EasyDeL is a JAX/Flax framework designed to accelerate and optimize the training and serving of machine learning models, particularly for large-scale deployments on TPUs and GPUs. It targets researchers and engineers seeking efficient, flexible, and production-ready solutions for modern deep learning architectures.

How It Works

Built on Flax NNX, EasyDeL offers a modular and customizable architecture. It provides specialized trainers for tasks like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), along with support for various attention mechanisms (FlashAttention2, Ring Attention, etc.) and quantization techniques (NF4, A8BIT). Its vInference engine and OpenAI-compatible API server enable efficient serving.

Quick Start & Requirements

• Install via pip: pip install easydel
• Requires JAX, Transformers, and Datasets. GPU/TPU recommended for performance.
• Official Documentation: https://erfanzar.github.io/EasyDeL/

Highlighted Details

• Supports diverse model architectures including Transformers, Mamba, and RWKV.
• Integrates platform-specific optimizations like TRITON, XLA, and Pallas.
• Offers advanced quantization methods (NF4, A8BIT, A8Q, A4Q) for memory and speed improvements.
• Includes a vInference engine for efficient LLM serving and an OpenAI-compatible API server.

Maintenance & Community

• Primarily maintained by Erfan Zare Chavoshi.
• Contributing guidelines are available in the repository.

Licensing & Compatibility

• Released under the Apache v2 license.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The framework is built on modern Flax NNX, which is still evolving. While it supports many architectures, specific model compatibility or performance tuning might require deeper understanding of JAX and Flax internals.