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name: examples-conversion description: "Plan Fengshen example-family workflows and conversion utilities without launching downloads, training, services, or checkpoint mutation." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Fengshenbang-LM examples and conversion planning

Use this sub-skill when the user asks for Fengshen model-family example recipes, CLUE/classification recipe adaptation, NLG/NLT task recipes, Taiyi Stable Diffusion usage or fine-tuning requirements, Ziya inference/quantization/fine-tuning/conversion planning, or checkpoint conversion utilities. This skill is for safe planning and dry-run checklists; it does not run training, servers, downloads, or checkpoint conversion.

First route the task

User intent	Start here	Route elsewhere when needed
CLUE leaderboard, high-level classification scripts, AFQMC/TNEWS/OCNLI/C3/CHID/CSL/IFLYTEK/WSC/CMRC2018 recipe	references/clue-and-task-recipes.md	Common fengshen-pipeline command mechanics and schema fixture generation belong to ../pipelines-cli/SKILL.md; data loaders and Trainer/checkpoint flags belong to ../data-training/SKILL.md.
Summary, question generation, generative QA, translation, medical close-book QA, reasoning generation	references/nlg-nlt-recipes.md	Core model/config/tokenizer classes belong to ../model-zoo/SKILL.md; universal dataloaders and scheduler details belong to ../data-training/SKILL.md.
Taiyi Stable Diffusion inference, CPU/full precision vs FP16 CUDA, fine-tune, DreamBooth	references/taiyi-diffusion.md	Taiyi CLIP/model internals belong to ../model-zoo/SKILL.md; distributed training argument details belong to ../data-training/SKILL.md.
Ziya LLaMA inference, bitsandbytes/llama.cpp quantization, full-parameter fine-tune, HF/Fengshen/TP conversion	references/ziya-llama.md and scripts/plan_ziya_conversion.py	LLaMA class/config internals belong to ../model-zoo/SKILL.md; full training loop and Deepspeed mechanics belong to ../data-training/SKILL.md.
Delta weights, TF checkpoint, Diffusers-to-original Stable Diffusion, LLaMA HF/Fengshen/TP conversions	references/conversion-utilities.md and scripts/plan_ziya_conversion.py	If the user wants an actual conversion, require explicit source/output paths, dependency/hardware checks, backup plan, and mutation acknowledgement before using package utilities.
FastDemo/Streamlit/FastAPI demo caveats	references/troubleshooting.md	Production API/server design is outside this repo skill; keep to caveats and minimal request shape.
Unsure which family applies	references/model-family-recipes.md and scripts/check_recipe_requirements.py	Model class selection goes to ../model-zoo/SKILL.md.

Safety contract

• Treat all bundled scripts as non-mutating dry-run helpers. They may print plans/checklists but must not install packages, download models or datasets, start services, launch training, call from_pretrained, or write checkpoint outputs.
• Do not copy or run source example shell scripts verbatim. Many examples encode local machine paths, Slurm settings, checkpoint output directories, or GPU-only assumptions.
• Before any real training/conversion outside this skill, ask for: model/checkpoint source, target output directory, whether network downloads are permitted, device/VRAM, expected precision, and whether overwriting/removing output directories is allowed.
• For checkpoint conversion, assume outputs are mutating and large. Require a new empty output directory or backup plan. Never point output to the same directory as an input checkpoint.
• For CPU-only environments, keep to help/static planning for Ziya, Taiyi FP16, diffusion fine-tuning, DreamBooth, Deepspeed, and full LLaMA fine-tuning.

Bundled helpers

python scripts/check_recipe_requirements.py --recipe taiyi-inference --device cpu --precision fp32
python scripts/check_recipe_requirements.py --recipe taiyi-inference --device cuda --precision fp16 --gpus 1 --vram-gb 16
python scripts/plan_ziya_conversion.py --source-format hf --target fs-finetune --model-size 13b --gpus 8 --vram-gb 80
python scripts/plan_ziya_conversion.py --source-format hf --target fs-finetune --model-size 13b --gpus 24 --vram-gb 24

The helpers are checkout-independent and safe to run from any directory.

Verification expectations for future agents

Use this sub-skill to satisfy these difficult cases without opening the source checkout:

1. Decide whether a Ziya HF checkpoint should be converted to Fengshen format and then tensor-parallel shards before full-parameter fine-tuning. The answer must identify delta merge prerequisites, HF-to-Fengshen conversion, TP choice, output mutation risk, dependencies, and resource gates.
2. Choose between Taiyi Stable Diffusion CPU/full-precision and FP16 CUDA inference. The answer must distinguish full precision from FP16, list diffusers/accelerate/torch dependency gates, explain network/offline model-cache effects, and avoid launching image generation.

Non-goals

• Do not document every Trainer, dataloader, checkpoint, or optimizer flag here; link to ../data-training/SKILL.md.
• Do not explain core model classes, auto config keys, or tokenizer internals here; link to ../model-zoo/SKILL.md.
• Do not reproduce common fengshen-pipeline mechanics here; link to ../pipelines-cli/SKILL.md.
• Do not claim native execution of heavy examples was verified. The portable verification scope is static planning, help/checklist behavior, and non-mutating script execution.