Applications — Fine-tuning
Your model, your data, your behavior.
An off-the-shelf model speaks generic. Fine-tuning shapes it to your organization: your terminology, your tone, your compliance requirements. We modify open-weight models efficiently using LoRA and QLoRA. One GPU. Days, not months.
Overview
What is fine-tuning?
Fine-tuning is partial retraining of an open-weight model on your data. The model keeps its general intelligence (language, reasoning, knowledge) but learns to behave differently.
- Tone — Formal for legal, conversational for customer service
- Terminology — Your industry jargon, product names, internal acronyms
- Output format — Structured JSON, specific templates, compliance language
- Behavior — Refusal boundaries, escalation rules, domain-specific reasoning
- The model's core capabilities: language, reasoning, knowledge
- Your data stays yours. The fine-tuned model runs on your infrastructure
Method
How we do it.
We train a small add-on (an 'adapter', the LoRA/QLoRA method) that adjusts how the model behaves, leaving the original model untouched. That's what makes it fast (hours to days), cheap (a single GPU, since QLoRA cuts memory needs by ~4×), and easy to version, A/B test, and swap.
Data preparation
We help you curate and format training examples. Quality over quantity. A thousand well-crafted examples typically beat a hundred thousand noisy ones.
Training
LoRA/QLoRA on your infrastructure or hosted GPU.
Evaluation
Side-by-side comparison against the base model. We measure what changed.
Deployment
The fine-tuned model replaces or runs alongside the base model.
Example
Base model, then tuned.
Can I share this patient data with a third-party lab?
“Yes, you can share it using secure file transfer protocols. Make sure to encrypt the data and use a secure channel.”
“Sharing patient data with third parties requires a signed BAA and HIPAA-compliant transfer. Under our data governance policy (section 4.2), all external data sharing must be approved by the compliance officer. Would you like me to retrieve that policy?”
The fine-tuned model knows your rules, speaks your language, and refuses appropriately. The base model gives generic, and potentially risky, advice.
Applications
Use cases by industry.
Banking & Finance
Fine-tune for compliance tone. The model learns to refuse appropriately, cite regulations, and format outputs for audit.
Healthcare
Fine-tune for medical terminology in Spanish (or English). Diagnostic assistance, clinical documentation, patient communication.
Utilities & Energy
Fine-tune for operational protocols. The model understands grid topology, outage procedures, and regulatory reporting formats.
Legal
Fine-tune for jurisdiction-specific language. Puerto Rico law, US federal, LATAM regulatory frameworks.
Customer Service
Fine-tune for your brand voice and escalation rules. When to answer, when to hand off to a human.
Comparison
Fine-tuning vs. Knowledge (RAG): when to use which.
| Fine-tuning | Knowledge (RAG) | |
|---|---|---|
| What it changes | How the model behaves | What the model knows |
| Best for | Tone, format, style, refusal patterns | Facts, documents, current information |
| Data needed | Curated examples of desired behavior | Organized documents |
| Updates | Retrain when behavior needs to change | Add documents anytime |
| They work together | Fine-tune for behavior… | …RAG for knowledge. |
Contact
What behavior do you want your AI to learn?
Bring us your use case. We'll tell you if fine-tuning is the right tool, and what data you'll need.
Discuss your fine-tuning needs →