To be able to make all procedures without pain you should have physical keyboard or just install ssh server and connect to your device shell from computer.
Folow this guide to setup ssh server.
To be able to make all procedures without pain you should have physical keyboard or just install ssh server and connect to your device shell from computer.
Folow this guide to setup ssh server.
"C:\Program Files\Blender Foundation\Blender 3.4\blender.exe" --background --python flip_glb.py | |
import os | |
import bpy | |
from math import pi | |
# put the location to the folder where the glb are located here in this fashion | |
path_to_obj_dir = os.path.join('C:\\', 'Users\\Username\\Documents\\File_Folder\\') | |
# get list of all files in directory | |
file_list = sorted(os.listdir(path_to_obj_dir)) |
from typing import Optional, Any | |
import torch | |
from transformers.utils import is_accelerate_available, is_bitsandbytes_available | |
from transformers import ( | |
AutoTokenizer, | |
AutoModelForCausalLM, | |
GenerationConfig, | |
pipeline, |
The package that linked you here is now pure ESM. It cannot be require()
'd from CommonJS.
This means you have the following choices:
import foo from 'foo'
instead of const foo = require('foo')
to import the package. You also need to put "type": "module"
in your package.json and more. Follow the below guide.await import(…)
from CommonJS instead of require(…)
.GNU ld (GNU Binutils for Ubuntu) 2.25.1 | |
Supported emulations: | |
elf_x86_64 | |
elf32_x86_64 | |
elf_i386 | |
i386linux | |
elf_l1om | |
elf_k1om | |
i386pep | |
i386pe |
# Google Analytics Bypassing Adblockers | |
## Client | |
change www.googletagmanager.com => your.domain.com | |
``` | |
<!-- Global site tag (gtag.js) - Google Analytics --> | |
<script async src="https://your.domain.com/gtag/js?id=UA-123456789-1"></script> | |
``` |
First of all, you find the install path of your Electron app. If you found it, find the resources folder. If you found it, you'll have to install asar globally, by running:
I'm too lazy to write this as official documentation so I'm transcribing my experiences here for reference.
This is high level and does not cover how to setup your peer, only how to use the API itself.
This is not a tutorial.
If you are just getting started, this tutorial by DevLogLogan is worth watching.
# This supports merging as many adapters as you want. | |
# python merge_adapters.py --base_model_name_or_path <base_model> --peft_model_paths <adapter1> <adapter2> <adapter3> --output_dir <merged_model> | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from peft import PeftModel | |
import torch | |
import os | |
import argparse |