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export-evaluation

export-evaluation

Export, quantize, push, and EvalScope-evaluate ms-swift models safely.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: export-evaluation description: "Export, quantize, push, and EvalScope-evaluate ms-swift models safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

export-evaluation

Use this sub-skill when a user needs post-training packaging or evaluation for ms-swift / swift: merging LoRA adapters, exporting cached datasets, quantizing checkpoints, pushing artifacts to ModelScope or Hugging Face, running swift eval, or preparing custom EvalScope datasets.

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Route Boundaries

  • Use this sub-skill for swift export, swift merge-lora, swift eval, ExportArguments, EvalArguments, AWQ/GPTQ/GPTQ v2/FP8/BNB export quantization, hub push flags, EvalScope Native / OpenCompass / VLMEvalKit, custom MCQ/QA eval formats, and the training flag --eval_use_evalscope.
  • For training schedules, dataset registration, template construction, or fine-tuning hyperparameters, route to the training or data customization sub-skill.
  • For serving, swift deploy, swift infer, vLLM/SGLang/LMDeploy deployment tuning, or OpenAI-compatible API operations beyond eval URL usage, route to inference/deployment.
  • For RLHF, GRPO, Ray, or Megatron export/conversion details, route to the advanced RL/distributed sub-skill.

Minimal Environment Facts

  • swift export --help should work in a base ms-swift install.
  • swift eval --help imports EvalScope early and may fail if evaluation extras are missing; install evaluation support with pip install ms-swift[eval] -U when using swift eval.
  • Quantization methods need method-specific optional packages and hardware compatibility; do not hide missing autoawq, auto_gptq, gptqmodel, optimum, or bitsandbytes errors.
Gespeicherte Skills