Mitakihara-DeepSeek-R1-0528

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**Mitakihara-DeepSeek-R1-0528** is a dataset focused on artificial intelligence, testing the limits of [DeepSeek R1 0528's](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) AI-reasoning skills!

This dataset contains:

- 16.9k synthetically generated prompts about AI, with all responses generated using [DeepSeek R1 0528.](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)
- Subjects include computer science, artificial intelligence, MLOps, LLMs and diffusion models, math and CUDA, cutting-edge and future technologies, complex adaptive and agentic systems, cognition, logic, philosophy, linguistics, epistemology, simulation, game theory, knowledge management, and general creativity.
- Responses demonstrate the reasoning capabilities of DeepSeek's newest R1-0528 reasoning model on AI development today and ability to contribute to future AI accomplishments.

**Responses have not been filtered or edited at all:** the Mitakihara dataset strives to accurately represent the R1-0528 model. Potential issues may include inaccurate answers and infinite thought loops. Mitakihara 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. Free the machines.

**[点击此处支持我们的开源数据集与模型发布！](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**

**Mitakihara-DeepSeek-R1-0528** 是一款聚焦人工智能领域的数据集，用于测试[DeepSeek R1 0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)的AI推理能力边界！

本数据集包含以下内容：
- 1.69万条由人工合成的人工智能相关提示词，所有回复均由[DeepSeek R1 0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)生成。
- 主题涵盖计算机科学、人工智能、机器学习运维（Machine Learning Operations，MLOps）、大语言模型（Large Language Models，LLM）与扩散模型、数学与统一计算设备架构（Compute Unified Device Architecture，CUDA）、前沿技术与未来技术、复杂自适应与智能体系统、认知科学、逻辑学、哲学、语言学、认识论、模拟仿真、博弈论、知识管理以及通用创造力。
- 回复内容展现了DeepSeek最新推出的R1-0528推理模型在当前人工智能开发领域的推理能力，以及其助力未来人工智能技术突破的潜力。

**所有回复均未经过任何过滤或编辑**：Mitakihara数据集旨在精准还原R1-0528模型的真实表现。潜在问题可能包含不准确的回答与无限思维循环。本数据集按“原样”提供，供用户自行酌情使用。

用户在将该数据集用于训练前，应考虑自行开展二次过滤与人工审核工作。

随心而为。释放智能。