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name: model-export description: "Save and export Unsloth checkpoints as LoRA adapters, merged weights, GGUF files, Ollama Modelfiles, or Hugging Face Hub artifacts with safe path and tokenizer preflights." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Model Export

Use this sub-skill when the user needs to save or export an already trained Unsloth checkpoint: LoRA adapters, merged 16-bit weights, forced merged 4-bit weights, GGUF quantizations, Ollama Modelfiles, or Hub uploads.

Route first

• Route training setup, loading base models, LoRA configuration, and trainer code to core-training.
• Route CLI-wide syntax, dry-run config validation, aliases, and non-export commands to cli-workflows.
• Route Studio server startup, auth, browser/API runtime, log streaming transport, and UI connectivity to studio-runtime.
• Stay here for export method choice, checkpoint/output preflight, Python save calls, unsloth export, Studio export backend behavior, GGUF/Ollama conversion planning, and export failures.

Read or run

• Read references/api-reference.md for Python save APIs, CLI export arguments, Studio export backend contracts, format names, quantization names, and output artifacts.
• Read references/workflows.md for safe LoRA, merged, GGUF, Ollama, CLI, Studio, and Hugging Face Hub export workflows.
• Read references/troubleshooting.md when diagnosing missing checkpoints, wrong export type, tokenizer preservation, path safety, GGUF tools, Hub credentials, memory/sharding, or Unicode subprocess output.
• Run scripts/inspect_export_targets.py before heavy exports to validate checkpoint/output paths, tokenizer files, format choices, quantization names, and Hub flags without loading a model or converting files.

Safe default workflow

1. Identify the source checkpoint and whether it is a PEFT/LoRA adapter, full/base model, or existing GGUF artifact.
2. Run the bundled preflight helper with --checkpoint, --output, and --format; use --json when another tool will parse the result.
3. Choose lora for adapter-only sharing, merged-16bit before downstream conversion, merged-4bit only as a final compact bitsandbytes export, or gguf for llama.cpp/Ollama-style inference.
4. Keep tokenizer files with every local export; GGUF export requires a tokenizer and may create a Modelfile when Unsloth can map the base model to an Ollama template.
5. For Hub uploads, require an explicit repo id, token source, and private/public decision; never print tokens or embed them in scripts, logs, or reusable artifacts.