It didn't had any quality loss, since the PSNT as quantization it's mainly to convert the model over the console constraints (you can convert any model you want, even when i trained a model for this hw); it's q8 quantization, so quality loss is negligible for these sizes. For the speed, I will fix the Tok/sec count since now drops 0 always for showing measures
PS: Thank you! And forgot to mention PSNT also supports bitnet models, they work like crap tho
I wondered the same, but the rendering seems right, the output was almost instant. I'll recheck the token counter; anyway as you say, fast isn't practical. Actually I had to develop my own tiny model https://huggingface.co/xaskasdf/brandon-tiny-10m-instruct to fit something "usable", and it's basically a liar or disinformation machine haha
Actually is purely bandwidth-bound, the major bottleneck of the whole process, for me in this case, is the B450 mobo I got that's only capable of pcie3 and 1x8 in the pcie lanes for gpu instead of 1x16; so I'm capped until I get an X570 maybe. I should get around twice or triple the tok speed with that upgrade alone
I updated the documentation to provide more info for the patching process, I added the patches themselves too and provided some risk info about the patches
I did it, but with different quantization compressions, It ran into quality issues, I will try to rerun with the same quants if that fixes the issue, but the most that looks unused, its being used by rotating layers that are being swapped by the cpu from the ram itself, that manages to keep layers warm, ready to use while inferencing and discarding already used ones
The idea was basically to run a llm on a ps2, then I ran into some problems as the 32mb ram cap with 4mb vram cap; so I had to figure out a way to stream layers on the forward pass. Given that ps2 manages to give instructions directly to the vram that's capable of 32bit addresses, it gave an insane amount of tok/s, then I wondered if I could do the same on my puter
That would be nice to see. Actually I was thinking about getting another 3090 and a mobo upgrade since I'm bottlenecked by pcie3 to tryna run glm 4.7 or 5 at q4_k_m, it should be possible.
yeah, actually I wanted to see if this was possible at all. I managed to get around 3000 tokens/s on a ps2 with classic transformers, since the emotion engine is capable of 32 bit addresses, but it has like 32gb of ram. So I ran into the question of why was that fast and I couldn't get that speed even with small models, and the deal is that the instructions went right of the memory to the gpu and that's the main difference that does when a regular computer does inference: it has to request the instructions to the cpu every time. As I mentioned too, on professional cards you can avoid these problems naturally, since they got instructions precisely for this, but sadly I don't have 30k bucks to spare on a gpu :(
PS: Thank you! And forgot to mention PSNT also supports bitnet models, they work like crap tho