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Merge a base HF model dir with an extra HF model dir by index diff — take tensors only present in extra, append them to a new output dir in HF-standard shard layout. Base wins on overlap.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/InternLM/xtuner --skill model-patch命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Add or audit an XTuner chat-template implementation and its loss mask from an official Hugging Face model repository or local model directory. Use when supporting a new model under xtuner/v1/data_proto/messages, checking an existing implementation against tokenizer or processor apply_chat_template, deciding which assistant output and stop/EOS tokens receive loss, adding regression tests, or validating that exported training behavior matches vLLM and SGLang inference.
Implement a missing XTuner Hugging Face config export from the official HF model when possible, then validate it against the installed Transformers round-trip and versioned inference-engine field contracts. Use when adding a model, when hf_config is missing or returns None, when changing from_hf/hf_config/save_hf, when reviewing config.json differences, or when debugging vLLM/SGLang checkpoint-load or inference failures. Also use for requests named check_hf_config_save.
Use when the user wants to instrument, inspect, or debug XTuner traces to reconstruct a sample/request execution path, follow cross-process call chains, or identify latency hotspots, bottleneck stages, and abnormal paths from span data.
基于 SOC 职业分类
正在显示 SKILL.md
| name | model_patch |
| description | Merge a base HF model dir with an extra HF model dir by index diff — take tensors only present in extra, append them to a new output dir in HF-standard shard layout. Base wins on overlap. |
Produce out = base ∪ (extra \ base) at the tensor level, by diffing the
two model.safetensors.index.json files. Useful when extra is a sibling
checkpoint (different training run, fine-tune, or fp8 conversion) that holds a
few tensors the base is missing — e.g. an updated head, a newly-added MTP
block, or auxiliary projections — and you want a self-contained merged dir
without re-running the whole conversion pipeline.
Overlap rule: base wins. Any tensor present in both indexes is taken from
base; the corresponding entry in extra is ignored.
.claude/skills/model_patch/
├── SKILL.md # this file
└── model_patch.py # CLI + library
python model_patch.py \
--base /path/to/base-model \
--extra /path/to/extra-model \
--out /path/to/merged-model
model-{i:05d}-of-{N:05d}.safetensors shards (total N = base shards +
newly-packed shards holding the extra-only tensors)model.safetensors.index.jsonbase — config.json, tokenizer files,
modeling/configuration .py, generation_config.json, README, etc. —
copied verbatim so out is loadable on its own. Subdirectories are
copied recursively. Existing entries in out are overwritten.extra's aux files are deliberately not copied. The skill's overlap
rule is "base wins" at the tensor level, and that extends to configs: extra
often carries engine-specific edits or modality keys that don't match the
tensor topology we keep. If you need fields from extra's config.json
(e.g. a new modality), merge them into out/config.json as a separate,
explicit step.
-of-N total. By default the
bytes are fully copied so out is independent of base. Pass
--hardlink to use os.link instead (fast, but the two trees share
inodes — editing one mutates the other; deleting one keeps the other
intact since hardlinks are symmetric).--shard-size-gb GiB (default 4), appended after the base shards.So a base with 12 shards plus 5 GB of extra-only tensors yields:
model-00001-of-00014.safetensors … model-00012-of-00014.safetensors
(copies of the base shards) and
model-00013-of-00014.safetensors / model-00014-of-00014.safetensors
(freshly written, containing only the extra tensors).
| Situation | Use this? |
|---|---|
extra contains a few tensors base is missing; everything else is identical. | Yes |
| You need to merge two checkpoints whose overlapping tensors differ in value. | No — this skill silently keeps base; you probably want a manual decision per key. |
extra is a full quantization of base and you want the quantized weights. | No — use model_normalize with --reference extra instead. |
You want a single model.safetensors instead of standard shards. | No — repack downstream. |
Before invoking model_patch.py:
--base, --extra, --out) with the user
via AskUserQuestion. Never infer them from earlier conversation.model.safetensors.index.json files and reporting:
After a successful run, do not ask the user where the output should go —
--out is already the final destination the user named. Just report what
landed there (shard count, index path, total size, and that base's aux files
were mirrored over). Unlike model_normalize, model_patch has no
staging-area concept: there is no temp dir, and the caller already made the
placement decision when they passed --out.
If the user wanted any of extra's aux files (e.g. a config.json that
documents a new modality), call that out — those are not copied by the
skill and must be merged in by hand.