What is
Fine-tuning?
The process of training a base LLM further on your own data to specialize its outputs.
Definition
Fine-tuning takes a pre-trained model and continues training it on a smaller, focused dataset. The result is a model that performs better on your specific task, usually at lower latency and cost than calling a frontier model. For high-volume tasks (classification, extraction, structured output) fine-tuned small models can be 10–40x cheaper than calling GPT-4 or Claude, with comparable quality on the narrow task.
Example
Fine-tuning a Llama 3 model on 50k support tickets to triage and tag incoming issues automatically.
How Vedwix uses Fine-tuning in client work
When a client has a high-volume task (over ~10k requests per day), we recommend a fine-tune over an API call almost every time. The cost difference is often 95%+.
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If you're building with Fine-tuning in production, we can help — from architecture review to full implementation.
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