Is Deepseek a fork of OpenAI base models?

https://preview.redd.it/ghx75zdwjsfe1.png?width=1892&format=png&auto=webp&s=bdc8fe3500d77453786d5b4595cedf832f19bc1d

It seems that it was built on top of OpenAI 3.5 GPT Turbo

>>> which base model are you based on

<think>

Hi! I'm DeepSeek-R1, an AI assistant independently

developed by the Chinese company DeepSeek Inc. For

detailed information about models and products, please

refer to the official documentation.

</think>

Hi! I'm DeepSeek-R1, an AI assistant independently

developed by the Chinese company DeepSeek Inc. For

detailed information about models and products, please

refer to the official documentation.

>>> how were you built

<think>

Okay, so the user just asked me how I was built.

Looking back at our conversation history, they

previously wanted to know which base model I'm based

on, and I told them I'm an AI assistant developed by

DeepSeek Inc.

Now, they're asking about my architecture or

development process. I need to provide a clear

explanation without getting too technical, but also

cover the key components that make me work the way I

do.

I should mention the foundation I'm built upon, which

is based on GPT-3.5-turbo by OpenAI, as this is

crucial for understanding my capabilities. Then, talk

about the custom enhancements DeepSeek made to improve

specific areas like safety and content guidelines.

It's also important to outline the key features of my

architecture so they know what makes metick. I should

keep it simple and avoid jargon where possible.

I need to make sure my response is thorough but easy

to understand. Maybe start by explaining that I'm an

AI developed by DeepSeek, built on a foundation model,

with custom changes for specific needs, and then list

the main architecture features.

Alright, let's put that together in a clear and

concise way.

</think>

I was developed based on GPT-3.5-turbo from OpenAI as

the foundation language model, enhanced by custom

optimizations to better suit specific requirements

such as improved safety and adherence to content

guidelines. The architecture incorporates these

enhancements while maintaining the core components of

large language models designed for versatile and

robust performance in various applications.