"Building FinOptix-14B: A FinOps Architect in 14 Billion Parameters"
ccortezb
• • 1☁️ FinOptix-14B
Your FinOps architect in 14 billion parameters.
A QLoRA fine-tuned Qwen 2.5 14B model specialized in AWS Cloud Governance, FinOps Cost Optimization, and Infrastructure as Code (Terraform HCL) auditing.
Developed as a submission for the Hugging Face Build Small Hackathon.
🚀 Key Capabilities
- Terraform HCL Auditor — Scans IaC for cost waste, governance violations, rightsizing opportunities. Outputs compliant code.
- AWS Cost JSON Parser — Extracts anomalies, burn rates, and actionable insights from Cost Explorer / Anomaly Detection payloads.
- BYaML Governance Engine — Validates architecture YAML against a 38-type component catalog and 13 governance policies.
- FinOps Q&A — Expert answers on Reserved Instances, Savings Plans, tagging strategies, unit economics, and more.
🛠️ Training Details
| Parameter | Value |
|---|---|
| Base Model | |
| Method | QLoRA (4-bit NF4, double quantization) |
| LoRA Config | r=16, alpha=32, dropout=0.05, target_linear=True |
| Hardware | NVIDIA A100-80GB (Modal) |
| Duration | ~40 minutes |
| Epochs | 3 |
| Dataset | 265 gold synthetic examples (FinOps, Terraform, AWS Cost, BYaML) |
| Sequence Length | 2048 tokens |
| Optimizer | paged_adamw_8bit, lr=2e-4, cosine schedule |
📊 Dataset Composition
| Category | Count | Description |
|---|---|---|
| Terraform HCL | 100 | Audit + refactor (rightsizing, tags, encryption, lifecycle) |
| AWS Cost JSON | 80 | Anomaly detection, budget alerts, cost-by-service |
| BYaML Governance | 50 | Schema validation, policy checks, relationship verification |
| FinOps Q&A | 15 | Deep expert answers (frameworks, strategies, tooling) |
| Bash/Python Scripts | 20 | Real boto3 + bash for cloud automation |
All data is 100% synthetic — no client or proprietary information. Modeled after real-world AWS patterns.
💻 Usage
With PEFT (recommended)
🎯 Demo
Try it live: FinOptix-14B Space
🏕️ Hackathon Context
- Track: Backyard AI — solving a real FinOps problem for my own startup (Brickstore AI)
- Constraint: ≤ 32B parameters (this model is 14B ✅)
- Builder: Carlos Cortez (@ccortezb) — AWS Community Hero, Lima, Peru
- Badges: 🎯 Well-Tuned · 🎨 Off-Brand · 📓 Field Notes
⚖️ License
Apache 2.0 — inheriting from Qwen 2.5 base model license.
🙏 Acknowledgments
- Qwen Team for the exceptional base model
- Modal for 50 GPU credits (training infrastructure)
- Hugging Face for hosting and hackathon organization
- Axolotl for the training framework
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