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/.
843 B
843 B
| name | description |
|---|---|
| dgx-spark-pytorch-fine-tune | Use Pytorch to fine-tune models locally — on NVIDIA DGX Spark. Use when setting up pytorch-fine-tune on Spark hardware. |
Fine-tune with Pytorch
Use Pytorch to fine-tune models locally
This playbook guides you through setting up and using Pytorch for fine-tuning large language models on NVIDIA Spark devices.
Outcome: You'll establish a complete fine-tuning environment for large language models (1-70B parameters) on your NVIDIA Spark device. By the end, you'll have a working installation that supports parameter-efficient fine-tuning (PEFT) and supervised fine-tuning (SFT).
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/pytorch-fine-tune/README.md