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ctx-0/lazyllama

★ 17PythonAudience · developerComplexity · 1/5Setup · easy

TLDR

A command-line tool that scans your computer for locally installed AI language models across HuggingFace, Ollama, and LM Studio and shows them all in one unified view with file sizes, storage locations, and metadata.

Mindmap

mindmap
  root((lazyllama))
    What it does
      List local AI models
      Unified view
      Scan multiple sources
    Supported sources
      HuggingFace
      Ollama
      LM Studio
      Custom directories
    Output formats
      Summary view
      Detailed file list
      Tree view
      JSON export
    Model metadata
      File size
      Quantization
      Format
      Timestamps
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Code map

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Things people build with this

USE CASE 1

See every AI model installed across HuggingFace, Ollama, and LM Studio in a single unified list, with file sizes and storage paths.

USE CASE 2

Export a full model inventory as JSON -- including name, source, size, format, quantization, and timestamps -- to use in scripts or pipe into other tools.

USE CASE 3

Scan custom directories where your models live outside the default HuggingFace, Ollama, or LM Studio locations.

Tech stack

Pythonpip

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

lazyllama is a small command-line tool for people who run AI language models on their own computer. If you use tools like HuggingFace, Ollama, or LM Studio to download and run models locally, the files can end up scattered across different folders on your machine. lazyllama scans all of those locations and gives you a unified view of every model you have installed, along with details like file sizes and where things are stored. Once installed via pip, you run it by typing its name in a terminal. With no options it shows a compact summary. You can switch to a more detailed view that lists individual filenames, or a tree view that shows the folder hierarchy. There are also flags to pull out just the model directories, output a tab-separated list, or dump everything as raw JSON for use in scripts. The JSON output is the most thorough option. It includes metadata like model name, source, size, file count, format, quantization level, and timestamps. This makes the tool useful not just for browsing but also for piping data into other tools or building your own scripts on top of it. You can also point it at custom directories if your models live somewhere outside the standard locations that HuggingFace, Ollama, or LM Studio normally use. A debug mode shows which directories were scanned and how long the scan took.

Copy-paste prompts

Prompt 1
I have models scattered across HuggingFace cache, Ollama, and LM Studio. How do I use lazyllama to get a tree view of all of them organized by folder?
Prompt 2
I want to pipe lazyllama's JSON output into a script that lists every model larger than 10GB. Show me the JSON output flag and a small shell command that reads the size field to filter results.
Prompt 3
Some of my models are stored in a custom directory outside the standard HuggingFace cache path. How do I tell lazyllama to scan that directory, and will it show duplicates if a model appears in two places?
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