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If I were apple, I would be developing something in total secrecy and then release something ahead of the rest of competition when people least expect it. very big ifs but siri can be updated everywhere overnight and I dont see them rushing into anything like this
If I were apple, I would just buy one of the major LLM companies. They have the cash.
If I was Apple I'd be quaking in my boots. They are getting too far behind to ever catch up. Nokia in 2010 vibes.
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> Phi-3-mini 3.8b: 71.2 Per the paper, phi3-mini (which is english-only) quantised to 4bit uses 1.8gb RAM and outputs 1212 tokens/sec (correction: 12 tokens/sec) on iOS. A model on par with GPT-3.5 running on phones! (weights haven't been released, though)
> (weights haven't been released, though) Phi-1, Phi-1.5, and Phi-2 have all had their weights released, and those weights are available under the MIT License. Hopefully Microsoft will continue that trend with Phi-3. > outputs 1212 tokens/sec on iOS I think you meant "12 tokens/sec", which is still nice, just a little less exciting than a kilotoken/sec.
Thanks! The HTML version on archive.is has messed up markup and shows 1212 instead: https://archive.is/Ndox6
Less tokens than Llama 3 (3.3T vs 15T) yet better outcome. No doubt more information dense training data. The interesting thing is the use of synthetic data which they don't talk about.
Actually the original Phi papers did talk about their synthetic data strategy, and it's very cool -- essentially invert high quality textbook text using GPT-4 to create prompts, where the textbooks supply the answers. There may be more undisclosed, but it remains in my mind as one of the best ideas of the last twelve months -- so smart, and interesting, and apparently, it works well.
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Phi-2 wasn't chat/instruct tuned, so it didn't act good in chat, it was a base model. But the benchmark #s were real.
I'm pretty naive so please forgive it's a stupid question. To me, what the parent comment is saying is that even though the benchmarks are cool, it's not super helpful to the every day person. Because if you can't chat with it very well (even for a narrow context) what utility does it have with great benchmarks?
Less tokens than Llama 3 (3.3T vs 15T) yet better outcome. No doubt more information dense training data. The interesting thing is the use of synthetic data which they don't talk about.
Actually the original Phi papers did talk about their synthetic data strategy, and it's very cool -- essentially invert high quality textbook text using GPT-4 to create prompts, where the textbooks supply the answers. There may be more undisclosed, but it remains in my mind as one of the best ideas of the last twelve months -- so smart, and interesting, and apparently, it works well.
Everyone needs to take these benchmark numbers with a big grain of salt. According to what I've read, Phi-2 was much worse than its benchmark numbers suggested. This model follows the same training strategy. Nobody should be assuming these numbers will translate directly into a high ranking on the LMSYS leaderboard, or usefulness in everyday tasks. Let's not dethrone Llama 3 until some real world testing can be done.…
Phi-2 wasn't chat/instruct tuned, so it didn't act good in chat, it was a base model. But the benchmark #s were real.
It felt a lot like it was overfitted to the exact type of tasks (ie, not a data leak) in the benchmarks but if you were trying something a bit off track if didn't know what to do. At the time my hypothesis was that the small model just didn't have the capacity to generalise well enough, but since then Gemma 2B has come out and seems to be ok.
So now I have no idea why, but yes: the benchmarks for Phi-2 didn't represent how it worked for me on real world tasks where you'd expect it top be ok.
Incredible, rivals Llama 3 8B with 3.8B parameters after less than a week of release. And on LMSYS English, Llama 3 8B is on par with GPT-4 (not GPT-4-Turbo), as well as Mistral-Large. Source: https://chat.lmsys.org/?leaderboard (select English in the dropdown) So we now have an open-source LLM approximately equivalent in quality to GPT-4 that can run on phones? Kinda? Wild. (I'm sure there's a lot of nuance to it, f…
Can’t wait to see some Phi-3 fine tunes! Will be testing this out locally, such a small model that I can run it without quantization. Feels incredible to be living in a time with such neck breaking innovations. What are chances we’ll have a <100B parameter GPT4/Claude Opus model in the next 5 years?