What is a self-hosted small LLM actually good for (<= 3B)
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Currently I've been using a local AI (a couple different kinds) to first - take the audio from a Twitch stream; so that I have context about the conversation, convert it to text, and then use a second AI; an LLM fed the first AIs translation + twitch chat and store 'facts' about specific users so that they can be referenced quickly for a streamer who has ADHD in order to be more personable.
That way, the guy can ask User X how their mothers surgery went. Or he can remember that User K has a birthday coming up. Or remember that User G's son just got a PS5 for Christmas, and wants a specific game.
It allows him to be more personable because he has issues remembering details about his users. It's still kind of a big alpha test at the moment, because we don't know the best way to display the 'data', but it functions as an aid.
sounds like salesforce for a twitch setting. cool use case, must make fun moments when he mentions such things.
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sounds like salesforce for a twitch setting. cool use case, must make fun moments when he mentions such things.
Esp. if the LLM just hallucinates 50% of the "facts" a about the users
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Oh—do you happen to have any recommendations for that?
DeepSeek-R1-Distill-Qwen-1.5B
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I've tried coding and every one I've tried fails unless really, really basic small functions like what you learn as a newbie compared to say 4o mini that can spit out more sensible stuff that works.
I've tried explanations and they just regurgitate sentences that can be irrelevant, wrong, or get stuck in a loop.
So. what can I actually use a small LLM for? Which ones? I ask because I have an old laptop and the GPU can't really handle anything above 4B in a timely manner. 8B is about 1 t/s!
I've used smollm2:135m for projects in DBeaver building larger queries. The box it runs on is Intel HD graphics with an old Ryzen processor. Doesn't seem to really stress the CPU.
UPDATE: I apologize to the downvoter for not masochistically wanting to build a 1000 line bulk insert statement by hand.
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Esp. if the LLM just hallucinates 50% of the "facts" a about the users
wrote last edited by [email protected]That hasn't been a problem at all for the 200+ users it's tracking so far for about 4 months.
I don't know a human that could ever keep up with this kind of thing. People just think he's super personable, but in reality he's not. He's just got a really cool tool to use.
He's managed some really good numbers because being that personal with people brings them back and keeps them chatting. He'll be pushing for partner after streaming for only a year and he's just some guy I found playing Wild Hearts with 0 viewers one day...
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Currently I've been using a local AI (a couple different kinds) to first - take the audio from a Twitch stream; so that I have context about the conversation, convert it to text, and then use a second AI; an LLM fed the first AIs translation + twitch chat and store 'facts' about specific users so that they can be referenced quickly for a streamer who has ADHD in order to be more personable.
That way, the guy can ask User X how their mothers surgery went. Or he can remember that User K has a birthday coming up. Or remember that User G's son just got a PS5 for Christmas, and wants a specific game.
It allows him to be more personable because he has issues remembering details about his users. It's still kind of a big alpha test at the moment, because we don't know the best way to display the 'data', but it functions as an aid.
Hey, you're treating that data with the respect it demands, right? And you definitely collected consent from those chat participants before you Hoover'd up their [re-reads example] extremely Personal Identification Information AND Personal Health Information, right? Because if you didn't, you're in violation of a bunch of laws and the Twitch TOS.
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Hey, you're treating that data with the respect it demands, right? And you definitely collected consent from those chat participants before you Hoover'd up their [re-reads example] extremely Personal Identification Information AND Personal Health Information, right? Because if you didn't, you're in violation of a bunch of laws and the Twitch TOS.
Most US states are single party consent. https://recordinglaw.com/united-states-recording-laws/one-party-consent-states/
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Currently I've been using a local AI (a couple different kinds) to first - take the audio from a Twitch stream; so that I have context about the conversation, convert it to text, and then use a second AI; an LLM fed the first AIs translation + twitch chat and store 'facts' about specific users so that they can be referenced quickly for a streamer who has ADHD in order to be more personable.
That way, the guy can ask User X how their mothers surgery went. Or he can remember that User K has a birthday coming up. Or remember that User G's son just got a PS5 for Christmas, and wants a specific game.
It allows him to be more personable because he has issues remembering details about his users. It's still kind of a big alpha test at the moment, because we don't know the best way to display the 'data', but it functions as an aid.
Surely none of that uses a small LLM <= 3B?
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I've tried coding and every one I've tried fails unless really, really basic small functions like what you learn as a newbie compared to say 4o mini that can spit out more sensible stuff that works.
I've tried explanations and they just regurgitate sentences that can be irrelevant, wrong, or get stuck in a loop.
So. what can I actually use a small LLM for? Which ones? I ask because I have an old laptop and the GPU can't really handle anything above 4B in a timely manner. 8B is about 1 t/s!
Have you tried RAG? I believe that they are actually pretty good for searching and compiling content from RAG.
So in theory you could have it connect to all of you local documents and use it for quick questions. Or maybe connected to your signal/whatsapp/sms chat history to ask questions about past conversations
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Have you tried RAG? I believe that they are actually pretty good for searching and compiling content from RAG.
So in theory you could have it connect to all of you local documents and use it for quick questions. Or maybe connected to your signal/whatsapp/sms chat history to ask questions about past conversations
No, what is it? How do I try it?
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No, what is it? How do I try it?
RAG is basically like telling an LLM "look here for more info before you answer" so it can check out local documents to give an answer that is more relevant to you.
You just search "open web ui rag" and find plenty kf explanations and tutorials
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I've tried coding and every one I've tried fails unless really, really basic small functions like what you learn as a newbie compared to say 4o mini that can spit out more sensible stuff that works.
I've tried explanations and they just regurgitate sentences that can be irrelevant, wrong, or get stuck in a loop.
So. what can I actually use a small LLM for? Which ones? I ask because I have an old laptop and the GPU can't really handle anything above 4B in a timely manner. 8B is about 1 t/s!
I installed Llama. I've not found any use for it. I mean, I've asked it for a recipe because recipe websites suck, but that's about it.
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I've integrated mine into Home Assistant, which makes it easier to use their voice commands.
I haven't done a ton with it yet besides set it up, though, since I'm still getting proxmox configured on my gaming rig.
What are you using for voice integration? I really don't want to buy and assemble their solution if I don't have to
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I've tried coding and every one I've tried fails unless really, really basic small functions like what you learn as a newbie compared to say 4o mini that can spit out more sensible stuff that works.
I've tried explanations and they just regurgitate sentences that can be irrelevant, wrong, or get stuck in a loop.
So. what can I actually use a small LLM for? Which ones? I ask because I have an old laptop and the GPU can't really handle anything above 4B in a timely manner. 8B is about 1 t/s!
I've run a few models that I could on my GPU. I don't think the smaller models are really good enough. They can do stuff, sure, but to get anything out of it, I think you need the larger models.
They can be used for basic things, though. There are coder specific models you can look at. Deepseek and qwen coder are some popular ones
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Hey, you're treating that data with the respect it demands, right? And you definitely collected consent from those chat participants before you Hoover'd up their [re-reads example] extremely Personal Identification Information AND Personal Health Information, right? Because if you didn't, you're in violation of a bunch of laws and the Twitch TOS.
wrote last edited by [email protected]If I say my name is Doo doo head, in a public park, and someone happens to overhear it - they can do with that information whatever they want. Same thing. If you wanna spew your personal life on Twitch, there are bots that listen to all of the channels everywhere on twitch. They aren't violating any laws, or Twitch TOS. So, *buzzer* WRONG.
Right now, the same thing is being done to you on Lemmy. And Reddit. And Facebook. And everywhere else.
Look at a bot called "FrostyTools" for Twitch. Reads Twitch chat, Uses an AI to provide summaries of chat every 30 minutes or so. If that's not violating TOS, then neither am I. And thousands upon thousands of people use FrostyTools.
I have the consent of the streamer, I have the consent of Twitch (through their developer API), and upon using Twitch, you give the right to them to collect, distribute, and use that data at their whim.
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Surely none of that uses a small LLM <= 3B?
wrote last edited by [email protected]Yes. The small LLM isn't retrieving data, it's just understanding context of text enough to know what "Facts" need to be written to a file. I'm using the publicly released Deepseek models from a couple of months ago.
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I installed Llama. I've not found any use for it. I mean, I've asked it for a recipe because recipe websites suck, but that's about it.
you can do a lot with it.
I heated my office with it this past winter.
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I've run a few models that I could on my GPU. I don't think the smaller models are really good enough. They can do stuff, sure, but to get anything out of it, I think you need the larger models.
They can be used for basic things, though. There are coder specific models you can look at. Deepseek and qwen coder are some popular ones
Been coming to similar conclusions with some local adventures. It's decent but not as able to process larger contexts.
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What are you using for voice integration? I really don't want to buy and assemble their solution if I don't have to
I just use the companion app for now. But I am designing a HAL9000 system for my home.
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RAG is basically like telling an LLM "look here for more info before you answer" so it can check out local documents to give an answer that is more relevant to you.
You just search "open web ui rag" and find plenty kf explanations and tutorials
wrote last edited by [email protected]I think RAG will be surpassed by LLMs in a loop with tool calling (aka agents), with search being one of the tools.
