--- title: Train AI models with Unsloth and Hugging Face Jobs for FREE link: https://huggingface.co/blog/unsloth-jobs source: huggingface-co published: 2026-02-20T00:00:00Z updated: 2026-02-20T00:00:00Z first_seen: 2026-10-04T12:50:30.804479567Z content: extracted html: 2026-02-20-train-ai-models-with-unsloth-and-hugging-face-jobs-for-free.html --- This blog post covers how to use [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face Jobs for fast LLM fine-tuning (specifically [`LiquidAI/LFM2.5-1.2B-Instruct`](https://huggingface.co/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. [![Watch the video](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/unsloth-jobs/screenshot.png)](http://www.youtube.com/watch?feature=player_embedded&v=Gh5P4niIFNA) ## You will need We are giving away free credits to fine-tune models on Hugging Face Jobs. Join the [Unsloth Jobs Explorers](https://huggingface.co/unsloth-jobs) organization to claim your free credits and one-month Pro subscription. - A [Hugging Face](https://huggingface.co) account (required for HF Jobs) - Billing setup (for verification, you can monitor your usage and manage your billing in your [billing page](https://huggingface.co/settings/billing)). - 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: ```sh 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](https://huggingface.co/datasets/unsloth/jobs/blob/main/sft-lfm2.5.py) and [Hugging Face Jobs documentation](https://huggingface.co/docs/hub/jobs) for more details. ## Installing the Skill Hugging Face model training skill lowers barrier of entry to train a model by simply prompting. First, install the skill with your coding agent. ### Claude Code Claude Code discovers skills through its [plugin system](https://code.claude.com/docs/en/discover-plugins), so we need to install the Hugging Face skills first. To do so: 1. Add the marketplace: ```text /plugin marketplace add huggingface/skills ``` 2. Browse available skills in the `Discover` tab: ```text /plugin ``` 3. Install the model trainer skill: ```text /plugin install hugging-face-model-trainer@huggingface-skills ``` For more details, see the [documentation](https://huggingface.co/docs/hub/en/agents-skills) on using the hub with skills or the Claude Code [Skills docs](https://code.claude.com/docs/en/skills). ### Codex Codex discovers skills through [`AGENTS.md`](https://developers.openai.com/codex/guides/agents-md) files and [`.agents/skills/`](https://developers.openai.com/codex/skills) directories. Install individual skills with `$skill-installer`: ```text $skill-installer install https://github.com/huggingface/skills/tree/main/skills/huggingface-llm-trainer ``` For more details, see the [Codex Skills docs](https://developers.openai.com/codex/skills) and the [AGENTS.md guide](https://developers.openai.com/codex/guides/agents-md). ### Anything else A generic install method is simply to clone the [skills repository](https://github.com/huggingface/skills) and copy the [skill](https://github.com/huggingface/skills/tree/main/skills/huggingface-llm-trainer) to your agent's skills directory. ```text git clone https://github.com/huggingface/skills.git mkdir -p ~/.agents/skills && cp -R skills/skills/hugging-face-model-trainer ~/.agents/skills/ ``` ## Quick Start Once the skill is installed, ask your coding agent to train a model: ```text Train LiquidAI/LFM2.5-1.2B-Instruct on mlabonne/FineTome-100k using Unsloth on HF Jobs ``` The agent will generate a training script based on an [example in the skill](https://github.com/huggingface/skills/blob/main/skills/huggingface-llm-trainer/scripts/unsloth_sft_example.py), submit the training to HF Jobs, and provide a monitoring link via Trackio. ## How It Works Training jobs run on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs), fully managed cloud GPUs. The agent: 1. Generates a UV script with inline dependencies 2. Submits it to HF Jobs via the `hf` CLI 3. Reports the job ID and monitoring URL 4. Pushes the trained model to your Hugging Face Hub repository ### Example Training Script The skill generates scripts like this based on the example in the [skill](https://github.com/huggingface/skills/blob/main/skills/huggingface-llm-trainer/scripts/unsloth_sft_example.py). ```python # /// script # dependencies = ["unsloth", "trl>=0.12.0", "datasets", "trackio"] # /// from unsloth import FastLanguageModel from trl import SFTTrainer, SFTConfig from datasets import load_dataset model, tokenizer = FastLanguageModel.from_pretrained( "LiquidAI/LFM2.5-1.2B-Instruct", load_in_4bit=True, max_seq_length=2048, ) model = FastLanguageModel.get_peft_model( model, r=16, lora_alpha=32, lora_dropout=0, target_modules=[ "q_proj", "k_proj", "v_proj", "out_proj", "in_proj", "w1", "w2", "w3", ], ) dataset = load_dataset("trl-lib/Capybara", split="train") trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=dataset, args=SFTConfig( output_dir="./output", push_to_hub=True, hub_model_id="username/my-model", per_device_train_batch_size=4, gradient_accumulation_steps=4, num_train_epochs=1, learning_rate=2e-4, report_to="trackio", ), ) trainer.train() trainer.push_to_hub() ``` | 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 | For a full overview of Hugging Face Spaces pricing, check out the guide [here](https://huggingface.co/docs/hub/en/spaces-overview#hardware-resources). ## Tips for Working with Coding Agents - Be specific about the model and dataset to use, and include Hub IDs (for example, `Qwen/Qwen2.5-0.5B` and `trl-lib/Capybara`). Agents will search for and validate those combinations. - Mention Unsloth explicitly if you want it used. Otherwise, the agent will choose a framework based on the model and budget. - Ask for cost estimates before launching large jobs. - Request Trackio monitoring for real-time loss curves. - Check job status by asking the agent to inspect logs after submission. ## Resources - [Hugging Face Skills Repository](https://github.com/huggingface/skills) - [Free credits for Unsloth Jobs Explorers](https://huggingface.co/unsloth-jobs) - [Unsloth Tutorial on Hugging Face Jobs](https://unsloth.ai/docs/basics/inference-and-deployment/deploying-with-hugging-face-jobs) - [Example Unsloth Jobs scripts](https://huggingface.co/datasets/unsloth/jobs)