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name: fine-tuning description: "Route supervised fine-tuning, DeepSpeed, LoRA, data preprocessing, and post-training loading for Baichuan2-7B-Base." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Baichuan2 Fine-Tuning

Use this sub-skill when the task is to prepare, explain, validate, or launch supervised fine-tuning for Baichuan2-7B-Base. It covers the training data format, the bundled supervised trainer, DeepSpeed ZeRO-3 launch structure, multi-machine hostfiles, LoRA toggling, and how to load the saved fine-tuned result.

Do not use this sub-skill for inference demos, chat APIs, quantized loading, or CPU deployment. Route those tasks to the appropriate inference or deployment guidance instead.

Read first

• For an end-to-end run plan, read references/workflows.md.
• For JSON training data requirements and label construction, read references/data-format.md.
• For ZeRO-3 and ds_config interpretation, read references/deepspeed-config.md.
• For dependency expectations and environment checks, read references/installation.md.
• For failure diagnosis, read references/troubleshooting.md.

Bundled helpers

• scripts/validate_training_data.py: validates Belle-style multi-turn JSON, can write a tiny fixture, and can optionally estimate token truncation with a Baichuan tokenizer.
• scripts/train_supervised.py: self-contained supervised trainer adapted for Baichuan2-7B-Base with schema validation, dry-run mode, DeepSpeed config generation, and LoRA options.

Routing checklist

1. Confirm the user is fine-tuning a Base checkpoint, normally baichuan-inc/Baichuan2-7B-Base, not asking for inference or quantized deployment.
2. Validate the JSON data before launching any distributed job.
3. Decide full-parameter versus LoRA fine-tuning. Use LoRA when VRAM or wall-clock budget is limited, and keep target_modules=W_pack unless the model architecture has been intentionally changed.
4. Create or verify a DeepSpeed ZeRO-3 config and match it with the Hugging Face TrainingArguments flags.
5. For one machine, use an empty or omitted hostfile. For multiple machines, prepare a DeepSpeed hostfile with one hostname slots=N line per node.
6. After training, load a full fine-tuned output with AutoModelForCausalLM.from_pretrained(...); load LoRA adapter output with peft.AutoPeftModelForCausalLM.from_pretrained(...).

Safe default posture

Before launching training, prefer:

python scripts/validate_training_data.py \
  --data_path /path/to/train.json \
  --model_max_length 512

python scripts/train_supervised.py \
  --dry_run True \
  --data_path /path/to/train.json \
  --model_name_or_path baichuan-inc/Baichuan2-7B-Base \
  --output_dir /path/to/output \
  --model_max_length 512

Only move from dry-run validation to DeepSpeed training when dependencies, CUDA visibility, hostfile formatting, and data schema all pass.