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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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill
설치 방법 보기

포함된 파일(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(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

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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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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