---
title: Titanium2.1-DeepSeek-R1
canonical_url: "https://www.modelscope.cn/datasets/sequelbox/Titanium2.1-DeepSeek-R1"
md_url: "https://www.modelscope.cn/datasets/sequelbox/Titanium2.1-DeepSeek-R1.md"
repository: sequelbox/Titanium2.1-DeepSeek-R1
last_updated: 2025-07-10
license: "Apache License 2.0"
storage_size: "285 MB"
downloads: 352
stars: 0
---

# Titanium2.1-DeepSeek-R1

> Titanium2.1-DeepSeek-R1 - sequelbox 在 ModelScope 开源的数据集。Click here to support our open-source dataset and model releases!

sequelbox/Titanium2.1-DeepSeek-R1 是 ModelScope 魔搭社区上的数据集，存储大小 285 MB，采用 Apache License 2.0 许可。

- **Repository**: sequelbox/Titanium2.1-DeepSeek-R1
- **License**: Apache License 2.0
- **Storage size**: 285 MB
- **Downloads**: 352
- **Stars**: 0
- **Last updated**: 2025-07-10

Source: https://www.modelscope.cn/datasets/sequelbox/Titanium2.1-DeepSeek-R1

---

**[Click here to support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**

**Titanium2.1-DeepSeek-R1** is a dataset focused on architecture and DevOps, testing the limits of [DeepSeek R1's](https://huggingface.co/deepseek-ai/DeepSeek-R1) architect and coding skills!

This dataset contains:

- 31.7k synthetically generated prompts focused on architecture, cloud, and DevOps. All responses are generated using [DeepSeek R1.](https://huggingface.co/deepseek-ai/DeepSeek-R1) Primary areas of expertise are architecture (problem solving, scenario analysis, coding, full SDLC) and DevOps (Azure, AWS, GCP, Terraform, shell scripts) 
- Synthetic prompts are generated using [Llama 3.1 405b Instruct.](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct)
- Responses demonstrate the reasoning capabilities of DeepSeek's 685b parameter R1 reasoning model.

**Responses have not been filtered or edited at all:** the Titanium dataset strives to accurately represent the R1 model. Potential issues may include inaccurate answers and infinite thought loops. Titanium is presented as-is to be used at your discretion.

Users should consider applying their own sub-filtering and manual examination of the dataset before use in training.

Do as you will.