A JAX-based framework for streamlined training, fine-tuning, and high-performance serving of large language and multimodal models.
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.
Accelerate, Optimize performance with streamlined training and serving options with JAX.
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Supports over 70 model architectures including LLaMA, Qwen, Mistral, and multimodal models like LLaVA, as highlighted in the README's Supported Models section.
eSurge provides continuous batching, paged KV cache, and an OpenAI-compatible API server, enabling efficient serving with features like streaming and monitoring.
Offers 16 unified trainers for SFT, DPO, ORPO, GRPO, and knowledge distillation, simplifying advanced fine-tuning workflows documented in the Training & Fine-Tuning section.
Built on Flax NNX with clean, modular code and HuggingFace-style APIs, making it easy to inspect and modify components, as emphasized in the Philosophy and Customization sections.
Requires familiarity with JAX, distributed computing, and complex configuration options like sharding axes and attention mechanisms, which can be daunting for newcomers.
Some attention mechanisms like BLOCKWISE and PAGED_ATTENTION are listed in the enum but not registered in OperationRegistry, as noted in the Advanced Recipes section, leading to potential runtime errors.
Optimal performance relies on TPU/GPU clusters and specific backends like Triton or Pallas, making it less effective for resource-constrained environments without such infrastructure.