AI Solutions
Adapting Models to Your Domain
Fine-tuning or adapting a model is sometimes the right answer and often not. We help you decide first, then do the work carefully if it is justified.
Overview
Custom LLM Development
Many teams assume they need their own model when better prompts, retrieval or a larger hosted model would do. Codemotives starts by testing those cheaper options against your task. Customization becomes worthwhile when you need consistent style or format, specialized terminology, lower latency or cost at volume, or deployment in an environment where hosted APIs are not acceptable.
When it is justified, the real work is data. A fine-tune needs a clean, representative set of examples, a held-out evaluation set and a clear baseline to beat. We handle dataset preparation, training with techniques such as parameter-efficient fine-tuning, comparison against the unmodified model and deployment, while being clear that customization will not remove the need for oversight or guarantee factual accuracy.
Context
Problems we solve
- 01
Fine-tuning as a reflex
Training is costly and often unnecessary. Prompting and retrieval should be tried and measured first.
- 02
Training data quality
A model learns whatever the examples contain, including their errors and inconsistencies. Data preparation dominates the effort.
- 03
No clear baseline
Without a benchmark, nobody knows if the tuned model is actually better. Evaluation must be designed before training.
- 04
Ongoing maintenance
Base models change and data drifts. A custom model needs retraining plans and an owner.
Capabilities
What we deliver
- 01
Fit assessment
Side-by-side tests of prompting, retrieval and fine-tuning on your task to see whether customization pays off.
- 02
Dataset preparation
Collection, cleaning, labeling guidance and splitting of training and evaluation data, with privacy review.
- 03
Efficient fine-tuning
Parameter-efficient methods on open-weight models, or provider fine-tuning services where suitable.
- 04
Benchmarking
Held-out tests and human review comparing the tuned model with the baseline across realistic cases.
- 05
Model serving
Deployment on managed or self-hosted infrastructure with scaling, monitoring and versioning.
- 06
Retraining plan
Documented process and triggers for updating the model as data and requirements change.
Where it fits
- Consistent brand voice in generated content
- Domain terminology in technical classification
- Smaller private model for restricted environments
- Lower cost per request on a high-volume task
- Structured output in a specialized format
Technologies we use
- PyTorch
- Hugging Face Transformers
- LoRA
- PEFT
- vLLM
- Ollama
- MLflow
- Weights & Biases
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
Do we need our own LLM?
Usually not. Most business needs are met by a hosted model with good prompts and retrieval. We will test that first and recommend customization only if results show a real gap.
How much data do we need?
Quality matters more than raw volume, and the amount needed depends on the task and method. A small, carefully reviewed set often beats a large messy one. We assess what you have before promising anything.
Does fine-tuning teach the model new facts?
Not reliably. It is better at shaping behavior, tone and format than at storing knowledge. For facts that change, retrieval over your documents is generally more dependable.
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.