Simply model., fuse usingtorch.quantizationthe result not same: def model_equivalence(model_1, model_2, device, rtol=1e-05, atol=1e-08, num_tests=100, input_size=(1, 3, 32, 32)): model_1.to(device) model_2.to(device) for _ in range(num_tests): x = torch.rand(size=input...
For more info: https://github.blog/changelog/2024-03-07-github-actions-all-actions-will-run-on-node20-instead-of-node16-by-default/ Show more
Uranus 清华大学 计算机系博士在读 我们支持 chatglm3 啦 | 升级到最新版本 Xinference: `pip install -U xinference`升级后,通过 UI 或命令行一键加载 chatglm3: `xinference launch --model-name chatglm3 --size-in-billions 6 --model-format pytorch --quantization none`2023-10-30 发布 赞同...
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
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 can be feasibly combined with various existing quantization approaches (e.g., AWQ,...
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime - intel/neural-compressor
[ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models" - ModelTC/QLLM
[EMNLP 2024 Industry Track] This is the official PyTorch implementation of "LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit". - ModelTC/llmc
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@9fee87e
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