বাংলাদেশের গর্ব, আমাদের নিজেদের কৃত্রিম বুদ্ধিমত্তা বাহ (AI) - ফ্রি!
Bangladesh's Pride, A New Era of Artificial Intelligence

Features • Architecture • Performance • Usage • Roadmap • Contribute

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🌟 Features

Advanced AI

Multilingual

Ethical AI

Advanced Analytics

Robust Security

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🏗 Architecture

Component Diagram

graph TD
    A[User Interface] --> B[API Gateway]
    B --> C[Authentication Service]
    B --> D[Likhon 3.5 Core]
    D --> E[Knowledge Base]
    D --> F[Inference Engine]
    D --> G[NLP Processor]
    B --> H[Analytics Service]
    B --> I[Monitoring Service]

    style A fill:#f9d5e5,stroke:#333,stroke-width:2px
    style D fill:#eeac99,stroke:#333,stroke-width:4px
    style E fill:#e06377,stroke:#333,stroke-width:2px
    style F fill:#c83349,stroke:#333,stroke-width:2px
    style G fill:#5b9aa0,stroke:#333,stroke-width:2px

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Caption: This component diagram illustrates the high-level architecture of Likhon 3.5. The core AI model interacts with various services through an API Gateway, ensuring secure and efficient processing of user queries.

Sequence Diagram

sequenceDiagram
    participant User
    participant UI as User Interface
    participant API as API Gateway
    participant L3.5 as Likhon 3.5 Core
    participant KB as Knowledge Base

    User->>UI: Input Query
    UI->>API: Send Request
    API->>L3.5: Process Query
    L3.5->>KB: Fetch Relevant Data
    KB-->>L3.5: Return Data
    L3.5-->>API: Generate Response
    API-->>UI: Return Result
    UI-->>User: Display Answer

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Caption: This sequence diagram shows the flow of a user query through the Likhon 3.5 system, from input to response generation and display.

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📊 Performance

Benchmark Comparison

gantt
    title Model Performance Comparison
    dateFormat X
    axisFormat %s

    section Likhon 3.5
    GPQA     : 0, 92
    MMLU     : 0, 95
    HumanEval: 0, 88

    section GPT-4
    GPQA     : 0, 89
    MMLU     : 0, 93
    HumanEval: 0, 85

    section Claude 3.5
    GPQA     : 0, 87
    MMLU     : 0, 90
    HumanEval: 0, 84

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Caption: This chart compares Likhon 3.5's performance against GPT-4 and Claude 3.5 across three key benchmarks: GPQA (Graduate-level Problem-solving and Question Answering), MMLU (Massive Multitask Language Understanding), and HumanEval (Code Generation and Problem Solving).

Response Time Distribution

pie
    title "Likhon 3.5 Response Time Distribution"
    "<50ms" : 30
    "50-100ms" : 50
    "100-150ms" : 15
    "150-200ms" : 4
    ">200ms" : 1

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Caption: This pie chart illustrates the distribution of response times for Likhon 3.5. The majority of queries (80%) are processed within 100ms, showcasing the model's efficiency.

Multilingual Capability

graph TD
    A[Likhon 3.5 Multilingual Proficiency] --> B[Bangla]
    A --> C[English]
    A --> D[Hindi]
    A --> E[Urdu]
    A --> F[Arabic]
    
    style A fill:#f9d5e5,stroke:#333,stroke-width:4px
    style B fill:#eeac99,stroke:#333,stroke-width:2px
    style C fill:#e06377,stroke:#333,stroke-width:2px
    style D fill:#c83349,stroke:#333,stroke-width:2px
    style E fill:#5b9aa0,stroke:#333,stroke-width:2px
    style F fill:#45b7d1,stroke:#333,stroke-width:2px

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Caption: This diagram highlights Likhon 3.5's multilingual capabilities, showcasing its proficiency in Bangla, English, Hindi, Urdu, and Arabic, making it particularly suited for the South Asian and Middle Eastern markets.

Performance Scaling

Caption: This chart demonstrates how Likhon 3.5's performance scales with increasing model size and computational resources, showing near-linear improvement up to 1 trillion parameters.

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💻 Usage

Quick Start Guide

# Clone the repository
git clone https://github.com/likhonsheikh/likhon-3.5.git

# Navigate to the project directory
cd likhon-3.5

# Install dependencies
pip install -r requirements.txt

# Run the model
python likhon35_local.py

Advanced Configuration

model:
  name: Likhon3.5
  version: 3.5.0
  parameters:
    layers: 24
    attention_heads: 16
    hidden_size: 1024

training:
  batch_size: 32
  learning_rate: 2e-5
  epochs: 10
  optimizer: AdamW

inference:
  temperature: 0.7
  top_p: 0.9
  max_tokens: 100

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🚀 Development Roadmap

timeline
    title Likhon 3.5 Development Roadmap
    2024 Q3 : Enhance Bangla language understanding
             : Implement advanced context retention
    2024 Q4 : Launch specialized model for Bangladesh government services
             : Integrate with national education platforms
    2025 Q1 : Develop explainable AI features for transparency
             : Expand to cover all major South Asian languages
    2025 Q2 : Achieve superhuman performance in Bangladesh-specific domains
             : Host "AI for Bangladesh" innovation challenge

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Caption: This timeline outlines the key milestones in Likhon 3.5's development, focusing on enhancing its capabilities for Bangladesh and the broader South Asian region.

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👥 Key Contributors

Likhon Sheikh 🚀 🏗️

Dr. Aisha Rahman 🛡️ 🔬

Md. Kamal Hossain 🧠 ⚡

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📊 Project Analytics

Commit Activity: Showing steady increase in development activity over the past year.	Language Usage: Python (60%), C++ (30%), CUDA (10%) for optimal performance.
Code Frequency: Consistent code additions with periodic refactoring for optimization.	Contribution Distribution: Wide range of contributors from academia and industry in Bangladesh.

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🤝 Join the AI Revolution in Bangladesh

বাংলাদেশের ভবিষ্যৎ আমাদের হাতে। আসুন, একসাথে এই যাত্রায় অংশ নেই।
The future of Bangladesh is in our hands. Let's embark on this journey together.

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Made with ❤️ in Bangladesh 🇧🇩
© 2024 Likhon Sheikh. All rights reserved.

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