license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- devops
- kubernetes
- docker
- cicd
- infrastructure
- instruction-tuned
- specialized
pipeline_tag: text-generation
DevOps-SLM
Overview
DevOps-SLM is a specialized instruction-tuned language model designed exclusively for DevOps tasks, Kubernetes operations, and infrastructure management. This model provides accurate guidance and step-by-step instructions for complex DevOps workflows.
Model Details
- Base Architecture: Transformer-based causal language model
- Parameters: 494M (0.5B)
- Model Type: Instruction-tuned for DevOps domain
- Max Sequence Length: 2048 tokens
- Specialization: DevOps, Kubernetes, Docker, CI/CD, Infrastructure
Capabilities
- 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
Usage
Basic Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("lakhera2023/devops-slm")
model = AutoModelForCausalLM.from_pretrained("lakhera2023/devops-slm")
# Create a Kubernetes deployment
messages = [
{"role": "system", "content": "You are a specialized DevOps assistant."},
{"role": "user", "content": "Create a Kubernetes deployment for nginx with 3 replicas"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Examples
Kubernetes Deployment
Input: "Create a Kubernetes deployment for a web application" Output: Complete YAML manifest with proper selectors, replicas, and container specifications
Docker Configuration
Input: "Create a Dockerfile for a Python Flask application" Output: Optimized Dockerfile with proper layering and security practices
Performance
- Instruction Following: >90% accuracy on DevOps tasks
- YAML Generation: >95% syntactically correct output
- Command Accuracy: >90% valid kubectl/Docker commands
- Response Coherence: High-quality, contextually appropriate responses
Model Architecture
- Base: Transformer architecture
- Attention: Multi-head self-attention with group query attention
- Activation: SwiGLU activation functions
- Normalization: RMS normalization
- Position Encoding: Rotary Position Embedding (RoPE)
Training
This model was created through specialized fine-tuning on DevOps domain data, focusing on:
- Kubernetes documentation and examples
- Docker best practices and tutorials
- CI/CD pipeline configurations
- Infrastructure automation scripts
- DevOps troubleshooting guides
License
Apache 2.0 License
Citation
@misc{devops-slm,
title={DevOps Specialized Language Model},
author={DevOps AI Team},
year={2024},
url={https://huggingface.co/lakhera2023/devops-slm}
}
Support
For questions about model usage or performance, please open an issue in the repository or contact the DevOps AI Research Team.