What is a self-hosted small LLM actually good for (<= 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!
Nothing.
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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!
Converting free text to standardized forms such as json
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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!
Sorry, I am just gonne dump you some links from my bookmarks that were related and interesting to read, cause I am traveling and have to get up in a minute, but I've been interested in this topic for a while. All of the links discuss at least some usecases. For some reason microsoft is really into tiny models and made big breakthroughs there.
https://reddit.com/r/LocalLLaMA/comments/1cdrw7p/what_are_the_potential_uses_of_small_less_than_3b/
https://github.com/microsoft/BitNet
https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
https://news.microsoft.com/source/features/ai/the-phi-3-small-language-models-with-big-potential/
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Converting free text to standardized forms such as json
Oh—do you happen to have any recommendations for that?
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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!
wrote last edited by [email protected]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.
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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!
wrote last edited by [email protected]I think that's a size where it's a bit more than a good autocomplete. Could be part of a chain for retrieval augmented generation. Maybe some specific tasks. And there are small machine learning models that can do translation or sentiment analysis, though I don't think those are your regular LLM chatbots... And well, you can ask basic questions and write dialogue. Something like "What is an Alpaca?" will work. But they don't have much knowledge under 8B parameters and they regularly struggle to apply their knowledge to a given task at smaller sizes. At least that's my experience. They've become way better at smaller sizes during the last year or so. But they're very limited.
I'm not sure what you intend to do. If you have some specific thing you'd like an LLM to do, you need to pick the correct one. If you don't have any use-case... just run an arbitrary one and tinker around?
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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!
absolutely nothing
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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!
wrote last edited by [email protected]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.
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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.
