Titanium 2.1 — DevOps Dataset and LLM Model Architecture
Dataset of technical prompts oriented towards DevOps, cloud computing, shell scripting and software architecture for language models.
Description
Titanium 2.1 — DeepSeek R1 is a corpus of 31,700 synthetic prompts focused on complex software architecture, DevOps, and cloud scenarios. The answers are generated automatically by the DeepSeek R1 model, simulating concrete cases of the software life cycle: design, infrastructure scripts, multi-cloud management (Azure, AWS, GCP), Terraform and much more.
What is this dataset for?
- Evaluate the performance of LLMs on concrete DevOps tasks
- Train specialized models in cloud infrastructure and automation
- Test technical reasoning skills in software architecture
Can it be enriched or improved?
Yes, you can add human annotations on the quality or correctness of responses, include variant prompts with additional constraints, or create multi-stage scenarios to simulate a complete DevOps pipeline.
🔎 In summary
🧠 Recommended for
- LLM DevOps fine-tuning
- Cloud reasoning tests
- AI platforms for system engineers
🔧 Compatible tools
- LangChain
- OpenAI API
- VLLM
- DeepSeek
- Manual annotation with Label Studio
💡 Tip
Use an LLM self-assessment score (e.g. GPT-4) to rank responses before fine-tuning.
Frequently Asked Questions
Is this dataset suitable for training models for multi-cloud tasks?
Yes, it covers scenarios on Azure, AWS, GCP and can be used to train agents specialized in infrastructure management.
Have the answers been validated manually?
No, all responses are generated automatically. It is advisable to filter or note the quality before use.
Is it suitable for business use?
Yes, the Apache 2.0 license allows commercial use, as long as you check the validity of the content before production.
