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.
Resources
1Install
npx skillscat add svngoku/coding-agents-skills/unsloth-hf-jobs Install via the SkillsCat registry.
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
Resources
Categories
Install
npx skillscat add svngoku/coding-agents-skills/unsloth-hf-jobs Install via the SkillsCat registry.