---

name: xtuner description: "Use XTuner for large-model SFT, pretraining, MLLM fine-tuning, data preparation, model/backend configuration, RL/GRPO post-training, and legacy XTuner tooling." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

XTuner repo skill

Use this root skill when a task names XTuner or asks for operating guidance around XTuner V1 large-model training, data preparation, MoE/model backend choices, GRPO/RL post-training, or legacy XTuner utilities.

XTuner V1 is a PyTorch-based training engine aimed at large dense, MoE, and multimodal models. Most real training/RL work needs accelerator resources, model/data assets, and optional backend packages. This generated skill gives safe command builders, validators, and routing guidance; it does not require the original source checkout.

Start here

1. Check the package and backend surface. Read references/package-and-environment.md, then run scripts/check_xtuner_install.py in the user's XTuner environment.
2. Route to the right workflow. Use the sub-skill map below. Do not mix V1 direct CLIs with the old legacy xtuner MODE ... router.
3. Validate data before training. Use data-preparation for SFT, MLLM, and RL JSONL schemas before building training or RL launch commands.
4. Treat accelerator checks as real gates. CPU imports and synthetic cases do not prove CUDA/NPU/FP8/Ray rollout execution. Use references/native-verification.md before claiming backend success.
5. Use troubleshooting early. Cross-cutting install/import/backend failures are in references/troubleshooting.md; workflow-specific failures live in each sub-skill.

Sub-skill routes

Task signal	Read
V1 SFT, pretraining, MLLM fine-tuning, TrainingArguments, TrainerConfig, torchrun, FSDP/checkpoint/resume, training logs	sub-skills/training/SKILL.md
SFT/MLLM/RL JSONL validation, media paths, chat templates, tokenizer configs, dataset caching, packing, GSM8K conversion	sub-skills/data-preparation/SKILL.md
Dense/MoE/VLM model configs, get_model_config, FSDP/TP/EP/HSDP, attention/router/dispatcher, FP8, CUDA/NPU optional backends	sub-skills/model-backends/SKILL.md
RL/GRPO launch planning, Ray resources, rollout engines, agent loops, judgers, reward data, replay buffers, evaluation, traces	sub-skills/reinforcement-learning/SKILL.md
Legacy xtuner MODE ..., old config-zoo search/copy, model conversion planning, chat/eval/preprocess tools, HF Trainer examples	sub-skills/cli-and-tools/SKILL.md

Package facts to remember

• Public package/distribution name: xtuner.
• Source snapshot used for this skill: see references/repo-provenance.md.
• Python requirement from package metadata: >=3.10.
• Verified package version in the construction environment: 0.2.0.
• V1 installed-package entry points used by generated helpers:
  • SFT/training: python -m xtuner.v1.train.cli.sft ...
  • RL: python -m xtuner.v1.train.cli.rl ...
• Optional RL support uses Ray plus a rollout backend such as LMDeploy, SGLang, or vLLM.
• Optional acceleration surfaces include FlashAttention, bitsandbytes quantization, GroupedGEMM, AdaptiveGEMM, FP8 kernels, DeepEP, and NPU/vendor stacks. Verify these in the target environment before relying on them.

Minimal install and smoke check

Use the package/environment reference for details. A typical installed-package check is:

python -m pip install xtuner
python scripts/check_xtuner_install.py --json
python -m xtuner.v1.train.cli.sft --help

For RL planning, install Ray and a chosen rollout backend in the target environment, then check:

python -m xtuner.v1.train.cli.rl --help

If python -m xtuner.v1.train.cli.sft --help prints warnings about missing FlashAttention or bitsandbytes CUDA binaries but still exits successfully, treat those as optional acceleration limitations unless the user explicitly needs that feature.

Guardrails

• Do not launch training, Ray clusters, model downloads, benchmark evaluation, or model conversion unless the user supplies assets/resources and approves side effects.
• Do not claim GPU/NPU/FP8/backend verification from CPU imports alone.
• Do not ask future agents to open or run original repository docs, examples, tests, configs, or scripts. Use the generated references and bundled helper scripts here.
• For source checkout freshness, compare the current checkout to references/repo-provenance.md; run refresh-repo-skill when it diverges.