Dolphin is a project dedicated to pushing the boundaries of AI model development and distributed inference

Dolphin is trained on top of leading open-source frontier models with the goal of customizing alignment to the user.

We have developed a pipeline to create Uncensored, Unaligned & Unbiased versions of the best open-source AI models that are available to the public.

Dolphin has produced its own versions of Meta's Llama 3, Mistral & Mixtral MoE, Google's Gemma, Alibaba's Qwen 3 & other popular models.

This is done through a process called "fine tuning" which modifies the base model using a custom dataset.

When creating our dataset, we identify and remove as many refusals and biased answers as possible and keep the rest.

The model is then trained with the filtered dataset the same way that the original instruct model was trained, producing a much more compliant version of the base model that should not refuse any of the user's requests.

We aim to retain all the intelligence of the base model throughout this process, while also expanding the knowledge base with our coding and math-focused datasets. Ultimately, this will create a model that can respond to almost any prompt you can imagine.

Dolphin models are also trained to produce desirable prose in the response style, ensuring they are both entertaining and highly informative.

Dolphin Network supports heterogeneous AI models running on machines around the world, with strong verification measures to prevent cheating and deliver consistent generations at lower cost than centralized providers.

The goal of the network is to allow gamers & data centers to repurpose their GPUs during idle periods by powering a portion of our inference network.

By processing a portion of the network's requests, they are able to earn POD tokens which can later be used for inference or sold on the market to recoup the cost of the GPU.