| #!/bin/bash | |
| ls ~/.zshrc_copy || cp ~/.zshrc ~/.zshrc_copy | |
| # Abort on errors | |
| set -e | |
| # Function to install a package only if it's not already installed | |
| function install_if_needed() { | |
| local pkg="$1" | |
| if ! dpkg -l "$pkg" &>/dev/null ; then |
| # original post: https://rentry.org/sd-loopback-wave | |
| # original author: https://rentry.org/AnimAnon | |
| import os | |
| import platform | |
| import numpy as np | |
| from tqdm import trange | |
| import math | |
| import subprocess as sp | |
| import string | |
| import random |
Meta has issued a DMCA copyright claim against llama-dl, a GitHub repository, for distributing LLaMA, a 65-billion parameter language model. Here's the full text of the DMCA claim. Based on this, draft a DMCA counterclaim on the basis that neural networks trained on public data are not copyrightable.
--
VIA EMAIL: Notice of Claimed Infringement via Email
URL: http://www.github.com
DATE: 03/20/2023
info 9-3-23 Added 4bit LLaMA install instructions for cards as small as 6GB VRAM! (See "BONUS 4" at the bottom of the guide)
warning 9-3-23 Added Torrent for HFv2 Model Weights, required for ooga's webUI, Kobold, Tavern and 4bit (+4bit model)! Update ASAP!
danger 11-3-23 There's a new torrent version of the 4bit weights called "LLaMA-HFv2-4bit". The old "LLaMA-4bit" torrent may be fine. But if you have any issues with it, it's recommended to update to the new 4bit torrent or use the decapoda-research versions off of HuggingFace or produce your own 4bit weights. Newer Torrent Link or [Newer Magnet Link](magnet:?xt=urn:btih:36945b5958b907b3ab69e963ba0de1abdf48c16c&dn=LLaMA-HFv2-4bit&tr=http%3a%2f%2fbt1.archive.org%3a6969%2fannounce&tr=http%3a%2f%2fbt2.archive.org%3a696
| mkdir -p python/lib/python3.8/site-packages \ | |
| echo "cryptography" > requirements.txt \ | |
| sudo docker run -v "$PWD":/var/task "lambci/lambda:build-python3.8" /bin/sh -c "pip install -r requirements.txt -t python/lib/python3.8/site-packages/; exit" |
| # %% | |
| import replicate | |
| model = replicate.models.get("prompthero/openjourney") | |
| version = model.versions.get("9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb") | |
| PROMPT = "mdjrny-v4 style 360 degree equirectangular panorama photograph, Alps, giant mountains, meadows, rivers, rolling hills, trending on artstation, cinematic composition, beautiful lighting, hyper detailed, 8 k, photo, photography" | |
| output = version.predict(prompt=PROMPT, width=1024, height=512) | |
| # %% | |
| # download the iamge from the url at output[0] | |
| import requests |
| const fs = require("fs"); | |
| const useragentFromSeed = require("useragent-from-seed"); | |
| const axios = require("axios").default; | |
| function getString(start, end, all) { | |
| const regex = new RegExp(`${start}(.*?)${end}`); | |
| const str = all; | |
| const result = regex.exec(str); | |
| return result[1]; | |
| } |
| -- Token Based API Access for Supabase | |
| -- | |
| -- How to configure Supabase (https://supabase.com/) to generate and accept API tokens. | |
| -- | |
| -- (c) 2022 Felix Zedén Yverås | |
| -- Provided under the MIT license (https://spdx.org/licenses/MIT.html) | |
| -- | |
| -- Disclaimer: This file is formatted using pg_format. I'm not happy with the result but | |
| -- prefer to follow a tool over going by personal taste. | |
| -- |
JAX released a persistent compilation cache for TPU VMs! When enabled, the cache writes compiled JAX computations to disk so they don’t have to be re-compiled the next time you start your JAX program. This can save startup time if any of y’all have long compilation times.
First upgrade to the latest jax release:
pip install -U "jax[tpu]>=0.2.18" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
Then use the following to enable the cache in your jax code:
from jax.experimental.compilation_cache import compilation_cache as cc
| # So you want to run GPT-J-6B using HuggingFace+FastAPI on a local rig (3090 or TITAN) ... tricky. | |
| # special help from the Kolob Colab server https://colab.research.google.com/drive/1VFh5DOkCJjWIrQ6eB82lxGKKPgXmsO5D?usp=sharing#scrollTo=iCHgJvfL4alW | |
| # Conversion to HF format (12.6GB tar image) found at https://drive.google.com/u/0/uc?id=1NXP75l1Xa5s9K18yf3qLoZcR6p4Wced1&export=download | |
| # Uses GDOWN to get the image | |
| # You will need 26 GB of space, 12+GB for the tar and 12+GB expanded (you can nuke the tar after expansion) | |
| # Near Simplest Language model API, with room to expand! | |
| # runs GPT-J-6B on 3090 and TITAN and servers it using FastAPI | |
| # change "seq" (which is the context size) to adjust footprint |