Quantized (Q4_K_S) version of the amazing RealFlux 1.0b model, all credits to the original author SG_161222 .
Why I made this quantized version? because my gpu is not powerful enough to run the full version and I thought it would be useful for everyone else who like me want to try a version of this wonderful model but for our humble gpus. 😅😁
If you want to know more details of the original model as version information or Recommended Settings, I invite you to go through the original model page and give a lot of love to its author😊.
If you are the author of the original model and you don't like or are against the upload of this quantized version, either because you have already uploaded your own quantized version or because you don't like the idea, let me know and I will remove it. ✌🏻
Description
Quantized GGUF Q4_K_S
FAQ
Comments (3)
This quantized version is smaller ( about half) , but does that mean it would be faster as well ? I mean on consumer grade GPUs like RTX 3000 or so.
It's a very good question. I know that by quantizing we reduce the bits of the parameters, which means it will require fewer resources to load the model (and for which we sacrifice a bit of precision in the results). I would say that inference times are also reduced, but I'm not completely sure since I can't test the full models because my GPU would explode into a thousand pieces.
If this is the difference between fitting in VRAM, and having to swap to main memory, then yes it'll be faster. But compared to another model that also fits in your VRAM? Not really. The main point of the quantization is to make the model work on low spec hardware. There are similarly quantized models that are much larger, but still operate on low spec GPUs, so it's not just the size of the file that matters.
If you want speed, look for models that require fewer steps. It's my experience that these models tend to be good at one or two things in particular and not so good elsewhere, so your mileage may vary.