LLM Fine-Tuning Services
A model that speaks your domain, not the internet's.
Fine-tuning language models on your domain-specific data for better accuracy, on-brand behaviour and lower inference cost.
About this service
Fine-tuning is worth considering once prompting and retrieval reach their limits, it can teach a model your task, tone, and terminology, often at lower running cost.
We fine-tune open and commercial models so they follow your formats and vocabulary on high-volume tasks, using LoRA and QLoRA to make this practical.
What we deliver
Use-case assessment
Deciding whether fine-tuning, RAG, or prompting fits your goal, often combining them.
Data curation
Building clean, diverse, correctly formatted training sets.
Efficient tuning
LoRA, QLoRA, and PEFT methods, with swappable task-specific adapters.
Domain adaptation
Models that follow your formats, tone, and terminology.
Evaluation framework
Held-out test sets and task metrics.
Deploy & serve
Self-hosted or managed serving of models or adapters.
Technology we use
The stack behind this service
- LoRA / QLoRA
- Hugging Face TRL
- Unsloth
- Axolotl
- Together AI
- Fireworks AI
- Predibase
- Google Vertex AI
- Amazon Bedrock
- Llama / Mistral / Gemma
Our approach & delivery process
Fit assessment
Confirming fine-tuning is the right lever and setting success metrics up front.
Data preparation
Curating, cleaning, and formatting training data.
Train & evaluate
Running efficient tuning and measuring against a held-out set.
Deploy & iterate
Serving the model or adapter and re-tuning as new data arrives.
What clients say about working with us
One more client quote appears above, in Trust & credibility. Here's the rest.
General client testimonials, shown as the client wrote them, not written specifically about this service.
MagikCoder found and fixed security gaps our previous vendor missed entirely. The weekly reports made the whole engagement easy to manage from our side.
“We hired them through Upwork expecting a freelancer and got an entire senior team. On-time delivery, no scope creep, no drama.”
Shachar AmarCEO, Apolloecom · Hired via Upwork
Trust & credibility
- Years of experience
- 8+
- Projects delivered
- 295+
- Average client rating
- 4.9/5*
- Countries served
- 12+*
Security, certifications & partnerships
- ISO 27001-audited information security practices.
- Role-based access and permission-aware handling of client data.
- No client data is used to train third-party models.
- Developing Apps for ShopifyShopify · Credly verified
- Liquid Storefronts for Theme DevelopersShopify · Credly verified
- Google CloudPartner Network
- ClutchTop B2B Firm 2026
- GoodFirmsTop Developer
- ISO 27001Certified
Team & support
7+ yrs*Average hands-on engineering experience per developer
- Ongoing support, bug fixes, performance monitoring, and feature updates after launch.
- Typical first response within 24 hours on active engagements.*
* Illustrative placeholder, shown here as a sample until confirmed with real company data.
Proof in practice
Independently reviewed on Clutch and GoodFirms by past clients, and in their own words:
“They rebuilt our checkout in three weeks and conversion jumped almost immediately. Communication was tighter than any agency we'd used before.”
Frequently asked questions
Should we fine-tune or use RAG?
RAG suits current or frequently changing facts, while fine-tuning teaches tasks, formats, or style, many systems use both.
How much data do we need?
It depends on the task, often hundreds to a few thousand high-quality examples, quality matters more than volume.
Can we keep the model in-house?
Yes, open-weight models like Llama or Mistral can be deployed on your own infrastructure.
Teach a model your task, tone and terminology.
Tell us about the high-volume task you'd fine-tune for, and we'll scope the data and training approach.