31–40 of 252 posts
"64K tokens context window" I do wish they had managed to extend it to at least 128K to match the capabilities of GPT-4 Turbo Maybe this limit will become a joke when looking back? Can you imagine reaching a trillion tokens context window in the future, as Sam speculated on Lex's podcast?
maybe we'll look back at token context windows like we look back at how much ram we have in a system.
Pricing? Found it: https://mistral.ai/technology/#pricing It'd useful to add a link to the blog post. While it's an open model, most will only be able to use it via the API.
I'm considering switching my function calling requests from OpenAI's API to Mistral. Are they using similar formats? What's the easiest way to use Mistral? Is it by using Huggingface?
Pricing? Found it: https://mistral.ai/technology/#pricing It'd useful to add a link to the blog post. While it's an open model, most will only be able to use it via the API.
Pricing? Found it: https://mistral.ai/technology/#pricing It'd useful to add a link to the blog post. While it's an open model, most will only be able to use it via the API.
It's open source, you can just download and run it for free on your own hardware.
Does anyone have a good layman's explanation of the "Mixture-of-Experts" concept? I think I understand the idea of having "sub-experts", but how do you decide what each specialization is during training? Or is that not how it works at all?
Does anyone have a good layman's explanation of the "Mixture-of-Experts" concept? I think I understand the idea of having "sub-experts", but how do you decide what each specialization is during training? Or is that not how it works at all?
It typically won't behave like human experts - you might find one of the networks is an expert in determining where to place capital letters or full stops for example.
MoE's do not really improve accuracy - instead they are to reduce the amount of compute required. And, assuming you have a fixed compute budget, that in turn might mean you can make the model bigger to get better accuracy.
Earlier quoted context omitted.
maybe we'll look back at token context windows like we look back at how much ram we have in a system.
I agree with this in the sense that once you have enough, you stop caring about the metric.
I just find it hilarious how approximately 100% of models beat all other models on benchmarks.
Great to see such free to use and self-hostable models, but it's said that open now means only that. One cannot replicate this model without access to the training data.
There's a large amount of liability in disclosing your training data.