transformerlab/transformerlab-app — stars, momentum and ranking

The open source research environment for AI researchers to seamlessly train, evaluate, and scale models from local hardware to GPU clusters.

The Operating System for AI Research Labs

Designed for ML Researchers. Local, on-prem, or in the cloud. Open source.

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[⬇️ Install for Individuals](https://lab.cloud/docs/install/)
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[🏢 Install for Teams](https://lab.cloud/for-teams/install)
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[📖 Documentation](https://lab.cloud/for-teams/)
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[🎬 Demo](https://youtu.be/tY5TAvKviLo)
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[💬 Discord](https://discord.gg/transformerlab)

Transformer Lab Demo

✨ What is Transformer Lab?

Transformer Lab is an open-source machine learning platform that unifies the fragmented AI tooling landscape into a single, elegant interface. It is available in two editions:

👤 For Individuals

Perfect for researchers and hobbyists working on a single machine.

Local Privacy: No data leaves your machine.
Full Toolkit: Train, fine-tune, chat, and evaluate models.
Cross-Platform: Runs natively on macOS (Apple Silicon), Linux, and Windows (WSL2).
No Cloud Costs: Use your own hardware.

🏢 For Teams

Built for research labs scaling across GPU clusters.

Unified Orchestration: Submit jobs to Slurm clusters or SkyPilot clouds (AWS, GCP, Azure) from one UI.
Collaborative: Centralized experiment tracking, model registry, and artifact management.
Interactive Compute: One-click Jupyter, VSCode, and SSH sessions on remote nodes.
Resilience: Auto-recovery from checkpoints and spot instance preemption.

🛠️ Key Capabilities

🧠 Foundation Models & LLMs

Universal Support: Download and run Llama 3, DeepSeek, Mistral, Qwen, Phi, and more.
Inference Engines: Support for MLX, vLLM, Ollama, and HuggingFace Transformers.
Format Conversion: Seamlessly convert between HuggingFace, GGUF, and MLX formats.
Chat Interface: Multi-turn chat, batched querying, and function calling support.

🎓 Training & Fine-tuning

Unified Interface: Train on local hardware or submit tasks to remote clusters using the same UI.
Methods: Full fine-tuning, LoRA/QLoRA, RLHF (DPO, ORPO, SIMPO), and Reward Modeling.
Hardware Agnostic: Optimized trainers for Apple Silicon (MLX), NVIDIA (CUDA), and AMD (ROCm).
Hyperparameter Sweeps: Define parameter ranges in YAML and automatically schedule grid searches.

🎨 Diffusion & Image Generation

Generation: Text-to-Image, Image-to-Image, and Inpainting using Stable Diffusion and Flux.
Advanced Control: Full support for ControlNets and IP-Adapters.
Training: Train custom LoRA adaptors on your own image datasets.
Dataset Management: Auto-caption images using WD14 taggers.

📊 Evaluation & Analytics

LLM-as-a-Judge: Use local or remote models to score outputs on bias, toxicity, and faithfulness.
Benchmarks: Built-in support for EleutherAI LM Evaluation Harness (MMLU, HellaSwag, GSM8K, etc.).
Red Teaming: Automated vulnerability testing for PII leakage, prompt injection, and safety.

🔌 Plugins & Extensibility

Plugin System: Extend functionality with a robust Python plugin architecture.
Lab SDK: Integrate your existing Python training scripts (import lab) to get automatic logging, progress bars, and artifact tracking.
CLI: Power-user command line tool for submitting tasks and monitoring jobs without a browser.

🗣️ Audio Generation

Text-to-Speech: Generate speech using Kokoro, Bark, and other state-of-the-art models.
Training: Fine-tune TTS models on custom voice datasets.

📥 Quick Start

1. Install

curl https://lab.cloud/install.sh | bash

2. Run

cd ~/.transformerlab/src
./run.sh

3. Access

Open your browser to http://localhost:8338.

Requirements

Platform	Requirements
macOS	Apple Silicon (M1/M2/M3/M4)
Linux	NVIDIA or AMD GPU
Windows	NVIDIA GPU via WSL2 (setup guide)

🏢 Enterprise & Cluster Setup

Transformer Lab for Teams runs as an overlay on your existing infrastructure. It does not replace your scheduler; it acts as a modern control plane for it.

To configure Transformer Lab to talk to Slurm or SkyPilot:

Follow the Teams Install Guide.
Configure your compute providers in the Team Settings.
Use the CLI (lab) or Web UI to queue tasks across your cluster.

👩‍💻 Development

Frontend

# Requires Node.js v22
npm install
npm start

Backend (API)

cd api
./install.sh   # Sets up Conda env + Python deps
./run.sh       # Start the API server

Lab SDK

pip install transformerlab

🤝 Contributing

We are an open-source initiative backed by builders who care about the future of AI research. We welcome contributions! Please check our issues for open tasks.

📄 License

AGPL-3.0 · See LICENSE for details.

📚 Citation

@software{transformerlab,
  author = {Asaria, Ali and Salomone, Tony},
  title = {Transformer Lab: The Operating System for AI Research},
  year = 2023,
  url = {https://github.com/transformerlab/transformerlab-app}
}

💬 Community

Built with ❤️ by Transformer Lab in Canada 🇨🇦