---

name: training description: "Train, fine-tune, or prepare datasets for LLM4Decompile models." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Training

Use this sub-skill when the user wants to train or fine-tune an LLM4Decompile checkpoint, prepare a training dataset, or inspect the repo's DeepSpeed, ColossalAI, or LLaMA-Factory launch paths.

Covers

• supervised fine-tuning with train/finetune.py
• AnghaBench-style dataset generation with train/compile.py
• continual-pretraining dataset splicing with train/colossalai_llm4decompile/prepare_pretrain_dataset.py
• LLaMA-Factory dataset registry and YAML examples under train/llama_factory_llm4decompile/
• launcher patterns in train/run_training.sh and train/colossalai_llm4decompile/run_llm4decompile_train.sh

Excludes

• benchmark scoring and inference-only flows → use evaluation
• Ghidra pseudo-code extraction or refinement → use ghidra-refine
• SK²Decompile preprocessing, RL, and BringUpBench evaluation → use sk2decompile

Start Here

1. Read references/training-workflows.md for the training family map.
2. Read references/data-formats.md before editing configs or datasets.
3. Read references/troubleshooting.md if the environment or launcher fails.
4. Use the bundled scripts in this sub-skill's scripts/ directory rather than source-checkout paths.

Common routes

Supervised fine-tuning

Use this route when the request mentions:

• deepspeed
• finetune.py
• llm4binary_v1
• pseudo2norm
• norm2code

Good entry points:

• scripts/run_llamafactory_train.sh
• scripts/finetune.py

Dataset generation

Use this route when the request mentions:

• compiling raw C sources into trainable examples
• AnghaBench or similar source corpora
• JSONL output with optimization-level assembly maps

Good entry points:

• scripts/compile_dataset.py
• scripts/prepare_pretrain_dataset.py

ColossalAI pretraining

Use this route when the request mentions:

• continual pretraining
• spliced Arrow datasets
• colossalai run
• prepare_pretrain_dataset.py

Good entry points:

• scripts/prepare_pretrain_dataset.py
• scripts/run_colossalai_train.sh

Model and data signals

• The repository's example dataset registry key is llm4binary_v1.
• The example LLaMA-Factory configs are pseudo2norm-example.yaml and norm2code-example.yaml.
• Training commands in the repo assume a CUDA-capable torch stack, BF16-capable hardware when bf16 is enabled, and a checkpoint path that matches the selected family.

When to read the bundled references

• Use the workflow reference for command templates and decision points.
• Use the data-format reference to validate JSON, JSONL, and registry names before launching a long run.
• Use the troubleshooting reference for dependency, CUDA, or launcher failures.