Train models with Unsloth and Hugging Face Jobs for free | Keryc
Unsloth and Hugging Face Jobs let you fine-tune small language models quickly, cheaply and accessibly. Want to try LFM2.5-1.2B-Instruct and pay only a few dollars (or use free credits)? Here I walk you step by step: what Unsloth does and how to use code agents to automate the flow.
What the news offers and why it matters
Hugging Face announced integration with Unsloth for accelerated fine-tuning of small LLMs, plus free credits if you join the Unsloth Jobs Explorers organization. Unsloth promises roughly 2x faster training and about 60% less VRAM usage compared to standard methods.
The result: training small models like LiquidAI/LFM2.5-1.2B-Instruct can cost just a few dollars and run on devices with under 1 GB of memory. Why focus on small models? Because they’re cheap, fast to iterate on, and increasingly competitive at specific tasks.
LFM2.5-1.2B-Instruct is tuned for on-device deployment: CPUs, phones and laptops can serve what you fine-tune. That changes the equation if your product needs low latency or edge deployment. Who wouldn’t want inference that’s fast and local?
Requirements and free credits
Hugging Face account (required for HF Jobs).
Billing configured (verification; you can monitor usage on your billing page).
A Hugging Face token with write permissions.
Optional: a code agent like Open Code, Claude Code or Codex.
They also offer free credits and one month of Pro if you join the Unsloth Jobs Explorers org. If you’re trying this for the first time, that can cover several experiments — handy, right?
Technical flow: from the CLI to the push to the Hub
Install the hf CLI (mac or linux):
curl -LsSf https://hf.co/cli/install.sh | bash
Submit a job to HF Jobs using the ready example for LFM2.5:
The example uses unsloth.FastLanguageModel with load_in_4bit to shrink memory use and speed up inference. It complements this with PEFT (LoRA) to train only light adaptations, which significantly cuts compute cost.
Key fragment from the script:
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"],
)
trainer = SFTTrainer(...)
trainer.train()
trainer.push_to_hub()
Important points:
load_in_4bit=True reduces memory using quantization; ideal for small GPUs and edge deployment.
LoRA with r=16 and lora_alpha=32 lets you adapt the model without retraining all weights.
SFTTrainer and SFTConfig handle batching, gradient accumulation and reporting (for example to Trackio).
Integration with code agents (Claude Code, Codex, Open Code)
If you prefer an agent to generate and launch the job:
Claude Code: install the skill from its marketplace and add the Hugging Face Model Trainer skill.
Once the skill is installed, ask the agent: "Train LiquidAI/LFM2.5-1.2B-Instruct on mlabonne/FineTome-100k using Unsloth on HF Jobs." The agent will generate the UV script, submit it, report the job ID and give the Trackio monitoring link. Super useful if you want to automate from prompts.
Cost estimates and GPU selection
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
Practical tips:
Be specific with model and dataset IDs (for example LiquidAI/LFM2.5-1.2B-Instruct and trl-lib/Capybara). Agents validate combinations.
If you want them to use Unsloth explicitly, mention it in the prompt; otherwise the agent will pick based on budget.
Ask for cost estimates before launching large jobs.
Request Trackio monitoring to see loss curves in real time.
After submitting, check logs and metrics with the agent or via the HF Jobs console.
Recommendations to get started quickly
Use the free credits if you qualify: experiment with 1 epoch and a small dataset to validate the pipeline.
Start with a small per_device_train_batch_size and use gradient_accumulation_steps to simulate large batches without high VRAM.
Push to your own repo on the Hub to version and deploy easily.
Training models is no longer only for big teams with huge budgets. With tools like Unsloth, PEFT and Hugging Face Jobs you can iterate fast, spend little and deploy to real devices. Ready to try your first fine-tune?
