Train AI models with Unsloth and Hugging Face Jobs for FREE

Train AI models with Unsloth and Hugging Face Jobs for FREE Train AI models with Unsloth and Hugging Face Jobs for FREE Published February 20, 2026 Update on GitHub Upvote 111 ben burtenshaw burtenshaw Daniel (Unsloth) danielhanchen unsloth Michael Han shimmyshimmer unsloth Maxime Labonne mlabonne LiquidAI Daniel van Strien davanstrien shaun smith evalstate 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.

This PriceChange is relevant to the technology intelligence record because it involves Hugging Face, GitHub, Claude, qwen. The source article should remain the factual reference for follow-up coverage.

• Train AI models with Unsloth and Hugging Face Jobs for FREE Published February 20, 2026 Update on GitHub Upvote 111 ben burtenshaw burtenshaw Daniel (Unsloth) danielhanchen unsloth Michael Han shimmyshimmer unsloth Maxime Labonne mlabonne LiquidAI Daniel van Strien davanstrien shaun smith evalstate 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.
• 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.