---
license: apache-2.0
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- finops
- aws
- terraform
- cloud-governance
- qlora
- peft
language:
- en
- es
pipeline_tag: text-generation
library_name: peft
datasets:
- ccortezb/finoptix-training-data
model-index:
- name: FinOptix-14B
  results: []
---

# ☁️ 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](https://huggingface.co/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](https://huggingface.co/spaces/build-small-hackathon/finoptix14b)**

## 🏕️ 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](https://huggingface.co/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](https://huggingface.co/Qwen) for the exceptional base model
- [Modal](https://modal.com) for 50 GPU credits (training infrastructure)
- [Hugging Face](https://huggingface.co) for hosting and hackathon organization
- [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) for the training framework