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unsloth-hf-jobs

Fine-tune LLMs and VLMs using Unsloth on HF Jobs (Hugging Face on-demand cloud GPUs). Use when users want to fine-tune language models, train VLMs (Vision Language Models), do continued pretraining, domain adaptation, or run UV scripts on HF Jobs. Triggers on requests involving Unsloth training, HF Jobs GPU training, Qwen3-VL fine-tuning, Gemma VLM training, or LoRA fine-tuning on cloud GPUs.

svngoku/coding-agents-skills

インストール方法を見る

含まれるファイル（9）

SKILL.md5.2 KB
eval.yaml986 B
graders/check.py3.9 KB
instructions/qwen3-vl-finetune-hf-jobs.md1.5 KB
rubrics/quality.md1.5 KB
scripts/continued-pretraining.py12.0 KB
scripts/sft-gemma3-vlm.py11.0 KB
scripts/sft-qwen3-vl.py19.6 KB
solutions/reference-qwen3-vl-finetune-hf-jobs1.6 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Unsloth Training on HF Jobs

Fine-tune LLMs and VLMs using Unsloth on HF Jobs with UV scripts that handle dependencies automatically.

Prerequisites

Hugging Face account with token
HF CLI: curl -LsSf https://hf.co/cli/install.sh | bash
Dataset on the Hub (see data formats below)

Available Scripts

Script	Base Model	Task
`scripts/sft-qwen3-vl.py`	Qwen3-VL-8B	VLM fine-tuning
`scripts/sft-gemma3-vlm.py`	Gemma 3 4B	VLM fine-tuning (smaller)
`scripts/continued-pretraining.py`	Qwen3-0.6B	Domain adaptation

Remote URL (for HF Jobs): https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/

Data Formats

VLM Fine-tuning

Requires images and messages columns:

{
    "images": [<PIL.Image>],
    "messages": [
        {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What's in this image?"}]},
        {"role": "assistant", "content": [{"type": "text", "text": "A golden retriever playing fetch."}]}
    ]
}

Example dataset: davanstrien/iconclass-vlm-sft

Continued Pretraining

Any dataset with a text column (use --text-column if named differently):

{"text": "Your domain-specific text here..."}

Usage Patterns

VLM Fine-tuning

hf jobs uv run \
  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-qwen3-vl.py \
  --flavor a100-large --secrets HF_TOKEN --timeout 4h \
  -- --dataset <username>/<dataset> \
     --num-epochs 1 \
     --eval-split 0.2 \
     --output-repo <username>/<model-name>

Continued Pretraining

hf jobs uv run \
  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/continued-pretraining.py \
  --flavor a100-large --secrets HF_TOKEN \
  -- --dataset <username>/<dataset> \
     --text-column content \
     --max-steps 1000 \
     --output-repo <username>/<model-name>

With Trackio Monitoring

Add --trackio-space <username>/trackio to any command for live monitoring.

Common Options

Option	Description	Default
`--dataset`	HF dataset ID	required
`--output-repo`	Where to save trained model	required
`--max-steps`	Training steps	500
`--num-epochs`	Train for N epochs (overrides steps)	-
`--eval-split`	Fraction for evaluation	0
`--batch-size`	Per-device batch size	2
`--gradient-accumulation`	Accumulation steps	4
`--lora-r`	LoRA rank	16
`--learning-rate`	Learning rate	2e-4
`--merge-model`	Upload merged model (not just adapter)	false
`--streaming`	Stream large datasets	false

Quick Tips

Verify setup with --max-steps 10 before full runs
Use --eval-split 0.1 to detect overfitting
Check GPU pricing: hf jobs hardware (A100-large ~$2.50/hr, L40S ~$1.80/hr)
First step may take minutes (CUDA kernel compilation)
View script options: uv run <script-url> --help

Anti-Patterns to Avoid

Skipping the smoke test — always verify setup with --max-steps 10 before a full run; config errors cost GPU hours
Wrong data format for VLMs — VLM fine-tuning requires images + messages columns with image + text content parts; plain text datasets silently fail or train nonsense
Forgetting --secrets HF_TOKEN — the job fails at model download/login; pass the secret explicitly
Batch size 1 without gradient accumulation — on A100 with 4-bit LoRA you can afford larger batches; accumulation of 1 wastes throughput
Uploading only the adapter when you need a merged model — set --merge-model if the target is deployment, not another fine-tuning pass
Training without --eval-split — you cannot detect overfitting or pick a checkpoint without a held-out split
Ignoring --streaming on huge datasets — disk may fill up; stream when the dataset doesn't fit locally

When to Use / Not Use

Use this skill when:

Fine-tuning LLMs or VLMs with Unsloth on HF on-demand cloud GPUs
Doing LoRA/QLoRA fine-tuning, continued pretraining, or domain adaptation via UV scripts
You have no local GPU or want ephemeral, pay-per-hour training

Do NOT use this skill when:

You have local GPUs and prefer on-premises training — use the unsloth skill directly
The model works with prompting/RAG and doesn't need fine-tuning at all
You need full control over a custom training stack (TRL, custom loops) — this skill is scoped to the provided UV scripts

Resources

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