DevSecOps Model Platform
Train a secure model on the best data, then deploy it securely.
Start Here: Train Your Model
| Dataset | Size | What It Gives You | Command |
|---|---|---|---|
| tulu-3-sft-mixture | 940K | Math, code, safety, chat (BEST) | python model/train_tulu3.py |
| OpenThoughts-114k | 114K | Reasoning, chain-of-thought | python model/train_openthoughts.py |
allenai/tulu-3-sft-mixture is from Allen AI Tulu 3 - current SOTA open instruction-tuned model. Proven on Llama-3.1-8B: MMLU 53.5, GSM8K 79.9, HumanEval 76.8.
LoRA config from LoRA Without Regret (Schulman 2025): r=256, alpha=16, all-linear = matches full fine-tuning at 67% compute.
Repository Structure
model/ THE MODEL - train, serve, enhance
train_tulu3.py Primary: 940K best data (zero preprocessing)
train_openthoughts.py Reasoning: 114K CoT traces
finetune_configurable.py Multi-dataset configurable trainer
rag_pipeline.py RAG for DevSecOps knowledge
DATASETS.md Why these datasets, proven recipes
deployment/ SERVE IT - Kubernetes + Docker + vLLM
deployment.yaml ML inference K8s manifest
mlflow-deployment.yaml Experiment tracking
Dockerfile.ml-inference Hardened multi-stage image
security/ PROTECT IT - scanning + policies
scanning/ Trivy, Semgrep, Checkov, SBOM
policies/ Kyverno, OPA Gatekeeper
infrastructure/ RUN IT - Terraform + monitoring + CI/CD
terraform/ VPC, EKS, RDS, S3, IAM, KMS, GuardDuty, Macie
monitoring/ Prometheus, Alertmanager, OTEL, Grafana
ci-cd/ GitHub Actions DevSecOps pipeline
compliance/ CERTIFY IT - SOC2, NIST, CIS
controls-mapping.yaml SOC2 Type II
nist-800-53-mapping.yaml NIST 800-53 Rev5
cis-eks-k8s.yaml CIS Benchmarks
Quick Commands
# Train on best data (A100, ~6h)
python model/train_tulu3.py
# Quick test (any GPU)
python model/train_tulu3.py --max_steps 100 --no_push
# Security scan
python security/scanning/security_audit.py
# Deploy model to K8s
kubectl apply -f deployment/deployment.yaml
# Infrastructure (Terraform)
cd infrastructure/terraform/environments/prod && terraform apply
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