lim-mm-cli 0.1.6

CLI tool for LIM-compatible micro model project scaffolding

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

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

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:

   pip install lim-mm-cli
   

2. Create a new model:

   mm start my-model
   cd my-model
   

3. Configure & Validate: Edit meta.json and run:

   mm validate
   mm run
   

4. Deploy:

   mm build
   mm push
   

Technology Stack

• Backend: Python, Quart (Async)
• Infra: Docker, MySQL
• Ops: Loki, Grafana
• Interface: CLI, REST API