dgx-spark-playbooks/skills/dgx-spark-unsloth/SKILL.md

name	description
dgx-spark-unsloth	Optimized fine-tuning with Unsloth — on NVIDIA DGX Spark. Use when setting up unsloth on Spark hardware.

Unsloth on DGX Spark

Optimized fine-tuning with Unsloth

• Performance-first: It claims to speed up training (e.g. 2× faster on single GPU, up to 30× in multi-GPU setups) and reduce memory usage compared to standard methods.
• Kernel-level optimizations: Core compute is built with custom kernels (e.g. with Triton) and hand-optimized math to boost throughput and efficiency.
• Quantization & model formats: Supports dynamic quantization (4-bit, 16-bit) and GGUF formats to reduce footprint, while aiming to retain accuracy.
• Broad model support: Works with many LLMs (LLaMA, Mistral, Qwen, DeepSeek, etc.) and allows training, fine-tuning, exporting to formats like Ollama, vLLM, GGUF, Hugging Face.
• Simplified interface: Provides easy-to-use notebooks and tools so users can fine-tune models with minimal boilerplate.

Outcome: You'll set up Unsloth for optimized fine-tuning of large language models on NVIDIA Spark devices, achieving up to 2x faster training speeds with reduced memory usage through efficient parameter-efficient fine-tuning methods like LoRA and QLoRA.

Duration: 30-60 minutes for initial setup and test run

Full playbook: /home/runner/work/dgx-spark-playbooks/dgx-spark-playbooks/nvidia/unsloth/README.md