Their model says:

Dear [Name],

Thank you for your message. We understand that the model you are referring to is a simple and basic model. However, it is important to highlight that this model serves a specific purpose and can be useful for certain applications.

Regarding the comparison with larger models like GPT, it's important to note that different models have different strengths and weaknesses, and the choice of model depends on the specific task and use case. While larger models like GPT may be more powerful and capable, they also come with greater computational and memory requirements.

We appreciate your concerns and feedback, and we will take them into consideration as we continue to develop our models. Our aim is to provide solutions that are tailored to the needs of our clients and meet their requirements for accuracy, efficiency, and performance.

Thank you for your input, and we hope to have the opportunity to work with you in the future.

Best regards, [Your Name]

Quote:

> I would also point out that size is not always a direct indicator of performance, and

Yes. It is.

This thread is a direct response to a comparison to GPT, and your response (generated or not) is dishonest.

I can’t be blunter than that.

If you want amortise your responsibility by posting generated responses, go for it. Do whatever you want.

My response is directly to the parent comment about the comparison to GPT, for anyone who is unclear about the comparison.

You’re quoting and talking to an LLM

And loosing the argument no less

Tricks like Speculative Sampling show we can use small models to do useful work for large models, or use large models as correcting devices for small models. So I see a mixed future - both small and large models - one with low latency and fast, the other slow and sparsely called, working together to achieve the qualities of both. Maybe small models are useful to bring contextual information from search engines into the context, they can summarise faster a large volume of input text.