Your data — curated, cleaned, and formatted to instruction-tuning standards. Most clients have more usable training data than they think.
LoRA / QLoRA / DPO with eval harnesses on every checkpoint. You get numeric scores, not vibes-only training runs.
vLLM or TGI serving on your infrastructure. API-compatible endpoint, cost-per-token monitoring, on-prem capable.
Domain-specific fine-tuning adapts an open-source base model — Llama 3, Mistral, Qwen — to your industry vocabulary, output format, and tone. We use LoRA and QLoRA for parameter-efficient training that runs on modest GPU budgets.
Every training run is evaluated against a golden dataset we build with you upfront. You see accuracy, latency, and cost metrics after each checkpoint — not just qualitative impressions of whether it feels better.
Book a discovery callFine-tuning without rigorous evaluation is expensive guessing. We build the eval harness before touching training data — because you need to measure improvement, not just observe that the model seems different.
Data quality determines model quality. Most enterprise datasets need significant curation work before they are useful for training. We audit your data first and give you an honest picture of what you have and what it will produce.
We train on open-source models using parameter-efficient methods (LoRA/QLoRA) that run on accessible GPU budgets. The weights belong to you. After delivery, you run the model on your own infrastructure — no dependency on our servers, no usage fees.
A fine-tuned 7B model, evaluated rigorously on your domain, will outperform GPT-4 on your specific tasks — and cost 10x less to run at scale.
Massar Digital Fine-Tuning Team
Whether you are validating the approach or shipping to production, we scope the engagement to where you actually are.
