While not hard to find the issue and set the backend explicitly, it would be nice if it's possible to get some kind of warning that we were using a different backend than what was used during quantization (or even when the model was saved). This would have been more dangerous if the ...
edited by pytorch-probotbot model=resnet18(num_classes=num_classes,pretrained=False)model.to(cpu_device)print(model)# Make a copy of the model for layer fusionfused_model=copy.deepcopy(model)model.train()# The model has to be switched to training mode before any layer fusion.# Otherwise ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Add model name, quantization and device to gpt_fast micro benchmark o… · pytorch/pytorch@f37121b
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact This repository contains the PyTorch implementation of IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact. IntactKV is a simple and orthogonal method to enhance the quantized LLMs. It ...
pytorch, size: 6, quantization: 8-bit 2024-05-06 19:58:13,617 xinference.api.restful_api 596 ERROR [address=0.0.0.0:23614, pid=660] Model not supported, name: fuzimingcha_v1.0, format: pytorch, size: 6, quantization: 8-bit Traceback (most recent call last): File "/root/anaconda...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - torch._export.aot_compile reports an error when compiling the model after int8 quantization · pytorch/pytorch@1b07d42
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime - intel/neural-compressor
model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net);2、 pruning: normal、regular and group convol
File:torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py,Entity:quantization_perchannel_hook,Line: 122,Description: First line should be in imperative mood (perhaps 'Apply', not 'Applies') File:torch/distributed/algorithms/model_averaging/averagers.py,Entity:average_parameters,Line: 106,...
[ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models" - ModelTC/QLLM