🚀 DevOps Specialized Language Model (SLM)

A specialized AI model trained for DevOps tasks, Kubernetes operations, Docker containerization, CI/CD pipelines, and infrastructure management.

• Kubernetes Operations: Pod management, deployments, services, configmaps, secrets
• Docker Containerization: Container creation, optimization, and best practices
• CI/CD Pipeline Management: Pipeline design, automation, and troubleshooting
• Infrastructure Automation: Infrastructure as Code, provisioning, scaling
• Monitoring and Observability: Logging, metrics, alerting, debugging
• Cloud Platform Operations: Multi-cloud deployment and management

• Base Architecture: Qwen (494M parameters)
• Specialization: DevOps, Kubernetes, Docker, CI/CD, Infrastructure
• Max Sequence Length: 2048 tokens
• Model Type: Instruction-tuned for DevOps domain

Option 1: Interactive Web UI (Recommended for Beginners)

Access the UI:

• Main URL: https://huggingface.co/spaces/lakhera2023/devops-slm-chat/

How to Use the UI:

1. Enter Your Question: Type your DevOps question in the text box
2. Adjust Parameters (optional):
   • Max Tokens: 50-500 (controls response length)
   • Temperature: 0.1-2.0 (controls creativity/randomness)
   • Top-p: 0.1-1.0 (controls response diversity)
   • Top-k: 1-100 (controls vocabulary selection)
3. Generate Response: Click "Generate Response" or press Enter
4. Try Example Prompts: Click any of the pre-loaded example questions

The model provides a Hugging Face Inference Endpoint for programmatic access.

1. Test Basic DevOps Question:

curl --max-time 300 -X POST \
  "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "How do I deploy a microservice to Kubernetes?",
    "parameters": {"max_new_tokens": 100, "temperature": 0.7}
  }'

2. Test Container Security Question:

curl --max-time 300 -X POST \
  "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "What are the best practices for container security?",
    "parameters": {"max_new_tokens": 150, "temperature": 0.5}
  }'

3. Test Docker Optimization:

curl --max-time 300 -X POST \
  "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "How do I optimize a Docker image for production?",
    "parameters": {"max_new_tokens": 200, "temperature": 0.7}
  }'

4. Test CI/CD Pipeline:

curl --max-time 300 -X POST \
  "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "How do I set up a CI/CD pipeline for a Python project?",
    "parameters": {"max_new_tokens": 180, "temperature": 0.6}
  }'

5. Test Health Check:

curl "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud"

Basic Python Example:

import requests

# Test DevOps question
response = requests.post(
    "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud",
    json={
        "inputs": "How do I set up a CI/CD pipeline?",
        "parameters": {
            "max_new_tokens": 150,
            "temperature": 0.7
        }
    },
    timeout=300
)
print("Response:", response.json())

# Test Kubernetes question
response = requests.post(
    "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud",
    json={
        "inputs": "Explain Docker vs Kubernetes",
        "parameters": {
            "max_new_tokens": 200,
            "temperature": 0.5
        }
    },
    timeout=300
)
print("Response:", response.json())

Advanced Python Example with Error Handling:

import requests
import json

def ask_devops_question(question, max_tokens=150, temperature=0.7):
    """Ask a DevOps question to the inference endpoint"""
    try:
        response = requests.post(
            "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud",
            json={
                "inputs": question,
                "parameters": {
                    "max_new_tokens": max_tokens,
                    "temperature": temperature
                }
            },
            timeout=300
        )
        response.raise_for_status()
        result = response.json()
        return result[0]["generated_text"] if result else "No response generated"
    except requests.exceptions.Timeout:
        return "Request timed out. Try again with a shorter question."
    except requests.exceptions.RequestException as e:
        return f"Error: {e}"

# Example usage
answer = ask_devops_question("How do I monitor Kubernetes pods?")
print(answer)

Inference Endpoint Response:

[
  {
    "generated_text": "How do I deploy a microservice to Kubernetes? To deploy a microservice to Kubernetes, you'll need to follow these steps:\n\n1. Install the Kubernetes CLI (Kubectl) and create a new cluster.\n\n2. Define your microservice's deployment YAML file in YAML format using the `kubectl apply -f ` command.\n\n3. Apply the deployment YAML file by running `kubectl rollout status  | grep \"Ready\"`. This will show you whether the deployment is ready or not.\n\n4. If the deployment is ready, you can access your microservice through the Kubernetes service."
  }
]

Health Check Response:

"Ok"

For Non-Technical Users:

1. Visit: https://huggingface.co/spaces/lakhera2023/devops-slm-chat/
2. Type your DevOps question in the text box
3. Click "Generate Response" or press Enter
4. Try the example prompts for inspiration

For Developers:

1. Test API connectivity: curl "https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud"
2. Try a simple question: Use the cURL examples above with the inference endpoint
3. Integrate into your app: Use the Python examples with the working inference endpoint

• DevOps Consulting: Get expert advice on infrastructure decisions
• Learning: Understand complex DevOps concepts with examples
• Code Generation: Generate YAML, Dockerfiles, and scripts
• Troubleshooting: Debug deployment and infrastructure issues
• Best Practices: Learn industry-standard DevOps practices
• Documentation: Generate technical documentation and guides

Kubernetes

• "How do I create a Kubernetes namespace?"
• "What's the difference between a Deployment and a StatefulSet?"
• "How do I set up a Kubernetes service with load balancing?"

Docker

• "Create a multi-stage Dockerfile for a Node.js application"
• "How do I optimize Docker image size?"
• "What are Docker best practices for production?"

CI/CD

• "How do I set up a GitHub Actions pipeline for a Python project?"
• "What's the difference between continuous integration and continuous deployment?"
• "How do I implement blue-green deployment?"

Infrastructure

• "How do I set up monitoring with Prometheus and Grafana?"
• "What are the benefits of Infrastructure as Code?"
• "How do I implement auto-scaling in Kubernetes?"

• Model: https://huggingface.co/lakhera2023/devops-slm
• Live Demo: https://huggingface.co/spaces/lakhera2023/devops-slm-chat/
• API Endpoint: https://bcg2lrpnfylqamcz.us-east-1.aws.endpoints.huggingface.cloud

The ultimate goal of this documentation is to help you learn, build, and have fun with DevOps AI. Whether your use case is consulting, learning, code generation, or troubleshooting, this model is designed to provide expert-level DevOps assistance.

🔥 Let's make this the start of something amazing for the DevOps + AI community!