Open Source Windows Generative AI - Page 8
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Generative AI for Windows
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gpt2-client
Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, etc.
GPT-2 is a Natural Language Processing model developed by OpenAI for text generation. It is the successor to the GPT (Generative Pre-trained Transformer) model trained on 40GB of text from the internet. It features a Transformer model that was brought to light by the Attention Is All You Need paper in 2017. The model has 4 versions - 124M, 345M, 774M, and 1558M - that differ in terms of the amount of training data fed to it and the number of parameters they contain. Finally, gpt2-client is a wrapper around the original gpt-2 repository that features the same functionality but with more accessiblity, comprehensibility, and utilty. You can play around with all four GPT-2 models in less than five lines of code. Install client via pip. The generation options are highly flexible. You can mix and match based on what kind of text you need generated, be it multiple chunks or one at a time with prompts. -
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gptee
LLMs done the UNIX-y way
Output from a language model using standard input as the prompt. Now supporting GPT3.5 chat completions! gptee was designed for use within shell scripts and other programs and also works in interactive shells. You can compose commands and execute them in a script. Proceed with caution before running arbitrary shell scripts. Using a chat completion model (like gpt-3.5-turbo), you can then inject a system message with -s or --system messages. For davinci and other non-chat models, the output is prefixed to the prompt. Compose shell commands like you would in a script. Try with a custom model. By default gptee uses gpt-3.5-turbo. -
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hebrew-gpt_neo
Hebrew text generation models based on EleutherAI's gpt-neo
Hebrew text generation models based on EleutherAI's gpt-neo. Each was trained on a TPUv3-8 which was made available to me via the TPU Research Cloud Program. The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. -
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hexabot
Hexabot is an open-source AI chatbot / agent builder.
Hexabot is an open-source AI chatbot / agent solution. It allows you to create and manage multi-channel, and multilingual chatbots / agents with ease. Hexabot is designed for flexibility and customization, offering powerful text-to-action capabilities. Originally a closed-source project (version 1), we've now open-sourced version 2 to contribute to the community and enable developers to customize and extend the platform with extensions. -
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hfapigo
Unofficial (Golang) Go bindings for the Hugging Face Inference API
(Golang) Go bindings for the Hugging Face Inference API. Directly call any model available in the Model Hub. An API key is required for authorized access. To get one, create a Hugging Face profile. -
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langchain-prefect
Tools for using Langchain with Prefect
Large Language Models (LLMs) are interesting and useful - building apps that use them responsibly feels like a no-brainer. Tools like Langchain make it easier to build apps using LLMs. We need to know details about how our apps work, even when we want to use tools with convenient abstractions that may obfuscate those details. Prefect is built to help data people build, run, and observe event-driven workflows wherever they want. It provides a framework for creating deployments on a whole slew of runtime environments (from Lambda to Kubernetes), and is cloud agnostic (best supports AWS, GCP, Azure). For this reason, it could be a great fit for observing apps that use LLMs. RecordLLMCalls is a ContextDecorator that can be used to track LLM calls made by Langchain LLMs as Prefect flows. Run several LLM calls via langchain agent as Prefect subflows. -
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marqo
Tensor search for humans
A tensor-based search and analytics engine that seamlessly integrates with your applications, websites, and workflows. Marqo is a versatile and robust search and analytics engine that can be integrated into any website or application. Due to horizontal scalability, Marqo provides lightning-fast query times, even with millions of documents. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images. It can seamlessly handle image-to-image, image-to-text and text-to-image search and analytics. Marqo adapts and stores your data in a fully schemaless manner. It combines tensor search with a query DSL that provides efficient pre-filtering. Tensor search allows you to go beyond keyword matching and search based on the meaning of text, images and other unstructured data. Be a part of the tribe and help us revolutionize the future of search. Whether you are a contributor, a user, or simply have questions about Marqo, we got your back. -
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min(DALL·E)
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
This is a fast, minimal port of Boris Dayma's DALL·E Mini (with mega weights). It has been stripped down for inference and converted to PyTorch. The only third-party dependencies are numpy, requests, pillow and torch. The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16. Set the device to either cuda or "cpu". Once everything has finished initializing, call generate_image with some text as many times as you want. Use a positive seed for reproducible results. Higher values for supercondition_factor result in better agreement with the text but a narrower variety of generated images. Every image token is sampled from the top_k most probable tokens. The largest logit is subtracted from the logits to avoid infs. The logits are then divided by the temperature. If is_seamless is true, the image grid will be tiled in token space not pixel space. -
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node-markov-generator
Generates simple sentences based on given text corpus
This simple generator emits short sentences based on the given text corpus using a Markov chain. To put it simply, it works kinda like word suggestions that you have while typing messages in your smartphone. It analyzes which word is followed by which in the given corpus and how often. And then, for any given word it tries to predict what the next one might be. Here you create an instance of TextGenerator passing an array of strings to it - it represents your text corpus which will be used to "train" the generator. The more strings/sentences you pass, the more diverse results you get, so you'd better pass like hundreds of them, or even more! If you have your texts in an external file, you can pass the path to it as an argument for TextGenerator's constructor. -
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onnxt5
Summarization, translation, sentiment-analysis, text-generation, etc.
Summarization, translation, sentiment analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX. This package is still in the alpha stage, therefore some functionalities such as beam searches are still in development. The simplest way to get started for generation is to use the default pre-trained version of T5 on ONNX included in the package. Please note that the first time you call get_encoder_decoder_tokenizer, the models are being downloaded which might take a minute or two. Other tasks just require to change the prefix in your prompt, for instance for summarization. Run any of the T5 trained tasks in a line (translation, summarization, sentiment analysis, completion, generation) Export your own T5 models to ONNX easily. Utility functions to generate what you need quickly. Up to 4X speedup compared to PyTorch execution for smaller contexts. -
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pdf-extractor
Node.js module for rendering pdf pages to images, svgs and HTML files
Pdf-extractor is a wrapper around pdf.js to generate images, svgs, html files, text files and json files from a pdf on node.js. A DOM Canvas is used to render and export the graphical layer of the pdf. Canvas exports *.png as a default but can be extended to export to other file types like .jpg. Pdf objects are converted to svg using the SVGGraphics parser of pdf.js. Pdf text is converted to HTML. This can be used as a (transparent) layer over the image to enable text selection. Pdf text is extracted to a text file for different usages (e.g. indexing the text). This library is in it's most basic form a node.js wrapper for pdf.js. It has default renderers to generate a default output, but is easily extended to incorporate custom logic or to generate different output. It uses a node.js DOM and the node domstub from pdf.js do make pdf parsing available on node.js without a browser. -
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php-text-generator
Fast SEO text generator on a mask
Fast SEO text generator on a mask. Written in PHP. I do not use regular expressions and the fastest. I covered tests and simple! Supporting recursive text generation rules. It supports multiple encodings. This package implements the functionality of a similar package for Go Lang. It supports multiple encodings. Supporting recursive text generation rules. Fast! Does not use regular expressions. Easy wrapping thanks to the integrated interface. Covered tests. Written by PSR standards and 100% covered with documentation (PHP-Doc) Without external dependencies. The code is checked by the static analyzer PhpStan lvl 7. -
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pytorch-cpp
C++ Implementation of PyTorch Tutorials for Everyone
C++ Implementation of PyTorch Tutorials for Everyone. This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch (i.e. Section 1 to 3) Interactive Tutorials are currently running on LibTorch Nightly Version. Libtorch only supports 64bit Windows and an x64 generator needs to be specified. Create all required script module files for pre-learned models/weights during the build. Requires installed python3 with PyTorch and torch-vision. You can choose to only build tutorials in one of the categories basics, intermediate, advanced or popular. You can build and run the tutorials (on CPU) in a Docker container using the provided Dockerfile and docker-compose.yml files. -
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revChatGPT
Reverse engineered ChatGPT API
Reverse Engineered ChatGPT API by OpenAI. Extensible for chatbots etc. This is not an official OpenAI product. -
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ruDALL-E
Generate images from texts. In Russian
We present a family of generative models from SberDevices and Sber AI! Models allow you to create images that did not exist before. All you need is a text description in Russian or another language. Try to create unique images together with generative artists using your own formulations. Ask generative artists to depict something special for you as well. The Kandinsky 2.0 model uses the reverse diffusion method and creates colorful images on various topics in a matter of seconds by text query in Russian and other languages. You can even combine different languages within a single query. This neural network has been developed and trained by Sber AI researchers in close collaboration with scientists from Artificial Intelligence Research Institute using joined datasets by Sber AI and SberDevices. Russian text-to-image model that generates images from text. The architecture is the same as ruDALL-E XL. Even more parameters in the new version. -
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stable-diffusion-webui-colab
Stable diffusion webui colab
Stable Diffusion webui colab. lite has a stable WebUI and stable installed extensions. stable has ControlNet, a stable WebUI, and stable installed extensions. Nightly has ControlNet, the latest WebUI, and daily installed extension updates. If you want to use more models, you can download your model into Colab, which has an empty 50GB space. You can also free up more space by deleting the default model in your drive. If you don't plan to use ControlNet models, you can also free up space by deleting them. -
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terminalGPT
Get GPT like ChatGPT on your terminal
Get GPT like ChatGPT on your terminal Note: This doesn't use OpenAI ChatGPT, it uses text-davinci-003 model (by default) You'll need to have your own OpenAi apikey to operate this package. 1. Go to https://beta.openai.com 2. Select you profile menu and go to View API Keys 3. Select + Create new secret key 4. Copy generated key Get started: Using tgpt: npm -g install terminalgpt or yarn global add terminalgpt Run tgpt chat ps.: If it is your first time running it, it will ask for open AI key , paste generated key from pre-requisite steps -
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text-generator
Golang text generator for generate SEO texts
Golang text generator for generate SEO texts. Fast text generator on a mask. Written in Golang. I do not use regular expressions and the fastest. I covered tests and simple! Supporting recursive text generation rules. -
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texturize
Generate photo-realistic textures based on source images
Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture. A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab. -
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website-to-gif
Turn your website into a GIF
This Github Action automatically creates an animated GIF or WebP from a given web page to display on your project README (or anywhere else). In your GitHub repo, create a workflow file or extend an existing one. You have to also include a step to checkout and commit to the repo. You can use the following example gif.yml. Make sure to modify the url value and add any other input you want to use. WebP rendering will take a lot of time to benefit from lossless quality and file size optimization. -
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wechat-chatgpt
Use ChatGPT On Wechat via wechaty
Use ChatGPT On Wechat via wechaty Interact with WeChat and ChatGPT: Use ChatGPT on WeChat with wechaty and Official API Add conversation support Support command setting Deployment and configuration options: Add Dockerfile, deployable with docker Support deployment using docker compose Support Railway and Fly.io deployment Other features: Support Dall·E Support whisper Support setting prompt Support proxy (in development) -
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x-unet
Implementation of a U-net complete with efficient attention
Implementation of a U-net complete with efficient attention as well as the latest research findings. For 3d (video or CT / MRI scans).