Axolotl

Use this repo skill when the user asks about Axolotl, axolotl CLI commands, Axolotl YAML configs, LLM fine-tuning recipes, dataset/preprocess issues, LoRA/QLoRA, preference tuning, GRPO/EBFT, vLLM-assisted training, model architecture support, DeepSpeed/FSDP/Ray scaling, or Axolotl-specific troubleshooting.

Axolotl is config-driven: a single YAML config selects the base model, adapter/training method, datasets, preprocessing, launch/runtime settings, and output paths.

Start Here

1. Identify the user’s primary task family before editing YAML.
2. Read the nearest sub-skill route below, then only open deeper references as needed.
3. Prefer safe static helpers first; run axolotl preprocess <config> --debug only in the user’s Axolotl environment when tokenizer/model access and runtime cost are acceptable.
4. Treat bundled helper scripts as triage tools, not replacements for Axolotl’s installed config-schema, preprocess, or training runtime.
5. Read references/repo-provenance.md before judging whether this skill matches a current Axolotl checkout.

Route By Task

User task	Read
Write or debug YAML config structure, dataset columns, chat_template, prompt strategies, preprocessing, sample packing, or schema issues	data-and-configs
Build SFT, LoRA/QLoRA, full fine-tune, or continual pretraining recipes; diagnose SFT/pretraining loss, OOM, checkpoint, or resume issues	sft-and-pretraining
Configure DPO, IPO, KTO, ORPO, SimPO, reward model, process reward model, paired/unpaired preference data, or preference loss settings	preference-tuning
Configure GRPO, EBFT, custom reward functions, vLLM rollout servers, async online RL, NeMo Gym, or Hatchery reward hooks	rl-and-rewards
Choose base models, tokenizers/processors, chat templates, LoRA targets, QLoRA, quantization/QAT/PTQ, multimodal settings, architecture quirks, or new model support	model-loading-and-adapters
Configure DeepSpeed, FSDP, Ray, SLURM/multi-node, tensor/context/sequence/expert parallelism, precision, kernels, profiling, or performance troubleshooting	distributed-and-performance
Use axolotl commands, launchers, agent-docs, config-schema, fetch, inference, evaluate, merge-lora, vllm-serve, quantize, install checks, or command construction	cli-and-operations

Common Entry Points

• axolotl preprocess config.yaml --debug checks rendered/tokenized samples and label masking before expensive training.
• axolotl train config.yaml launches training; use --launcher torchrun, --launcher accelerate, or --launcher python deliberately.
• axolotl inference config.yaml, axolotl merge-lora config.yaml, axolotl evaluate config.yaml, axolotl vllm-serve config.yaml, and axolotl quantize config.yaml are operational routes covered by cli-and-operations.
• axolotl agent-docs --list, axolotl agent-docs <topic>, and axolotl config-schema --field <field> are preferred installed-package truth checks when available.

Cross-Cutting Helpers

• references/troubleshooting.md covers install/import, optional dependency, config-first triage, hardware/backend, and workflow-routing symptoms.
• scripts/check_axolotl_environment.py performs a safe package/CLI/schema availability check without loading models or starting training.

Routing Notes

• Reward-model and process-reward-model training belong to preference-tuning; callable reward functions for GRPO/EBFT belong to rl-and-rewards.
• Config validation usually starts in data-and-configs, then narrows to method/model/distributed/CLI-specific static helpers, then escalates to axolotl config-schema or axolotl preprocess in the user’s runtime.
• Model downloads, gated repositories, GPU kernels, vLLM services, distributed launchers, and long training runs require user environment readiness and should not be inferred from static helper success.

Refresh Guidance

Run refresh-repo-skill when the Axolotl commit, package version, CLI entry points, config schema, docs, examples, or training APIs have changed relative to references/repo-provenance.md.