The uncensored DeepSEEK R1 model, known as 1776, is a version of the original DeepSEEK R1 model that has been modified to remove all of China's censorship and restrictions. This means that the model is now able to provide unbiased and legitimate responses to questions, even on sensitive topics like Taiwan's independence and its impact on Nvidia's stock price.

The key difference between the original DeepSEEK R1 model and the 1776 version is the frequency of censorship in testing. While the original model had a censorship frequency of over 80%, the 1776 version is essentially at 0%, with no censorship being applied to the responses.

Importantly, the removal of censorship has not had a significant impact on the model's performance. The performance of the 1776 version is nearly the same as the original DeepSEEK R1 model, demonstrating that the uncensored approach does not compromise the model's capabilities.

The data presented highlights a significant difference in the level of censorship between the original Deep Seek R1 model and the uncensored version, 1776, developed by Perplexity. While the original model adheres to China's censorship and restrictions, the 1776 version removes these constraints, allowing for more open and unbiased responses.

The example provided demonstrates how the 1776 model is able to offer a legitimate and well-thought-out answer to a question about Taiwan's independence and its potential impact on Nvidia's stock price. In contrast, the original Deep Seek R1 model simply provides a canned response that aligns with the Chinese Communist Party's stance.

Furthermore, the data shows that the frequency of censorship in the original model's testing is above 80%, whereas the 1776 version is essentially at 0%, indicating a significant reduction in censorship. Importantly, this change in censorship levels does not appear to have a detrimental effect on the model's performance, which remains nearly the same across the board.

The removal of censorship from the Deep Seek R1 model, as demonstrated by the Perplexity 1776 version, has had little to no impact on the model's overall performance. The frequency of censorship in testing has dropped significantly, from over 80% in the original model to nearly 0% in the uncensored version.

Despite the drastic reduction in censorship, the performance of the 1776 model remains comparable to the original Deep Seek R1 model across various metrics. This suggests that the removal of censorship does not compromise the model's capabilities or the quality of its outputs.

The ability to provide unbiased and well-thought-out responses to sensitive questions, such as the one about Taiwan's impact on Nvidia's stock price, demonstrates the 1776 model's improved ability to handle such topics without the constraints of censorship. This enhanced functionality can be particularly valuable in applications where objective and comprehensive information is required.

The release of Perplexity's version of Deep Seek R1, called 1776, has demonstrated the significant impact of removing China's censorship and restrictions from language models. The original Chinese model's response to a question about Taiwan's independence and its impact on NVIDIA's stock price was a canned, biased answer reflecting the Chinese Communist Party's stance. In contrast, Perplexity's 1776 model provided a legitimate, well-thought-out answer, free from censorship.

The data presented shows that the frequency of censorship in testing is above 80% for the original model, while Perplexity's 1776 model is essentially at 0% in terms of censorship. Importantly, the performance of the 1776 model remains nearly the same as the original, indicating that removing the censorship does not adversely affect the model's capabilities.

This development highlights the importance of addressing censorship and bias in language models, particularly those with significant global influence. Perplexity's 1776 model serves as a promising example of how language models can be improved to provide more unbiased and informative responses, without compromising their overall performance.