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 10before full runs - Use
--eval-split 0.1to 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