otavio-lemos/oci-copilot-jr-dataset

Dataset Card: OCI Copilot Jr Dataset

Overview

This dataset contains 13,196 examples of high-quality training data for fine-tuning a Large Language Model to become the best knowledgeable in Oracle Cloud Infrastructure (OCI) — the "OCI Copilot Jr".

The dataset was synthetically generated using prompt templates with OCI CLI commands and real-world enterprise scenarios in Brazilian Portuguese (PT-BR).

Split	Examples	Percentage
Train	9,897	75%
Valid	1,979	15%
Eval	1,320	10%
Total	13,196	100%

Dataset Structure

Schema (Chat Format)

{
  "messages": [
    {
      "role": "system",
      "content": "Você é um arquiteto e especialista experiente em OCI focado no domínio de {category}. Forneça orientações técnicas, profundas e definitivas."
    },
    {
      "role": "user",
      "content": "Para o ambiente {environment} do nosso projeto {project}, precisamos realizar: {task}. Quais as melhores estratégias e comandos no OCI considerando a restrição: {restriction}?"
    },
    {
      "role": "assistant",
      "content": "## {task} — OCI Step-by-Step\n\n**Cenário**: {company}, projeto {project}, ambiente {environment}\n\n[detailed technical response with OCI CLI commands, Terraform, and best practices]"
    }
  ]
}

Categories (88 OCI Domains)

Pillar	Categories
Compute	instances, custom-images, scaling
Container	instances, OKE
Database	autonomous, autonomous-json, exadata, exadata-cloud, MySQL, NoSQL, PostgreSQL
DevOps	artifacts, CI/CD, resource-manager, secrets
FinOps	cost-optimization, rightsizing, showback-chargeback, storage-tiering
Governance	audit-readiness, budgets-cost, compartments, compliance, landing-zone, policies-guardrails, resource-discovery, tagging
Load Balancer	load-balancer
Migration	aws-database, azure-compute, azure-database, azure-storage, data-transfer, gcp-compute, gcp-database, gcp-storage, onprem-compute, onprem-database, onprem-storage, onprem-vmware
Networking	connectivity, security, VCN
Observability	APM, logging, monitoring, stack-monitoring
Platform	backup-governance, SRE-operations
Security	cloud-guard, dynamic-groups, encryption, federation, IAM-basics, policies, posture-management, vault-keys, vault-secrets, WAF, zero-trust
Serverless	api-gateway, functions
Storage	block, file, object
Terraform	compute, container, database, devops, load-balancer, networking, observability, provider, security, serverless, state, storage
Troubleshooting	authentication, compute, connectivity, database, functions, OKE, performance, storage

Data Generation Pipeline

flowchart LR
    A["generate_v7_combined.py\n(88 cats × 150 ex)"] --> B["validate_jsonl.py"]
    B --> C["clean_dataset.py"]
    C --> D["dedupe_embedding.py\n(threshold 0.97)"]
    D --> E["build_dataset_fixed.py\n(75/15/10 split)"]
    E --> F["train.jsonl\nvalid.jsonl\neval.jsonl"]

Generation Process

1. Template-based generation: Uses prompt templates with varied:

   • Company names (realistic Brazilian enterprises)
   • Project names
   • Environments (greenfield, brownfield, production, staging)
   • Personas (SRE, Platform Engineer, FinOps Analyst, Architect)
   • Restrictions (budget-limited, no-downtime, rollback-15min, etc.)
   • Regions and compartments

2. Quality Validation:

   • JSONL schema validation
   • Content cleaning (removes generic templates, incorrect CLI)
   • Semantic deduplication using embeddings (threshold 0.97)

Token Statistics

Metric	Value
Average tokens/example	883
Min tokens	410
Max tokens	934

Fine-Tuning Results

After fine-tuning Qwen 2.5 Coder 7B Instruct (4-bit) with LoRA on this dataset, the model achieved significant improvements:

External Judge Evaluation (mlx-community/Meta-Llama-3.1-8B-Instruct-4bit) - 200 samples

Metric	Base Model	Fine-Tuned	Delta
technical_correctness	3.00	3.73	+0.72
depth	3.06	3.82	+0.76
structure	3.50	4.63	+1.14
hallucination	3.62	4.46	+0.84
clarity	3.20	3.98	+0.77
Overall	3.27	4.12	+0.85

Top Gains by Topic

1. storage/object: +3.60
2. troubleshooting/performance: +3.80
3. observability/apm: +3.40
4. security/dynamic-groups: +3.40
5. database/postgresql: +3.40

Model Files

Resource	URL
Safetensors	https://huggingface.co/otavio-lemos/oci-copilot-jr-safetensors
GGUF	https://huggingface.co/otavio-lemos/oci-copilot-jr-gguf

Use and Limitations

Intended Use

This dataset is designed for:

• Fine-tuning LLMs for Oracle Cloud Infrastructure (OCI) operations
• Training technical assistants specialized in OCI CLI, Terraform, and best practices
• Building domain-specific RAG systems for cloud operations

Limitations

• Language: Only Brazilian Portuguese (PT-BR)
• Generated data: Not human-annotated, may contain occasional inaccuracies
• Knowledge cutoff: Based on OCI documentation available up to April 2026
• Scope: Focus on operational tasks (not development/architecture planning)

Citation

@dataset{lemos_2026_oci_copilot_jr,
  author    = {Otavio Lemos},
  title     = {OCI Copilot Jr Dataset},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/otavio-lemos/oci-copilot-jr-dataset}
}

License

MIT License - See LICENSE

---

Dataset generated using MLX-Tune pipeline on Apple Silicon M3 Pro