lim-mm-cli 0.1.7
CLI tool for LIM-compatible micro model project scaffolding
Requires Python: >=3.7

# LIM MM CLI: Command Line Interface for Micro Model Management

## 🎯 LIM Concept
**LIM (Large Integration Model)** aims to be an open source, realtime data retrieval and micro model training enhancement framework.

### Philosophy: "Human Socialism Model Network"
LIM represents a paradigm shift from monolithic "God Models" (LLMs) to a **decentralized, collaborative network of specialized intelligence**.

- **Socialism of Intelligence**: Democratized AI through community-driven micro models.
- **Model on the Air**: Intelligence is fluid. MMs are lightweight, rapidly trainable, and deployable (MaaS).
- **The Anti-Monolith**: Unlike traditional LLMs, LIM routes intent to the *exact* source of truth or specialist model.
- **LIM > MCP**: While MCP standardizes connections, LIM resolves the *execution* problem by autonomously routing queries.

## Core Architecture (The L-I-M Trinity)

### **L** - Large Integration Model (The Conductor)
- **Role**: Intent Understanding & Routing.
- **Function**: The "Generalist" that parses queries and directs them into the network.

### **I** - Integration Network (The Nervous System)
- **Role**: Discovery, Transport, & Protocol.
- **Function**: The infrastructure layer connecting the Conductor to the Specialists.

### **M** - Micro Model (The Specialist) / MMS
- **Role**: Domain-Specific Execution.
- **Philosophy**: Minimize context, rapid training, and hot-swappable.

## CLI Commands
The `lim` CLI is the primary interface for managing the Micro Model lifecycle.

### Project Management
```bash
mm start <name>         # Create a new micro model project from template
mm validate             # Verify MM compliance (checks /meta.json service health)
mm build                # Containerize the current MM
mm run                  # Execute the project locally (python run/start.py)
```

### Registry Operations
```bash
mm push                 # Publish the MM to the LIM repository
mm pull <mm1,mm2>       # Retrieve MMs into local models/ directory
mm list [name]          # Display metadata and status of an MM
```

## Micro Model Service (MMS) Standard
All MMs must adhere to the Open Source MMS Standard for interoperability:

### Structure
- **`meta.json`**: The contract. Defines input/output schemas and configuration.
- **`mms/`**: Service logic (API, Agents, RAG).
- **`models/`**: Model artifacts (weights, embeddings).
- **`data/`**: Specialized datasets or knowledge base.
- **`run/`**: Execution scripts & Docker config.

```
my-model/
├── meta.json           # Model configuration and metadata
├── mms/                # Service implementation
│   ├── app/            # Application logic
│   └── tests/          # Tests
├── models/             # AI Model artifacts
├── data/               # Domain-specific data
├── run/
│   ├── start.py        # Entry point
│   └── docker/         # Dockerfile & compose
├── requirements.txt    # Dependencies
└── README.md           # Documentation
```

### Protocol
- **Discovery**: Must expose `docker.internal.network:8000/meta.json`.
- **Git-Based**: MMs are versioned and distributed via Git repositories.

## Getting Started

1. **Install the CLI**:
   ```bash
   pip install lim-mm-cli
   ```

2. **Create a new model**:
   ```bash
   mm start my-model
   cd my-model
   ```

3. **Configure & Validate**:
   Edit `meta.json` and run:
   ```bash
   mm validate
   mm run
   ```

4. **Deploy**:
   ```bash
   mm build
   mm push
   ```

## Technology Stack
- **Backend**: Python, Quart (Async)
- **Infra**: Docker, MySQL
- **Ops**: Loki, Grafana
- **Interface**: CLI, REST API