🧠 Modlable

A platform for building, training, and running inference on TensorFlowJS-based language models with the assistance of LLM-powered agents — hosted on Google Cloud Run.

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📘 Overview

Modlable is an experimental platform that allows developers and researchers to build, train, and deploy lightweight language models (LMs) directly in JavaScript using TensorFlowJS, augmented by Large Language Model (LLM) agents for orchestration, optimization, and workflow automation.

The platform is cloud-native — it’s designed to run efficiently on Google Cloud Run, enabling serverless and scalable model operations with minimal DevOps overhead.

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🚀 Features

• 🧩 Modular Training Pipelines — Train custom token-based language models in TensorFlowJS.
• 🤖 LLM Orchestration Agents — Use pre-configured agents to guide hyperparameter tuning, dataset preparation, and model evaluation.
• ☁️ Cloud Run Deployment — Run models and inference endpoints on demand using containerized builds.
• 🔁 Seamless Inference API — Serve models as REST endpoints for text generation, embedding, or classification.
• 🧠 Local + Cloud Support — Develop locally, then deploy to GCP with one command.

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🧱 Project Structure

modlable/
├── frontend/
│   ├── modlable/            # The frontend Angular App
│
├── backend/
│   └── src/              # Genkit + Cloud Functions/Cloud Run for agents
│   ├── package.json      # Node project dependencies
├── Dockerfile               # Container configuration for running it all 
├── .env.example             # Example environment configuration
└── README.md                # You are here

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⚙️ Prerequisites

Before getting started, ensure you have:

• Node.js ≥ 18.x
• npm or yarn
• Docker (for containerization)
• Google Cloud SDK (gcloud) configured with billing and permissions
• A Google Cloud Project with Cloud Run, Artifact Registry, and Cloud Build enabled

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🧩 Installation

1. Clone the repository

   git clone https://github.com/yourusername/modlable.git
   cd modlable

2. Install dependencies

   npm install

3. Set up environment variables

   cp .env.example .env
   # Edit the .env file with your API keys and Cloud project info

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🧠 Local Development

Run the API locally for testing:

npm run dev

To start a local training session:

npm run train

Run inference on a local model:

npm run infer "Once upon a time"

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☁️ Deployment (Google Cloud Run)

1. Build and push Docker image

   gcloud builds submit --tag gcr.io/[PROJECT_ID]/modlable

2. Deploy to Cloud Run

   gcloud run deploy modlable \
       --image gcr.io/[PROJECT_ID]/modlable \
       --platform managed \
       --region [REGION] \
       --allow-unauthenticated

3. Access your endpoint

   https://modlable-[REGION]-a.run.app/infer
   

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🧮 API Endpoints

Method	Endpoint	Description
POST	/train	Initiate a model training session
POST	/infer	Run inference with a trained model
GET	/models	List all available trained models
POST	/agents/optimize	Use LLM agent for optimization guidance

Example Request:

curl -X POST https://modlable-[REGION]-a.run.app/infer \
  -H "Content-Type: application/json" \
  -d '{"prompt": "The future of AI is"}'

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🧰 Environment Variables

Variable	Description
GOOGLE_PROJECT_ID	Your GCP project ID
MODEL_BUCKET	Cloud Storage bucket for model checkpoints
OPENAI_API_KEY	API key for LLM agent integration (optional)
PORT	Server port (defaults to 8080)

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🧑‍💻 Contributing

Contributions are welcome!

1. Fork the repo
2. Create a new feature branch
3. Submit a pull request with a clear description

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🧾 License

MIT License © 2025 — Modlable Contributors

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🌐 Roadmap

• Web dashboard for model visualization and management
• Support for multimodal training data (text + image)
• Integration with Hugging Face model zoo
• Cross-runtime inference (Node, Browser, Edge)

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Made with ❤️ by the Modlable Team Empowering developers to build LMs anywhere.