nnll
_{neural network link library}

nnll (or null) is a project incubator and AI toolkit for managing and processing Diffusion and Large Language Models (LLMs). The project is divided into modular, ready-to-use components and may appeal to researchers or developers working in the general field of machine learning.

• Generative AI pipeline preparation & execution
• Extracting and classifying metadata from images/models
• Consumer-grade GPU/CPU inference optimization
• Misc UX/UI Experimentation
• 🧨Diffusers, 🤗Transformers, 🦙Ollama, 🍏MLX, 🌀DSPy, 🚅LiteLLM
  
  

setup
_{clone -> venv -> activate -> pip install "nnll[module]"}

clone repo

git clone https://github.com/darkshapes/nnll.git

Next-->

create virtual environment

python3 -m venv .venv_nnll

Next-->

3 (windows powershell) activate

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

3 ( linux | macos) activate

source .venv_nnll/bin/activate

Next-->

4 install

• install the bare minimum:

pip install -e nnll

• install select packages:

pip install -e "nnll[nnll_33,nnll_56]"

• install all packages :

pip install -e "nnll[dev]"

Done.

use

Some modules are full scripts and can be run from command line. These are written here:

zodiac - Experimental generative system
astra - Live diagnostic console
nnll-hash - Hash the layer metadata from models within a directory and write out to console.
nnll-parse - Process metadata headers from a model file or directory of models and write out to individual JSON files.
nnll-find - Search a local directory of model layer files (HuggingFace🤗 index.json, JSON from nnll-parse)

Each module contains 1-5 functions or 1-2 classes and its own test routines. There are multiple ways to integrate nnll into a project (sorted by level of involvement)

• Recommended : Add the project as a dependency including only modules that are needed with "nnll[nnll_04,nnll_16]" @ git+https://github.com/darkshapes/nnll
• Install the entire project as a dependency via nnll @ git+https://github.com/darkshapes/nnll
• Basic clone or fork of the project
• Use a submodule
• Filter a clone of the project to a single subfolder and include it in your own

nnll is a 'living' project. Like a spoken language, it evolves over time. For this reason, we prefer 'living' duplications of the repo. If you still want a static hard copy, you are welcome to copy and paste folders or code wherever you please.

contributing

* Environment  : uv
* Testing      : pytest -vv tests/*.py
* Formatting   : ruff/better align
* Linting      : ruff/pylint
* Type Checking: pylance/pyright
* Spelling     : typos vsc
* Docstrings   : sphinx