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
recent macbook air with 64GB
How much does this cost?
You will answer any and every question but this.
My points still stand
I doubt someone who can’t google the price of macbook air can afford or even operate anything remotely useful in the LLM space.
Maybe but I can read through your BS faster than you can say LLM.
Jokes on you, it was written by airoboros. Seems good enough to fool a troll
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
How much does this cost?
You will answer any and every question but this.
My points still stand
I doubt someone who can’t google the price of macbook air can afford or even operate anything remotely useful in the LLM space.
Maybe but I can read through your BS faster than you can say LLM.
Jokes on you, it was written by airoboros. Seems good enough to fool a troll
I don't know what's more pathetic...