| name | fine-tune |
| description | Train custom LLM models locally using conversations exported from LibrAgent.
Use when the user requests to fine-tune a model, train on past chats, export chat datasets,
or configure custom agent models.
Triggers on: "fine-tune model", "모델 학습시켜줘", "내 대화 데이터로 학습해줘", "export dataset", "대화 데이터셋 추출".
|
Fine-Tune
Export conversational data from LibrAgent and fine-tune a local language model (LLM) with it.
This skill automates the data extraction, hardware pre-flight checks, and training orchestration utilizing Llama-Factory.
Quick Process
- Export Dataset — Call
history__exportDataset to extract chats in ShareGPT or Alpaca format.
- Pre-flight Check — Validate local CPU/GPU, VRAM, and CUDA environments.
- Orchestrate Training — Execute
train.py to initiate Llama-Factory CLI.
- Deploy Model — Update assistant configurations to point to the newly fine-tuned local model.
Workflow
1. Export Dataset
Use the history builtin to export data (requires the history optional capability):
history__exportDataset({
"format": "llamaFactory",
"outputPath": "workspace/datasets/finetune_data.json",
"filters": {
"minTurns": 2,
"excludeErrors": true,
"excludeShort": true
}
})
To export a subset, call history__list or history__search first and pass sessionIds.
2. Run Pre-flight Check & Train
Execute the helper script to verify resources and launch training:
python scripts/train.py --data_path workspace/datasets/finetune_data.json --output_dir workspace/models/finetuned_model
3. Apply Fine-tuned Model
Once training completes, update the Assistant configuration to load the model path (e.g., via llama.cpp, Ollama, or local Hugging Face model settings).
Guidelines
- Hardware Safety — Always run pre-flight checks. If VRAM is less than 12GB, default to LoRA parameter-efficient training with low batch size.
- Privacy — Ensure no credentials or API keys exist in the exported dataset before launching training.
References
