It’s a good thing that real open source models are getting good enough to compete with or exceed OpenAI.
It’s a good thing that real open source models are getting good enough to compete with or exceed OpenAI.
I like the game, but agree with the over-tutorialed complaints. They have two difficulty modes, I wish only story mode got all the handholding. I think there’s enough obvious indicators to get you through all the game mechanics.
surely he’ll be less of a twat then. right?
Easiest shorting money I ever made.
Is this the new “Simpsons already did it”?
Cunk already did it…
(3:40 if you want to get right to it) https://www.youtube.com/watch?v=UoSUx1xyj1E
MAWP - Archer
Taking ollama for instance, either the whole model runs in vram and compute is done on the gpu, or it runs in system ram and compute is done on the cpu. Running models on CPU is horribly slow. You won’t want to do it for large models
LM studio and others allow you to run part of the model on GPU and part on CPU, splitting memory requirements but still pretty slow.
Even the smaller 7B parameter models run pretty slow in CPU and the huge models are orders of magnitude slower
So technically more system ram will let you run some larger models but you will quickly figure out you just don’t want to do it.
Boeing made $76B in revenue in 2023. This is slightly more than 1 day’s revenue for them ($210M / day) or a bit more than 10 days profit for them ($21M / day). They will keep doing what they’re doing, but increase their spending on a PR campaign to improve their public image.
FWIW they didn’t merge it, they closed the PR without merging, link to line that still exists on master.
The recent comments are from the announcement of the ladybird browser project which is forked from some browser code from Serenity OS, I guess people are digging into who wrote the code.
Not arguing that the new comments on the PR are good/bad or anything, just a bit of context.
Also, the few points others are talking about needing others, there’s a group-finder and I’d say most people running those raids in group finder groups don’t talk at all, so you can just pretend they’re NPCs if you want.
Been 100% linux for like 6-9 months now, these stories make me thankful for finally making the switch.
I’ve tried to make the switch 3-4 times in the past and was stopped by 2 main things:
The experience was so much better this time and I really have no regrets. I don’t imagine I’ll ever run Windows again outside of a VM
Elon “Nick Cannon” Musk
Rip up the Reddit contract and don’t use that data to train the model. It’s the definition of a garbage in garbage out problem.
First a caveat/warning - you’ll need a beefy GPU to run larger models, there are some smaller models that perform pretty well.
Adding a medium amount of extra information for you or anyone else that might want to get into running models locally
Tools
Models
If you look at https://ollama.com/library?sort=featured you can see models
Model size is measured by parameter count. Generally higher parameter models are better (more “smart”, more accurate) but it’s very challenging/slow to run anything over 25b parameters on consumer GPUs. I tend to find 8-13b parameter models are a sort of sweet spot, the 1-4b parameter models are meant more for really low power devices, they’ll give you OK results for simple requests and summarizing, but they’re not going to wow you.
If you look at the ‘tags’ for the models listed below, you’ll see things like
8b-instruct-q8_0
or8b-instruct-q4_0
. The q part refers to quantization, or shrinking/compressing a model and the number after that is roughly how aggressively it was compressed. Note the size of each tag and how the size reduces as the quantization gets more aggressive (smaller numbers). You can roughly think of this size number as “how much video ram do I need to run this model”. For me, I try to aim for q8 models, fp16 if they can run in my GPU. I wouldn’t try to use anything below q4 quantization, there seems to be a lot of quality loss below q4. Models can run partially or even fully on a CPU but that’s much slower. Ollama doesn’t yet support these new NPUs found in new laptops/processors, but work is happening there.