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README.md

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 # Droplychee AI Research
 
-### Building Open, Efficient, and Responsible Foundation Models
-
-<img src="assets/banner.png" alt="Droplychee AI Banner" width="100%">
-
-[![License](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](LICENSE)
-[![Research](https://img.shields.io/badge/Research-Open%20AI%20Models-success.svg)](#)
-[![Language](https://img.shields.io/badge/Languages-40%2B-orange.svg)](#)
-[![Context](https://img.shields.io/badge/Context-Up%20to%201M%20Tokens-purple.svg)](#)
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-</div>
-
----
-
-# About
-
-**Droplychee AI Research** is an open research organization focused on developing large language models, multilingual AI systems, long-context transformers, and practical deployment technologies.
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-Our mission is to build transparent, reproducible, and production-ready AI systems that empower developers, researchers, educators, and organizations worldwide.
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-We believe that high-quality AI research should be accessible through open documentation, engineering best practices, and responsible model development.
-
----
-
-# Mission
-
-Our goals include:
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-- Develop high-quality open language models
-- Advance multilingual AI research
-- Improve long-context reasoning
-- Support efficient inference and deployment
-- Promote transparent model documentation
-- Encourage reproducible AI research
-- Foster an open-source developer community
-
----
-
-# Research Areas
-
-- Large Language Models (LLMs)
-- Transformer Architectures
-- Long-Context Modeling
-- Multilingual Natural Language Processing
-- AI Agents
-- Retrieval-Augmented Generation (RAG)
-- Model Optimization
-- Quantization
-- Efficient Inference
-- Responsible AI
-- AI Safety
-- Benchmarking and Evaluation
-
----
-
-# Flagship Projects
-
-## Droplychee-2.0-40B
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-A multilingual decoder-only language model supporting long-context inference, instruction following, coding assistance, reasoning, and research workflows.
-
-Key features include:
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-- Up to 1M token context support (configuration dependent)
-- YaRN-based context extension
-- 40+ language support
-- Transformer compatibility
-- Production deployment support
-
----
-
-# Engineering Principles
-
-Our projects prioritize:
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-- Transparency
-- Reproducibility
-- Documentation
-- Responsible AI
-- Open Standards
-- Practical Engineering
-- Community Collaboration
-
----
-
-# Documentation
-
-Every major project includes comprehensive documentation such as:
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-- Model Card
-- System Card
-- Technical Report
-- Evaluation Report
-- Deployment Guide
-- Architecture Documentation
-- Safety Documentation
-- Reproducibility Guide
-
----
-
-# Community
-
-We welcome contributions including:
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-- Bug reports
-- Documentation improvements
-- Benchmark reproductions
-- Evaluation feedback
-- Deployment examples
-- Educational resources
-
-Please read the repository's **CONTRIBUTING.md** before submitting pull requests.
-
----
-
-# Responsible AI
-
-Our models are intended to assist users in research, education, software development, and general productivity.
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-They are **not intended** to replace qualified human judgment in high-impact domains such as healthcare, law, finance, or emergency decision-making.
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-Users should independently verify important outputs before relying on them.
-
----
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-# Repository Standards
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-Projects published under Droplychee AI Research aim to include:
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-- Clear documentation
-- Versioned releases
-- Open licensing (where applicable)
-- Reproducible research practices
-- Responsible disclosure processes
-- Transparent development workflows
-
----
-
-# License
-
-Unless otherwise specified, projects are released under the Apache License 2.0.
-
-See individual repositories for licensing details.
-
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-<div align="center">
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-**Building Open AI for Everyone**
-
-</div>
+Droplychee AI Research is an open AI research organization focused on building multilingual large language models, long-context transformers, and practical AI systems. We emphasize transparency, responsible AI, reproducible research, and high-quality documentation. Our projects support developers, researchers, and organizations through open-source models, evaluation frameworks, deployment tools, and educational resources. By combining modern AI research with production-ready engineering, we aim to make advanced language models more accessible, efficient, and reliable for real-world applications.