namanadep/qwen2.5-72b-aiops-qlora

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.

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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

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Key Technical Takeaways

1. 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).
2. Dominant Win Rate (88 / 100 Prompts):
   • Improved accuracy and gold-standard label alignment on 88 out of 100 test prompts.
3. 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.
4. 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.

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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.