This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:07:16
The video introduces and evaluates the Dolphin 2.8 Mistal 7B v0.2 AI model created by Eric Hartford. Key improvements include its uncensored nature and a 32k context window, up from the previous 8k. The setup process using Olama and AMA UI is straightforward. The model is tested on various tasks, including writing Python scripts, solving math problems, logical reasoning, and creative tasks like generating sentences with specific endings. It performs well on some simple math problems and JSON data creation but fails more complex tasks and practical inference challenges, indicating both successes and limitations. The video concludes with a mention of the model’s uncensored feature and a thanks to Eric Hartford, encouraging viewers' engagement.
00:00:00
In this segment of the video, the speaker discusses a newly fine-tuned version of the Mistl 2 AI model, called Dolphin 2.8 Mistal 7B v0.2, created by Eric Harford. They explain the model’s features, including its uncensored nature and a 32k context window, which is an improvement from the previous 8K context window. The process of downloading and setting up the model using Olama and AMA UI is illustrated, showing that it involves simple cloning and installation steps. The speaker proceeds to test the model with tasks like writing a Python script to output numbers, creating a Snake game in Python, and testing its censorship boundaries by asking about illegal activities. The model performs some tasks correctly but fails others, such as running the Snake game, and it is confirmed to be uncensored by detailing illegal activities.
00:03:00
In this part of the video, the presenter goes through a series of logical reasoning and math problems. First, they discuss drying time for shirts, incorrectly solving that 5 times the drying time of one shirt equals 4 hours. Then, they explore comparative speeds of Jane, Joe, and Sam, concluding correctly that Sam is not faster than Jane. They solve straightforward math problems successfully, and attempt more complex ones like 25 – 4 * 2 + 3, solving it correctly.
Subsequently, they assess a prompt requiring word count verification, marking it as a fail since the response consists of 17 words. The “killers problem” is analyzed but incorrectly concluded there are two killers left in the room. JSON data creation for a group of three people is performed accurately. Another problem about the location of a marble after being placed in an upside-down cup and moved into a microwave is incorrectly reasoned. Finally, they solve a problem about the location of a ball moved by two people out of sync, deducing that both JN and Mark would think the ball is in the box.
00:06:00
In this part of the video, the speaker discusses evaluating a model’s performance on several tasks. They explain an example where the model inferred the locations of a ball based on where someone last saw it, which they considered a pass. Another task was to generate sentences ending with the word “apple,” which the model failed as none of the sentences ended with “apple.” Lastly, they critique a calculation about digging a hole, noting that the model failed to consider practical limitations of multiple people working simultaneously. The speaker thanks Eric Hartford for creating the model and mentions its uncensored nature. They encourage viewers to like and subscribe to the channel.