LocalLLaMA

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Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

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[Question] Why is there no Q8 quantization for Phi-3-V? (programming.dev)

submitted 1 year ago by paintenzero@programming.dev to c/localllama@sh.itjust.works

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Hello! I am looking for some expertise from you. I have a hobby project where Phi-3-vision fits perfectly. However, the PyTorch version is a little too big for my 8GB video card. I tried looking for a quantized model, but all I found is 4-bit. Unfortunately, this model works too poorly for me. So, for the first time, I came across the task of quantizing a model myself. I found some guides for Phi-3V quantization for ONNX. However, the only options are fp32(?), fp16, int4. Then, I found a nice tool for AutoGPTQ but couldn't make it work for the job yet. Does anybody know why there is no int8/int6 quantization for Phi-3-vision? Also, has anybody used AutoGPTQ for quantization of vision models?

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[–] rufus@discuss.tchncs.de 3 points 1 year ago* (last edited 1 year ago)

I think most people use something like exllamav2 or vllm or use GGUF to do inference and it seems neither of those projects have properly implemented multimodality or this specific model architecture, yet.

You might just be at the forefront of things and there isn't yet any beaten path you could follow.

The easiest thing you could do is just use something that already exists, be it 4bit models, wait a few weeks and then upgrade. And I mean you can also always quantize models yourself and set the parameters however you like, if you have some inference framework that supports your model including the adapters for vision and has the quantization levels you're interested in...