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uncategorizedllm-fine-tuningai-automationmulti-agent-systemsllm-opsmodel-training

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

Understand the conceptual workflow of TREX, a multi-agent system designed to fully automate LLM fine-tuning. This action pack outlines how to define objectives and interact with such a system to simplify complex model adaptation and accelerate specialized LLM deployment.

intermediate15 min5 steps

The play

Define Fine-tuning Objective and Constraints
Clearly articulate the target task, desired performance metrics (e.g., F1-score, perplexity), and resource constraints (e.g., training hours, budget) for the fine-tuned LLM. This guides the TREX agents.
Provide Access to Data and Base Model
Specify the base LLM and the dataset for fine-tuning. This could involve referencing IDs in a registry or providing direct paths. TREX's data agents will handle preprocessing.
Initiate Automated Fine-tuning Process
Submit the defined objective, data source, and base model configurations to the TREX orchestrator. This triggers the multi-agent system to begin its tree-based exploration for optimal fine-tuning strategies.
Monitor Progress and Evaluate Results
Track the fine-tuning progress and agent decisions through the TREX dashboard or API. Review the performance metrics of candidate models as TREX automatically evaluates against your defined objectives.
Deploy the Best Performing Model
Once TREX identifies and validates an optimal fine-tuned LLM, use its integrated deployment interface to make the model available for inference, integrating it into your applications.

Starter code

fine_tuning_objective = {
    "task": "sentiment_analysis",
    "dataset_id": "customer_reviews_2023_q4",
    "target_metric": "f1_score",
    "min_f1_score": 0.85,
    "max_training_hours": 24,
    "model_base": "llama-2-7b-chat",
    "optimization_goal": "maximize_f1_score_minimize_latency"
}