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Cake day: July 14th, 2023

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  • Why is 255 off limits? What is 127.0.0.0 used for?

    To clarify, I meant that specific address - if the range starts at 127.0.0.1 for local, then surely 127.0.0.0 does something (or is reserved to sometimes do something, even if it never actually does in practice), too.

    Advanced setup would include a reverse proxy to forward the requests from the applications port to the internet

    I use Traefik as my reverse proxy, but I have everything on subdomains for simplicity’s sake (no path mapping except when necessary, which it generally isn’t). I know 127.0.0.53 has special meaning when it comes to how the machine directs particular requests, but I never thought to look into whether Traefik or any other reverse proxy supported routing rules based on the IP address. But unless there’s some way to specify that IP and the IP of the machine, it would be limited to same device communications. Makes me wonder if that’s used for any container system (vs the use of the 10, 172.16-31, and 192.168 blocks that I’ve seen used by Docker).

    Well this is another advanced setup but if you wanted to segregate two application on different subnets you can. I’m not sure if there is a security benefit by adding the extra hop

    Is there an extra hop when you’re still on the same machine? Like an extra resolution step?

    I still don’t understand why .255 specifically is prohibited. 8 bits can go up to 255, so it seems weird to prohibit one specific value. I’ve seen router subnet configurations that explicitly cap the top of the range at .254, though - I feel like I’ve also seen some that capped at .255 but I don’t have that hardware available to check. So my assumption is that it’s implementation specific, but I can’t think of an implementation that would need to reserve all the .255 values. If it was just the last one, that would make sense - e.g., as a convention for where the DHCP server lives on each network.








  • Your comment wasn’t in a meta discussion; it was on a post where they were venting about people complaining about them having a women’s only space. There was certainly no indication that the regular community rules didn’t apply, nor any invitation for men to comment.

    Commenting that it’s hostile for them to have a women’s only space might be ironic, but couldn’t possibly be good faith, in that context. And if the same mod banned you from multiple communities, then either it was out of line and you could appeal it, or it was warranted due to the perceived likelihood of you causing problems in those other communities and the perceived low likelihood of you contributing anything of value to them.

    Even now, you’re acting like the mod(s) banned you because of her / their emotions. You don’t see how that’s misogynistic?

    It makes logical sense for bad actors to be preemptively banned. Emotions have nothing to do with it.


  • I think the best way to handle this would be to just encode everything and upload all files. If I wanted some amount of history, I’d use some file system with automatic snapshots, like ZFS.

    If I wanted to do what you’ve outlined, I would probably use rclone with filtering for the extension types or something along those lines.

    If I wanted to do this with Git specifically, though, this is what I would try first:

    First, add lossless extensions (*.flac, *.wav) to my repo’s .gitignore

    Second, schedule a job on my local machine that:

    1. Watches for changes to the local file system (e.g., with inotifywait or fswatch)
    2. For any new lossless files, if there isn’t already an accompanying lossy files (i.e., identified by being collocated, having the exact same filename, sans extension, with an accepted extension, e.g., .mp3, .ogg - possibly also with a confirmation that the codec is up to my standards with a call to ffprobe, avprobe, mediainfo, exiftool, or something similar), it encodes the file to your preferred lossy format.
    3. Use git status --porcelain to if there have been any changes.
    4. If so, run git add --all && git commit --message "Automatic commit" && git push
    5. Optionally, automatically craft a better commit message by checking which files have been changed, generating text like Added album: "Satin Panthers - EP" by Hudson Mohawke or Removed album: "Brat" by Charli XCX; Added album "Brat and it's the same but there's three more songs so it's not" by Charli XCX

    Third, schedule a job on my remote machine server that runs git pull at regular intervals.

    One issue with this approach is that if you delete a file (as opposed to moving it), the space is not recovered on your local or your server. If space on your server is a concern, you could work around that by running something like the answer here (adjusting the depth to an appropriate amount for your use case):

    git fetch --depth=1
    git reflog expire --expire-unreachable=now --all
    git gc --aggressive --prune=all
    

    Another potential issue is that what I described above involves having an intermediary git to push to and pull from, e.g., running on a hosted Git forge, like GitHub, Codeberg, etc… This could result in getting copyright complaints or something along those lines, though.

    Alternatively, you could use your server as the git server (or check out forgejo if you want a Git forge as well), but then you can’t use the above trick to prune file history and save space from deleted files (on the server, at least - you could on your local, I think). If you then check out your working copy in a way such that Git can use hard links, you should at least be able to avoid needing to store two copies on your server.

    The other thing to check out, if you take this approach, is git lfs. EDIT: Actually, I take that back - you probably don’t want to use Git LFS.





  • You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.


  • I’d just like to interject for a moment. What you’re referring to as Alpine Linux Alpine Linux is in fact Pine’s fork, Alpine / Alpine Linux Pine Linux, or as I’ve taken to calling it, Pine’s Alpine plus Alpine Linux Pine Linux. Alpine Linux Pine Linux is an operating system unto itself, and Pine’s Alpine fork is another free component of a fully functioning Alpine Linux Pine Linux system.


  • Wow, there isn’t a single solution in here with the obvious answer?

    You’ll need a domain name. It doesn’t need to be paid - you can use DuckDNS. Note that whoever hosts your DNS needs to support dynamic DNS. I use Cloudflare for this for free (not their other services) even though I bought my domains from Namecheap.

    Then, you can either set up Let’s Encrypt on device and have it generate certs in a location Jellyfin knows about (not sure what this entails exactly, as I don’t use this approach) or you can do what I do:

    1. Set up a reverse proxy - I use Traefik but there are a few other solid options - and configure it to use Let’s Encrypt and your domain name.
    2. Your reverse proxy should have ports 443 and 80 exposed, but should upgrade http requests to https.
    3. Add Jellyfin as a service and route in your reverse proxy’s config.

    On your router, forward port 443 to the outbound secure port from your PI (which for simplicity’s sake should also be port 443). You likely also need to forward port 80 in order to verify Let’s Encrypt.

    If you want to use Jellyfin while on your network and your router doesn’t support NAT loopback requests, then you can use the server’s IP address and expose Jellyfin’s HTTP ports (e.g., 8080) - just make sure to not forward those ports from the router. You’ll have local unencrypted transfers if you do this, though.

    Make sure you have secure passwords in Jellyfin. Note that you are vulnerable to a Jellyfin or Traefik vulnerability if one is found, so make sure to keep your software updated.

    If you use Docker, I can share some config info with you on how to set this all up with Traefik, Jellyfin, and a dynamic dns services all up with docker-compose services.


  • Look up “LLM quantization.” The idea is that each parameter is a number; by default they use 16 bits of precision, but if you scale them into smaller sizes, you use less space and have less precision, but you still have the same parameters. There’s not much quality loss going from 16 bits to 8, but it gets more noticeable as you get lower and lower. (That said, there’s are ternary bit models being trained from scratch that use 1.58 bits per parameter and are allegedly just as good as fp16 models of the same parameter count.)

    If you’re using a 4-bit quantization, then you need about half that number in VRAM. Q4_K_M is better than Q4, but also a bit larger. Ollama generally defaults to Q4_K_M. If you can handle a higher quantization, Q6_K is generally best. If you can’t quite fit it, Q5_K_M is generally better than any other option, followed by Q5_K_S.

    For example, Llama3.3 70B, which has 70.6 billion parameters, has the following sizes for some of its quantizations:

    • q4_K_M (the default): 43 GB
    • fp16: 141 GB
    • q8: 75 GB
    • q6_K: 58 GB
    • q5_k_m: 50 GB
    • q4: 40 GB
    • q3_K_M: 34 GB
    • q2_K: 26 GB

    This is why I run a lot of Q4_K_M 70B models on two 3090s.

    Generally speaking, there’s not a perceptible quality drop going to Q6_K from 8 bit quantization (though I have heard this is less true with MoE models). Below Q6, there’s a bit of a drop between it and 5 and then 4, but the model’s still decent. Below 4-bit quantizations you can generally get better results from a smaller parameter model at a higher quantization.

    TheBloke on Huggingface has a lot of GGUF quantization repos, and most, if not all of them, have a blurb about the different quantization types and which are recommended. When Ollama.com doesn’t have a model I want, I’m generally able to find one there.


  • I recommend a used 3090, as that has 24 GB of VRAM and generally can be found for $800ish or less (at least when I last checked, in February). It’s much cheaper than a 4090 and while admittedly more expensive than the inexpensive 24GB Nvidia Tesla card (the P40?) it also has much better performance and CUDA support.

    I have dual 3090s so my performance won’t translate directly to what a single GPU would get, but it’s pretty easy to find stats on 3090 performance.