empirical-philosophy VS serge

One way that I've been framing this in my head (and in an application I'm building) is that gpt-3 will be useful for analytic tasks where as gpt-4 will be required for synthetic tasks. I'm using "analytic" and "synthetic" in the same way as in this writeup https://github.com/williamcotton/empirical-philosophy/blob/m...

Hey Simon! I've been digging your writings on LLMs lately.

I've been having some decent luck with some of the approaches that I've discussed in the following articles and projects:

From Prompt Alchemy to Prompt Engineering: An Introduction to Analytic Augmentation: https://github.com/williamcotton/empirical-philosophy/blob/m...

https://www.williamcotton.com/articles/writing-web-applicati...

https://github.com/williamcotton/transynthetical-engine

I'd love to hear your thoughts on the matter!

You’re not actually doing any research.

Here is my research: https://github.com/williamcotton/empirical-philosophy/blob/m...

It is clear that analytic augmentations will result in more factual information.

Your claims are unfounded and untested.

Take a look at

https://github.com/williamcotton/empirical-philosophy/blob/m...

https://langchain.readthedocs.io/en/latest/

They can be taught!

I've been developing a methodology around prompt engineering that I have found very useful:

https://github.com/williamcotton/empirical-philosophy/blob/m...

A few more edits and it's ready for me to submit to HN and then get literally no further attention!

That's not a rebuttable of a claim that Bing is more accurate.

A proper rebuttable would involve empirical evidence that Bing is no more accurate than other LLM tools that do not add analytical augmentations such as search results to their prompts.

Based on empirical evidence, I find that analytical augmentations do indeed result in more accurate results:

https://github.com/williamcotton/empirical-philosophy/blob/m...

Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).

You might be pleased to hear that nothing really stops you from doing this today. If you ran Serge[0] on a Mac with Tailscale, you could hack together a decently-accelerated Llama chatbot.

[0] https://github.com/serge-chat/serge

Cannot help you with libraries specifically but both Serge and ChatUI are built using SvelteKit, so the code might be of some use to you.

Serge made it really easy for me to get started, but it all CPU-based.

All that said this project probably solves your problem: https://github.com/serge-chat/serge

You can play a little bit with more straightforward local models (the simplest to setup is https://github.com/nsarrazin/serge ), to see that any LLM is basically a party trick.