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I had a hunch that writing the actual Upload/download speed tather than mbps was probably wrong. My bad, my internet provider lingo is rusted.
I had a hunch that writing the actual Upload/download speed tather than mbps was probably wrong. My bad, my internet provider lingo is rusted.
I don’t have a jellyfin server but 1MB/s (8mbps) for each person watching 1080p (3.6Gb per hour of content for each file) seems reasonable. ~3MB/s (24mbps) upload and as much download should work.
Z library. The official links are on the wiki page.
No. Quantization make it go faster. Not blazing fast, but decent.
Completely forgot to tell you to only use quantized models. Your pc can run 4bit quantized versions of the models I mentioned. That’s the key for running llms on at consumer level hardware. You can later read further about the different quantizations and toy with other ones like Q5_K_M and such.
Just read phi-3 got released and apparently it’s a 4B that reach gpt 3.5 level. Follow the news and wait for it to be add to ollama/llama.ccp
Thank you so much for taking the time to help me with that! I’m very new to the whole LLM things, and sorta figuring it out as I go
I became fascinated with llms after the first AI booms but all this knowledge is basically useless where I live, so might as well make it useful by teaching people what i know.
The key is quantized models. A full model wouldn’t fit but a 4bit 8b llama3 would fit.
Yeah, it’s not a potato but not that powerful eaither. Nonetheless, it should run a 7b/8b/9b and maybe 13b models easily.
running them in Python with Huggingface’s Transformers library (from local models
That’s your problem right here. Python is great for making llms but is horrible at running them. With a computer as weak as yours, every bit of performance counts.
Just try ollama or llama.ccp . Their github is also a goldmine for other projects you could try.
Llama.ccp can partially run the model on the gpu for way faster inference.
Piper is a pretty decent very lightweight tts engine that can be directly run on your cpu if you want to add tts capabilities to your setup.
Good luck and happy tinkering!
Specs? Try mistral with llama.ccp.
It shouldn’t happen for a 8b model. Even on CPU, it’s supposed to be decently fast. There’s definitely something wrong here.
Sadly, can’t really help you much. I have a potato pc and the biggest model I ran on it was Microsoft phi-2 using the candle framework. I used to tinker with Llama.cpp on colab, but it seems they don’t handle llama3 yet. ollama says it does , but I’ve never tried it before. For the speed, It’s kinda expected for a 70b model to be really slow on the CPU. How much slow is too slow ? I don’t really know…
You can always try the 8b model. People says it’s really great and even replaced the 70b models they’ve been using.
Run 70b llama3 on one and have a 100% local, gpt4 level home assistant . Hook it up with coqui.Ai xttsv2 for mind baffling natural language speech (100% local too ) that can imitate anyone’s voice. Now, you got yourself Jarvis from Ironman.
Edit : thought they were some kind of beast machines with 192gb ram and stuff. They’re just regular middle-low tier pcs.
https://megathread.pages.dev/linux
Torminatorr seems to have a dedicated gog section but requires making an account.
Avoid using any link that isn’t on the Z-Library wikipedia article. The singlelogin one shared above is the official link.
I heard that Germany anti-piracy laws are no joke. Maybe that’s why no one wants to be involved ?
Pardon me if I am mistaken, but it wouldn’t surprise me if piracy is a niche and taboo subject in Germany.
The wikipedia page of Z-Library contains all the links you need.
A pirate is always free.
convenience mostly. Having a streaming like UX is pleasant and It’s easier to browse directly from the app instead of looking for a movie to watch , search for it on torrent sites and then download it to watch it.
I mostly watch in 720p and This is why I watch directly on pirate streaming sites instead of torrenting. It’s more convenient for me.
Also, I don’t think piracy is against anything, it’s about making people’s life easier. At least, that’s what piracy is for me.
The difference is that with this kind of setup you have a somewhat similar experience to streaming services. You browse directly from the app and directly play the movie.
Torrent tip : if you make the download sequential you can start watching the movie using a player like VLC without it having to fully download. you start the download, wait a bit for it to load a few pourcentages and you can start watching while it’s still loading.
It depends on your internet speed, the file size and the movie length but if you’re internet is fast enough then no worries.
I don’t think warp change your ip. You’re still traceable.
If you guys like hiking and stuff, there’s this cool open source app called trail sense on f-droid and it’s just so much feature packed…
I don’t hike, so I only use it for it’s pedometer capabilities and a hypothetical situation where “I might get really lost” but the amount of features it has for hiking and survival is crazy and so I think deserves to be more known.