on your tasks.

Step 1: Data Preparation

Fine-tuning quality is 80% data quality. You need 500–1,000 high-quality examples in the instruction-response format your task requires. For most enterprise use cases — document classification, internal Q&A, decision support — this means curating real examples from your historical data.

json

{
  "instruction": "Classify this support ticket by urgency.",
  "input": "System down, cannot process payments",
  "output": "CRITICAL"
}

Step 2: Choosing Your Base Model

For most enterprise tasks, a 7B parameter model fine-tuned on your data will outperform a 70B general model on your specific use case — at a fraction of the inference cost. Start with a proven open-source base (Mistral, Llama 3, Gemma) and fine-tune using LoRA adapters.

Model architecture diagram showing LoRA adapter injection points — LoRA adapters inject trainable parameters at key attention layers without touching base model weights.

Step 3: Training Configuration

Use QLoRA for most enterprise fine-tuning — it reduces GPU memory requirements by 4× while maintaining 95%+ of full fine-tuning quality. A standard run on a single A100 for a 7B model takes 4–8 hours. AstraCore's fine-tuning API abstracts this into a single call.

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QLoRA fine-tuning on a 7B model costs approximately $15–40 for a 1,000-example dataset on cloud GPU infrastructure.

Step 4: Evaluation & Safe Deployment

Split your data 80/10/10 (train/validation/test) before fine-tuning. Evaluate on task-specific metrics and run human evaluation on 100+ examples. Before production, run red-teaming evaluations and check for PII leakage. AstraCore handles monitoring and automatic rollback.