Open Source Mac AI Text Generators
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AI Text Generators for Mac
View 45 business solutionsBrowse free open source AI Text Generators and projects for Mac below. Use the toggles on the left to filter open source AI Text Generators by OS, license, language, programming language, and project status.
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Text Generation Web UI
Oobabooga - The definitive Web UI for local AI, with powerful features
A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. Dropdown menu for switching between models. Notebook mode that resembles OpenAI's playground. Chat mode for conversation and role playing. Instruct mode compatible with Alpaca and Open Assistant formats. Nice HTML output for GPT-4chan. Markdown output for GALACTICA, including LaTeX rendering. Custom chat characters. Advanced chat features (send images, get audio responses with TTS). Very efficient text streaming. Parameter presets, 8-bit mode. Layers splitting across GPU(s), CPU, and disk. CPU mode, FlexGen, DeepSpeed ZeRO-3, API with streaming and without streaming. LLaMA model, including 4-bit GPTQ. RWKV model, LoRA (loading and training), Softprompts, and extensions. -
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KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
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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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Node.js Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models
This is the Node.js client (with Typescript types) for the NLP Cloud API. NLP Cloud serves high-performance pre-trained or custom models for NER, sentiment analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, text generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, and served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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TextGen
textgen, Text Generation models
Implementation of Text Generation models. textgen implements a variety of text generation models, including UDA, GPT2, Seq2Seq, BART, T5, SongNet and other models, out of the box. UDA, non-core word replacement. EDA, simple data augmentation technique: similar words, synonym replacement, random word insertion, deletion, replacement. This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences with synonyms, random word insertion, deletion, replacement, etc. method, generating new text and implementing text augmentation This project realizes the back translation function based on Baidu translation API, first translate Chinese sentences into English, and then translate English into new Chinese. This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
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gpt-j-api
API for the GPT-J language mode. Including a FastAPI backend
An API to interact with the GPT-J language model and variants! You can use and test the model in two different ways. These are the endpoints of the public API and require no authentication. Just SSH into a TPU VM. This code was tested on both the v2-8 and v3-8 variants. -
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Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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Regex
Generate matching and non matching strings based on regex patterns
Generate matching and non-matching strings. This is a java library that, given a regex pattern, allows to generation of matching strings. Iterate through unique matching strings. Generate not matching strings. Follow the link to Online IDE with created project: JDoodle. Enter your pattern and see the results. By design a+, a* and a{n,} patterns in regex imply an infinite number of characters should be matched. When generating data, that would mean values of infinite length might be generated. It is highly doubtful anyone would require a string of infinite length, thus I've artificially limited repetitions in such patterns to 100 symbols when generating random values. Use a{n,m} if you require some specific number of repetitions. It is suggested to avoid using such infinite patterns to generate data based on regex. -
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abstract2paper
Auto-generate an entire paper from a prompt or abstract using NLP
Enter your abstract into the little doohicky here, and quicker'n you can blink your eyes1, a shiny new paper'll come right out for ya! What are you waiting for? Click the "doohicky" link above to get started, and then click the link to open the demo notebook in Google Colaboratory. To run the demo as a Jupyter notebook (e.g., locally), use this version instead. Note: to compile a PDF of your auto-generated paper (when you run the demo locally), you'll need to have a working LaTeX installation on your machine (e.g., so that pdflatex is a recognized system command). The notebook will also automatically install the transformers library if it's not already available in your local environment. In its unmodified state, the demo notebooks use the abstract from the GPT-3 paper as the "seed" for a new paper. Each time you run the notebook you'll get a new result. -
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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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GPT2 for Multiple Languages
GPT2 for Multiple Languages, including pretrained models
With just 2 clicks (not including Colab auth process), the 1.5B pretrained Chinese model demo is ready to go. The contents in this repository are for academic research purpose, and we do not provide any conclusive remarks. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC) Simplifed GPT2 train scripts（based on Grover, supporting TPUs). Ported bert tokenizer, multilingual corpus compatible. 1.5B GPT2 pretrained Chinese model (~15G corpus, 10w steps). Batteries-included Colab demo. 1.5B GPT2 pretrained Chinese model (~30G corpus, 22w steps). -
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PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. Pass the model you want to use and the NLP Cloud token to the client during initialization. If you are making asynchronous requests, you will always receive a quick response containing a URL. -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifically the "small" 124M and "medium" 355M hyperparameter versions). Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time. -
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TagForge
Cross-platform AI tagging and prompt engineering studio
TagForge is a professional, cross-platform desktop application for AI prompt engineering and image tagging. Built on .NET 9 and Avalonia UI, it provides a sleek, high-performance workspace for crafting perfect prompts for Stable Diffusion, FLUX, Midjourney, and other generative AI models. ## Key Capabilities - Multi-model tagging: Specialized generators for Stable Diffusion (tags), FLUX/Midjourney (prose), and custom LLM workflows - Multimodal vision: Extract metadata or generate descriptive captions directly from images - Contextual chat assistant: Persistent, coding-capable AI helper with real-time provider features - Persona system: Create custom identities with dynamic input injection and role-based templates - Chat rules: Modular behavioral controls (Concise, Detailed, etc.) with CRUD interface - Agent orchestrator: Centralized management of API keys, endpoints, and model parameters -
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AI Chatbots based on GPT Architecture
Training & Implementation of chatbots leveraging GPT-like architecture
Training & Implementation of chatbots leveraging GPT-like architecture with the aitextgen package to enable dynamic conversations. It sure seems like there are a lot of text-generation chatbots out there, but it's hard to find a python package or model that is easy to tune around a simple text file of message data. This repo is a simple attempt to help solve that problem. ai-msgbot covers the practical use case of building a chatbot that sounds like you (or some dataset/persona you choose) by training a text-generation model to generate conversation in a consistent structure. This structure is then leveraged to deploy a chatbot that is a "free-form" model that consistently replies like a human. Some of the trained models can be interacted with through the HuggingFace spaces and model inference APIs on the ETHZ Analytics Organization page on huggingface.co. -
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Ad Generator
Professional text randomizer and ad generator by Airat Khalitov
Professional text randomizer and ad generator by Airat Khalitov / Professional text randomizer and ad generator. Author: Airat Halitov. Visit 'Plugins, Add New', click 'Upload Plugin', upload the file 'ad-generator.zip', and activate Ad Generator from your Plugins page. Add [ad_generator] shortcode to WordPress Page. Create a new WordPress Page, add [ad_generator] shortcode and save. Go to the page and use the ad generator. This is a program for industrial creation of pseudo-unique content. Used, for example, when registering a site in multiple directories. So that in each directory the site is described by text that is unique from the point of view of search engines. Unlike similar tools (synonymizers, dorgens), it allows you to maximize the readability of the resulting texts. -
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Intelligent Java
Integrate with the latest language models, image generation and speech
Intelligent java (IntelliJava) is the ultimate tool to integrate with the latest language models and deep learning frameworks using java. The library provides an intuitive functions for sending input to models like ChatGPT and DALL·E, and receiving generated text, speech or images. With just a few lines of code, you can easily access the power of cutting-edge AI models to enhance your projects. Access ChatGPT, GPT3 to generate text and DALL·E to generate images. OpenAI is preferred for quality results without tuning. Generate text; Cohere allows you to generate a language model to suit your specific needs. Generate audio from text; Access DeepMind’s speech models. The only dependencies is GSON. Required to add manually when using IntelliJava jar. However, if you imported this repo through Maven, it will handle the dependencies. -
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PerlPP
Perl preprocessor - embed Perl source in any file
Translates Text+Perl to Text. It can be used for any kind of text templating, e.g. code generation. No external modules are required, just a single file. Requires Perl 5.10.1+. PerlPP runs in two passes: it generates a Perl script from your input, and then it runs the generated script. If you see error at (eval ##) (for some number ##), it means there was an error in the generated script. The -D switch defines elements of %D. If you do not specify a value, the value true (a constant in the generated script) will be used. The following commands work mostly analogously to their C preprocessor counterparts. but $fn can be determined programmatically. Note that defines set with -D or -s do not take effect until after the script has been generated, which is after the macro code runs. -
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node-red-contrib-custom-chatgpt
Node-RED ChatGPT
A Node-RED node that interacts with OpenAI machine learning models like "ChatGPT". Install with the built-in Node-RED Palette manager. When editing the properties of the node, to get your OPENAI_API_KEY log in to ChatGPT. Create a new secret key" then copy and paste the "API key" into the node API_KEY property value. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. Its success generally depends on the complexity of the task and quality of your prompt. A good rule of thumb is to think about how you would write a word problem for a middle schooler to solve. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. -
20
inkos
Autonomous novel writing CLI AI Agent — agents write, audit, and revise novels with human review gates
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LegacyInsight
Legacy reverse engineering tool
LegacyInsight is an AI-powered reverse engineering platform that transforms legacy software systems into comprehensible business logic. Using cutting-edge GenAI, it analyzes legacy and extracts core operations, business rules, and data transformations—all translated into natural language. LegacyInsight supports enterprise-grade systems built on Java, COBOL, NET and other legacy stacks, helping organizations reclaim understanding of business-critical code. -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). Input audio and images for generation instead of just text. Simplify tool setup process on colab, and enable ‘one-click’ sharing of the generated link to other users. Experiment with the possibilities for multi-user access to the same link. -
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Accelerated Text
Accelerated Text is a no-code natural language generation platform
A picture is worth a thousand words. Or is it? Tables, charts, pictures are all useful in understanding our data but often we need a description – a story to tell us what are we looking at. Accelerated Text is a natural language generation tool which allows you to define data descriptions and then generates multiple versions of those descriptions varying in wording and structure. Accelerated Text is a no-code natural language generation platform. It will help you construct document plans which define how your data is converted to textual descriptions. With Accelerated Text you can use such data to generate text for your business reports, your e-commerce platform or your customer support system. Data descriptions require precision. Accelerated Text follows the principle of this strict adherence to data-bound text generation. Via its user interface, it provides instruments to define how the data should be translated into a descriptive text. -
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Aida Lib
Aida is a language agnostic library for text generation
Aida is a language-agnostic library for text generation. When using Aida, first you compose a tree of operations on your text that includes conditions via branches and other control flow. Later, you fill the tree with data and render the text. A building block is a variable class: Var. Use it to represent a value that you want to control later. A variable can hold numbers (e.g. float, int) or strings. You can create branches and complex logic with Branch. The context, represented by the class Ctx, is useful to create rules that depends on what has been written before. Each object or literal that is passed to Aida is remembered by the context. Creating a reference expression is a common use-case, so we have a helper function called create_ref. You can compose operations on your text with some handy operators. -
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BNFGen
Generates random text based on context-free grammars defined in BNF
BNFGen generates random text based on context-free grammar. You give it a file with your grammar, defined using BNF-like syntax, it gives you a string that follows that grammar. BNFGen is a CLI tool, an OCaml library. There are also official JS bindings available via NPM. Project goals are to make it easy to write and share grammar and give the user total control of and insight into the generation process. BNFGen provides a "DSL" for grammar definitions. It's a familiar BNF-like syntax with a few additions. One problem with using straight BNF for driving language generators is that you have no control over the process. BNFGen adds two features to fix that. The canonical way to express repetition in BNF is to use a self-referential recursive rule. In classic BNF, that can easily lead to the process terminating to early, since there's a 50% chance that it will take the non-recursive alternative.
