🏗️ Loom-Train

A Simple & Efficient Training Framework for Long-Context LLMs

Optimized for scalability, memory efficiency, and seamless integration — built to unlock the full potential of long-context large language models.

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📅 Update Log

• 📅 2025-10-07 — 🚀 Initial Release: Loom-Train is now live!
  ✅ Native support for 🤗 Hugging Face Trainer
  ✅ Optimized attention with 🌀 Ring-Flash-Attention
  ✅ Lightweight, plug-and-play design for long-sequence training (128K+ tokens)

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✨ Key Features

• 🔧 Plug-and-Play: Drop-in replacement for HF Trainer — no major code changes needed.
• 🚀 Memory-Efficient: Leverages Ring-Flash-Attention to reduce GPU memory footprint by up to 50%.
• 📈 Scalable: Seamlessly scales to 100K+ context lengths without sacrificing speed.
• ⚡ Fast Setup: Minimal dependencies, easy installation via pip install loom-train.

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💻 Environment & Installation

To install theloomtrain package from the gitee repository, run:

git clone https://github.com/LCM-Lab/LOOM-Train.git
conda create -n loom_train python=3.10 -y
conda activate loom_train
cd LOOM-Train/loomtrain
pip install -e .

To install flash attention, run the command below to obtain the required flah-attn version:

loomtrain-required-flash-attn

Download the suitable version of flash_attn from https://github.com/Dao-AILab/flash-attention/releases

pip install <path_to_flash_attn_whl_file>
pip install ring_flash_attn

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🛠️ Getting Started

Then just swap your Trainer with LoomTrainer:

from loomtrain import LoomTrainer

trainer = LoomTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    # ... rest unchanged!
)

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🤝 Contributing

We welcome contributions! Whether it’s bug fixes, new features, or documentation improvements — feel free to open an issue or PR.
Let’s build the future of long-context training, together. 💪

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📬 Contact

Questions? Suggestions? Reach out at: [email protected] and [email protected]