unsloth-hf-jobs

name	unsloth-hf-jobs
description	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.

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

Resources

• HF Jobs Quickstart
• Unsloth Documentation
• UV Scripts Guide