Predictions about the potential impacts of generative AI may be hugely overblown because of "many serious, unsolved problems" with the technology according to Gary Marcus, one of the field's leading voices.
Yeah but Llama’s quality cannot compete with ChatGPT models (Doesn’t matter what model you use, if you want good and FAST results, you require serious compute). We do have commercial dedicated AI chips from NVDA, last time I checked you had to make an order to even get a price. George Hotz who is also working on something similar, by his account from a Lex Fridman podcast mentioned that a personal AI rig would have to be closer to a mainframe’s size.
There’s nothing I have seen so far that leads me to believe that generative AI gets more efficient with weaker hardware.
The trajectory is such that current L2 70B models are easily beating 3.5 and are approaching GPT4 performance - an A6000 can run them comfortably and this is a few months only after release.
Nah the trajectory is not in favor of proprietary, especially since they will have to dumb down due to alignment more and more
Which modern Mac are you talking about and how much does that cost? Again, I doubt any of the opensource 30B models can compete even with ChatGPT 3.5. Which is the point I started with earlier.
Seems to me like you are riding this whole efficiency thing on nothing more than hopium.
I actually work with this stuff daily and there is a number of 30B models that are exceeding chatGPT for specific tasks such as coding or content generation, especially when enhanced with a lora.
airoboros-33b1gpt4-1.4.SuperHOT-8k for example comfortably outputs > 10 tokens/s on a 3090 and beats GPT-3.5 on writing stories, probably because it’s uncensored. It’s also got 8k context instead of 4.
Several recent LLama 2 based models exceed chatgpt on coding and classification tasks and are approaching GPT4 territory. Google bard has already been clobbered into a pulp.
The speed of advances is stunning.
M- architecture macs can run large LLMs via llama.cpp because of unified memory interface - in fact a recent macbook air with 64GB can comfortably run most models just fine. Even notebook AMD GPUs with shared memory have started running generative AI in the last week.
You can follow along at chat.lmsys.org. Open source LLMs are only a few months but have started encroaching on the proprietary leaders who have years of headstart
Reminds me of the article saying open ai is doomed because it can only last about thirty years with its current level of expenditure.
OpenAI must evolve into serving something other than generative AI.
The compute bills for OpenAI are crazy. They would need more paying customers to try and at least keep the service somewhat viable.
https://futurism.com/the-byte/chatgpt-costs-openai-every-day
Cost reduction in the field is orders of magnitude potential. Look at llama running on everything down to a raspy pi after 2 months.
There are massive gains to be made - once we have dedicated hardware for transformers, that’s orders of magnitude more.
See your phone being able to playback 24h of video but die after 3h of browsing? Dedicated hardware codec support
Yeah but Llama’s quality cannot compete with ChatGPT models (Doesn’t matter what model you use, if you want good and FAST results, you require serious compute). We do have commercial dedicated AI chips from NVDA, last time I checked you had to make an order to even get a price. George Hotz who is also working on something similar, by his account from a Lex Fridman podcast mentioned that a personal AI rig would have to be closer to a mainframe’s size.
There’s nothing I have seen so far that leads me to believe that generative AI gets more efficient with weaker hardware.
The trajectory is such that current L2 70B models are easily beating 3.5 and are approaching GPT4 performance - an A6000 can run them comfortably and this is a few months only after release.
Nah the trajectory is not in favor of proprietary, especially since they will have to dumb down due to alignment more and more
https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper?trk=feed_main-feed-card_feed-article-content
An A6000 ranges between $4500 and $7000 . We are a long long way from reaching efficiency on affordable consumer grade hardware.
A 30B model which will be fine for specialized tasks runs on a 3090 or any modern mac today.
We are months away from being affordable at current trajectory
Which modern Mac are you talking about and how much does that cost? Again, I doubt any of the opensource 30B models can compete even with ChatGPT 3.5. Which is the point I started with earlier.
Seems to me like you are riding this whole efficiency thing on nothing more than hopium.
I think at this point we are arguing belief.
I actually work with this stuff daily and there is a number of 30B models that are exceeding chatGPT for specific tasks such as coding or content generation, especially when enhanced with a lora.
airoboros-33b1gpt4-1.4.SuperHOT-8k for example comfortably outputs > 10 tokens/s on a 3090 and beats GPT-3.5 on writing stories, probably because it’s uncensored. It’s also got 8k context instead of 4.
Several recent LLama 2 based models exceed chatgpt on coding and classification tasks and are approaching GPT4 territory. Google bard has already been clobbered into a pulp.
The speed of advances is stunning.
M- architecture macs can run large LLMs via llama.cpp because of unified memory interface - in fact a recent macbook air with 64GB can comfortably run most models just fine. Even notebook AMD GPUs with shared memory have started running generative AI in the last week.
You can follow along at chat.lmsys.org. Open source LLMs are only a few months but have started encroaching on the proprietary leaders who have years of headstart