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transformerlab-app

transformerlabAGPL-3.0活跃维护

面向 AI 研究者的开源实验环境，可在本地硬件到 GPU 集群之间无缝训练、评估和扩展大模型与扩散模型。

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

v0.40.1发布于 2026-06-19 19:10 UTC5,200star552fork同步于 2026-11-10 17:37 UTC
模型训练模型评估分布式训练扩散模型LoRA微调
对比 GitHub 官网diffusiondiffusion-modelselectronllamallms

怎么上手

由 README 提炼，执行前请确认
原文 
curl https://lab.cloud/install.sh | bash
cd ~/.transformerlab/src
./run.sh
硬件要求按 README 说明

macOS 需 Apple Silicon（M1/M2/M3/M4）；Linux 需 NVIDIA 或 AMD GPU；Windows 需通过 WSL2 使用 NVIDIA GPU。README 未给出具体显存数值。

支持 Apple Silicon需要 CUDA支持 ROCm / AMD
下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼
没人工点评？写下你的经验

开源 AI 研究平台，用一个界面统一本地与集群上的模型训练、微调、评测和推理

面向 AI 研究者的开源实验环境，可在本地硬件到 GPU 集群之间无缝训练、评估和扩展大模型与扩散模型。

核心能力

  • 统一界面在本地硬件或 Slurm、SkyPilot 集群上提交训练任务
  • 支持全量微调、LoRA/QLoRA、DPO/ORPO/SIMPO 与奖励建模
  • 集成 MLX、vLLM、Ollama、HuggingFace 多种推理引擎
  • 内置 LM Evaluation Harness 基准与 LLM-as-a-Judge 评测
  • 提供 Python 插件架构与 lab CLI 扩展和脚本集成

容易踩的坑

安装脚本会把代码装到 ~/.transformerlab/src，需进入该目录才能运行 run.sh；Windows 不能直接运行，必须先配置 WSL2 与 CUDA；集群功能需按 Teams 安装指南另行配置 Slurm 或 SkyPilot 计算提供方。

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

最近发版

5 个版本v0.40.1
全部 release
  1. v0.40.1最新正式版2026-06-19 · 2026-06-19 19:10 UTC

    What's Changed

    Full Changelog: https://github.com/transformerlab/transformerlab-app/compare/v0.40.0...v0.40.1

  2. v0.40.02026-06-12 · 2026-06-12 13:57 UTC
    查看更新说明

    What's Changed

    Full Changelog: https://github.com/transformerlab/transformerlab-app/compare/v0.39.0...v0.40.0

  3. v0.39.02026-06-04 · 2026-06-04 17:31 UTC
    查看更新说明

    What's Changed

  4. v0.38.02026-05-13 · 2026-05-13 19:51 UTC
    查看更新说明

    What's Changed

  5. v0.37.22026-05-05 · 2026-05-05 19:40 UTC
    查看更新说明

    What's Changed

    Full Changelog: https://github.com/transformerlab/transformerlab-app/compare/v0.37.1...v0.37.2

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    README 原文

    未翻译
    在 GitHub 打开

    ✨ 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

    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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
    PlatformRequirements
    macOSApple Silicon (M1/M2/M3/M4)
    LinuxNVIDIA or AMD GPU
    WindowsNVIDIA 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:

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

    👩‍💻 Development

    # Requires Node.js v22
npm install
npm start

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

    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}
}

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