Official HF skill — orchestrates the fine-tune on Hugging Face Jobs from
inside this coding session, so you don't leave the agent to run training.

BEFORE ANYTHING: IS FINE-TUNING EVEN RIGHT?
Worth 30 seconds. If you want the model to answer FROM your tickets,
that's RAG, not fine-tuning. If you want it to answer LIKE your team
(tone, format, escalation style), that's fine-tuning. Support tickets
usually want both — RAG for the facts, a fine-tune for the voice.
Assuming you want the voice:

DATA — the step that decides everything
  Format: instruction → response pairs. Ticket question → your team's
  actual reply. Strip PII first; a fine-tune memorizes.
  Quality over quantity: 500 clean, on-voice pairs beat 5,000 noisy
  ones. Drop the tickets where the reply was "see attached" or a
  one-word close — they teach the model nothing but shape.

MODEL
  A small open model (e.g. Qwen) is the right default here — support
  replies don't need a frontier model, and small means cheap to train
  and cheap to serve.

TRAINING — launched on HF Jobs
  The skill configures and submits the job: LoRA (not full fine-tune —
  cheaper, and you can swap adapters later), sane LR, a couple epochs.
  It runs on HF's GPUs; you watch from the session.

EVALUATE — hold out before you train, not after
  Reserve 10% of tickets the model never sees. After training, judge on
  those: does it match your team's tone and escalate correctly? Eyeball
  20 side by side. A loss number won't tell you if it sounds like you.

THE FAILURE TO WATCH FOR
Overfitting to phrasing — the model parrots exact past replies instead
of generalizing. If eval answers feel copy-pasted, you have too many
epochs or too little data variety. Fix the data, not the LR.

Share a few sanitized ticket→reply pairs and I'll spec the dataset
format the job expects.