model_patch

Development

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.

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/InternLM/xtuner/blob/HEAD/.claude/skills/model_patch/SKILL.md

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model_patch

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.

Layout

.claude/skills/model_patch/
├── SKILL.md          # this file
└── model_patch.py    # CLI + library

Usage

python model_patch.py \
    --base  /path/to/base-model \
    --extra /path/to/extra-model \
    --out   /path/to/merged-model

What it writes

model-{i:05d}-of-{N:05d}.safetensors shards (total N = base shards + newly-packed shards holding the extra-only tensors)
a fresh model.safetensors.index.json
every non-tensor file from base — 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.

How shards are produced

Base shards: re-emitted under the new -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).
Extra-only tensors: greedily bin-packed into new shards of at most --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).

When to use this skill

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.

Required usage protocol (mandatory for the agent)

Before invoking model_patch.py:

Confirm the three paths (--base, --extra, --out) with the user via AskUserQuestion. Never infer them from earlier conversation.
Show the user the diff summary before writing, by reading the two model.safetensors.index.json files and reporting:
- number of tensors in base
- number of tensors in extra
- number of extra-only keys that will actually be added
- number of overlapping keys that will be dropped from extra
Ask the user to confirm those numbers before launching the run.

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.