---

name: x-turing description: "Route xTuring dataset preparation, model loading and inference, fine-tuning and DPO alignment, CLI/API/UI serving, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

xTuring

Use this skill for the public xTuring package (xturing) when you need to prepare data, choose or load models, fine-tune or align a causal model, serve the model through CLI/API/UI, or run the built-in evaluation scaffold.

Install and smoke-check

Install from the repository root:

pip install -e .

If you need the full training, quantization, or provider stack, make sure the environment can satisfy the package dependencies in pyproject.toml and the backend notes in the troubleshooting guide.

Quick import smoke:

python -I -c "import importlib.metadata as md; import xturing; from xturing.models import BaseModel; from xturing.datasets import TextDataset, InstructionDataset, PreferenceDataset; from xturing.evaluation import LMEvalAdapter; print(md.version('xturing'))"

For a broader readiness check, run scripts/check_xturing_environment.py.

Route to the right sub-skill

Task family	Start here	Notes
Dataset construction, validation, Alpaca conversion, or self-instruct generation	sub-skills/data-prep-and-generation/SKILL.md	Use for TextDataset, InstructionDataset, PreferenceDataset, JSONL, and API-backed data generation.
Model choice, load/save, generation, hub paths, or registry lookup	sub-skills/models-and-inference/SKILL.md	Use for BaseModel.create, BaseModel.load, generate, and model catalog questions.
SFT fine-tuning, LoRA, quantized variants, or DPO alignment	sub-skills/training-and-alignment/SKILL.md	Use for model.finetune(...), model.dpo_finetune(...), and trainer config issues.
xturing chat, xturing api, xturing ui, or FastAPI/Gradio behavior	sub-skills/cli-api-ui/SKILL.md	Use for terminal chat, the API server, and the playground UI.
Perplexity scoring or adapter-based evaluation artifacts	sub-skills/evaluation/SKILL.md	Use for model.evaluate(...), run_eval_adapter(...), and JSON result persistence.

Shared notes

• The package name is xturing; the public repo skill id is x-turing.
• references/repo-provenance.md records the source commit and package snapshot used to generate this skill.
• references/troubleshooting.md collects cross-cutting install, import, optional dependency, and backend issues.
• references/source-script-inventory.md lists the source examples and maintenance helpers that were distilled into bundled skill helpers or references.
• references/repo-routing-metadata.json is consumed by the repo-skill router during later import or refresh flows.

When you are unsure

• If the request mentions a dataset schema, start with the data-prep skill.
• If it mentions a model key, checkpoint, or generate(...), start with the model skill.
• If it mentions LoRA, CPU int8, k-bit, or DPO, start with the training skill.
• If it mentions a command-line or HTTP route, start with the CLI/API/UI skill.
• If it mentions perplexity or EvalRunResult, start with the evaluation skill.

Do not depend on the original repository checkout for any runtime guidance; use the bundled references and scripts inside this skill tree instead.