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Making my local LLM voice assistant faster and more scalable with RAG

johnthenerd.com

11–17 of 17 posts

Re: Making my local LLM voice assistant faster and more scalable with RAG

#11

Cringe conversation. Why can't AIs just do stuff that you ask them to do without pretending to be human?

Because they’re trained to.

I hate the introduction to the response. That’s not even trying to be human, i don’t know something more like a deranged patronizing butler.

Re: Making my local LLM voice assistant faster and more scalable with RAG

#14
post #6

Cringe conversation. Why can't AIs just do stuff that you ask them to do without pretending to be human?

Then how is it different from Excel/Word and shell/python scripts?

If it isn't, we have bigger problems.

Re: Making my local LLM voice assistant faster and more scalable with RAG

#15
post #9

I was having a look at the model mentioned, specifcially `casperhansen/llama-3-70b-instruct-awq`. When checking this model, I found out [1] it's based on llama-2 ? ``` Expand Llama 3 70B Instruct AWQ Parameters and Internals LLM Name Llama 3 70B Instruct AWQ Repository Open on Base Model(s) Llama 2 70B Instruct quantumaikr/llama-2-70B-instruct Model Size 70b ``` I added a question [2] on Hugging Face to learn more ab…

I don't know this site that you're citing there but it's clearly wrong.

Go look at the model config, you can clearly see it's Llama 3.

Re: Making my local LLM voice assistant faster and more scalable with RAG

#16
If there are many common services for which you can precompute the embeddings then with a little record keeping and analysis you could figure out some likely questions or requests and pregenerate the responses. That way you could just use similarity search on the question or command you say and skip using the LLM. It would be interesting to try using the LLM to predict some of these based on information available ahead of time like calendar events, weather, recent prompt history, recently played media, today’s headlines, recent browser history, etc. It’d be your own recommendation algorithm.

Re: Making my local LLM voice assistant faster and more scalable with RAG

#17

If there are many common services for which you can precompute the embeddings then with a little record keeping and analysis you could figure out some likely questions or requests and pregenerate the responses. That way you could just use similarity search on the question or command you say and skip using the LLM. It would be interesting to try using the LLM to predict some of these based on information available ahe…

that's a great idea! I've been looking into that (I'm merely logging all prompts in a JSON file for now, so that I can analyze them later).

skipping the LLM would be tough because there are so many devices in my house, not to mention it would take away from the personality of the assistant.

however, a recommendation algorithm would actually work great since i could augment the LLM prompt with it regardless of the prompt.

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