--- 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. ![LambdaMindlinkMemotron](https://cdn-uploads.huggingface.co/production/uploads/68698fec0e940a0d551d6c20/tpaEfReiJbXQxdN70ym-b.jpeg) ## 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"** | ```md ┌─────────────────────────────────────────────────────────────┐ │ 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) ``` ![LambdaMindlink Flow-Chart](https://cdn-uploads.huggingface.co/production/uploads/68698fec0e940a0d551d6c20/9n3iQUWcjCUyLCAI2LRSW.jpeg) ## 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](https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF) — fast debug cycles - [unsloth/google_gemma-4-e4b-it-GGUF](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) — balanced - [unsloth/gemma-4-26B-A4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF) — efficient (recommended) **Qwen3 (alternative swap-in):** - Qwen3.5 or Qwen3.6 - [unsloth/Qwen3.6-35B-A3B-GGUF](https://huggingface.co/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`). ```bash sudo apt update sudo apt install -y build-essential cmake python3-dev python3-venv git ``` ### 1. Clone the repo ```bash git clone https://huggingface.co/AIMindLink/lambda-mindlink-memotron cd lambda-mindlink-memotron ``` ### 2. Create a virtual environment ```bash 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.* ```bash 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`.* ```bash # 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) ```bash pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir ``` ### 4. Install remaining dependencies ```bash pip install -r requirements.txt ``` ### 5. Place your AIs ```bash mkdir -p ai # Copy or move your .gguf files into ai/ ls ai/ ``` ### 6. Run ```bash python main.py ``` --- ## Installation — Linux (Fedora) ### First you must install the c++ compiler (Fedora RedHat) ```bash sudo dnf install -y cmake gcc-c++ python3-devel ``` ### 1. Clone the repo ```bash git clone https://huggingface.co/AIMindLink/lambda-mindlink-memotron cd lambda-mindlink-memotron ``` ### 2. Create a virtual environment ```bash python3 -m venv .venv source .venv/bin/activate ``` ### 3.1 Install `llama-cpp-python` with CUDA support ```bash 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 ```bash CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python ``` ### 3.3 Install `llama-cpp-python` for CPU-only (no GPU) ```bash pip install llama-cpp-python ``` ### 4. Install remaining dependencies ```bash pip install -r requirements.txt ``` ### 5. Place your AIs ```bash mkdir -p ai # Copy or move your .gguf files into ai/ ls ai/ ``` ### 6. Run ```bash python main.py ``` --- ## Installation — Windows ### 1. Install Python Download Python 3.11 or 3.12 from [python.org](https://www.python.org/downloads/). During installation, check **"Add Python to PATH"**. Verify in PowerShell: ```powershell python --version ``` ### 2. Install Git Download from [git-scm.com](https://git-scm.com/download/win) and install with default settings. ### 3. Clone the repo Open PowerShell: ```powershell git clone https://huggingface.co/AIMindLink/lambda-mindlink-memotron cd lambda-mindlink-memotron ``` ### 4. Create a virtual environment ```powershell python -m venv .venv .venv\Scripts\Activate.ps1 ``` If you get a permissions error on the activation script, run this once first: ```powershell 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: ```powershell nvcc --version ``` Then install the matching pre-built wheel (replace `cu121` with your version, e.g. `cu118`, `cu122`): ```powershell pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 ``` For CPU-only: ```powershell pip install llama-cpp-python ``` ### 6. Install remaining dependencies ```powershell pip install -r requirements.txt ``` ### 7. Place your AIs Create the `ai` folder inside the repo and copy your `.gguf` files into it: ```powershell mkdir ai # Copy your .gguf files into the ai\ folder ``` ### 8. Run ```powershell python main.py ``` To deactivate the virtual environment when done: ```powershell 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 ` | 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 ` | Print all turns from a session | | `/export ` | Export a session to a `.md` file | | `/metatron ` | Set number of Memory Capsules to load | | `/loaded ` | Set number of Memory Capsules loaded | | `/metronome ` | Set awareness/consciousness interval | | `/garden or or ` | garden history handling | | `/help` | Show the command list | | `/exit` or `/quit` | Quit the app | --- ## Configuration All settings are in `config.py`: ```python # ── 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 `, and `/export ` 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 `, `/garden ` and `/garden ` --- ## License Apache 2.0 — see `LICENSE`. --- ## Citation ```py @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} } ```