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name: cli-workflows description: "Use the Unsloth Typer CLI for training, inference, chat, export, Studio, connect, dry-run config validation, pass-through flags, and parser troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Unsloth CLI Workflows

Use this sub-skill when the user wants command-line Unsloth workflows instead of direct Python APIs: unsloth train, inference, chat, export, list-checkpoints, studio, run, or connect.

Route first

• Use unsloth train --dry-run before any expensive training to validate config resolution without model or dataset downloads.
• Route direct Python API code, trainer internals, and notebook-style finetuning to ../core-training/SKILL.md.
• Route export backend behavior, format tradeoffs, and checkpoint conversion details to ../model-export/SKILL.md.
• Route Studio backend/API internals and server implementation work to ../studio-runtime/SKILL.md.

Read or run

• Read references/cli-reference.md for the command catalog, safe command examples, Studio run alias behavior, connect targets, and pass-through rules.
• Read references/configuration.md for the YAML/JSON config schema, CLI override mapping, token precedence, and dry-run workflow.
• Read references/troubleshooting.md when diagnosing parser errors, missing config/model/dataset inputs, System32 guard failures, Studio exposure flags, connect side effects, or alias conflicts.
• Run scripts/unsloth_cli_smoke.py --help to see the safe smoke checker; run it against an installed unsloth CLI to validate help output and train --dry-run without downloads.
• Copy or adapt scripts/training_config_template.yaml as a starter config, then replace placeholder model/dataset/output values before a real training run.

Safe default workflow

1. Convert the user request into scripts/training_config_template.yaml-style YAML with explicit model, one dataset source, training.training_type, and output settings.
2. Run unsloth train --config config.yaml --dry-run and inspect the resolved YAML before starting training.
3. For one-shot inference or chat, prefer unsloth inference <model> <prompt> or unsloth chat <model>; add --no-server if a running Studio server should not be reused.
4. For a local Studio API server with a loaded model, use unsloth run --model <model-or-gguf-repo> or unsloth studio run --model <model-or-gguf-repo>; both use the same implementation.
5. For agent integration, start Studio first, then use unsloth connect <agent> --model <model> --no-launch to review environment/config changes before launching an agent.