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Get a model that speaks your domain and costs less to run. Talk to ProsperaSoft about whether fine-tuning is right for your use case.
Custom LLM Training
We fine-tune open-source models such as Llama, Mistral, Qwen and Gemma with LoRA and QLoRA using Hugging Face tooling, and use the hosted fine-tuning APIs of OpenAI and cloud platforms when that is simpler. Fine-tuned models can then run on your own GPUs, in your cloud or on managed endpoints.
Fine-tuning teaches style, format and task behaviour; it is not the best way to teach facts that change. For knowledge we usually combine a tuned model with retrieval-augmented generation, and we compare both approaches on your data before you invest.
Why ProsperaSoft for Fine-Tuning
The model is only as good as the training data. Most of our effort goes into collecting, cleaning, labelling and balancing examples, and into an evaluation set that shows whether the tuned model is really better.
Our PyTorch and ML engineers handle the full path from data pipelines and training runs to quantization, serving and monitoring, so the result is a model in production, not a notebook.
Fine-Tuning Services
Feasibility Assessment
Compare prompting, RAG and fine-tuning on your tasks with a small evaluation before committing budget.
Training Data Preparation
Collect, clean, anonymise and format examples from your documents, tickets and conversations.
Open-Source Model Fine-Tuning
LoRA and QLoRA fine-tuning of Llama, Mistral, Qwen and Gemma on your cloud or GPU servers.
Hosted Model Fine-Tuning
Fine-tuning through OpenAI and cloud AI platforms when managed hosting is preferred.
Evaluation and Safety
Task metrics, regression tests and safety checks comparing tuned and base models.
Deployment and Serving
Quantized models served with vLLM or managed endpoints, with autoscaling and monitoring.
When Fine-Tuning Helps
Consistent Output Formats
Structured outputs and house style that prompts alone do not hold reliably.
Domain Language
Medical, legal, technical or internal terminology used correctly.
Lower Cost at Scale
A small tuned model replacing a large general model for a narrow task.
Private Deployment
Models that run fully inside your environment for sensitive data.
TECHNICAL EXPERTISE
Frequently asked questions
When should we fine-tune an LLM instead of using RAG?
Fine-tune to change behaviour, style, format or to make a small model perform a narrow task well. Use RAG to give a model up-to-date facts and documents. Many production systems use both.
How much training data do we need?
For many tasks a few hundred to a few thousand high-quality examples are enough with LoRA fine-tuning. Quality and coverage matter more than volume.
Which models can you fine-tune?
Open-source models such as Llama, Mistral, Qwen and Gemma, and hosted models through the OpenAI and cloud provider fine-tuning services.
Can the fine-tuned model run on our own servers?
Yes. Open-source fine-tuned models can be quantized and served on your own GPUs or in your private cloud with tools such as vLLM.
How do you know the fine-tuned model is better?
We build an evaluation set from your real tasks before training and compare the tuned model with the base model and with prompting and RAG alternatives.