Fine-Tuning Open-Source LLMs: From Experiment to Production in 30 Days

Why Fine-Tune?

Fine-tuning transforms a general-purpose model into a specialist. Instead of prompting a model to "act like a legal expert," you train it on thousands of real legal documents until it genuinely becomes one.

Data Strategy

Quality > Quantity. 5,000 carefully curated examples outperform 50,000 scraped ones.

Data format:

{
  "instruction": "Extract all payment terms from this contract clause",
  "input": "[contract text]",
  "output": "Payment due within 30 days of invoice date. Late payment penalty: 1.5% monthly."
}

Training with QLoRA

from transformers import AutoModelForCausalLM, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-8B-Instruct",
    quantization_config=bnb_config,
    device_map="auto",
)

Evaluation Framework

Never deploy without a comprehensive eval suite:

• ▸Task accuracy: Does it do the right thing?
• ▸Hallucination rate: Does it make things up?
• ▸Latency: Is it fast enough for your use case?
• ▸Regression: Did fine-tuning break existing capabilities?