mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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a680d0472b
Adds a Claude Code plugin structure that exposes each NVIDIA DGX Spark
playbook as a triggerable skill, with an index skill ('dgx-spark') that
routes users to the right leaf based on intent and encodes the
relationship graph between playbooks (prerequisites, alternatives,
composes-with, upgrade paths).
Structure:
- overrides/*.md hand-curated frontmatter + Related sections
- scripts/generate.mjs zero-dep Node generator: nvidia + overrides → skills
- scripts/install.sh symlinks skills into ~/.claude/skills (--plugin mode available)
- skills/ committed, browsable, installable without Node
- .github/workflows/ auto-regenerates skills/ when playbooks/overrides change
Initial curated leaves: ollama, open-webui, vllm, connect-to-your-spark.
Remaining 37 leaves use generator fallback (title + tagline + summary
extracted from README) and can be curated incrementally via overrides/.
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1.5 KiB
| 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: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/unsloth/README.md