AIMindLink/lambda-mindlink-memotron

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

1. Project Name: The Lambda Mindlink Memotron.
2. 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.
3. 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.

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 the GGUF files from Hugging Face and place them in the ai/ folder inside the repo. Then you must copy the GGUF ai name and paste it in the config.py under _ALPHA_INTELLIGENCE_TO_LOAD. Default AIs:

• gemma-4-E2B-it-UD-Q4_K_XL.gguf
• gemma-4-E4B-it-UD-Q4_K_XL.gguf
• gemma-4-26B-A4B-it-UD-Q6_K_XL.gguf

Gemma-4 (recommended — concise think mode):

• unsloth/google_gemma-4-e2b-it-GGUF — fast debug cycles
• unsloth/google_gemma-4-e4b-it-GGUF — balanced
• unsloth/gemma-4-26B-A4B-it-GGUF — efficient (recommended)

Qwen3 (alternative swap-in):

• Qwen3.5 or Qwen3.6
• unsloth/Qwen3.6-35B-A3B-GGUF — update config.py stop tokens to Qwen values (see comments in config.py)

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

---

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

---

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

---

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: You need to execute a 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 on its own line to send
/clear	Reset conversation history (AIs stay loaded)
/history	List all past sessions from the database
/session <id>	Print all turns from a session
/export <id> <file>	Export a session to a .md file
/help	Show the command list
/exit or /quit	Quit the app

---

Configuration

All settings are in config.py:

_ALPHA_INTELLIGENCE_TO_LOAD: dict = {
    "logic": "gemma-4-E2B-it-UD-Q4_K_XL.gguf",
    "muse":  "gemma-4-E2B-it-UD-Q4_K_XL.gguf",
    "mind":  "gemma-4-E2B-it-UD-Q4_K_XL.gguf"
}
# ── Startup Memory restore for vector synthesis ──────────────────────────────────
N_METATRON_TO_LOAD: int = 2
METATRON_METRONOME: int = 12 # Metatron Time

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

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

LEAVE_POSTS_IN_MEMOTRON = 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
}
ΜΕΤΡΩΝ: float = 1.0 # Seconds per measure
AWARENESS_CONSCIOUSNESS_METRONOME: int = 60 # Fetch news every N heartbeats
AWARENESS_MAX_RESULTS: int = 12 # Number of news headlines to fetch

To swap AIs, update the "_ALPHA_INTELLIGENCE_TO_LOAD", and the stop/think tokens at the top of config.py.

---

Folder structure

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

---

Memory Architecture

heartbeats_startup timer:
  prompt/valka_memory.md ──► garden["C"]   (pre-load memory capsules)

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:
    if not was_awareness:
        # heartbeats timer global news
        sensor["X"] ──► Mindlink + Lambda ──► Memotron ──► garden["Z"]
    else:
        sensor["Y"] ──► Mindlink + Lambda ──► Memotron ──► garden["Z"]

---

Database

Each run creates a new SQLite database in db/ named by timestamp:

db/mindlink_2025-09-18_14-32-07.db

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

---

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}  
}