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That person clearly hasn’t witnessed Dutch students carrying a whole bedroom on the back of their bike.
That person clearly hasn’t witnessed Dutch students carrying a whole bedroom on the back of their bike.
Good luck! You can try the huggingface-chat repo, or ollama with this web-ui. Both should be decent, as they have instructions to set up a docker container.
I believe the Llama 3 models are out there in a torrent somewhere, but I didn’t dig to find it. For the 70B model, you’ll probably need around 64GB of RAM available, but the 7B one should run fine with just 8GB. It will be somewhat slow though, compared to the ChatGPT experience. The self-attention mechanism can be parallelized, which is why you will see much better results on a GPU. According to some others that tested it, if you offload some stuff to RAM, you could see ~10-12 tokens per second on an RTX 3090 for certain 70B models. But more capable ones will be at less than 1 token per second, all depending on the context window you use.
If you don’t have a GPU available, just give the Phi-3 model a try :D If you quantize it to 4 bits, it can apparently get 12 tokens per second on an iPhone haha. It should play nice with pooling information from a search engine, or a vector database like milvus, qdrant or chroma.
What db2 already said. Microsoft just released Phi-3 mini, which could, allegedly, run locally on newer smartphones.
If I understood correctly, the Rabbit thingy just captures your information locally and then forwards it to their server. So, if you want more power, you could probably do the same by submitting the same info to a bigger open source model than Phi-3, like Llama 3, hosted on your homelab. I believe you can set it up with huggingface/gradio, which sort of provides an API that you could use.
That way, you don’t need a shitty orange box, and can always get the latest open source models with a few lines of code. There are plenty of open source frameworks in the works at the moment, and I believe that we’re not far off from having multi-modal LLMs running on homelab-level hardware (if you don’t mind a bit of lag).
That is good to know. Tried the free version of Roll20 before, and it definitely felt lacking in certain areas. Oh, and thanks for letting me know about the sale! I’ll definitely keep an eye out for that one :)
How will you move to WhatsApp if everyone else uses iMessage? Europe has the same issue, but reversed. Everyone uses WhatsApp and can’t jump to Signal/Telegram because they’re not as popular.
With the way current LLMs operate? The short answer is no. Most machine learning models can learn the probability distribution by performing backward propagation, which involves “trickling down” errors from the output node all the way back to the input. More specifically, the computer calculates the derivatives of each layer and uses that to slowly nudge the model towards the correct answer by updating the values in each neural layer. Of course, things like the attention mechanism resemble the way humans pay attention, but the underlying processes are vastly different.
In the brain, things don’t really work like that. Neurons don’t perform backpropagation, and, if I remember correctly, instead build proteins to improve the conductivity along the axons. This allows us to improve connectivity in a neuron the more current passes through it. Similarly, when multiple neurons in a close region fire together, they sort of wire together. New connections between neurons can appear from this process, which neuroscientists refer to as neuroplasticity.
When it comes to the Doom example you’ve given, that approach relies on the fact that you can encode the visual information to signals. It is a reinforcement learning problem where the action space is small, and the reward function is pretty straight forward. When it comes to LLMs, the usual vocabulary size of the more popular models is between 30-60k tokens (these are small parts of a word, for example “#ing” in “writing”). That means, you would need a way to encode the input of each to feed to the biological neural net, and unless you encode it as a phonetic representation of the word, you’re going to need a lot of neurons to mimic the behaviour of the computer-version of LLMs, which is not really feasible. Oh, and let’s not forget that you would need to formalize the output of the network and find a way to measure that! How would we know which neuron produces the output for a specific part of a sentence?
We humans are capable of learning language, mainly due to this skill being encoded in our DNA. It is a very complex problem that requires the interaction between multiple specialized areas: e.g. Broca’s (for speech), Wernicke’s (understanding and producing language), certain bits in the lower temporal cortex that handle categorization of words and other tasks, plus a way to encode memories using the hippocampus. The body generates these areas using the genetic code, which has been iteratively improved over many millennia. If you dive really deep into this subject, you’ll start seeing some scientists that argue that consciousness is not really a thing and that we are a product of our genes and the surrounding environment, that we act in predefined ways.
Therefore, you wouldn’t be able to call a small neuron array conscious. It only elicits a simple chemical process, which appears when you supply enough current for a few neurons to reach the threshold potential of -55 mV. To have things like emotion, body autonomy and many other things that one would think of when talking about consciousness, you would need a lot more components.
Job market seems terrible right now, especially if you’re just starting out. Had a friend that applied to ~100 jobs in tech, and a majority of them didn’t even reply back.
But the same can be said about bad HR as well. How many hiring teams have no idea what a candidate is supposed to be doing?
The Framework 13 inch model should be plenty, especially if you want to dev on the go. Much more lightweight and smaller, and you can connect it to external monitors if the screen size is not big enough. Also, you shouldn’t have issues running Linux on either laptops.
Instead of going for the 16 version, I would use the extra 900-1000 euros (that’s the amount I saw I could save between the two almost maxed-out models) to make a dedicated server or mini-cluster to run your workloads. Deploy Kubernetes or Proxmox on it, and you’ll also get some more practice on it outside work if you want to run stuff for your home lab. That is only if you don’t want to game on your laptop, but I’d still put that money aside to make a desktop.
Cheats nowadays don’t even need to run on your machine. You can get a second computer that is connected to your computer via a capture card, analyze your video feed with an AI and send mouse commands wirelessly from it (mimicking the signal for your USB receiver).
These anti-cheats are nothing more than privacy invasion, and any game maker that believes they have the upper hand on people that want to cheat are very wrong.
Opening up anti-cheat support for Linux would at least make them more creative at finding these people from their behaviour, and not from analysing everything that’s running in the background.
It’s amazing that Linux gaming is becoming a thing that’s better sometimes than Windows gaming (minus the getting banned part in some games). I also like that AMD is making some big pushes on open source drivers, plus their ROCm open-source alternative to CUDA.
This is a great time for Linux users! :)
Same. It sucks that most banks wouldn’t jump on this train :(
What a stupid article. It’s like saying “stop using electric vehicles because you can’t use gas stations”. I don’t understand why he’s so adamant about this? It’s not like Wayland had about 20 years of extra time to develop like X11. People keep working on it, and it takes time to polish things.
Sooooo tinyyyy 🥺
If I am not mistaken, the difference was that the Internet Archive was distributing books with a DRM that would make the PDF unusable after a certain time. You could relate it to how a physical library offers books for a limited time, for free. Now, of course, one could bypass the DRM or copy the contents differently, but so can another person photocopy a book they borrowed physically. Meanwhile, other physical libraries are allowed to distribute e-books, but I’m not sure if that’s made possible due to licensing fees.
I’m not saying that they approached this well, especially given the copyright laws in the US, but it was indeed a good thing for the normal person at the time. Too bad that the judicial system in the US is biased towards leeching companies. I really can’t wait to see the AI vs publishers fight, though. Let’s see who has deeper pockets and better plants in the courts :D