---

name: llm-workflows description: "Plan ExecuTorch LLM export and on-device runner workflows, including native export_llm, Optimum ExecuTorch, tokenizer/model assets, backend choices, Android/iOS/C++ runners, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

llm-workflows

Use this sub-skill when the user wants to export or run LLMs with ExecuTorch, including Llama/Gemma/Qwen/Whisper-like examples, export_llm, Optimum ExecuTorch, C++ LLM runners, Android/iOS deployment, tokenizer handling, or QNN/iOS/CUDA/Metal LLM backend choices.

First Questions

1. Which model family and source weights/tokenizer are available locally?
2. Is the user using native ExecuTorch LLM export or Optimum ExecuTorch?
3. Which backend/runtime is the target: CPU, CUDA, Metal/MPS, Vulkan, QNN, iOS, Android, or C++ host?
4. Is this only command planning, or are large downloads/builds/device runs authorized?

Safe Planner

python scripts/plan_llm_export.py --model llama --backend cpu --method native --output-dir artifacts/llama-cpu
python scripts/plan_llm_export.py --model llama --backend qnn --method optimum --quantization 4bit

The planner prints required assets and command shapes; it does not download weights, build runners, or run inference.

Cross-Links

• Export fundamentals: ../export-runtime/SKILL.md.
• Backend choice: ../backend-selection/SKILL.md.
• Qualcomm LLM deployment: ../qualcomm/SKILL.md.
• Build runners/libraries: ../setup-build/SKILL.md.
• Performance and ETDump: ../profiling-debugging/SKILL.md.