The video reviews the Dolphin 3 model, based on the Llama 3.1 architecture, highlighting its 8 billion parameters and context size of 12,000 tokens, while testing its performance in various tasks such as coding and generating meal plans. The host notes inconsistencies in the model’s responses, particularly in coding capabilities, and emphasizes the need for tailored prompts to improve interactions, inviting viewers to share their experiences.
In the video, the host reviews the newly released Dolphin 3 model, which is based on the Llama 3.1 architecture. The Dolphin 3 model is an 8 billion parameter model with a context size of 12,000 tokens, making it suitable for users with GPUs of 6GB or larger. The host discusses the different quantization options available, specifically Q4 and Q8, and mentions that they will be testing the Q8 version for better performance. The video also highlights the model’s training on various open-source datasets, emphasizing its steerability and adaptability for different tasks.
The host shares their experience with the Dolphin model, noting that it was one of the first AI models they experimented with for local hosting. They provide insights into their setup, which includes a powerful quad GPU rig running on Proxmox. The video includes a walkthrough of the installation process and troubleshooting tips for viewers interested in setting up their own AI systems. The host encourages viewers to check out previous videos for guidance on building their own AI rigs and using software tools effectively.
During the review, the host tests the Dolphin 3 model by asking a series of questions and tasks, including coding challenges and ethical dilemmas. They note that the model’s performance has been inconsistent, with some responses being accurate while others fall short. For instance, the model struggled with generating a Flappy Bird clone and failed to provide correct answers for some mathematical and logical queries. The host expresses disappointment in the coding capabilities of the Llama 3.1 base, suggesting that it may not be the best choice for programming tasks.
The host also explores the model’s ability to generate fitness and meal plans, which yielded a basic but somewhat repetitive response. They highlight that while the model followed instructions, it lacked creativity and depth in its suggestions. The review includes a variety of questions, from simple counting tasks to more complex ethical scenarios, showcasing the model’s strengths and weaknesses in understanding context and providing nuanced answers.
In conclusion, the host reflects on the overall performance of the Dolphin 3 model, noting that while it may not excel in coding tasks, it could still be useful for conversational purposes. They emphasize the importance of tailoring system prompts for better results and express a desire for more human-like interactions in AI models. The video wraps up with an invitation for viewers to share their thoughts and experiences in the comments, fostering a community discussion around the capabilities and limitations of the Dolphin 3 model.