| # coding=utf-8 | |
| # Copyright 2023 Mixtral AI and the HuggingFace Inc. team. All rights reserved. | |
| # | |
| # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX | |
| # and OPT implementations in this library. It has been modified from its | |
| # original forms to accommodate minor architectural differences compared | |
| # to GPT-NeoX and OPT used by the Meta AI team that trained the model. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. |
| """ | |
| 1. First checkout the trl branch: | |
| git clone https://github.com/huggingface/trl.git | |
| git checkout debug-dpo | |
| 2. Install deps with: | |
| make dev |
| from collections import defaultdict | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| import torch.nn as nn | |
| from datasets import load_dataset | |
| from rich.console import Console | |
| from rich.table import Table | |
| from transformers import ( | |
| AutoTokenizer, |
Cleaned up version of https://gist.github.com/mrsteyk/74ad3ec2f6f823111ae4c90e168505ac,
which is in turn based on the public_diff_vae.ConvUNetVAE from https://github.com/openai/consistencydecoder.
Install the consistency decoder code (for the inference logic) and download the extracted weights:
| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software |
| """ | |
| stable diffusion dreaming | |
| creates hypnotic moving videos by smoothly walking randomly through the sample space | |
| example way to run this script: | |
| $ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry | |
| to stitch together the images, e.g.: | |
| $ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4 |
| import torch | |
| import torch.utils.dlpack | |
| import jax | |
| import jax.dlpack | |
| # A generic mechanism for turning a JAX function into a PyTorch function. | |
| def j2t(x_jax): | |
| x_torch = torch.utils.dlpack.from_dlpack(jax.dlpack.to_dlpack(x_jax)) | |
| return x_torch |
| all: pdf epub kindle html examples | |
| BOOK_TITLE = A\ React\ Developer’s\ Guide\ to\ Hooks\ -\ Sebastien\ Castiel | |
| dist: | |
| @mkdir -p dist | |
| pdf: dist/${BOOK_TITLE}.pdf | |
| @echo '✅ PDF' |
| // | |
| // ContentView.swift | |
| // TestingMoreSwiftUI | |
| // | |
| // Created by Chris Eidhof on 04.06.19. | |
| // Copyright © 2019 Chris Eidhof. All rights reserved. | |
| // | |
| import SwiftUI | |
| import Combine |
| """Character based language modeling with multi-layer GRUs. | |
| To train the model: | |
| python3 tf_char_rnn.py --mode training \ | |
| --logdir path/to/logdir --corpus path/to/corpus.txt | |
| To generate text from seed words: | |
| python3 tf_char_rnn.py --mode sampling \ |
