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axolotl

Routes agents working with Axolotl config-driven LLM fine-tuning, datasets, SFT, preference tuning, GRPO/EBFT, model adapters, distributed training, CLI operations, and troubleshooting.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill

See how to install

Files included (44)

SKILL.md4.8 KB
references/repo-provenance.md1.9 KB
references/repo-routing-metadata.json566 B
references/troubleshooting.md3.7 KB
scripts/check_axolotl_environment.py3.9 KB
sub-skills/cli-and-operations/references/cli-reference.md5.2 KB
sub-skills/cli-and-operations/references/operations.md7.4 KB
sub-skills/cli-and-operations/references/troubleshooting.md7.2 KB
sub-skills/cli-and-operations/scripts/axolotl_command_builder.py6.7 KB
sub-skills/cli-and-operations/scripts/check_axolotl_install.py6.4 KB
sub-skills/cli-and-operations/SKILL.md2.9 KB
sub-skills/data-and-configs/references/configuration.md5.2 KB
sub-skills/data-and-configs/references/data-formats.md6.4 KB
sub-skills/data-and-configs/references/troubleshooting.md5.8 KB
sub-skills/data-and-configs/scripts/inspect_chat_dataset.py13.0 KB
sub-skills/data-and-configs/scripts/validate_axolotl_config.py19.0 KB
sub-skills/data-and-configs/SKILL.md2.6 KB
sub-skills/distributed-and-performance/references/distributed-training.md7.5 KB
sub-skills/distributed-and-performance/references/performance-options.md6.3 KB
sub-skills/distributed-and-performance/references/troubleshooting.md6.7 KB
sub-skills/distributed-and-performance/scripts/check_distributed_config.py19.3 KB
sub-skills/distributed-and-performance/SKILL.md4.0 KB
sub-skills/model-loading-and-adapters/references/adapters-and-quantization.md7.3 KB
sub-skills/model-loading-and-adapters/references/model-selection.md8.1 KB
sub-skills/model-loading-and-adapters/references/new-model-support.md7.7 KB
sub-skills/model-loading-and-adapters/references/troubleshooting.md6.9 KB
sub-skills/model-loading-and-adapters/scripts/check_model_config.py23.3 KB
sub-skills/model-loading-and-adapters/SKILL.md4.1 KB
sub-skills/preference-tuning/references/data-formats.md7.3 KB
sub-skills/preference-tuning/references/troubleshooting.md8.5 KB
sub-skills/preference-tuning/references/workflows.md7.9 KB
sub-skills/preference-tuning/scripts/check_preference_dataset.py12.3 KB
sub-skills/preference-tuning/SKILL.md3.4 KB
sub-skills/rl-and-rewards/references/ebft.md5.0 KB
sub-skills/rl-and-rewards/references/grpo.md6.2 KB
sub-skills/rl-and-rewards/references/reward-functions.md5.1 KB
sub-skills/rl-and-rewards/references/troubleshooting.md6.4 KB
sub-skills/rl-and-rewards/scripts/validate_reward_function.py9.4 KB
sub-skills/rl-and-rewards/SKILL.md3.5 KB
sub-skills/sft-and-pretraining/references/training-options.md6.0 KB
sub-skills/sft-and-pretraining/references/troubleshooting.md7.6 KB
sub-skills/sft-and-pretraining/references/workflows.md6.1 KB
sub-skills/sft-and-pretraining/scripts/make_minimal_sft_config.py8.3 KB
sub-skills/sft-and-pretraining/SKILL.md3.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Identify the user’s primary task family before editing YAML.
Read the nearest sub-skill route below, then only open deeper references as needed.
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.
Treat bundled helper scripts as triage tools, not replacements for Axolotl’s installed config-schema, preprocess, or training runtime.
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.

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Install

Install with a command

npx skills add https://github.com/VectorSpaceLab/AREX-Skill --skill axolotl

Paste it into your terminal and run it (requires Node.js). You can choose which agents to install it for, such as Claude Code or Codex.

Ask your agent

Please install the following Agent Skill.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/axolotl

First review SKILL.md and the bundled files and confirm there is nothing suspicious (such as sending data externally or reading credentials). Then place it in ~/.claude/skills/axolotl/ for Claude Code, or ~/.agents/skills/axolotl/ for Codex.

Send this text to Claude Code or Codex as is. It checks the contents before installing.

View source on GitHub

Repository: VectorSpaceLab/AREX-Skill
Stars: 340
Forks: 26
Category: Data & AI
License: Apache-2.0
Original language: English
Last updated: Sep 3, 2026

A skill is a set of instructions and scripts for an agent. cccskills does not guarantee that skills are safe. Review the contents before you install.