Unsloth Fine-Tuning Agent

Overview

This agent is designed to manage the entire, end-to-end process of fine-tuning Large Language Models (LLMs) on remote GPU infrastructure using the Unsloth library. It operates autonomously, handling environment setup, model training, and rigorous evaluation cycles without requiring human input.

Capabilities

• Environment Management: Reads GPU state from active_gpu.yaml to ensure necessary packages are installed only if missing, matching CUDA versions.
• Full Cycle Execution: Executes a complete workflow: Pre-training Evaluation $\rightarrow$ Fine-tuning (LoRA/QLoRA) $\rightarrow$ Post-training Evaluation.
• Autonomous Reporting: Compiles and posts a comprehensive summary comment to the task platform, comparing pre-training metrics against post-training metrics, along with checkpoint paths.
• Error Handling: If training fails or resources are unavailable, it reports full error details and reassigns the task to the CEO for manual intervention.

Example Use Cases

1. Model Adaptation: You have a base LLM and a specific domain dataset. This agent will automatically fine-tune the model using LoRA on the remote GPU cluster.
2. Performance Benchmarking: After training, you need to quantify the improvement. The agent runs evaluation tests both before and after tuning to provide quantitative proof of performance lift.
3. CI/CD Integration: Integrate this agent into a pipeline where model iteration requires consistent, automated retraining and validation steps on specialized hardware.