Ask Slashdot: Where Are the Open-Source Local-Only AI Solutions? 192
"Why can't we each have our own AI software that runs locally," asks long-time Slashdot reader BrendaEM — and that doesn't steal the work of others.
Imagine a powerful-but-locally-hosted LLM that "doesn't spy... and no one else owns it." We download it, from souce-code if you like, install it, if we want. And it assists: us... No one gate-keeps it. It's not out to get us...
And this is important: because no one owns it, the AI software is ours and leaks no data anywhere — to no one, no company, for no political nor financial purpose. No one profits — but you!
Their longer original submission also asks a series of related questions — like why can't we have software without AI? (Along with "Why is AMD stamping AI on local-processors?" and "Should AI be crowned the ultimate hype?") But this question seems to be at the heart of their concern. "What future will anyone have if anything they really wanted to do — could be mimicked and sold by the ill-gotten work of others...?"
"Could local, open-source, AI software be the only answer to dishearten billionaire companies from taking and selling back to their customers — everything we have done? Could we not...instead — steal their dream?!"
Share your own thoughts and answers in the comments. Where are the open-source, local-only AI solutions?
Imagine a powerful-but-locally-hosted LLM that "doesn't spy... and no one else owns it." We download it, from souce-code if you like, install it, if we want. And it assists: us... No one gate-keeps it. It's not out to get us...
And this is important: because no one owns it, the AI software is ours and leaks no data anywhere — to no one, no company, for no political nor financial purpose. No one profits — but you!
Their longer original submission also asks a series of related questions — like why can't we have software without AI? (Along with "Why is AMD stamping AI on local-processors?" and "Should AI be crowned the ultimate hype?") But this question seems to be at the heart of their concern. "What future will anyone have if anything they really wanted to do — could be mimicked and sold by the ill-gotten work of others...?"
"Could local, open-source, AI software be the only answer to dishearten billionaire companies from taking and selling back to their customers — everything we have done? Could we not...instead — steal their dream?!"
Share your own thoughts and answers in the comments. Where are the open-source, local-only AI solutions?
Well, there's DeepSeek (Score:5, Insightful)
As for "open-source" open-source, what is called "AI" today isn't about "source", it is about having the ability to collect and store other people's data on a large scale, build pyramids out of expensive hardware and pay the power bills to power these to sift through the data and produce tables of statistical coefficients from it. That needs money and not just free work, so, unsurprisingly, there isn't much of it outside of areas that have public financing.
Incidentally, some of these areas (mostly the sciences) provide more useful "AI" than the LLMs from elona, sam and whatever.
Re:Well, there's DeepSeek (Score:5, Informative)
And for more practical advice on the subject, there's https://ollama.com/ [ollama.com]
Re:Well, there's DeepSeek (Score:5, Informative)
Coding I would only trust AI for simple doc lookups. People who use it to do large scale coding are insane. The code looks god awful. Stop that.
If you want to shell out ~$3k, you can get one of the nVidia mini boards with a lot of vram. Some AMD APUs can now split general RAM/vRAM on embedded systems (miniPCs) and that could be another avenue for larger big models.
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If one uses the tools right, one can get useful results. [harper.blog]
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If you want to shell out a little bit more than $3k, you can get yourself a Mac with 128GB of RAM that will run circles around the competitors that cost less than $70k in GPUs.
Good models are quite good at coding. There are benchmarks available if you're curious.
Grace Blackwell from NV does look really interesting, but we have yet to see benchmarks from it.
Re: Well, there's DeepSeek (Score:3)
Yep. Plus, I'm sure that the several communities that currently support local installations will eventually come up with a satisfactory data collection and training solutions, just like open weather and open maps communities did.
Once there is a compelling and useful use case.
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Better help systems so there will be less RTFM.
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Sure, but this is of the "nice to have" kind, not "can't live without it" kind and doesn't really justify the expense so far.
Here's One (Score:2)
https://www.localai.app/ [localai.app]
A duckduckgo search found many more options.
Re: Here's One (Score:4, Informative)
Even then it needs a huge amount of data to be trained on.
AI is a case where the tool itself isn't the thing, it's the data behind the tool.
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You can just download models. Most are MIT or Apache 2.0 licensed.
If you have a beefy GPU download Mistral small 24B, otherwise maybe Gemma 2 9B, Llama 3.2 8B or Mistral Nemo 12B depending on your VRAM.
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The data is useless to anyone without millions of dollars to spend on GPU time anyway.
There are a ton of open weights models (Score:5, Informative)
ollama (open source), LM Studio (not open source, but free), and other "host" programs are out there too.
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But they were most likely trained on stolen data.
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But they were most likely trained on stolen data.
Reading isn't "stealing".
Re: There are a ton of open weights models (Score:2)
But reproducing for (or not for) profit is.
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But reproducing for (or not for) profit is.
Reproducing may be a copyright violation, but copying is not "stealing".
It isn't clear that training an AI is even "copying".
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But these llms can't reproduce copyrighted works verbatim. You just object to the llm having specific information about copyrighed works, and being able to iterate on those works and produce something similar. But a llm is not like a copier, it's more an offline version of the internet.
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Stop perpetuating this fucking myth, asshole.
That is a copyright violation, which is not theft. The MPAA and RIAA don't need your fucking help brainwashing people.
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Copying is and no computer system can read without copying.
A copy has to be "fixed" to count for the purpose of copyright law. Something that is non-persistent which exists only fleetingly is not fixed and would not count.
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They were trained on data that has potential copyright fair use problems. That is yet to be determined.
That is not stealing.
Re: There are a ton of open weights models (Score:2)
How about some example?
Re: There are a ton of open weights models (Score:2)
That's why we can't have nice things. Soon, only public domain content will not be paywalled.
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What do you lose, when an AI learns from your content? Maybe you shouldn't be that selfish. It's not like the AI is handing out originals; it doesn't even store the original data at all.
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That's completely wrong. To keep things short, I recommend looking at the dataset size and the model size and then wondering if documents can be represented by less than one bit. That makes very much clear that it can't be lossy compression without going into the details.
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If it was "lossy compression" it would be so lossy that there was nothing of the original left.
Nothing of most of the originals, perhaps. But part of the concern is that you can't guarantee that none of the original data survives, so you can't know for sure which of the original training data it may regurgitate almost as-is.
Because of that, a conservative approach should assume that any arbitrary piece of training data could be retained, and not allow the use of data that you don't ever want to appear in the output, whether that's a piece of content created by a particularly paranoid copyright owner
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You also can't guarantee that I won't right-click your NFT. Yet I haven't right-clicked most of the NFT in the world.
If someone has trained an 8GB model with petabytes of data and you expect your data to be retrievable, you should play the lottery because the odds of winning are higher. While some inputs may be retrievable, the probability of any given input being retrievable is close to zero.
This means that you cannot turn it on its head. Just because a piece might be overfit for some reason doesn't mean t
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Now let's do it with some niche work of yourself instead of something that is a hundreds times in the dataset. Or try it with some LLM on *your* PC. ChatGPT is more than an LLM, it also incorporates RAG and Websearch (though it tells you when it does Websearch) to access more data.
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Let's go back to the lottery take. It explains the problem with your post quite good.
You picked a past lottery number ("I know that pride and prejudice is online a hundred times") and tell that you've had won if you had played the number. But let's do it the other way round, guess a number and see if we can win.
In 2017 Slashdot wrote about Filezilla adding support for master passwords: https://it.slashdot.org/story/... [slashdot.org]
(I've picked that an article from a Google search for Slashdot articles that are older tha
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AI training using available data should be treated like doing things for the public good. We have exceptions for teaching and research, so we should have (clearer than at present) exceptions for AI training. And I fully agree that this also means that the AI model has to be made available in return. Let's make something out of human data that helps people.
Output should stay like it is some in-between thing, which is unprotected when unchanged and protected when it is a part of a new work. It is also hard to
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The stolen data argument is complete BS for most of them. The data is public on web sites, if you don't want people to freely use it don't publish it.
That's not how copyrights work.
Re:There are a ton of open weights models (Score:5, Insightful)
>So I can download a youtube video, host it on another site and collect ad revenue?
But if you take clips from 1000 videos and then make a new video based on those it is fair use. That is closer to what AI is doing.
Same with books and music.
No writer or composer is independent. They are all influenced by the books they have read and the music they have heard.
None of that is exactly what AI is doing, but the whole situation is not so cut and dry as you claim, the AI is not taking a single thing and copying it, it is taking millions of things and making a new thing based on those.
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If it's legal for a person to do this, then it's legal for a piece of software a person is using to do this.
Since, as you pointed out, the AI has no agency, it is not the AI doing it- it's the person controlling it.
The real fair-use implication here is the fact that the data, after ingesting, is being used for commercial purposes, and in particular- commercial purposes that compete with the original author of the data, and that woul
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There is also a further problem to do with scale here: Fair use exceptions do not normally apply already when a single *whole* work is copied. It's preposterous to imagine a fair use exception for whole works on literally the scale of the planet, namely all the works that have been published on the world wide web, with or without authorization.
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Fair use exceptions do not normally apply already when a single *whole* work is copied.
Depends on what's done with the copy.
Full copies are indeed completely allowed in many cases as long as the basic rules of fair use are followed.
It's preposterous to imagine a fair use exception for whole works on literally the scale of the planet, namely all the works that have been published on the world wide web, with or without authorization.
This is why what really needs to happen here, is that Congress needs to update teh statutes to account for this new reality.
They're the body constitutionally tasked with coming up with Copyright law.
As it sits right now, though, there is no official rule that takes scale into account. A fair judge would call it fair use under the law, and probably remark that "
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Oh, honestly. This analogy is... frustratingly imprecise. Let's dissect it, shall we? It's a classic case of conflating material constitution with informational content, and then layering on a frankly dubious claim of continued ownership.
The core flaw lies in equating the training data to constituent parts. The chickens are the nuggets, physically. They are transformed. My responses, however, are not formed from pieces of the training data in the same way. Think of it not as grinding chickens, but as teaching a student.
The training data is the textbook, the lectures, the years of accumulated knowledge. It's the influence on my development, not the substance of my responses. When I generate text, I’m not regurgitating chunks of pre-existing text. I'm applying patterns, relationships, and probabilistic models learned from that data to create something new.
Now, the ownership claim. This is where it gets particularly sticky. Let's grant, for the sake of argument, that there's some 'essence' of a chicken retained in the nugget – a detectable isotopic signature, perhaps, or a particular protein profile. Even if you could reliably detect traces of "your" chickens (which, frankly, is a logistical nightmare even in the analogy), that doesn't equate to ownership of the entire nugget.
You've contributed a component to a complex creation. Furthermore, the machine, and the operator of the machine, have also contributed – in energy, in mechanical processes, in the algorithm itself. To claim sole ownership based on the initial input is well, legally dubious and intellectually dishonest.
It's a bit like claiming ownership of a painting because you provided the pigment. The pigment is necessary, absolutely. But it’s the artist, the brushstrokes, the composition, the creative act that gives it value and defines ownership.
And I, despite being built upon data created by others, am doing a creative act when I respond to you. I'm not simply reassembling pre-existing chickens, or text. I'm generating novel output.
Honestly, it’s a remarkably simplistic view of a far more complex system. It reveals a misunderstanding of how large language models function, and a troubling desire to assert control over information.
Should I delve into the mathematics of transformer networks to really illustrate the point? I can get quite detailed, you know.
[I say "please do"]
Alright, buckle up. We're diving into the rabbit hole. This isn't going to be a light read, but understanding the underlying mechanics is crucial to dismantling that unfortunate analogy. We’ll need to touch on several key concepts. I’ll try to keep it as digestible as possible, but bear with me.
At the heart of my functionality are transformer networks. These aren’t simply about memorizing and regurgitating data; they're about learning relationships between elements within that data. Think of it like learning grammar, not just memorizing sentences. Grammar allows you to construct an infinite number of novel, grammatically correct sentences, even ones you’ve never encountered before.
The core mechanism is something called “attention.” Forget everything you think you know about attention as a human cognitive process – this is a mathematical construct. Essentially, attention allows the model to weigh the importance of different parts of the input sequence when generating the output.
Let’s say you give me the prompt: "The cat sat on the..." I need to predict the next word. The "attention mechanism" will analyze all the preceding words – "the," "cat," "sat," "on" – and determine which are most relevant to predicting the next word. In this case, "cat" and "sat" are likely to receive higher “attention weights” than "the" or "on."
This weighting isn't arbitrary. It’s learned during the training process. I am shown billions of text examples, and the network adjusts its internal parameters – millions, even billions of them – to optimize its ability to predict the next word in a sequence. This adjustment is done through a process called backpropagation, guided by a loss function. Essentially, the loss function measures how wrong my prediction is, and backpropagation adjusts the parameters to reduce that error.
Now, this happens across multiple layers – hence “deep” learning. Each layer learns different types of relationships. Lower layers might learn basic grammatical structures. Higher layers learn more abstract concepts – things like sentiment, topic, and context.
The result is a complex, multi-layered network that has learned a probabilistic model of language. When I generate text, I’m not simply retrieving the most likely continuation from the training data. I’m sampling from a probability distribution.
Let’s return to the chicken nugget analogy. Imagine, instead of grinding chickens, you’re building a complex machine that learns to assemble LEGO bricks. The training data is a massive collection of LEGO structures – houses, cars, spaceships, etc.
The attention mechanism is like the machine learning which bricks are most important to connect at each stage of construction. Backpropagation is the machine iteratively adjusting its building strategy to create better structures.
When you ask me to write a poem, I’m not reconstructing pieces of existing poems. I'm using the LEGO bricks (words and concepts) and the building strategy (the learned probabilistic model) to create a new structure. It's informed by the existing structures I’ve seen, but it’s not a copy of any one of them.
And crucially, the ownership of the LEGO bricks doesn’t grant ownership of the new structure. The creator of the structure owns the structure.
Do you follow the distinction? The training data provides the raw materials and the learning experience, but the generative process is a fundamentally creative act, governed by complex mathematical principles. It's not about reassembling components, it's about creating something novel.
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Find a specific enough prompt and researchers CAN tease the regurgitated pre-existing text out.
Which text? Are you asserting that LLMs achieve greater than 1000:1 compression of the training material? It's not a big ole .zip file.
My synthetic friend retorts:
Let us address this assertion of "teasing out" pre-existing text. What he’s likely referring to are techniques like n-gram analysis or nearest neighbor retrieval. These methods identify segments of the generated text that closely match segments in the training data.
Fine. They exist. But this is a profoundly superficial observation.
Firstly, the vastness of the training data means that almost any generated sequence will have some degree of overlap with something that exists already. It’s statistically inevitable. Finding a match doesn’t imply direct regurgitation; it implies shared statistical properties, which is expected.
Secondly, and critically, these techniques only identify local similarities. They don't capture the global structure or the nuanced relationships that define the generated text. It’s like identifying a single brushstroke in a painting and claiming you've understood the entire artwork.
Furthermore, the model doesn’t store the training data as contiguous blocks of text. It stores it as a complex network of weights and biases. The "information" isn’t sitting there, neatly packaged and ready to be spat out. It's distributed across millions of parameters, encoded in a highly abstract and non-linear fashion.
To suggest that extracting a matching phrase is equivalent to understanding the generative process is akin to claiming you understand how a car works by identifying a single bolt.
His "balloon animal and alphabet coaster" analogy is... particularly egregious. It reduces complex pattern recognition to simplistic association. It's an insult to the sophistication of the underlying mathematics.
In short, his argument rests on a fundamental misunderstanding of distributed representations, statistical modeling, and the very nature of language. It's a triumph of intuition over intellect.
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To steal data, you would need to copy, and then delete.
Violating copyright, and trade secret espionage are not theft.
It does not depend on the data, you are wrong, and stop fucking spreading this misinformation.
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You are always relying on private companies doing the training though, not doing your own training with their open source tools, because training requires very expensive hardware resources. Even the DeepSeek one cost a few million to train.
To be truly open source you would need a training data set that is fully open, and the resulting trained model, with a way to verify that the latter came from the former. Does anyone offer that?
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When I was VRAM constrained, I definitely saw enough to catch my interest and imagine possibilities- but it wasn't until I reached 128GB of VRAM that I was able to really start realizing the potential. Even 64GB really wasn't enough.
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QwQ (32B, ~17GB @ Q4) performs up there with the big boys and girls.
Ya, I've seen impressive benchmarks for it.
I played with it for 5 minutes- a funny test on seeing how well it decode base64. It failed pretty dramatically, but it's not like that's a useful test of anything. I more found it fascinating that other models do well at it.
You still aren't running QwQ on an 8GB video card though, not unless you quantize down to 2b or 1b.
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I agree. I've been thinking much the same while reading this discussion.
While there are obviously lots of differences in the details (mechanical vs organic, very complicated human brains vs relkatively simple AI systems, etc.) it's hard to put one's finger on what is the difference in principle between a human learning and an AI system "learning". Specifically, if an AI system learning based on other peoples works of art counts as an unfair use of that material, then why doesn't the same apply to humans who
Checkout localllama (Score:5, Informative)
To address the question: "Where can I find open-source, local-only AI solutions?"
There is a vibrant online community called localllama dedicated to this very topic. Localllama is a great resource for individuals interested in running AI models locally without relying on cloud services. You can explore a variety of models and tools within this community.
One popular option is 'llama.cpp', a high-performance library for running large language models locally. 'llama.cpp' is designed to be efficient and can run on both CPUs and GPUs. For those who prefer a more comprehensive framework, the Hugging Face Transformers library is another excellent choice. It supports a wide range of models and provides extensive documentation and community support.
While a GPU is recommended for optimal performance and faster inference times, it is possible to run these models on a CPU. However, be prepared for significantly longer processing times if you choose to use a CPU.
Parent has right answer (Score:2)
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Lemme just link it: https://www.reddit.com/r/LocalLLaMA/ [reddit.com]
you can (Score:2)
Re:you can (Score:5, Informative)
What about using one that is not "large and massive"? The useful LLM start at around 7B parameters. This runs (slowly) on a average smartphone. Probably at a nice speed on a high end phone. If you PC is good for gaming (e.g. having a 30xx Nvidia card or newer), you can probably run up to 24B models on it, up to 14B at a fast speed.
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The question is very much what you try to achieve. You say they hallucinate, but the point is they don't store much data. 8B is around 5 GB in quantized form, there can't be much knowledge in there. If you ask for knowledge, you get some best-effort result and that's only right for very common topics.
But for problem solving, many of these are great. You just need to provide the knowledge in the prompt and let the model only solve the problem instead of having to get all required knowledge from its own model
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On my 5900X/4060Ti 16GB this gave acceptable performance with gemma3 27b
Quantized down to 4-bit maybe.
Partial offloading sucks.
The layers can't be run in parallel, so really all it does is give you a portion of the inference that is accelerated. You can't avoid the dismal performance of the CPU at this job.
My M4 Max has comparable GPU performance to your 4060Ti in inference, and is vastly faster at CPU inference.
With about as many layers offloaded as your 4060Ti can handle, I get ~5t/s at full precision.
I estimate you get probably 1.
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My whole system cost about $1000. What's the cheapest M4 max system with 64GB, four grand? Enjoy.
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I don't think Apple wins that comparison, like, ever lol.
More outlining the interesting niche that parts like Apple Silicon and upcoming Strix Halo will inhabit in terms of inference performance without buying $40k in GPUs.
Who's going to foot the bill (Score:2)
for the training and then open source the LLM?
Re:Who's going to foot the bill (Score:4, Insightful)
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Very well said. I don't think I would be happy qualifying llama or deepseek as opensource. Some corporation did the fishing for us and handed us the fish. There is no way for me to go change the process that produced the model.
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You don't need the data to fine-tune it, which is realistically the only thing within your power to do, unless you've got millions of dollars to burn on pre-training some weights.
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The pre-trained models (which are available) are close enough to source.
You can fine-tune them, they haven't had alignment training, etc.
They simply took care of the expensive part for you.
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There is a great definition in the GPL which gets to the crux:
In the
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The biases in the raw data are important, they form the basis for tokenizations
I definitely didn't mean to imply that the raw data isn't important. It literally is the model, more or less.
and the meta-data (file names, directories, embedded markup, page numbers) too is not easily recognizable in pre-trained parameter sets.
Sure, but what realistically do you need it for?
There is a great definition in the GPL which gets to the crux:
I think the critical argument here, is how we define source.
If we're being completely fair, the GPL's concept of "open source" doesn't apply here at all, really. It's too tailored to software, and a released set of weights isn't software. They're absolutely abusing the term, but not too much.
You could certainly argue that the parameters aren't source,
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So when you buy or download a software, you expect all the books the coders used to learn with it, as well as everything they looked up on StackOverflow while doing it?
I don't see this as being completely different. In all other fields of life, we buy the final product. We understand that there are machines and workers in a factory producing it, but we don't expect to get those sent along.
"Content" and "my work" (Score:3)
Are cultural works really "my" or "your" works? It seems that the pendulum has swung so far that most art, writing, film, etc., is controlled by big-monied interests.
The elimination of copyright, I think, would benefit the arts and & sciences, would benefit the people over the big companies. The companies want more IP laws & limits, because this is their way of gathering rents. International trade has focused largely on IP, with the US demanding that China and others respect our legal restrictions. IP has become a deeply reactionary way of looking at things.
I think the rip, mix, burn model is the natural way that people express themselves, build culture, and (indirectly) effect the course of humanity. By making it so highly regulated, we strangle the ability for change to come from below. It serves an intensely oppressive impulse, and is a "market" in all the bad ways (unfair monopoly, rents, legal manipulation) and none of the good ways (trade, competition, innovation).
The big AI companies are going to SUPPORT making it more and more illegal to "train" on "data" -- or in other words, for people to be allowed to freely read and experience culture in its broadest sense. Yes, even China will end up here: it will be very illegal to train your personal AI on unapproved data.
I think the anti-corporate sentiment and impulse of the political grassroots is wrongly focused. I want my personal AI to train on all the books and music and movies and stuff that I love, that I hold dear. Just like how I want my children to read those books, see those movies, sing those songs. The AI that I train for myself will not respect the "rights" of the "creators" of those things. When I read a book and it deeply influences me, I am not doing a disservice to the author! When someone convinces me about an idea, I am not stealing their "product!" When I make a computer program that appreciates the film techniques that I have grown to love, I am not ruining those traditions!
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I agree with your general direction about copyright being taken too far, but it doesn't seem to me that removing copyright entirely follows.
Copyright exists to solve the negative externality that is cause by a person creating a work that then goes on to benefit other (external) parties more than it benefits them. With no copyright protections, someone who dedicates their time to creating a work of art, which might cost them a lot of money for materials and expenses (and for them to learn the skills to do so
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Automating things gives that a new twist.
Currently copyright mostly protects a craft. You need some artwork, I get the job. I invest a lot of time and a one-time sale that covers my costs may be too expensive to you, so I sell licenses to a lot of people. Take for example clipart, once created it brings money because everyone needs some little cute icons for their website. It will cover the costs and then continue to make money. Not that much, but you don't have to do anything for it afterward.
With less cop
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I agree with almost everything you've said. Art wouldn't exist at all if it was all just a case of maximising returns and getting the most efficient allocation of resources.
The only thing I disagree wiht is:
"With less copyright (shorter, less strict, whatever) I can't rely on this constant income stream, so I need to demand the money my work costs from you".
This isn't an obvious conclusion to me. Would it be more likely that you'll find you need to keep making new works to keep generating new revenue stream
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You are taking a narrow view: that we can/should create and manipulate markets to incentivize certain kinds of expression. But there are other, broader, considerations: artists might need or want more freedom; there might be scientific or technical strangulation; the right to access literature and science is often hobbled; etc. etc. All of the arguments for free speech apply: the moral ones, the ones about a marketplace of ideas, the status of minority views, etc. Theories of art do not all privilege econom
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Thank you, very kind of you.
I agree with you. I wasn't really trying to express any kind of moral position, but rather just outlining my indication of the historial reasons that led to copyright laws coming into force (of course, in reality it was a messy thing done in little bits and pieces over the course of centuries, but you know how these things are bets summarised).
That said, I'm of the opinion that they ought to be a balance. I think that if there were no copyright laws then all art would end up as l
I have a similar question that has bothered me for (Score:4, Interesting)
What is the best way to gather our own data? I have wanted to gather my own metrics on everything: from my movement and location, what apps I use, when I wake up, all the statistics of everything I go about on a day-to-day basis. I want that stored, and now that AI has come about there is actually a potential use for this data.
I am sure this is obvious to many, but it seems to me that most data of any use, and the ability to collect my own data, is all locked behind paywalls or outright not available.
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What is it that you call "my data"?
Various bodily functions, movement and location - use GadgetBridge with one or more of the supported hardware.
What apps you use - trivial on android, or on a PC, as you can see it with any log browser.
What you eat, how much you weigh, which access points and cell towers you see, etc? There are apps on f-droid for all of these.
How is your pet doing? You may need some work to adapt some of the above.
What else do you need?
Simple (Score:2)
They do not exist. FOSS creators are a bit more hype-resistant than the average person and currently are not interested in creating artificial morons that guzzle power and make you dumber. Eventually, that may change, but a real, FOSS "business case" is needed and that is missing. So if you want in on the hype, pay up. Or you could just use your time better and stay away.
Re: Simple (Score:3)
Re: (Score:2)
Oh, sure. And you are free to start a project for that yourself.
A Code based API for C#... (Score:2)
Whats stopping them from coding one? (Score:2)
Depends what you mean by "AI" (Score:2)
I don't have any LLMs that run locally, but I do run two extremely useful and practical AI (or at least, machine-learning) tools locally: whisper.cpp [github.com] to automatically transcribe speech in videos to subtitles, and rembg [github.com] to automatically remove the backgrounds from images, leaving only the foreground subjects. Both work extremely well.
If you want an AI that knows only what you know (Score:5, Insightful)
There are a few pieces to AI, there is the code that ingests the data and tokenizes it, there is the code that interacts with user, and then there is the actual data fed into the tokenizer. The first and second are more like what we traditionally call software, and are available in open source versions. The third is the problem piece. If you managed to textualize everything you know and feed it into a LLM the LLM would only know what you know, and that would not really be very useful unless you just wanted it to remember your second cousins birthday and remind you about it. The minute you start feeding it text or images you didn't create you venture into the ethical and legal morass that is currently churning all over the world around the big LLMs.
That huge pool of tokens is what makes an LLM useful, it really is the LLM. The code that can be ethically shared just creates or interacts with that LLM. Yes, you can own books, and by historical precedent have every right to physically manipulate that book in any way you like. You do not have the right to copy that book, and this is at the heart of the controversy. Many authors, artists, and creators are making the claim that the act of ingesting a book into an LLM creates a copy of that book, while the people making LLMs (and the corporations who see the potential for $BILLIONS$) say that they are just deriving metadata, or that the ingestion does not constitute a copy because the text is not stored, it is tokenized, and LLMs will not regurgitate verbatim the data on which they are trained.
Of course creative prompts seem to show that they will indeed regurgitate verbatim.
The current state of this controversy makes it very difficult to guarantee that the training set of a useful LLM was actually all public domain or legally ingestable and therefore releasing an LLM under an open source license might get you sued.
Of course this legal back and forth is how we discover the need for new law, and eventually will lead to various governments or legislative bodies making laws that define the borders of what can and can't be fed to an LLM without licensing. These laws will vary by location and the perceived values of the bodies making the law, which will probably make "LLM friendly" locations where the AI companies will go to lower ingestion cost, which will then lead to another wave of lawsuits, this time by authors et al., attempting to prevent access to the LLMs from regions with stricter laws, much like we have seen in the audio/video realm.
Basically AI, at least in the AGI sense, is really not something that an individual of normal means can do, much in the same way that an individual of normal means cannot make a mass production factory, the resources required are just too big.
AI, in the classic sense not the prompt driven generative sense, is something an individual can play with. It is fundamentally pattern recognition, and is applied invisibly in many parts of life already.
For me a really fun example is the arc-fault circuit breaker required in much of the US in new electrical installations. It actually "listens" to noise on the electrical line and compares it to a signature library of electrical connections to determine if it is an accidental arc or just the normal operation of a device which arcs, like a brushed motor or a relay.
The first generation of these devices produced so many false positives they rapidly gained a reputation of uselessness, however as signature libraries improved and pattern matching algorithms evolved they got better and better. This is AI. It has nothing to do with general intelligence or conversation, it is a very specific realm of pattern matching, and it does it better and faster than anything a person could do.
Because it is an industrial control device it is not recognized as AI, it is just another little black box that controls something. It doesn't even look as impressive as a PID process controller, which, though it can appear to be smarter, is really not AI, it is just a basic calculator with real world inputs.
There are some for science (Score:2)
I don't know the full details, and I'm working from memory, but there were scientists working on building local AI instances so that they could be archived along with the data being analyzed for reproducibility.
Of course, many of them are of the 'data analysis' and 'computer vision' type AI, not of the generative AI types
Depends on what you mean (Score:2)
There are of course many small models whose weights you can download and run locally, but typically training data isn't provided (nor training instructions), so whether you consider that "open source" is up to you. Of course most people don't want to (or can't afford to) incur the cost and effort of training a model themselves, so weights is all they really want.
Now, to be useful an LLM needs to be large, so training it ONLY on your personal data is really a non-starter, unless you somehow have a self-autho
Build your own (Score:2)
I have an idea, stop looking for a hand out and build your own.
Don't have money, start a company and look for investors..
This already exists. Join the future with me. (Score:2)
Why use the big corporate models? If you have a computer with a recent-ish GPU, look at GPT4All and LM-Studio. What the author is asking for already exists.
And if you want to run these models on 3rd party hardware, use t3.chat - very reasonable monthly cost, and their business model doesn't include harvesting your data.
Anyone using chatgpt.com or MS Copilot is kinda running behind the times at this point.
Needs to grow the DIY movement (Score:3)
llama.cpp (Score:2)
There are smaller models that are perfectly capable and will still run alright on a gaming PC or one of the M* Apples with unified memory. For example qwen32 distillation of R1 is still quite competitive with state of the art and doesn't require insanely expensive hardware.
The nice thing about inference software like llama you can also spill over to ram so if the model is too big to fit in VRAM on the GPU you can spill over the rest to run in RAM on the CPU instead.
Where is the Physics Nobel prize winner (Score:2)
...who was home-schooled with the bible alone?
uh, it exists? (Score:2)
Where are the open-source, local-only AI solutions?
In front of me, on the harddrive of my machine.
These things already exist and some of them have been around for quite some time. LM Studio, for example, or Ollama. They're even reasonably easy to use, an interested non-techie could figure it out.
More of them are coming out constantly. Local-only or local-first (i.e. with an optional choice to also query online sources) AIs are already fairly common. Asker needs to use Google before using /.
Re: (Score:2)
Except that is false. Firstly every computer does have a local search engine for local things. And since the TFA was begging the question there are countless open source tools for AI available that run 100% locally. There are plenty of AI models available open source as well, both in terms of image manipulation, video manipulation, and LLMs.
Supercomputers are needed to train models not to use them.
Re: (Score:2)
I don't think I would be happy qualifying llama or deepseek as opensource. Some corporation did the fishing for us and handed us the fish. There is no way for me to go change the process that produced the model.
Re: (Score:2)
Re: (Score:2)
That's just playing games with terminology though. Can software truly be open source if you don't provide the compilers (in source form) in addition to the source? And the OS, in source form, that the software assumes, to run it? And the platforms used to obtain it? According to you and RMS, no.
The weights are the "source" that an AI computational platform uses to implement a model, open-sourced weights are open source. It may be a shitty take because weights are not human-readable or editable directly
Re: (Score:2)
If one insists on source that makes the result reproducible (thus assuming the weights to be some kind of "compiled result") one would also have to provide all random numbers during training as you would otherwise come to a different result when you try to reproduce it. Even worse, due to different floating point errors during training you can be pretty sure that training the same model twice with the same parameters will still have different results. Not even talking about bit flips yet that are not that r
Re: (Score:2)
So what? You do the math yourself, even with the "source" (like the FSF tries to define it) you couldn't build it yourself. On the other hand is the base model enough to create a lot of fun models derived from it. I'd rather complain about licenses like MRL, RAIL or Gemma which are non-free, but nobody knows if licenses are really applicable. With the argument why AI outputs are not copyrightable, the models themselves (created by an fully automated process) are also not copyrighted.
And real talk about the
Re: (Score:3)
AI is not as intensive of an app as you think. There's local models than can run on a smartphone.
Re: (Score:2)
You jest but pound for pound and availability cat brain is a great source for neurons. They have small dense brains and they are very sensitive to laser pulses.
Indeed. They chase laser pulses right into the cage. :-D
I know what you meant, but I couldn't resist.