LLM Fine-Tuning Services Domain-Expert AI From Your Data
Transform foundation models into specialized AI that understands your industry terminology, follows your conventions, and delivers 90-95% domain accuracy. LoRA, QLoRA, and PEFT techniques on secure GPU infrastructure you control.
What Fine-Tuning Delivers
Parameter-efficient techniques that adapt billion-parameter models at a fraction of the cost of training from scratch.
Parameter-Efficient Training
LoRA and QLoRA reduce trainable parameters by 99%+, enabling 70B+ model fine-tuning on a single GPU. Achieve domain performance without prohibitive compute costs.
Domain Adaptation
Transform general-purpose models into domain experts for healthcare, legal, financial, defense, and technical applications with specialized vocabulary understanding.
Secure Training Infrastructure
Training data and model weights never leave your control. On-premises GPU infrastructure and air-gapped environments satisfy HIPAA, CMMC, and data sovereignty requirements.
Rigorous Evaluation
Domain-specific benchmarking against your test sets. We measure accuracy, hallucination rates, latency, and task completion quality with quantitative evidence of improvement.
Fine-Tuned vs. Base Model Performance
70-80% Domain Accuracy
Generic models lack your industry's specialized vocabulary and formatting conventions.
Surface-Level Knowledge
Prompted terminology only. No internalized understanding of your domain's reasoning patterns.
Variable Output Quality
Inconsistent formatting, citation styles, and quality standards across generated outputs.
90-95% Domain Accuracy
Internalized domain vocabulary delivers significantly higher accuracy on your specific tasks.
Deep Domain Expertise
Model develops internal representations of your domain's knowledge structures and reasoning patterns.
Consistent Quality
Outputs follow your formatting standards, citation conventions, and quality requirements reliably.
What We Fine-Tune
Foundation Models
- Llama 3 (8B-405B): General-purpose with permissive licensing
- Mistral/Mixtral (7B-8x22B): High efficiency per parameter
- Qwen 2.5, Phi, Gemma: Specialized multilingual and compact models
GPU Infrastructure
- 3x NVIDIA RTX PRO 6000 (288GB VRAM combined)
- RTX 5090 (32GB) and DGX Spark clusters
- Unsloth, Axolotl, HuggingFace TRL training frameworks
How Fine-Tuning Works
Data Audit and Curation
Base Model Selection
Training Dataset Preparation
LoRA/QLoRA Fine-Tuning
Domain Benchmarking
Secure Deployment
Frequently Asked Questions
How much training data do we need?
As few as 500 high-quality instruction-response pairs can deliver significant improvements. Data quality accounts for 40-60% of fine-tuning outcomes. We audit your data assets and identify gaps before training begins.
Should we use fine-tuning or RAG?
Fine-tuning internalizes domain knowledge into the model. RAG retrieves current documents at query time. For best results, combine both. See our RAG vs. Fine-Tuning guide for a detailed comparison.
Is our training data secure during fine-tuning?
Your training data never leaves your control. We provide on-premises GPU infrastructure and air-gapped training environments that satisfy HIPAA, CMMC, and data sovereignty requirements.
How do you measure fine-tuning success?
We build domain-specific test sets covering your actual query types and output expectations. Every fine-tuned model includes accuracy benchmarks, hallucination rates, and model cards for stakeholder approval.
What does fine-tuning cost?
Projects range from $15,000 for focused domain adaptation to $100,000+ for comprehensive multi-model fine-tuning with custom evaluation frameworks. We provide cost modeling during the assessment phase.
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Ready to Fine-Tune Your AI?
Transform a general-purpose model into a domain expert that understands your industry, your terminology, and your quality standards.