The smartest model on the internet, made yours.
We fine tune frontier and open source LLMs on your data, voice, and edge cases, using SFT, DPO, RLHF, and LoRA. Domain specific accuracy at a fraction of the cost of training from scratch.
Every modern
tuning method.
There's no single fine tuning recipe. We pick the technique that matches the data you have, the behavior you want to change, and the budget you're working with.
Supervised Fine Tuning (SFT)
Curated input output pairs to teach the model your task. The right starting point for most projects.
Preference Optimization (DPO)
When good vs. better matters more than right vs. wrong, we tune the model on your team's preferences directly.
RLHF & RLAIF
Reinforcement learning from human or AI feedback for nuanced behaviors, tone, safety, multi turn coherence.
LoRA & Adapter Tuning
Parameter efficient methods that train fast, cost less, and let you swap behaviors per customer or use case.
Eval First Workflow
We build your eval harness before tuning, so we can prove the model improved instead of just hoping it did.
Production Deployment
Tuned models served on your infrastructure with monitoring, versioning, and rollback baked in.
From baseline to measurably better.
Most fine tuning engagements ship a model that beats the baseline on your evals inside six weeks.
Baseline & Eval
We build a custom eval harness for your task and benchmark every candidate model, so you know exactly what better looks like.
Data Preparation
Cleaning, formatting, and augmentation. We often discover your data needs more work than your model does.
Tune & Iterate
Multiple short training runs with intermediate evals, picking the right method and stopping when gains plateau.
Deploy & Monitor
Tuned model deployed alongside the baseline for shadow comparison, then promoted with full monitoring and rollback.
Questions about
Model Fine Tuning
Try prompting and RAG first. Fine tune when you've squeezed those dry and still need consistent tone, format, or domain accuracy that the base model can't reliably hold across thousands of inputs.
Surprisingly little for SFT, often a few thousand high quality examples beat tens of thousands of mediocre ones. We'll review what you have and tell you what's missing.
Open: Llama, Mistral, Qwen, Gemma, Phi, DeepSeek. Closed: OpenAI and Anthropic where they expose tuning APIs. We pick based on quality, license, and where you need to deploy.
Catastrophic forgetting is a real risk. We mitigate it with eval suites that cover both target and adjacent tasks, conservative learning rates, and LoRA where appropriate to leave the base weights intact.
Yes, when fine tuning open weight models. With closed models, ownership is governed by the provider's terms; we'll walk you through the trade offs at scoping time.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. We'll review your data, scope the right tuning method, and tell you honestly whether fine tuning is even the right answer.
info@croncore.com