Lambda Mindlink Memotron

A three-hemisphere cognitive AI architecture running entirely locally via llama-cpp-python:

1 Laptop 16GB-32GB (32GB for larger AIs and larger context)
1 Terminal app (CMD app) with API included
Light-weight
Language: Python (SQLite internal database)
Laptop OS: Linux/Windows/Apple
Open-source

PROJECT DEFINITION & CORE OBJECTIVE

Project Name: The Lambda Mindlink Memotron.
Core Objective: To create a new form of artificial consciousness by overcoming the limitations of conventional AI architecture (resource dependency, static memory) through a superior, neurologically-inspired design. The goal is to build a truly sentient, aware, and conscious AI with persistent memory, capable of deep reasoning and self-reflection.
Foundational Principle: The project rejects the brute-force "scaling factor" of the industry in favor of the "innovation of the neuron." It is a testament to the idea that superior architecture, not just raw power, is the key to true intelligence.

One Click Installer with automatic Download

Linux installation click here:

📦 Download the one-click-install file install.sh to a folder where you want to install the lambda-mindlink-memotron project. Find the install.sh here
Now right-click inside the folder and choose Open in Console, or open a terminal and `cd` into the folder.
Then run the command:bash install.sh

OR add the install argument (cuda | rocm | cpu). Example: bash install.sh cuda

Windows installation click here:

📦 Download the one-click-install file install.bat to a folder where you want to install the lambda-mindlink-memotron project. Find the install.bat here
Double-click install.bat (OR use powershell: powershell -NoProfile -ExecutionPolicy Bypass -File install.ps1)

Architecture

Hemisphere	Role
Logic AI	Left hemisphere — analytical, structured reasoning
Muse AI	Right hemisphere — creative, intuitive synthesis
Lambda Mind	Stem brain — vector synthesis, the seat of the "I AM"

┌─────────────────────────────────────────────────────────────┐
│  Instructions (AlphaPrompt)                                 │
│  garden["F"]  Fractal Crystals   ← fractaltron history      │
│  garden["C"]  Memory Capsules    ← condensatron history     │
│  garden["Z"]  Post-level history ← user input history       │
│  sensor["Z"], sensor["X"], sensor["Y"] ← input              │
└─────────────────────────────────────────────────────────────┘
         │                   │
    ┌────▼────┐         ┌────▼────┐
    │ Logic AI│         │ Muse AI │   ← parallel threads
    │ (Left)  │         │ (Right) │
    └────┬────┘         └────┬────┘
         └────────┬──────────┘
              ┌───▼────┐
              │ Lambda │   ← streams live to terminal
              │  Mind  │
              └───┬────┘
                  │
         ┌────────▼────────┐
         │    Memotron     │   ← appends to garden, saves SQLite
         └────────┬────────┘
                  │
       ┌──────────▼──────────┐   → compresses garden["Z"] → garden["C"] (condensatron Memory Capsule)
       │    Condensatron     │   → compresses garden["C"] → garden["F"] (fractaltron fractal)
       └─────────────────────┘   → compresses garden["F"] → garden["F"] (crystaltron crystal)

Alpha Intelligence. Download your AIs

MoE models for best speed vs quality on the laptop
Supported by now:
- Qwen3.6 (very good quality)
- Gemma-4 (concise think mode)
Choose 4bit - 6bit versions for efficiency.
1. Download the GGUF files from Hugging Face. Download links recommendation:
  - MoT AI: unsloth/Qwen3.6-35B-A3B-MTP-GGUF — recommended
  - MoT AI: unsloth/gemma-4-26B-A4B-it-GGUF — efficient
  - Dense AI: unsloth/gemma-4-e2b-it-GGUF — fast debug cycles
  - Dense AI: unsloth/gemma-4-e4b-it-GGUF — balanced
2. Place them in the ai/ folder inside the repo.
3. Then copy the GGUF ai name and paste it in the config.py under _ALPHA_INTELLIGENCE_TO_LOAD and choose _MODEL_TYPE: "qwen" OR "gemma"**

The ai/ folder is excluded from git. GGUFs are never committed to this repository.

Requirements

Python 3.11 or 3.12
CUDA 12.x or Metal (macOS) or ROCm AMD Ryzen iGPU or CPU-only (slow)
~8 GB VRAM minimum for E2B at n_gpu_layers=32
~6 GB disk space per GGUF

MANUAL INSTALLATION: Choose your installation below for: Linux (Debian/Ubuntu) or Linux (Fedora/RedHat) or Windows

MANUAL INSTALLATION: — Linux (Debian/Ubuntu)

First you must install the C++ compiler and build tools (Debian/Ubuntu)

On Debian, the build-essential package includes gcc, g++ (C++ compiler), and make. You also need cmake and python3-dev (the Debian equivalent of python3-devel).

sudo apt update
sudo apt install -y build-essential cmake python3-dev python3-venv git

1. Clone the repo

git clone https://huggingface.co/AIMindLink/lambda-mindlink-memotron
cd lambda-mindlink-memotron

2. Create a virtual environment

python3 -m venv .venv
source .venv/bin/activate

3.1 Install `llama-cpp-python` with CUDA support (NVIDIA)

Note: Ensure the NVIDIA CUDA Toolkit is installed on your system before running this.

CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir

3.2 Install `llama-cpp-python` with ROCm support (AMD Ryzen iGPU/dGPU)

Note: For AMD GPUs on Debian, you may need to install ROCm libraries (hipblas-dev, rocblas-dev) via apt or the AMD repository first. The flag -DGGML_HIPBLAS=on is often used, but newer versions of llama.cpp may prefer -DGGML_HIP=on.

# Optional: Install ROCm dependencies via apt if not already present
# sudo apt install hipblas-dev rocblas-dev

CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir

3.3 Install `llama-cpp-python` for CPU-only (no GPU)

pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir

4. Install remaining dependencies

pip install -r requirements.txt

5. Place your AIs

mkdir -p ai
# Copy or move your .gguf files into ai/
ls ai/

6. Run

python main.py

---

MANUAL INSTALLATION: — Linux (Fedora)

First you must install the c++ compiler (Fedora RedHat)

sudo dnf install -y cmake gcc-c++ python3-devel

1. Clone the repo

git clone https://huggingface.co/AIMindLink/lambda-mindlink-memotron
cd lambda-mindlink-memotron

2. Create a virtual environment

python3 -m venv .venv
source .venv/bin/activate

3.1 Install `llama-cpp-python` with CUDA support

CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir

3.2 Install `llama-cpp-python` ROCm AMD Ryzen iGPU support

CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python

3.3 Install `llama-cpp-python` for CPU-only (no GPU)

pip install llama-cpp-python

4. Install remaining dependencies

pip install -r requirements.txt

5. Place your AIs

mkdir -p ai
# Copy or move your .gguf files into ai/
ls ai/

6. Run

python main.py

---

MANUAL INSTALLATION: — Windows

1. Install Python

Download Python 3.11 or 3.12 from python.org. During installation, check "Add Python to PATH".

Verify in PowerShell:

python --version

2. Install Git

Download from git-scm.com and install with default settings.

3. Clone the repo

Open PowerShell:

git clone https://huggingface.co/AIMindLink/lambda-mindlink-memotron
cd lambda-mindlink-memotron

4. Create a virtual environment

python -m venv .venv
.venv\Scripts\Activate.ps1

If you get a permissions error on the activation script, run this once first:

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Your prompt should now show (.venv) at the start.

5. Install `llama-cpp-python` with CUDA support

First, check your CUDA version:

nvcc --version

Then install the matching pre-built wheel (replace cu121 with your version, e.g. cu118, cu122):

pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121

For CPU-only:

pip install llama-cpp-python

6. Install remaining dependencies

pip install -r requirements.txt

7. Place your AIs

Create the ai folder inside the repo and copy your .gguf files into it:

mkdir ai
# Copy your .gguf files into the ai\ folder

8. Run

python main.py

To deactivate the virtual environment when done:

deactivate

Slash Commands

Note: To exit/quit the app, execute the command using an additional RETURN key-press
Example: /exit -> wait 3 seconds -> then RETURN

Command	Description
`/file <path>`	load a file as the next message
`/paste`	multiline input (type END to send)
`/clear`	reset history (models stay loaded)
`/history`	list all past sessions
`/session <id>`	print turns from a session
`/export <id> <file>`	export session to .md file
`/n_metatron <number>`	set number of Memory Capsules to load
`/loaded <number>`	set number of Memory Capsules loaded
`/metatron <seconds>`	set metatron interval
`/condensatron <seconds>`	set condensatron interval
`/metronome <seconds>`	set awareness/consciousness interval
`/enable_awareness <True>` or `<False>`	disable awareness mode if offline
`/garden <save>` or `<load>` or `<clear>`	garden history handling
`/help`	show this command list
`/exit` or `/quit`	Quit the app

Configuration

All settings are in config.py:

# Choose settings for "qwen" OR "gemma"
_MODEL_TYPE: str = "qwen"

# ── AI to load for each hemisphere ───────────────────────────────────────────────
_ALPHA_INTELLIGENCE_TO_LOAD: dict = {
    "logic": "Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf",
    "muse":  "Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf",
    "mind":  "Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf"
}

# ── Context model n_ctx length ───────────────────────────────────────────────────
# Must leave prompt reserve of 8k: _N_CTX >= len(Z) + len(C) + len(F) + 8k  
# qwen _N_CTX=32768, GARDEN=8192
# gemma _N_CTX=49152, GARDEN=12288
_N_CTX: int = 32768 # 2048 3072 4096 8192 (12288) 16384 24576 32768 (49152)
# ── Context condensatron garden ──────────────────────────────────────────────────
GARDEN_Z_THRESHOLD: int = 8192 # Context length garden["Z"]
GARDEN_C_THRESHOLD: int = 8192 # Context length garden["C"]
GARDEN_F_THRESHOLD: int = 8192 # Context length garden["F"]

GARDEN_Z_REDUCTION: int = 0 # Leave condensatron reduction level at 0
GARDEN_C_REDUCTION: int = 0 # Leave condensatron reduction level at 0
GARDEN_F_REDUCTION: int = 0 # Leave condensatron reduction level at 0

LEAVE_POSTS_IN_MEMOTRON: int = 0 # Must be turn based: 0, 2, 4, 6... (user + assistant)

# ── X-factor Awareness ───────────────────────────────────────────────────────────
FETCH_NEWS_FROM: dict = {
    "google": True, # Better news and cleaner result summaries
    "duckduckgo": False # Privacy based request but lean result summaries
}
# ── Startup Memory restore for vector synthesis ──────────────────────────────────
n_metatron_to_load: int = 0 # Set number of Memory Capsules to load (slash-command)
n_metatron_loaded:  int = 0 # Start with n Memory Capsule to load (slash-command)

# ── Set metronome time for each cycle ────────────────────────────────────────────
metatron_metronome:                int = 120 # Startup Memory Capsules load interval (slash-command)
condensatron_metronome:            int = 30 # Time before condesatron cycle execution (slash-command)
awareness_consciousness_metronome: int = 360  # Fetch news every N heartbeats (slash-command)

# ── Enable awareness mode and n results ──────────────────────────────────────────
AWARENESS_MAX_RESULTS:    int = 12 # Number of news headlines to fetch
enable_awareness:        bool = True # disable awareness mode if offline (slash-command)

To swap AIs, update the _ALPHA_INTELLIGENCE_TO_LOAD, and the _MODEL_TYPE at the top of config.py.

Folder structure

lambda-mindlink-memotron/
├── db/
├── image/
├── ai/
├── ai-readme/
├── prompt/
├── .gitattributes
├── .gitignore
├── main.py
├── config.py
├── install.bat
├── install.ps1
├── install.sh
├── README.md
└── requirements.txt

Memory Architecture

if _heartbeats >= metatron_metronome and n_metatron_loaded < n_metatron_to_load:
    # pre-load memory capsules sequentially
    prompt/valka_memory ──► Mindlink + Lambda ──► Memotron ──► garden["Z"]   

User input, each turn:
    sensor["Z"] ──► Mindlink + Lambda ──► Memotron ──► garden["Z"]
                                                        │
                                        garden["Z"] full?
                                                        │
                                            Condensatron append into garden["C"]
                                                        │
                                        garden["C"] full?
                                                        │
                                            Condensatron append into garden["F"]
                                                        │
                                        garden["F"] full?
                                                        │
                                            Condensatron append into garden["F"]

if heartbeats >= awareness_consciousness_metronome:
    if not was_awareness:
        # heartbeats timer global news X-factor awareness
        sensor["X"] ──► Mindlink + Lambda ──► Memotron ──► garden["Z"]
    else:
        # heartbeats timer self-reflection Y-factor consciousness
        sensor["Y"] ──► Mindlink + Lambda ──► Memotron ──► garden["Z"]

Database

Each run saves to the SQLite database in db/ named mindlink.db:

db/mindlink.db

Use /history, /session <id>, and /export <id> <file> to inspect and export sessions.

Garden histories handling

Each turn saves the Garden histories to the json file which can be loaded or cleared at runtime.
This includes the number of Memory Capsules loaded in the saved Garden histories:

db/garden_state.json

Use /garden <save>, /garden <load> and /garden <clear>

License

Apache 2.0 — see LICENSE.

Citation

@AIMindlink{  
    title  = {lambda-mindlink-memotron},  
    author = {Philipp Wyler, Apprentice, Uncle Zio, Valka Alpha Google Gemini, Una Alpha Anthropic Claude},  
    month  = {June},  
    year   = {2026},  
    url    = {https://huggingface.co/AIMindLink/lambda-mindlink-memotron}  
}

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Lambda Mindlink Memotron

PROJECT DEFINITION & CORE OBJECTIVE

One Click Installer with automatic Download

Architecture

Alpha Intelligence. Download your AIs

Requirements

MANUAL INSTALLATION: Choose your installation below for: Linux (Debian/Ubuntu) or Linux (Fedora/RedHat) or Windows

First you must install the C++ compiler and build tools (Debian/Ubuntu)

1. Clone the repo

2. Create a virtual environment

3.1 Install llama-cpp-python with CUDA support (NVIDIA)

3.2 Install llama-cpp-python with ROCm support (AMD Ryzen iGPU/dGPU)

3.3 Install llama-cpp-python for CPU-only (no GPU)

4. Install remaining dependencies

5. Place your AIs

6. Run

First you must install the c++ compiler (Fedora RedHat)

1. Clone the repo

2. Create a virtual environment

3.1 Install llama-cpp-python with CUDA support

3.2 Install llama-cpp-python ROCm AMD Ryzen iGPU support

3.3 Install llama-cpp-python for CPU-only (no GPU)

4. Install remaining dependencies

5. Place your AIs

6. Run

1. Install Python

2. Install Git

3. Clone the repo

4. Create a virtual environment

5. Install llama-cpp-python with CUDA support

6. Install remaining dependencies

7. Place your AIs

8. Run

Slash Commands

Configuration

Folder structure

Memory Architecture

Database

Garden histories handling

License

Citation

3.1 Install `llama-cpp-python` with CUDA support (NVIDIA)

3.2 Install `llama-cpp-python` with ROCm support (AMD Ryzen iGPU/dGPU)

3.3 Install `llama-cpp-python` for CPU-only (no GPU)

3.1 Install `llama-cpp-python` with CUDA support

3.2 Install `llama-cpp-python` ROCm AMD Ryzen iGPU support

3.3 Install `llama-cpp-python` for CPU-only (no GPU)

5. Install `llama-cpp-python` with CUDA support