Hugging Face Jobs Fine-Tuning
Prepare datasets, run budgeted LoRA fine-tuning on Hugging Face Jobs, and retrieve checkpoints with held-out evaluation.
Before & after
Same prompt, same agent — the only difference is the skill.
Fine-tuning a small model on your support tickets could work well. Here's the general process: 1. **Prepare your data** — format your tickets into input/output pairs 2. **Choose a base model** — a small open model like Qwen or Llama works for this 3. **Set up training** — configure hyperparameters (learning rate, epochs, batch size) 4. **Train** — run the fine-tuning job, ideally on a GPU 5. **Evaluate** — test on held-out examples 6. **Deploy** — serve the fine-tuned model You'll need GPU access for training. Services like Hugging Face, Modal, or a cloud provider can provide that. Consider whether fine-tuning is actually necessary — sometimes good prompting or RAG gets you there without the training cost. What does your ticket data look like?
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.
About this skill
A training job can finish successfully and still leave you with a model that learned the wrong tokens, memorized your test set, or saved its only checkpoint to temporary storage. This workflow turns a custom dataset into a reproducible Hugging Face Jobs fine-tuning run, with a bounded pilot, recoverable artifacts, and a comparison against the base model.
What it does
- Audits dataset schema, conversation boundaries, duplicates, and train/evaluation leakage.
- Configures LoRA training around sequence lengths, loss masking, effective batch size, and available GPU memory.
- Verifies the installed Jobs CLI, hardware options, package versions, authentication, and model access before submission.
- Plans paid runs with a stated budget, short pilot, timeout, and explicit stop conditions.
- Diagnoses out-of-memory failures, malformed training examples, unstable loss, and missing remote checkpoints.
- Retrieves the adapter and tokenizer, tests reloading, and compares held-out task performance with the unchanged base model.
What you get
A launch-ready training plan and inline training code or commands sized to your request. When execution is available and authorized, you also get job status, persisted checkpoint locations, evaluation results, and a Hub handoff that identifies the exact base model and adapter dependencies.
Best for
Developers fine-tuning small open-weight language models on custom support, extraction, classification, or instruction datasets using Hugging Face Jobs instead of local GPUs.
SKILL.md preview
---
name: hugging-face-jobs-fine-tuning
description: Prepare and execute small language-model fine-tuning on Hugging Face Jobs using dataset audits, LoRA pilots, explicit resource budgets, persistent checkpoints, and held-out comparisons. Use when the user says "fine-tune this model on Hugging Face Jobs", "train Qwen on my support tickets", "prepare my dataset for LoRA", "my training job keeps running out of memory", "my fine-tuned model is worse than the base model", "retrieve my training checkpoint", or "turn this training script into an HF Job".
---
# Hugging Face Jobs Fine-Tuning
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