Microsoft has unveiled Phi-2, a small but mighty AI model with just 2 billion parameters — and it's outperforming giants like LLaMA 2–7B and Mistral! 😲 How is such a compact model able to compete with much larger ones? Dive into the details with AI Revolution as we explore the architecture, benchmarks, and real-world potential of this breakthrough! 💥
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00:00microsoft has just announced the launch of phi2 one of the smallest and most powerful language
00:04models in the world it is a 2.7 billion parameter model that beats some of the much bigger language
00:10models out there like google's gemini nano 2 and meta's llama 27b in this video we will dive deep
00:16into phi2 exploring its technical innovations practical uses and the broader impact it could
00:22have on ai's future but before we proceed remember to subscribe and hit the notification bell to stay
00:27updated on my latest videos all right so phi2 is microsoft's latest small language model in the phi
00:33series that builds on its earlier versions phi1 and phi1.5 but it's better in size and performance
00:40phi1 came out in june 2023 as a model with 1 billion parameters capable of writing coherent text in many
00:47languages it learned from a huge data set called common crawl which had tons of web text then phi1.5
00:55was released in september 2023 an upgrade of phi1 it used a more varied data set called webtext plus
01:01featuring texts from news social media books and wikipedia phi2 aims to be even better than the
01:07earlier models it stands out in two ways first it can create realistic images from text descriptions
01:12a unique feature among small language models second it improves itself by learning from different
01:18sources like books wikipedia code and scientific papers looking at different language models we see
01:23that phi2 despite having only half the parameters compared to models like llama 2 and mistral still
01:29performs better in benchmarks llama 27b mistral and gemini nano 2 all have 7 billion parameters and use
01:37the common crawl data set mistral also includes additional data from webtext plus wikipedia books news
01:44articles social media posts code repositories scientific papers and various book related content it scores 0.95 on
01:52the topper scale higher than llama 2 0.9 and gemini nano 2 0.8 phi1 with 1 billion parameters from common crawl
02:01doesn't have a topper score mentioned phi 1.5 includes knowledge transfer but its details aren't specified
02:07despite having fewer parameters phi2 outshines these models this indicates that phi2 is not only more
02:13compact but also more efficient and adaptable than other small language models it's capable of producing high
02:19quality text and handling various language tasks with fewer resources and less time now phi2 has some
02:25amazing abilities thanks to a few key technical advances let's talk about what makes phi2 special
02:32first of all phi2 has a unique way of working that uses text to image synthesis this means it can create
02:38lifelike varied pictures from just a text description next phi2 uses a top-notch training method called textual
02:45knowledge transfer this helps it learn better and handle a wider range of tasks it can take in
02:50information from many sources like books wikipedia code repositories and scientific papers and add it
02:56to what it already knows which gives it an edge over other slms allowing it to deal with more complex and
03:02varied challenges textual knowledge transfer is about learning from outside data that wasn't part of the
03:08original training if phi1 only learned from common crawl it might struggle with tasks needing specific
03:14knowledge or facts but if it's trained on webtext plus and also learns from other sources it can access
03:21more info and do better this method is great for slms because they often work with language tasks that
03:27need a lot of context and smart thinking for instance an slm might need to summarize a news article answer a
03:34question or write a product description to do this well it needs a wide and deep understanding of the world and
03:40language now microsoft developed ways to help slms with textual knowledge transfer one way is knowledge
03:47distillation which means taking the knowledge from a big model and fitting it into a smaller one for example if
03:53phi1 learned from a huge amount of data and had billions of parameters it would be big and costly to use but if we can
04:00transfer its knowledge to a smaller slm like phi2 we can keep the performance high without the big cost
04:07another way is knowledge augmentation which means adding new data to an existing model to make it
04:12perform better or no more for instance if phi1 learned from a lot of data but didn't know much about books or
04:18wikipedia it would be limited in what it can do but if we add info from these sources to the data it learned
04:24from phi2 can benefit and become more capable microsoft has shown these methods work well with their
04:30own data and tasks they've proven that textual knowledge transfer can greatly improve phi2's
04:35performance on different tests where it achieved the best results compared to other models i've been
04:40saying how great phi2 is because it's built in a special way and trained using advanced techniques
04:46but the cool part is all the things you can do with it basically it does everything a big language
04:51model can do but it needs less computer power and costs less that's what makes it really stand out
04:57all right so what's your take on phi2 drop your thoughts and ideas in the comments if you found
05:03this information helpful and want to stay in the loop with more insights into the ever-evolving world
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05:13content like this to you thanks for watching and see you in the next one