EasyDeL
A JAX-based framework for streamlined training, fine-tuning, and high-performance serving of large language and multimodal models.
What is EasyDeL?
EasyDeL is an open-source framework built on JAX and Flax NNX that streamlines the training, fine-tuning, and serving of large language and multimodal models. It solves the complexity of scaling model development by providing production-ready tools, specialized trainers, and a high-performance inference engine optimized for TPU and GPU clusters.
Machine learning researchers and engineers working with large language models, vision-language models, or other transformer architectures who need scalable training, advanced fine-tuning (like DPO/RLHF), and efficient model serving.
Developers choose EasyDeL for its unique combination of hackability and performance. It offers a clean, modular codebase inspired by HuggingFace Transformers for easy customization, alongside JAX-optimized kernels and distributed training capabilities for production-scale workloads, all through a unified API.
Overview
Accelerate, Optimize performance with streamlined training and serving options with JAX.
Use Cases
Best For
- Fine-tuning large language models with preference optimization techniques like DPO or ORPO.
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