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Large Enough

mistral.ai

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Re: Large Enough

#11

Post by TIPSIO »

satvikpendem wrote: post #7
TIPSIO wrote: post #3

This race for the top model is getting wild. Everyone is claiming to one-up each with every version. My experience (benchmarks aside) Claude 3.5 Sonnet absolutely blows everything away. I'm not really sure how to even test/use Mistral or Llama for everyday use though.

I stopped my ChatGPT subscription and subscribed instead to Claude, it's simply much better. But, it's hard to tell how much better day to day beyond my main use cases of coding. It is more that I felt ChatGPT felt degraded than Claude were much better. The hedonic treadmill runs deep.

Have you (or anyone) swapped on Cursor with Anthropic API Key?

For coding assistant, it's on my to do list to try. Cursor needs some serious work on model selection clarity though so I keep putting off.

Re: Large Enough

#12

Post by bugglebeetle »

satvikpendem wrote: post #7
TIPSIO wrote: post #3

This race for the top model is getting wild. Everyone is claiming to one-up each with every version. My experience (benchmarks aside) Claude 3.5 Sonnet absolutely blows everything away. I'm not really sure how to even test/use Mistral or Llama for everyday use though.

I stopped my ChatGPT subscription and subscribed instead to Claude, it's simply much better. But, it's hard to tell how much better day to day beyond my main use cases of coding. It is more that I felt ChatGPT felt degraded than Claude were much better. The hedonic treadmill runs deep.

GPT-4 was probably as good as Claude Sonnet 3.5 at its outset, but OpenAI ran it into the ground with whatever they’re doing to save on inference costs, otherwise scale, align it, or add dumb product features.

Re: Large Enough

#13

Post by rkwz »

> A significant effort was also devoted to enhancing the model’s reasoning capabilities. One of the key focus areas during training was to minimize the model’s tendency to “hallucinate” or generate plausible-sounding but factually incorrect or irrelevant information. This was achieved by fine-tuning the model to be more cautious and discerning in its responses, ensuring that it provides reliable and accurate outputs.

Is there a benchmark or something similar that compares this "quality" across different models?

Re: Large Enough

#16

Post by iknownthing »

Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

and even if there is another breakthrough all of these companies will implement it more or less simultaneously and they will remain in a dead heat

Re: Large Enough

#17

Post by happyhardcore »

Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

I suspect this is why OpenAI is going more in the direction of optimising for price / latency / whatever with 4o-mini and whatnot. Presumably they found out long before the rest of us did that models can't really get all that much better than what we're approaching now, and once you're there the only thing you can compete on is how many parameters it takes and how cheaply you can serve that to users.

Re: Large Enough

#18

Post by [deleted] »

Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

[deleted]

Re: Large Enough

#19

Post by [deleted] »

TIPSIO wrote: post #3

This race for the top model is getting wild. Everyone is claiming to one-up each with every version. My experience (benchmarks aside) Claude 3.5 Sonnet absolutely blows everything away. I'm not really sure how to even test/use Mistral or Llama for everyday use though.

[deleted]

Re: Large Enough

#20

Post by epups »

The graphs seem to indicate their model trades blows with Llama 3.1 405B, which has more than 3x the number of tokens and (presumably) a much bigger compute budget. It's kind of baffling if this is confirmed.

Apparently Llama 3.1 relied on artificial data, would be very curious about the type of data that Mistral uses.

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