language	library_name	license_name
en	nnll	MPL-2.0 + Commons Clause 1.0

nnll
_{neural network link library: Flexible code for multimodal AI apps}

nnll (or null)is a toolkit for researchers and developers working with AI models like Diffusion and Large Language Models (LLMs). It provides modular, reusable, and efficient components as a foundation to simplify the process of building and managing these complex systems.

• Generative AI pipeline preparation & execution
• Writing metadata from images
• Consumer-grade GPU/CPU inference optimization
• Misc UX/UI Experimentation
• 🧨Diffusers, 🤗Transformers
• 
  
  

Quick Guide

Install

Install uv, then run these terminal commands

• git clone https://github.com/darkshapes/nnll
  cd nnll
  uv sync --group dev
  

Use

Enter a terminal and activate the python environment in

• Linux/Macos:

  source .venv/bin/activate
  

  Windows Powershell:

  Set-ExecutionPolicy Bypass -Scope Process -Force; .venv\Scripts\Activate.ps1
  

nnll stores dependency versions in branches. In order to prevent failures, the repository should NEVER be rebased.

Important

Classes, Methods & Constants :

ReadModelTag
          `---------------------------------Universal model tag reader
ExtensionType
          `---------------------------------Model extension constants
JSONCache
       `------------------------------------Json read operations

Available terminal commands:

• nnll-autocard
• nnll-info

nnll-autocard

usage: nnll-autocard black-forest-labs/FLUX.1-Krea-dev -u exdysa -f FLUX.1-Krea-dev-MLX -l mlx -q 8

Create a new HuggingFace RepoCard.

    Retrieve HuggingFace repository data, fill out missing metadata,create a model card.
    Optionally download and quantize repo to a desired folder that will be ready for upload.
    Online function.

positional arguments:
  repo                  Relative path to HF repository

options:
  -h, --help            show this help message and exit
  -l, --library {gguf,schnell,dev,mlx}
                        Output model type [gguf,mlx,dev,schnell] (optional, default: 'mlx') NOTE: dev/schnell use MFLUX.
  -q, --quantization {8,6,4,3,2}
                        Set quantization level (optional, default: None)
  -d, --dry_run         Perform a dry run, reading and generating a repo card without converting the model (optional, default: False)
  -u, --user USER       User for generated repo card (optional)
  -f, --folder FOLDER   Folder path for downloading (optional, default: /Users/unauthorized/Downloads)
  -p, --prompt PROMPT   A prompt for the code example (optional, default: 'Test Prompt')

**Valid pipeline tags**:

         text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other

A link to example output of the nnll-autocard command

nnll-info

Immediate diagnostic system status information

usage: nnll-info

Detailed instructions for all version branches:

Discussion topics, issue requests, reviews, and code updates are encouraged. Build with us! Talk to us in our Discord!