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