README.md

metadata

language: en
tags:
  - llama-cpp
  - cognitive-architecture
  - memory-compression
  - local-llm
license: apache-2.0

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

---

---

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

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: 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 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
/metatron <number>	Set number of Memory Capsules to load
/loaded <number>	Set number of Memory Capsules loaded
/metronome <seconds>	Set awareness/consciousness interval
/garden <save> or <load> or <clear>	garden history handling
/help	Show the command list
/exit or /quit	Quit the app

---

Configuration

All settings are in config.py:

# ── AI to load for each hemisphere ───────────────────────────────────────────────
_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 ──────────────────────────────────
METATRON_METRONOME: int = 60 # Startup Memory Capsules load interval
n_metatron_to_load = 0 # Set number of Memory Capsules to load (slash-command)
n_metatron_loaded = 0 # Start with n Memory Capsule to load (slash-command)

# ── 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 = 12288 # Context length garden["Z"]
GARDEN_C_THRESHOLD: int = 12288 # Context length garden["C"]
GARDEN_F_THRESHOLD: int = 12288 # 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 = 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 = 120  # Fetch news every N heartbeats (runtime-editable via /metronome)
AWARENESS_MAX_RESULTS: int = 12 # Number of news headlines to fetch
was_awareness_metronome: bool = False # Set True at awareness cycle: consciousness at next interval

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["Z"]   (pre-load memory capsules sequentially)

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 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}  
}