Fine Tune

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

1. Export Dataset — Call history__exportDataset to extract chats in ShareGPT or Alpaca format.
2. Pre-flight Check — Validate local CPU/GPU, VRAM, and CUDA environments.
3. Orchestrate Training — Execute train.py to initiate Llama-Factory CLI.
4. 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__listSessions or history__searchHistory 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

• Hardware guide
• Llama-Factory setup