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.

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.

Prerequisites

• 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)

Using HF Jobs CLI

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, install the hf CLI:

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

Next, 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

Using Coding Agents with Skills

Hugging Face model training skill lowers the barrier of entry to train a model by simply prompting.

Claude Code

• Add the marketplace: /plugin marketplace add huggingface/skills
• Browse available skills: /plugin
• Install the model trainer skill: /plugin install hugging-face-model-trainer@huggingface-skills

Codex

Install individual skills with $skill-installer:

$skill-installer install https://github.com/huggingface/skills/tree/main/skills/hugging-face-model-trainer

Once the skill is installed, ask your coding agent to train a model: Train LiquidAI/LFM2.5-1.2B-Instruct on mlabonne/FineTome-100k using Unsloth on HF Jobs

The agent will generate a training script, submit the job to HF Jobs, and provide a monitoring link via Trackio.

Pricing Overview

| Model Size | Recommended GPU | Approx Cost/hr | |---|---|---| | <1B params | t4-small | ~$0.40 | | 1-3B params | t4-medium | ~$0.60 | | 3-7B params | a10g-small | ~$1.00 | | 7-13B params | a10g-large | ~$3.00 |

Best Practices

• Be specific about the model and dataset to use, and include Hub IDs.
• Mention Unsloth explicitly if you want it used.
• Ask for cost estimates before launching large jobs.
• Request Trackio monitoring for real-time loss curves.