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README.md

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-title: README
-emoji: 👁
+title: EasyDeL
+emoji: 🔮
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 colorTo: blue
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-# EasyDeL 🔮
+<p align="center">
+  <a href="https://github.com/erfanzar/EasyDeL">
+    <img src="https://raw.githubusercontent.com/erfanzar/easydel/main/images/easydel-logo-with-text.png" height="80" alt="EasyDeL" />
+  </a>
+</p>
 
-[**Key Features**](https://github.com/erfanzar/EasyDeL?tab=readme-ov-file#key-features)
-| [**Latest Updates**](https://github.com/erfanzar/EasyDeL?tab=readme-ov-file#latest-updates-)
-| [**Vision**](https://github.com/erfanzar/EasyDeL?tab=readme-ov-file#future-updates-and-vision-)
-| [**Quick Start**](https://github.com/erfanzar/EasyDeL?tab=readme-ov-file#quick-start)
-| [**Reference docs**](https://easydel.readthedocs.io/en/latest/)
-| [**License**](https://github.com/erfanzar/EasyDeL?tab=readme-ov-file#license-)
+<p align="center">
+  <a href="https://github.com/erfanzar/EasyDeL">
+    <img src="https://img.shields.io/badge/GitHub-erfanzar%2FEasyDeL-111?logo=github&style=flat-square" alt="GitHub" />
+  </a>
+  <a href="https://pypi.org/project/easydel/">
+    <img src="https://img.shields.io/pypi/v/easydel?style=flat-square" alt="PyPI" />
+  </a>
+  <a href="https://easydel.readthedocs.io/en/latest/">
+    <img src="https://img.shields.io/badge/Docs-ReadTheDocs-1f72ff?logo=readthedocs&style=flat-square" alt="Docs" />
+  </a>
+  <a href="https://discord.gg/FCAMNqnGtt">
+    <img src="https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&style=flat-square" alt="Discord" />
+  </a>
+</p>
 
-EasyDeL is an open-source framework designed to enhance and streamline the training process of machine learning models, with a primary focus on JAX. It provides convenient and effective solutions for training and serving JAX models on TPU/GPU at scale.
+# EasyDeL
 
-With EasyDeL, you're not constrained by rigid frameworks. Instead, you have a flexible, powerful toolkit that adapts to your needs, no matter how unique or specialized they may be. Whether you're conducting cutting-edge research or building production-ready ML systems, EasyDeL provides the freedom to innovate without limitations.
+EasyDeL is an open-source framework for building, training, fine-tuning, converting, and serving modern ML models in **JAX** at scale. It is designed for people who want **the performance benefits of JAX** without giving up the **practical ergonomics** of the Hugging Face ecosystem.
+
+## Purpose
+
+JAX is extremely powerful, but scaling real training/inference workloads can still feel fragmented: model code, sharding, kernels, training loops, serving, and conversions often live in separate places. EasyDeL’s goal is to provide a cohesive toolkit where these pieces work together—while still staying readable and hackable.
+
+## What EasyDeL focuses on
+
+- **Scale-first**: multi-device training/inference across GPU/TPU with sharding-aware utilities.
+- **Production inference**: a dedicated serving stack built for throughput and low latency.
+- **Interoperability**: straightforward workflows with Hugging Face models and assets.
+- **Hackability**: implementations you can actually read, debug, and modify.
+
+## Core components
+
+- **Model library (70+ architectures)**: text models, vision-language models, speech models (Whisper), and more.
+- **Training & fine-tuning**: supervised fine-tuning plus multiple alignment paradigms (preference optimization and RL-style training), and distillation workflows.
+- **eSurge (serving engine)**: continuous batching, paged KV cache, and OpenAI-compatible APIs (plus multimodal endpoints where applicable).
+- **eLargeModel (ELM)**: a configuration-driven interface for end-to-end workflows (load → shard → train → evaluate → serve).
+- **Conversion utilities**: convert PyTorch checkpoints to EasyDeL checkpoints, and generate model cards/metadata during export.
+
+## Where it fits
+
+EasyDeL is a good fit if you:
+
+- want to train or serve large models on JAX with real sharding strategies,
+- need a unified training + serving stack rather than a pile of scripts,
+- care about performance but also want to iterate quickly and customize internals,
+- want compatibility with common Hugging Face assets and workflows.
+
+## License
+
+EasyDeL is released under the Apache-2.0 license.