Train AI models with Unsloth and Hugging Face Jobs for FREE

This blog post covers how to use Unsloth and Hugging Face Jobs for fast LLM fine-tuning (specifically LiquidAI/LFM2.5-1.2B-Instruct ) through coding agents like Claude Code and Codex. Unsloth provides ~2x faster training and ~60% less VRAM usage compared to standard methods, so training small models can cost just a few dollars.

Why a small model? Small language models like LFM2.5-1.2B-Instruct are ideal candidates for fine-tuning. They are cheap to train, fast to iterate on, and increasingly competitive with much larger models on focused tasks. LFM2.5-1.2B-Instruct runs under 1GB of memory and is optimized for on-device deployment, so what you fine-tune can be served on CPUs, phones, and laptops.

You will need

We are giving away free credits to fine-tune models on Hugging Face Jobs. Join the Unsloth Jobs Explorers organization to claim your free credits and one-month Pro subscription.

A Hugging Face account (required for HF Jobs)
Billing setup (for verification, you can monitor your usage and manage your billing in your billing page).
A Hugging Face token with write permissions
(optional) A coding agent (Open Code, Claude Code, or Codex)

Run the Job

If you want to train a model using HF Jobs and Unsloth, you can simply use the hf jobs CLI to submit a job.

First, you need to install the hf CLI. You can do this by running the following command:

# mac or linux
curl -LsSf https://hf.co/cli/install.sh | bash

Next you can run the following command to submit a job:

hf jobs uv run https://huggingface.co/datasets/unsloth/jobs/resolve/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

Check out the training script and Hugging Face Jobs documentation for more details.