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name: training-and-optimization description: "Helps build LMFlow fine-tuning and optimization commands for full training, LoRA, QLoRA, LISA, and custom optimizers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Optimization

Use this sub-skill when the task is to fine-tune or continue training an LMFlow model, choose an optimizer variant, or prepare a safe command for a training run.

Typical Triggers

• finetune, train, LoRA, QLoRA, LISA
• custom optimizer, customized_optim, adamw_schedule_free, sgdp, adabelief
• accelerate, deepspeed, FSDP, wandb
• output_dir, overwrite_output_dir, resume_from_checkpoint

What This Sub-Skill Owns

• Full fine-tuning and parameter-efficient fine-tuning command construction.
• Custom optimizer name selection and common optimizer arguments.
• Safe output/checkpoint choices and W&B reporting choices.
• The command builder that prints a self-contained LMFlow training snippet.

Read These First

• references/workflows.md for the training variants and their required inputs.
• references/cli-reference.md for the key training dataclass fields.
• references/optimization-reference.md for the optimizer names and when to use them.
• references/troubleshooting.md for OOM, output-path, W&B, and optional dependency issues.
• scripts/build_finetune_command.py to render a copyable training command.

Cross-Links

• Dataset and conversation-template details live in ../data-and-templates/SKILL.md.
• Non-training generation and evaluation live in ../inference-and-evaluation/SKILL.md.
• Reward-model, DPO, RAFT, and LoRA-merge flows live in ../post-training-alignment/SKILL.md.

Workflow

1. Confirm the model, dataset, and output directory.
2. Choose the method: full, LoRA, QLoRA, LISA, or custom optimizer.
3. Decide whether W&B is enabled or explicitly disabled.
4. Check the dataset template and block-size settings.
5. Render the command with the builder script before a long run.

Common Decisions

• Use full training when the user wants the simplest baseline and memory is sufficient.
• Use LoRA for most parameter-efficient adaptation tasks.
• Use QLoRA when memory is tight and bitsandbytes is available.
• Use LISA when the user explicitly wants the memory-efficient layer-activation strategy.
• Use a custom optimizer only when the user already knows which optimizer variant they need.

What Not To Do

• Do not assume the command should run with the current checkout's shell scripts.
• Do not mix vLLM or SGLang concerns into this sub-skill.
• Do not hide output-overwrite or checkpoint-resume decisions.
• Do not claim a GPU workflow is validated just because a CPU import worked.