Overview
Foundation models are trained on the internet — not on your clinical protocols, your credit policies, or your proprietary research. When the gap between general capability and domain-specific performance is too large to close with prompting alone, fine-tuning is the answer. We design and execute fine-tuning programmes that adapt LLMs to your domain vocabulary, output style, and task-specific performance requirements. We handle everything from data curation and labelling strategy through training runs and evaluation — and we are rigorous about measuring whether fine-tuning actually improves performance over prompt engineering before recommending it.
How It Works with a21
Baseline & Decision
Establish baseline performance of the foundation model on your task. Determine whether fine-tuning is warranted or whether prompt engineering and RAG can close the gap — we recommend fine-tuning only when it demonstrably wins.
Data Curation & Training
Design the training dataset — sourcing, labelling, quality control, and formatting. Select the fine-tuning approach (full fine-tune, LoRA, QLoRA) and execute training runs with hyperparameter optimisation.
Evaluation & Deployment
Evaluate fine-tuned models rigorously against held-out test sets and human evaluation. Deploy to your infrastructure with monitoring for performance drift.
What We Offer
Task-Specific Fine-Tuning
Adapt models for classification, extraction, summarisation, generation, or reasoning tasks — with training data and evaluation matched to your specific requirements.
Domain Adaptation
Fine-tune models on your proprietary data to embed domain vocabulary, regulatory language, and subject-matter expertise into the model weights.
Instruction Tuning
Train models to follow your specific instructions, output formats, and communication styles consistently without elaborate prompting.
Parameter-Efficient Fine-Tuning (LoRA/QLoRA)
Apply low-rank adaptation techniques to fine-tune large models cost-effectively — achieving strong performance without full model retraining.
Training Data Curation
Design, source, and quality-control training datasets — including synthetic data generation where labelled examples are scarce.
Fine-Tuning Evaluation
Rigorous evaluation against held-out test sets and human raters — measuring domain accuracy, format adherence, and hallucination rates before and after fine-tuning.
Why Choose a21
Evidence-Based Recommendations
We measure baseline performance before recommending fine-tuning. We only proceed when the data shows it will outperform prompt engineering and RAG.
Proprietary Data Security
Your training data stays in your environment. We deploy fine-tuning pipelines in your cloud tenancy — your data never leaves your control.
Regulated Industry Standards
We apply model documentation and evaluation standards appropriate for regulated industries — so your fine-tuned models can be validated and audited.
Model-Agnostic
We fine-tune across open-source (Llama, Mistral, Falcon) and proprietary models (GPT-4, Claude) — matched to your performance, cost, and data residency requirements.
Success Stories
Problem
A consumer lender needed AI to generate consistent, compliant credit decision narratives — but generic LLMs did not know the regulatory language or internal credit policy framework.
Solution
Fine-tuned a Llama 3 model on 8,000 human-approved credit narratives using LoRA. Established an evaluation suite measuring regulatory compliance, format adherence, and factual accuracy.
Problem
A CRO needed AI to draft clinical trial protocol sections in the specific structural format and scientific language required by regulatory bodies.
Solution
Curated 3,500 approved protocol sections as training data and fine-tuned a domain-adapted model with instruction tuning for each protocol section type.
Tech Stack & Tools
Hugging Face Transformers
LoRA / QLoRA
Axolotl / LLaMA-Factory
W&B
OpenAI Fine-Tuning API
AWS SageMaker / Azure ML
vLLM / TGI
Get Started
Adapt AI to your domain. Talk to a21 about whether fine-tuning is right for your use case.
