---
title: "Train AI models with Unsloth and Hugging Face Jobs for FREE"
description: "The post presents a workflow for fine-tuning the LiquidAI/LFM2.5-1.2B-Instruct small language model with Unsloth on Hugging Face Jobs, including optional use of Claude Code, Codex, or another coding agent. It explains that Unsloth is described as providing about 2x faster training and 60% lower VRAM use than standard methods, while the model runs under 1GB of memory and is optimized for on-device deployment. The tutorial covers claiming free Hugging Face Jobs credits through the Unsloth Jobs Explorers organization, installing the hf CLI or a model-training skill, and submitting a job with a dataset, GPU flavor, timeout, and output repository. An agent can generate a UV training script using 4-bit loading and LoRA, submit the managed cloud-GPU job, report Trackio monitoring details, and push the trained model to the Hugging Face Hub."
---

# Train AI models with Unsloth and Hugging Face Jobs for FREE

[Hugging Face](https://yomu.fyi/company/hugging-face) · ben burtenshaw, Daniel (Unsloth), Michael Han, Maxime Labonne, Daniel van Strien, shaun smith · Feb 20, 2026

**Type:** Tutorial

## Summary

The post presents a workflow for fine-tuning the LiquidAI/LFM2.5-1.2B-Instruct small language model with Unsloth on Hugging Face Jobs, including optional use of Claude Code, Codex, or another coding agent. It explains that Unsloth is described as providing about 2x faster training and 60% lower VRAM use than standard methods, while the model runs under 1GB of memory and is optimized for on-device deployment. The tutorial covers claiming free Hugging Face Jobs credits through the Unsloth Jobs Explorers organization, installing the hf CLI or a model-training skill, and submitting a job with a dataset, GPU flavor, timeout, and output repository. An agent can generate a UV training script using 4-bit loading and LoRA, submit the managed cloud-GPU job, report Trackio monitoring details, and push the trained model to the Hugging Face Hub.

## Context

The post addresses the cost and complexity of fine-tuning language models by focusing on a small model that is cheap to train, fast to iterate on, and suitable for on-device deployment. It also describes free credits available through the Unsloth Jobs Explorers organization.

## Approach / What changed

Use Unsloth with Hugging Face Jobs to submit a managed GPU training job through the hf CLI or a coding-agent skill. The generated training script loads the model in 4-bit mode, applies LoRA through PEFT, trains with SFTTrainer, reports to Trackio, and pushes the resulting model to a Hugging Face Hub repository.

## Takeaways

- Unsloth is described as delivering about 2x faster training and approximately 60% lower VRAM usage than standard methods.
- The example uses 4-bit model loading, LoRA configuration, SFTTrainer, one training epoch, and Trackio reporting for monitoring.
- Hugging Face Jobs provides managed cloud GPUs, with listed approximate hourly costs ranging from $0.40 for a T4-small to $3.00 for an A10G-large.

**Tags:** [Deployment](https://yomu.fyi/topic/deployment), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Python](https://yomu.fyi/topic/python)

- Source: [Hugging Face](https://huggingface.co/blog/unsloth-jobs)
- Source URL: https://huggingface.co/blog/unsloth-jobs
- Ingested by Yomu: 2026-08-28T20:00:49.728Z

[Read original post](https://huggingface.co/blog/unsloth-jobs)