uv-scripts/unsloth-jobs

🦥 Unsloth Training Scripts for HF Jobs

UV scripts for fine-tuning LLMs and VLMs using Unsloth on HF Jobs (on-demand cloud GPUs). UV handles dependency installation automatically, so you can run these scripts directly without any local setup.

These scripts can also be used or adapted by agents to train models for you.

Prerequisites

• A Hugging Face account
• The HF CLI installed and authenticated (hf auth login)
• A dataset on the Hub in the appropriate format (see format requirements below). A strong LLM agent can often convert your data into the right format if needed.

Data Formats

LLM Fine-tuning (SFT)

Requires conversation data in ShareGPT or similar format:

{
    "messages": [
        {"from": "human", "value": "What is the capital of France?"},
        {"from": "gpt", "value": "The capital of France is Paris."}
    ]
}

The script auto-converts common formats (ShareGPT, Alpaca, etc.) via standardize_data_formats. See mlabonne/FineTome-100k for a working dataset example.

VLM Fine-tuning

Requires images and messages columns:

{
    "images": [<PIL.Image>],  # List of images
    "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 in a park."}
            ]
        }
    ]
}

See davanstrien/iconclass-vlm-sft for a working dataset example, and davanstrien/iconclass-vlm-qwen3-best for a model trained with these scripts.

Continued Pretraining

Any dataset with a text column:

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

Use --text-column if your column has a different name.

Usage

View available options for any script:

uv run https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-lfm2.5.py --help

LLM fine-tuning

Fine-tune LFM2.5-1.2B-Instruct, a compact and efficient text model from Liquid AI:

hf jobs uv run \
  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-lfm2.5.py \
  --flavor a10g-small --secrets HF_TOKEN --timeout 4h \
  -- --dataset mlabonne/FineTome-100k \
     --num-epochs 1 \
     --eval-split 0.2 \
     --output-repo your-username/lfm-finetuned

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 \
  -- --dataset your-username/dataset \
     --trackio-space your-username/trackio \
     --output-repo your-username/my-model

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 your-username/domain-corpus \
     --text-column content \
     --max-steps 1000 \
     --output-repo your-username/domain-llm

With Trackio monitoring

hf jobs uv run \
  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-lfm2.5.py \
  --flavor a10g-small --secrets HF_TOKEN \
  -- --dataset mlabonne/FineTome-100k \
     --trackio-space your-username/trackio \
     --output-repo your-username/lfm-finetuned

Scripts

Script	Base Model	Task
sft-lfm2.5.py	LFM2.5-1.2B-Instruct	LLM fine-tuning (recommended)
sft-qwen3-vl.py	Qwen3-VL-8B	VLM fine-tuning
sft-gemma3-vlm.py	Gemma 3 4B	VLM fine-tuning (smaller)
continued-pretraining.py	Qwen3-0.6B	Domain adaptation

Common Options

Option	Description	Default
--dataset	HF dataset ID	required
--output-repo	Where to save trained model	required
--max-steps	Number of training steps	500
--num-epochs	Train for N epochs instead of steps	-
--eval-split	Fraction for evaluation (e.g., 0.2)	0 (disabled)
--batch-size	Per-device batch size	2
--gradient-accumulation	Gradient accumulation steps	4
--lora-r	LoRA rank	16
--learning-rate	Learning rate	2e-4
--merge-model	Upload merged model (not just adapter)	false
--trackio-space	HF Space for live monitoring	-
--run-name	Custom name for Trackio run	auto

Tips

• Use --max-steps 10 to verify everything works before a full run
• --eval-split 0.1 helps detect overfitting
• Run hf jobs hardware to see GPU pricing (A100-large ~$2.50/hr, L40S ~$1.80/hr)
• Add --streaming for very large datasets
• First training step may take a few minutes (CUDA kernel compilation)

Links

• HF Jobs Quickstart
• Unsloth Documentation
• UV Scripts Guide