---

name: finetuning description: "Guide InternLM-XComposer supervised finetuning, data manifests, runnable launchers, DeepSpeed configs, LoRA settings, and adapter merge." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Finetuning Sub-skill

Use this sub-skill for supervised finetuning of InternLM-XComposer family models, especially the 2.5 training path. It covers:

• data.json and data.txt preparation for text-only, single-image, and multi-image samples;
• 2.5 placeholder rules, plus legacy 1.0/2.0 compatibility notes;
• torchrun + DeepSpeed templates and manual FSDP planning on the same finetune.py entrypoint;
• full-parameter and LoRA training arguments;
• PEFT adapter loading and merge_peft_adapter.py merge planning;
• the file-level data-mixing behavior used by the loader.

Route non-training tasks to sibling sub-skills:

• inference, Gradio, LMDeploy, and composition: model-inference;
• reward scoring or preference training: reward-model;
• benchmarks, converters, and related projects: evaluation-and-projects;
• OmniLive audio/video/service workflows: omnilive.

Quick compatibility notes

• InternLM-XComposer 2.5 uses conversations in actual JSON examples and loader code.
• 2.5 single-image samples do not require <ImageHere>; multi-image samples should keep one <ImageHere> token per image, in order.
• 2.0 finetuning examples require <ImageHere> on image-bearing samples.
• 1.0 legacy finetuning examples are placeholder-free in the published README guidance.

Operating checklist

1. Identify the target family: 2.5 default, or 2.0 / 1.0 compatibility guidance.
2. Validate the manifest with scripts/validate_finetune_data.py.
3. Render a safe command with scripts/render_finetune_command.py when planning only.
4. For approved execution, use the bundled runnable training bundle in entrypoints/xcomposer25/ instead of relying on source checkout files.
5. If using LoRA, keep both training and entrypoints/xcomposer25/merge_peft_adapter.py aligned with references/workflows.md.
6. Review references/troubleshooting.md before changing batch size, hd_num, max_length, or backend flags.

Bundled references

• references/data-formats.md
• references/workflows.md
• references/training-arguments.md
• references/troubleshooting.md

Bundled scripts and entrypoints

Safe planners/validators:

• scripts/validate_finetune_data.py
• scripts/render_finetune_command.py

Runnable self-contained entrypoints:

• entrypoints/xcomposer25/finetune.py
• entrypoints/xcomposer25/data_mix.py
• entrypoints/xcomposer25/ixc_utils.py
• entrypoints/xcomposer25/ds_config_zero2.json
• entrypoints/xcomposer25/launch_full.sh
• entrypoints/xcomposer25/launch_lora.sh
• entrypoints/xcomposer25/merge_peft_adapter.py
• entrypoints/xcomposer25/data.txt and entrypoints/xcomposer25/data/ source-format fixtures

Read entrypoints/xcomposer25/README.md before running any entrypoint. These files are real training/merge entrypoints and will import torch/Transformers/DeepSpeed/PEFT, load checkpoints, and write outputs when executed.

Boundaries

• Do not start actual training, checkpoint download, or merge execution in Creator mode.
• Do not depend on the source checkout remaining available after the skill is drafted; use the bundled references and scripts instead.
• Keep launcher advice self-contained and edit-safe; let the user choose exact paths, GPUs, and ports.