At least we might have a finally viable replacement in Photoshop soon. GIMP is getting NDE, Krita might be getting foreground extraction tool at some point, and Pixellator might have better tools though it’s NDE department is solid. The thing is all of them are missing something, but I’m betting on GIMP after CMYK_Student arrival to GIMP development.

I tried adding foreground selection based on guided selection, but was unable to fix noises on in-between selection and was unable to build Krita. We would have Krita with foreground selection if it weren’t for that.

I know that you are speaking truth, yet it still hurts

Solder doesn’t increase performance (the memory is soldered to something regardless, either the main board or an expansion board), but shorter physical distances mean lower latency and less power to transmit the same data. LPDDR4/5X are designed to take advantage of this additional efficiency.

It would seem to be rational that the less mass of metal in a connection, the faster that connection will charge or discharge voltage. Physical sockets require a lot more mass just to ensure solid contact.

Well, that too, but that’s not particularly common on laptops or GPUs. Even in Apple silicon it’s not the same die, but it is the same package.

Shorter physical distance means less latency and lower power. Some memory types like LPDDR4X are built with assumptions that only apply to soldered RAM.

Start off with the Tinyllama model, it’s under 1gb. It will even run on raspberry pi so on real PCs it rips even on CPU. You need a “quantized” model, they are distributed as GGUF files.

I would recommend 5 bit quantized. The less bits, the stupider to put it simply, and Tinyllama is already pretty stupid. But it’s still impressive for what it is, and you can learn the jargon which is the hard part.

Fastest software to run the model on is llama.cpp which is a rewrite from python to C++. Use -ngl <number> to offload layers from cpu to GPU.

Not sure what system you’re using, most AI development is done on Linux so if you’re on Windows I can’t guarantee anything will work.

Working right now on making a voice assistant for my house that can read all my MQTT data and give status reports, it’s neat when you get it running. Fun to tweak it with prompts and see what it can do. Tinyllama can’t seem to reliably handle MQTT and JSON but slightly smarter models can with ease.

Ok, I walked over to my PC to give you a working command line for llama.cpp. You need to make sure it is compiled with support for hipBLAS / ROCm which is the equivalent AMD framework to CUDA, if you want it to run on your GPU.

./main -ngl 24 -m models/tinyllama-1.1b-chat-v1.0.Q5_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -i -ins

This will put it into interactive mode so you can try to chat with it. Running on my GPU it cranks out almost 160 tokens per second, which is way faster than anyone can type. On CPU (-ngl 0) it will make 90 which is still fast. TinyLlama is not a great chatter and should be treated more as a prediction or answer engine. i.e:

>Write a paragraph about borscht.
Borscht is a traditional Russian soup made with beetroot, potatoes, and a variety of spices. It is often served during the winter months in Russia, Ukraine, Belarus, and other Soviet-style countries. Borscht is similar to borscht in Poland, but has different ingredients and a slightly different preparation method. In Poland, beets are boiled until they become tender, then blended with potatoes and vegetable broth to create the soup. In Russia, beetroot is removed from the pot before cooking and replaced with other vegetables such as carrots, celery, and onions. The resulting mixture is then simmered until it is thickened, creating a hearty and flavorful soup. Borscht is usually served cold or at room temperature, and can be accompanied by sour cream, slices of crusty bread, or grilled meats such as kebabs.

It does know a surprising amount, considering it would fit on a CDROM