Paper·arxiv.org

llmfine-tuningai-agentsautomationresearchmachine-learningevaluationdeploymenttrex

TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration

TREX is a novel multi-agent system that fully automates LLM fine-tuning using an agent-driven tree-based exploration strategy. It streamlines the entire training lifecycle, reducing manual effort and accelerating the deployment of specialized LLMs.

intermediate30 min5 steps

The play

Understand TREX's Core Innovation
Grasp how TREX's multi-agent architecture and tree-based exploration strategy automate the discovery of optimal LLM fine-tuning configurations, moving beyond manual trial-and-error.
Identify Fine-tuning Targets
Determine which base LLM (e.g., Llama-2, Mistral) and specific downstream tasks (e.g., summarization, code generation, sentiment analysis) you would want a system like TREX to optimize for.
Prepare Your Datasets
Outline the structure and quality requirements for the domain-specific datasets (e.g., JSONL, CSV) that TREX would use for fine-tuning. Ensure data is clean, relevant, and properly formatted.
Define Evaluation Metrics
Specify the objective performance criteria (e.g., ROUGE, BLEU, F1-score, accuracy) that TREX's agents would use to evaluate and optimize the fine-tuned models during its exploration process.
Envision Automated Deployment
Consider how a TREX-optimized LLM, once fine-tuned, would integrate into your existing MLOps pipeline for rapid experimentation, validation, and deployment into production environments.

Starter code

{
  "model_name_or_path": "meta-llama/Llama-2-7b-hf",
  "dataset_path": "./data/my_custom_domain_data.jsonl",
  "task_type": "text-generation",
  "evaluation_metric": "f1-score",
  "hyperparameters": {
    "learning_rate": "auto",
    "num_epochs": "auto",
    "batch_size": "auto",
    "lora_rank": "auto"
  },
  "output_dir": "./trex_optimized_llm_output"
}

Source

Paperarxiv.org