Industry Scenario — Media & Publishing

The team: a publisher's editorial + tech team. The goal: on-brand drafting, style-consistent summaries, and long-article assistance — owned, not rented by the token.

The journey

Build · 1 — connect your data

• Connect the editorial archive (published pieces, style guides, past drafts) as the tuning corpus. Metadata only — no copying the corpus out.

Build · 2 — train the model

• Fine-tune on past published pieces (instruction/output: draft, headline, summary) so the model writes in the house style.
• For long-article assistance, pick a long-context base (e.g. Kimi K3 / Llama 4 class) — context length is the choice you can't fix later.
• GPU smoke validates each run; confidence cache flags risky configs.

Build · 3 — post-train for format

• GRPO with a verifiable reward (e.g. headline within N chars, summary within a word budget, valid JSON for metadata) so the model learns to obey the house format — not just imitate it.

Ship — your own editorial API

• Deploy as a real-time endpoint; canary new styles before rollout.
• Gateway with per-user API keys + spending limits so the newsroom can call it without budget scares.

Monitor — keep the voice on-pitch

• Drift schedule on input (briefs change shape over time); self-heal keeps the endpoint up.

Own it — export the asset

• Export the tuned model (safetensors / GGUF) — the brand-voice weights are an editorial asset that moves with you, no vendor lock-in.

What you own at the end

• An on-brand drafting/summary model tuned on your archive.
• Format-disciplined output via verifiable-reward post-training.
• Exportable weights — your voice, your asset.

Key docs

Fine-tune · Post-train · Model catalog · Ship · Gateway · Export