LLM Fine-Tuning: Teach an AI Model Your Data
Send a data sample — we'll propose a strategy and quote.
What Is LLM Fine-Tuning and Why Bother?
Fine-tuning means continuing the training of an existing language model (LLM) on your own data — documents, conversations, instructions, code. The result: a model that speaks your company's language, knows your products and doesn't need a 10-page prompt to answer correctly.
- Open-source models: Llama 3, Mistral, Qwen, Phi, DeepSeek
- Methods: LoRA, QLoRA, full fine-tuning, DPO
- Training data: JSON, CSV, documents, conversations
- Your model — you can download and run it yourself
- Hosting of the trained model as a private API
When Is It Worth Fine-Tuning Your Own LLM?
ChatGPT and Claude are great — but they don't know your company, your procedures or your language. Fine-tuning solves specific problems.
Sensitive data can't go to OpenAI
Medical, legal, financial data? Train the model locally, host it in Poland. Nothing leaks to the US.
A generic model doesn't get your jargon
Industry acronyms, product names, internal procedures — fine-tuning teaches the model your vocabulary.
ChatGPT API costs eat your budget
At high volume, your own fine-tuned 7B–13B model is many times cheaper than GPT-4.
You need repeatable answers
A fine-tuned model sticks to format, style and tone. No hallucinations where it matters.
What Does the LLM Fine-Tuning Process Look Like?
- 1
Analysis and base model selection
We discuss the goal, measure task complexity and pick the base model (7B, 13B, 70B) and method: LoRA for a quick start, QLoRA for large models, full fine-tuning for top quality.
- 2
Training data preparation
Your documents, conversations or question–answer pairs become a clean dataset (JSONL, ChatML). Cleaning, dedup, validation, train/eval split — all handled on our side.
- 3
Training on NVIDIA GPUs
Training on A100/H100 cards in our data center in Poland. We monitor loss, perplexity and quality metrics. End-to-end — from hours to days, depending on model size and data volume.
- 4
Evaluation and comparison
We test the fine-tuned model on an eval set, compare against the base model and GPT-4. You get a report with concrete metrics: accuracy, BLEU, ROUGE, cost per 1000 tokens.
- 5
Deployment as a private API
We host the finished model on a dedicated GPU as a private OpenAI-compatible endpoint. You can also download the weights and run them yourself — they're yours.
Fine-Tuning Methods — Which One Will We Pick?
LoRA
Lightweight adapter fine-tuning. Fast, cheap, great for most business use cases. 7B in 2–6 hours.
QLoRA
LoRA on a quantized model. Lets you fine-tune even 70B on a single GPU. Ideal for large models.
Full fine-tuning
Full retraining of all weights. Highest quality when LoRA isn't enough. Needs more data and compute.
DPO / RLHF
Train the model from preferences (better answer vs worse). Ideal when you want to fit tone and style.
Why Fine-Tune Your LLM With Us?
Your data stays in Poland
Training and hosting on our servers in Poland. Full GDPR compliance. No data crossing to the US.
A100 and H100 GPUs
Latest NVIDIA GPUs with 80 GB VRAM. 7B trained in hours, 70B in days — not weeks.
The model weights are yours
After training you get the weights. Host with us, host yourself, host anywhere. No vendor lock-in.
Lower inference cost
A fine-tuned 7B model is 20–100x cheaper than GPT-4 on the same workload. ROI in weeks.
Measurable quality
You get an evaluation report: how the fine-tuned model beats the base and how much cheaper it is than GPT-4 on your task.
Engineering guidance
We help pick the model, method and prepare data. We don't leave you alone with the Hugging Face docs.
LLM Fine-Tuning Use Cases
Let's Talk About Your LLM
Tell us what you need the model for and what data you have. We'll propose a strategy, base model, method and quote.
Consultation is free. We reply within 24h.