AI Model Fine-Tuning
A general-purpose model gets you 80% of the way there; fine-tuning closes the gap — teaching a model your terminology, tone, and edge cases using LoRA/PEFT techniques that keep training costs proportional to the problem.
0%
Avg. Accuracy Improvement
-70%
Training Cost vs. Full Fine-Tune
4-8 wks
Typical Engagement
0+
Models Fine-Tuned
What's Included
Fine-tuned model with before/after benchmark
Curated and documented training dataset
Deployed inference API
How We Work
01
Dataset Curation
We help you assemble and clean a training dataset that actually represents your target task.
02
Fine-Tuning Approach
We select LoRA, PEFT, or full fine-tuning based on data volume, budget, and target model.
03
Training & Evaluation
We train and benchmark against the base model to confirm a genuine accuracy or quality lift.
04
Deployment
The fine-tuned model is deployed behind an API with version control for future updates.
What You'll Receive
- Fine-tuned model with before/after benchmark
- Curated and documented training dataset
- Deployed inference API
- Model versioning setup
- Retraining playbook for future updates
How Engagements Typically Work
Every project starts with scoping — here's the shape most engagements for this service take.
Dataset & Feasibility
A 1-2 week engagement to curate a sample dataset and confirm fine-tuning will meaningfully help.
Fine-Tuning Build
Full training, evaluation, and deployment — typically 4-8 weeks depending on data volume.
Ongoing Retraining
Periodic retraining as your data grows or the underlying base model is upgraded.
Technologies We Use
The tools and platforms our AI Model Fine-Tuning team works in day to day.
Fine-Tuning
MLOps
Frequently Asked Questions
LoRA-based fine-tuning can show real improvement with a few hundred to a few thousand examples, far less than full fine-tuning requires.
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