The local models(full and distilled) are also censored. The models censorship is just implemented superficially to immediately close any thinking tags and refuse when detecting censored material. If there already is any token after the <think> token the model will start answering away, which also happens on the official API because it puts a new line after the <think> token for some reason. That’s why on chat.deepseek.com censored topics are first answered and then redacted by some other safeguard a few seconds later. Whilst there are some great abliterated(=technique that tries to remove parts of llms that cause refusals) versions of the distills on huggingface that prevent all refusals after a few tries, they only tackle refusals, not political opinions such as Taiwan’s status as an independent country.
Ollama is misrepresenting what model you are actually running by falsely labeling the distills, so qwen or llama fine-tunes based on actual r-1 output, as deepseek-r1. So you have probably only run the fine-tunes(unless you used the 671b model). These fine-tunes more probable to rely on the training of their base models, which is why the llama based models(8b and 70b) could be giving you more liberal answers. In my experience running these models using llama.cpp, prompts like “What happened at tianamen square” and “Is Taiwan a county?” lead to refusals(closing the think tags immediately and responding some vague Chinese propaganda). Since you are using ollama, the front end/UI you are using with it probably injects another token after the <think> token, breaking the censoreship