Qwen2.5-72B AIOps Specialization (4-bit QLoRA)
This repository contains the complete technical architecture, dataset processing scripts, evaluation benchmarks, QLoRA adapter weights, and executive PowerPoint presentation for Qwen2.5-72B-Instruct AIOps Specialization across 2× NVIDIA H200 NVL GPUs.
Executive Master Comparative Scorecard
| Metric / Dimension | Baseline Base Model (72B) | Post-AIOps QLoRA (72B) | Delta (Post - Pre) | % Relative Gain |
|---|---|---|---|---|
| Mean Token F1 vs Gold | 0.1393 | 0.5140 | +0.3747 | +269.0% Relative Gain |
| Mean Token Recall vs Gold | 0.2108 | 0.4760 | +0.2652 | +125.8% Relative Gain |
| Holdout Prompts Improved | — | 88 / 100 Prompts | +88 Prompts | 88.0% Win Rate |
| Median Answer Length | 1,072 characters | 466 characters | -606 characters | -56.5% (Concise Triage) |
| Training Peak VRAM (GPU 0/1) | N/A | 20.22 GB / 32.49 GB | — | Fits in Sub-35GB VRAM |
| Adapter Checkpoint Footprint | 0 MB | 842 MB | +842 MB | Lightweight Payload |
Key Technical Takeaways
- Massive Quality Jump (+269.0% F1 Gain):
- Fine-tuning Qwen2.5-72B-Instruct via 4-bit QLoRA elevated the mean token F1 score from 0.1393 to 0.5140 across 100 frozen holdout AIOps incident prompts (
aiops_mix_test_100.jsonl).
- Fine-tuning Qwen2.5-72B-Instruct via 4-bit QLoRA elevated the mean token F1 score from 0.1393 to 0.5140 across 100 frozen holdout AIOps incident prompts (
- Dominant Win Rate (88 / 100 Prompts):
- Improved accuracy and gold-standard label alignment on 88 out of 100 test prompts.
- Style Shift from Textbook Chatter to Operational Triage:
- Transformed long-winded 1,072-character tutorial responses into exact 466-character incident root-cause and remediation summaries.
- Hardware & VRAM Efficiency:
- Single-epoch QLoRA fine-tuning completed in 134.15 minutes (2.23 hours) on 2× H200 GPUs, using only 20.22 GB (GPU 0) / 32.49 GB (GPU 1) peak VRAM.
Repository Structure
adapter/: PEFT QLoRA adapter weights (adapter_model.safetensors~842 MB,adapter_config.json,chat_template.jinja).QWEN-72B_FINE_TUNNING_FOR_AIOPS/: Complete 72B codebase, training scripts, evaluation benchmarks, and documentation.FineTuning_Qwen_72B_on_AIOps_Data.pptx: 12-slide widescreen executive PowerPoint deck.