Merge pull request #9 from LronDC/main

Update README.md

Author: LronDC

README.md

@@ -1,41 +1,74 @@
-![**DTX Logo**](https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/logo/Logo_DataTunerX%20-%20Horizontal%20-%20Color%20Light.png#gh-dark-mode-only)
-![**DTX Logo**](https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/logo/Logo_DataTunerX%20-%20Horizontal%20-%20Color%20Dark.png#gh-light-mode-only)
+![DTX Logo](https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/logo/Logo_DataTunerX%20-%20Horizontal%20-%20Color%20Light.png#gh-dark-mode-only)
+![DTX Logo](https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/logo/Logo_DataTunerX%20-%20Horizontal%20-%20Color%20Dark.png#gh-light-mode-only)
 
 ![Kubernetes](https://img.shields.io/badge/kubernetes-%23326ce5.svg?style=flat&logo=kubernetes&logoColor=white)
 ![release](https://img.shields.io/badge/version-0.1.0-blue)
 ![owner](https://img.shields.io/badge/daocloud.io-green)
 ![fine-tuning](https://img.shields.io/badge/fine--tuning-8B3E3)
 # Welcome 👋
 
-**Description**:  
-*DataTunerX*, short for *DTX*, is a powerful tool.
+***DataTunerX (DTX)*** is designed as a cloud-native solution integrated with distributed computing frameworks. Leveraging scalable *GPU* resources, it's a platform built for efficient fine-tuning *LLMs* with a focus on practical utility. Its core strength lies in facilitating batch fine-tuning tasks, enabling users to conduct multiple tasks concurrently within a single ***experiment***. ***DTX*** encompasses essential capabilities such as ${\color{#D8CBBE}dataset \space management}$, ${\color{#BDE7BD}hyperparameter \space control}$, ${\color{#F1A7A7}fine-tuning \space workflows}$, ${\color{#BADBF4}model \space management}$, ${\color{#F4CEDB}model \space evaluation}$, ${\color{#D2E3EE}model \space comparison \space inference}$, and a ${\color{#F9E195}modular \space plugin \space system}$.
 
-**Technology stack**:  
-Built using *Go*.
+**Technology stack**: 
+
+***DTX*** is built on cloud-native principles, employing a variety of [*Operators*](https://www.redhat.com/en/topics/containers/what-is-a-kubernetes-operator) that consist of distinct *Custom Resource Definitions (CRDs)* and *Controller* logic. Developed primarily in *Go*, the implementation utilizes the [*operator-sdk*](https://github.com/operator-framework/operator-sdk) toolkit. Operating within a [*Kubernetes (K8s)*](https://github.com/kubernetes/kubernetes) environment, ***DTX*** relies on the operator pattern for *CRD* development and management. Furthermore, ***DTX*** integrates with [*kuberay*](https://github.com/ray-project/kuberay) to harness distributed execution and inference capabilities.
 
 **Status**:  
+
 *Alpha (v0.1.0)* - Early development phase. [CHANGELOG](CHANGELOG.md) for details on recent updates.
 
-**Links to production or demo instances**:  
+**Quick Demo & More Documentation**:  
 
-[Demo Instance](https://github.com/DataTunerX/datatunerx-controller) (COMING SOON)
+- [Demo](https://github.com/DataTunerX/datatunerx-controller) (COMING SOON)
 
-[Documentation](https://github.com/DataTunerX/datatunerx-controller) (COMING SOON)
+- [Documentation](https://github.com/DataTunerX/datatunerx-controller) (COMING SOON)
+
+**Screenshot**:
 
-**Screenshot**:  
 ![**DTX Screenshot**](https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/screenshot/Job_Details.png)
 
-# Dependencies 🖇️
+# What DTX can do? 💪
 
-Describe any dependencies that must be installed for this software to work. This includes programming languages, databases, build tools, etc. Specify versions if necessary.
+***DTX*** empowers users with a robust set of features designed for efficient fine-tuning of large language models. Dive into the capabilities that make ***DTX*** a versatile platform:
 
-# Installation 📦
+## 1. Dataset Management 🗄️
+Effortlessly manage datasets by supporting both *S3* protocol (*http* is coming) and local dataset uploads. Datasets are organized with splits such as test, validate, and training. Additionally, feature mapping enhances flexibility for fine-tuning jobs.
 
-Detailed instructions on how to install, configure, and run the project are available in the [*INSTALL*](INSTALL.md) document.
+## 2. Fine-Tuning Experiments 🧪
+Conduct fine-tuning experiments by creating multiple fine-tuning jobs. Each job can employ different llms, datasets, and hyperparameters. Evaluate the fine-tuned models uniformly through the experiment's evaluation unit to identify the fine-tuning results.
+<div align="center">
+  <img src="https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/design/finetune.png" alt="FineTune" width="30%" />
+  <img src="https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/design/finetunjobe.png" alt="FineTuneJob" width="30%" />
+  <img src="https://raw.githubusercontent.com/DataTunerX/datatunerx-controller/main/assets/design/finetuneexperiment.png" alt="FineTuneExperiment" width="30%" />
+</div>
+
+## 3. Job Insights 📊
+Gain detailed insights into each fine-tuning job within an experiment. Explore job details, logs, and metric visualizations, including learning rate trends, training loss, and more.
+
+## 4. Model Repository 🗃️
+Store llms in the model repository, facilitating efficient management and deployment of inference services.
+
+## 5. Hyperparameter Group Management 🧰
+Utilize a rich parameter configuration system with support for diverse parameters and template-based differentiation.
 
-# Configuration ⚙️
+## 6. Inference Services 🚀
+Deploy inference services for multiple models simultaneously, enabling straightforward comparison and selection of the best-performing model.
 
-If the software is configurable, describe the configuration options in detail, either here or in other linked documentation. (COMING SOON)
+## 7. Plugin System 🧩
+Leverage the plugin system for datasets and evaluation units, allowing users to integrate specialized datasets and evaluation methods tailored to their unique requirements.
+
+## 8. More Coming 🤹‍♀️
+DTX offers a comprehensive suite of tools, ensuring a seamless fine-tuning experience with flexibility and powerful functionality. Explore each feature to tailor your fine-tuning tasks according to your specific needs.
+
+# Why DTX? 🤔
+
+# Architecture 🏛️
+
+Introducing the architectural design provides an overview of how DataTunerX is structured. This includes details on key components, their interactions, and how they contribute to the system's functionality.
+
+# Installation 📦
+
+Detailed instructions on how to install, configure, and run the project are available in the [*INSTALL*](INSTALL.md) document.
 
 # Usage 🖥️