Arman Afzar

AI Copilots & Agents

Evaluating language models for Persian: what to measure before fine-tuning

A model that excels in English isn’t necessarily good in Persian or your organisation’s vocabulary. A simple evaluation framework.

The Arman Afzar team2 min read

Model choice for Persian projects is usually made from English leaderboards — but Persian errors are different in kind: zero-width non-joiners, numerals, administrative terms and long sentences.

Build your own test set

Collect one to two hundred real questions from your domain, write the correct answer for each, and add some deliberately hard ones — Persian dates or rial amounts, for example.

Four criteria

Answer correctness, faithfulness to the source (in RAG), Persian language quality and instruction-following. Score each separately so you know which weakness fine-tuning must fix.

After fine-tuning

Run the same test set again. A before-and-after report is one of the deliverables of our enterprise LLM projects, because without it nobody can judge whether fine-tuning was worth it.

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  2. 02Automation without a baseline: why AI projects can’t show resultsIf you don’t know how long a process takes today, you’ll never be able to say how much automation helped.
  3. 03An LLM on your own servers: where to startWhy organisations run language models on-premise, and what a good pilot looks like.

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