Ghostty + Zoxide + Yazi + Oh-My-Zsh 完整配置
- Ghostty - 现代化 GPU 加速终端模拟器
- Zoxide - 智能目录跳转工具(cd 的智能替代)
- Yazi - 快速终端文件管理器
- Oh-My-Zsh - Zsh 配置框架
| """ | |
| Simplified Implementation of the Linear Recurrent Unit | |
| ------------------------------------------------------ | |
| We present here a simplified JAX implementation of the Linear Recurrent Unit (LRU). | |
| The state of the LRU is driven by the input $(u_k)_{k=1}^L$ of sequence length $L$ | |
| according to the following formula (and efficiently parallelized using an associative scan): | |
| $x_{k} = \Lambda x_{k-1} +\exp(\gamma^{\log})\odot (B u_{k})$, | |
| and the output is computed at each timestamp $k$ as follows: $y_k = C x_k + D u_k$. | |
| In our code, $B,C$ follow Glorot initialization, with $B$ scaled additionally by a factor 2 | |
| to account for halving the state variance by taking the real part of the output projection. |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| from argparse import ArgumentParser | |
| import torch | |
| import torch.distributed as dist | |
| from torch.nn.parallel import DistributedDataParallel as DDP | |
| from torch.utils.data import DataLoader, Dataset | |
| from torch.utils.data.distributed import DistributedSampler | |
| from transformers import BertForMaskedLM |
| #!/bin/bash | |
| # | |
| # script to extract ImageNet dataset | |
| # ILSVRC2012_img_train.tar (about 138 GB) | |
| # ILSVRC2012_img_val.tar (about 6.3 GB) | |
| # make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory | |
| # | |
| # https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md | |
| # | |
| # train/ |