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oobabooga/textgen

📈 Trending★ 47,159PythonAudience · developerComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

Run powerful AI language models on your own computer privately, with no internet or cloud service required. Chat, analyze images, attach files, and fine-tune models locally.

Mindmap

mindmap
  root((TextGen))
    What it does
      Local LLM chat
      Vision and files
      Tool calling
      Fine-tuning
    How it works
      Web server
      Desktop app
      llama.cpp backend
      OpenAI API compatible
    Why use it
      Privacy first
      No per-token cost
      Uncensored models
      Developer testing
    Tech stack
      Python
      llama.cpp
      Browser interface
    Supported platforms
      Linux
      Windows
      macOS

Things people build with this

USE CASE 1

Run a private AI assistant on your laptop without sending conversations to the cloud.

USE CASE 2

Build and test AI applications locally before deploying them to production.

USE CASE 3

Fine-tune an open-source model on your own documents or data to customize its responses.

USE CASE 4

Analyze images and PDFs in a chat interface while keeping all data on your machine.

Tech stack

Pythonllama.cppWeb serverBrowser

Getting it running

Difficulty · moderate Time to first run · 30min

Requires downloading a large language model file (1-50GB depending on model choice) which is the main time bottleneck.

Open-source software allowing free use, modification, and distribution for any purpose including commercial use.

In plain English

TextGen is a desktop application for running large language models (LLMs), the same type of AI that powers ChatGPT, entirely on your own computer, with no internet connection required and no data sent to any external service. The problem it solves is that most people who want to use powerful AI assistants depend on cloud services, which cost money per token, require sharing your data with a provider, and can be censored or restricted. TextGen lets you download an open-source language model once and run it locally as many times as you want, privately. The application provides a chat interface similar to what you see in consumer AI products, you type a message and the model responds in real time. It supports vision (attaching images to your messages for the model to analyze), file attachments like PDFs and Word documents, tool-calling (where the model can run small Python scripts to perform actions like web searches or calculations during a conversation), and fine-tuning (training a model on your own data to customize its behavior). Technically, it runs as a local web server that you access in your browser, or as a standalone desktop app for quick startup. It supports several inference backends including llama.cpp, which is optimized for running models efficiently on consumer hardware including CPU-only machines. It also exposes an API that is compatible with the OpenAI and Anthropic API formats, so existing applications built for those commercial services can point to your local TextGen instance instead. You would use this if you want to experiment with AI models without paying per use, if you have privacy requirements that preclude sending data to the cloud, if you want to use uncensored open-source models, or if you are a developer building applications who wants a local test environment. It is written in Python and supports Linux, Windows, and macOS.

Copy-paste prompts

Prompt 1
How do I install and run TextGen on my Windows machine to chat with a local AI model?
Prompt 2
Show me how to set up TextGen's API so my existing ChatGPT application can use my local model instead.
Prompt 3
What are the steps to fine-tune a language model using TextGen with my own training data?
Prompt 4
How do I enable vision and file attachment features in TextGen to analyze images and PDFs locally?
Prompt 5
What hardware do I need to run TextGen smoothly, and which inference backend should I choose for CPU-only machines?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.