Sovereign Fine‑Tuning Playbook for Arabic Workloads

Fine‑tuning is most valuable when you can keep sensitive data inside your own boundary while improving domain accuracy. This playbook focuses on how to build a high‑quality Arabic model without relying on external services or brittle translation layers.

1. Data Strategy

The fastest way to break a fine‑tuned model is to feed it inconsistent data. Start with clear intent categories (support, policy, product, operations) and use native Arabic responses rather than translated content. Remove duplicates, redact sensitive fields, and keep a holdout set for evaluation.

{"messages": [{"role": "system", "content": "You are a banking assistant."}, {"role": "user", "content": "كيف أفتح حساباً للشركات؟"}, {"role": "assistant", "content": "يمكنك فتح حساب شركات عبر ..."}]}
{"messages": [{"role": "system", "content": "You are a banking assistant."}, {"role": "user", "content": "ما هي رسوم التحويل؟"}, {"role": "assistant", "content": "تختلف الرسوم حسب ..."}]}

2. Tuning Recipe

Keep the recipe simple: choose a base model, run supervised fine‑tuning, and validate for tone, format, and factual alignment. Start small, then expand once the evaluation signal is stable.

model: mx4-atlas-core
train:
  epochs: 3
  learning_rate: 1.0
  batch_size: auto
validation:
  holdout_ratio: 0.15
  metrics: ["format", "faithfulness", "task_success"]

3. Evaluation Loop

Evaluate for real‑world tasks: support answers, policy wording, and domain‑specific workflows. Use human review and targeted test prompts to catch regressions early.

4. Deployment Checklist

Ship the tuned model to your Atlas deployment, enable versioned rollouts, and monitor request drift. Keep a rollback path to the previous model version.

• Version models and route traffic gradually.
• Keep telemetry local; export only what your policy allows.
• Re‑run evaluation after significant data or product changes.

5. Deployment Example

Below is a simple staged rollout example. Adjust naming and routing to match your Atlas setup.

1. Register the new model version in your local registry.
2. Route 10% of traffic to the new model and monitor quality signals.
3. Promote to 50% once metrics stabilize, then 100% after review.

release:
  model: mx4-atlas-core-v2
  stages:
    - traffic: 10%
      checks: ["quality", "latency"]
    - traffic: 50%
      checks: ["quality", "support_tickets"]
    - traffic: 100%
      checks: ["final_review"]

6. Common Pitfalls

Most failures come from weak data or unclear success criteria. Avoid tuning on noisy datasets, mixing incompatible styles, or shipping without review.

• Over‑fitting to a narrow intent set.
• Using translated answers instead of native Arabic responses.
• Skipping a human review loop before rollout.

7. Launch Checklist