# 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.

- Skill: `svngoku/unsloth-hf-jobs` (Agent Skill, multi-file: 9 files)
- Install (CLI): `npx skillmds@latest add svngoku/unsloth-hf-jobs`
- Raw SKILL.md: https://api.skillmd.com/api/skills/svngoku/unsloth-hf-jobs/raw
- Safety review: WARNING
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: svngoku (https://skillmd.com/u/svngoku)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/svngoku/unsloth-hf-jobs

---


# Unsloth Training on HF Jobs

Fine-tune LLMs and VLMs using [Unsloth](https://github.com/unslothai/unsloth) on [HF Jobs](https://huggingface.co/docs/hub/jobs) with UV scripts that handle dependencies automatically.

## Prerequisites

- Hugging Face account with [token](https://huggingface.co/settings/tokens)
- 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:

```python
{
    "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](https://huggingface.co/datasets/davanstrien/iconclass-vlm-sft)

### Continued Pretraining

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

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

## Usage Patterns

### VLM Fine-tuning

```bash
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

```bash
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

- [HF Jobs Quickstart](https://huggingface.co/docs/hub/jobs-quickstart)
- [Unsloth Documentation](https://docs.unsloth.ai/)
- [UV Scripts Guide](https://docs.astral.sh/uv/guides/scripts/)