ZENKEI
We adapt open models to your domain, tone and tasks with LoRA and QLoRA, evaluate the result against the base, and deploy it, with fully private options.
Rated 4.9 out of 5 by clients across AI, software and quantitative projects.
LLM fine-tuning is further training a pretrained language model on your own examples, so it adapts to your domain, follows your format and handles your tasks better than a general model. ZenkeiX fine-tunes open-weight models with LoRA and QLoRA, proves the gain with evaluation, and deploys the result where you need it, including fully private. Not sure it is the right tool? Read RAG vs fine-tuning.
Whether you need a model that writes in your house style, classifies your documents, or runs a narrow expert task on modest hardware, we build the dataset, run the training and measure the result against the base model, with an on-premise deployment when your data cannot leave the building. It sits alongside our broader custom LLM development. For a real example, see AI Certa, a private QLoRA model we fine-tuned for EU AI Act compliance.
Not a bigger prompt. A model that learned your domain.
Adapt an open model to your field's vocabulary, data and tasks with LoRA or QLoRA, so it performs where a general model is vague.
Teach the model your format, tone and rules, so its output is consistent, on-brand and follows the structure your systems expect.
Train and deploy open-weight models on your own infrastructure, quantized to run efficiently, so the data and the model stay in-house.
An evaluation suite built around your tasks, so you can see the gain over the base model and the best checkpoint before anything ships.
One session on your data and tasks. We leave with the case fine-tuning should own, the metric it moves, and an honest call on whether RAG fits better.
We build, clean and structure the dataset, choose the right base model, and run LoRA or QLoRA with a controlled, logged training run.
Evaluated on your tasks, deployed where you need it, private if required. The fine-tune earns its place or we keep iterating.
LLM fine-tuning is further training a pretrained language model on your own examples so it adapts to your domain, tone and tasks. Instead of training a model from scratch, you adjust an existing one, which is far cheaper and needs much less data.
Fine-tuning changes how a model writes, reasons and follows your format. RAG gives the model your current documents at answer time. Fine-tuning fits tone, structure and domain skills; RAG fits factual, changing knowledge. Many production systems use both.
Less than most people expect. A few hundred to a few thousand high-quality examples often move the needle, and for a narrow task even fewer can work. We help you build, clean and structure that dataset before any training runs, because quality matters more than volume.
Open-weight families such as Llama, Mistral, Qwen and Gemma, using LoRA and QLoRA for efficient fine-tuning, plus full fine-tuning when a project calls for it. We pick the base model for the task, accuracy, latency and where it needs to run.
Yes. Because we fine-tune open-weight models, the result can be deployed on your own infrastructure or private cloud, quantized to run efficiently, so your data and the model stay in-house and nothing is sent to a third-party API.
Full fine-tuning updates all of the model's weights, which is powerful but heavy and expensive. LoRA and QLoRA train a small set of adapter weights on top of a frozen, often quantized base, so training is cheaper and the result is portable. LoRA is enough for most projects.
Thirty minutes on your data and tasks. No slides, no fluff.