Metadata-Version: 2.4
Name: CarvusTrain
Version: 1.3.0
Summary: CarvusTrain: Multi-billion parameter AI training framework — Carvus Labs / Aadil Fazal
Home-page: https://github.com/Aadil-Fazal/CarvusTrain
Author: Aadil Fazal
Author-email: Aadil Fazal <aadilfazalb4u@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/Aadil-Fazal/CarvusTrain
Project-URL: Repository, https://github.com/Aadil-Fazal/CarvusTrain.git
Project-URL: Documentation, https://github.com/Aadil-Fazal/CarvusTrain/blob/main/README.md
Keywords: ai,deep-learning,carvustrain,carvus-labs,aadil-fazal,llm,transformer
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: build>=1.0; extra == "dev"
Requires-Dist: twine>=4.0; extra == "dev"
Requires-Dist: ruff>=0.1.0; extra == "dev"
Provides-Extra: all
Requires-Dist: numpy>=1.24; extra == "all"
Requires-Dist: pyyaml>=6.0; extra == "all"
Requires-Dist: scikit-learn>=1.0; extra == "all"
Provides-Extra: llm
Requires-Dist: torch>=2.0; extra == "llm"
Dynamic: license-file

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# ⚡ CarvusTrain

### The AI Development Ecosystem for the Modern Era

**Train · Build · Deploy · Evolve**

<p><i>Zero-to-inference in minutes — no mandatory dependencies required.</i></p>

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---

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<!-- TABLE OF CONTENTS -->
<!-- ==================================================================== -->
## 📋 Table of Contents

<details open>
<summary><b>Expand / Collapse</b></summary>

| # | Section | Description |
|---|---------|-------------|
| 1 | [🌟 Overview](#-overview) | What CarvusTrain is and why it exists |
| 2 | [✨ Core Features](#-core-features) | Feature showcase with cards and comparison tables |
| 3 | [🎯 Who Should Use It](#-who-should-use-carvustrain) | Target audience and use cases |
| 4 | [🏗️ Architecture](#%EF%B8%8F-architecture) | System architecture diagrams (Mermaid + ASCII) |
| 5 | [🗺️ Ecosystem Maps](#%EF%B8%8F-ecosystem-maps) | 22 Mermaid diagrams across the full stack |
| 6 | [🚀 Quick Start](#-quick-start) | Installation and first AI model in 30 seconds |
| 7 | [📊 Parameter Presets](#-parameter-presets) | Multi-billion parameter scales from 15M to 405B |
| 8 | [💻 Code Examples](#-code-examples) | Production-quality examples in Python, JS, Bash |
| 9 | [🎬 CLI Guide](#-cli-guide) | Full command reference with realistic output |
| 10 | [⚡ Performance & Scale](#-performance--scale) | Memory estimates and performance benchmarks |
| 11 | [🎥 GIF Demonstrations](#-gif-demonstrations) | Visual walkthrough placeholders |
| 12 | [📦 Project Structure](#-project-structure) | Codebase tree and module map |
| 13 | [🔐 Security](#-security--compliance) | Security features and compliance |
| 14 | [❓ FAQ](#-faq) | Frequently asked questions |
| 15 | [🗺️ Roadmap](#%EF%B8%8F-roadmap) | Completed, in-progress, and planned features |
| 16 | [📈 Repository Analytics](#-repository-analytics) | Stars, downloads, contributors |
| 17 | [🤝 Contributing](#-contributing) | How to contribute |
| 18 | [📚 Documentation](#-documentation) | Links to docs, API, examples, changelog |

</details>

---

<!-- ==================================================================== -->
<!-- SECTION 1: OVERVIEW -->
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## 🌟 Overview

> **CarvusTrain** is a pure-Python AI development ecosystem for building, training, deploying, and evolving intelligent systems — with **zero mandatory dependencies** and **multi-billion parameter support** from day one.

Built from scratch with **Python 3.10+**, CarvusTrain provides a complete AI toolkit that runs everywhere Python runs. No PyTorch, TensorFlow, or CUDA required — though they're supported when available.

### Why CarvusTrain?

| Problem | Solution |
|---------|----------|
| AI frameworks require heavy GPU dependencies | **Zero mandatory dependencies** — pure Python, works everywhere |
| Complex APIs with steep learning curves | **Clean, intuitive API** — create a model in 3 lines |
| Training requires deep learning expertise | **Auto AI Trainer** with smart defaults and learning validation |
| Deployment is complex and infrastructure-heavy | **Built-in REST API server** — deploy with one method call |
| Exporting models locks you into ecosystems | **5 export formats** — ONNX, GGUF, JSON, BIN, native `.ct` |
| Scaling from prototype to production is hard | **9 parameter presets** — 15M to 405B, no rearchitecture needed |
| No semantic understanding — just pattern matching | **TF-IDF knowledge base** with learning validation & retention tracking |

### Core Philosophy

1. **Zero-to-inference in minutes** — Install, import, create, learn, ask
2. **User-driven training** — You call `model.train()`, never the framework
3. **Safe by default** — No auto-subprocess, auto-downloads, or auto-training
4. **Scale without rearchitecture** — Change `parameters="7B"` to `parameters="70B"`
5. **Learn with understanding** — Knowledge retention, comprehension, and validation

<!-- Ecosystem Overview Diagram (Mermaid) -->
```mermaid
flowchart TB
    subgraph Training["📈 Training"]
        T1["Auto AI Trainer"]
        T2["Fine-tuning"]
        T3["Continuous Learning"]
        T4["Reinforcement Learning"]
        T5["Learning Validation"]
    end

    subgraph Models["🧠 Models"]
        M1["Transformer"]
        M2["CNN"]
        M3["RNN / LSTM"]
        M4["Custom Architectures"]
        M5["Multi-Billion Presets"]
    end

    subgraph Knowledge["📚 Knowledge"]
        K1["TF-IDF Semantic Search"]
        K2["Code Detection (20+ langs)"]
        K3["Grammar Engine"]
        K4["Math & Science Curriculum"]
        K5["Web Learning"]
    end

    subgraph Agents["🤖 Agents"]
        A1["Goal-Based Agents"]
        A2["Multi-Agent Teams"]
        A3["Plugin System"]
        A4["Personality Engine"]
        A5["Context Tracking"]
    end

    subgraph Deployment["🚀 Deployment"]
        D1["REST API Server"]
        D2["CLI Interface"]
        D3["Export (ONNX, GGUF, JSON, BIN)"]
        D4["Docker Support"]
        D5["Cloud SDK"]
    end

    Training --> Models
    Models --> Knowledge
    Knowledge --> Agents
    Agents --> Deployment
```

---

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<!-- SECTION 2: CORE FEATURES -->
<!-- ==================================================================== -->
## ✨ Core Features

<table width="100%">
<tr>
<td width="50%" valign="top">

### 🧠 Intelligent Core

| Feature | Description |
|---------|-------------|
| **Transformer Architecture** | Multi-head self-attention with positional encoding |
| **Multi-Model Support** | Transformer, CNN, RNN, LSTM, GRU, custom |
| **Knowledge Base** | TF-IDF semantic search — zero external deps |
| **Learning Validation** | Accuracy, comprehension, retention tracking |
| **Code Detection** | Auto language detection in 20+ programming languages |
| **Grammar Engine** | Built-in English grammar: parts of speech, tenses, rules |
| **Scalable Presets** | 9 parameter scales from 15M (tiny) to 405B (frontier) |

</td>
<td width="50%" valign="top">

### 🌐 Deployment & Export

| Feature | Description |
|---------|-------------|
| **REST API Server** | Built-in, zero-dependency HTTP server (stdlib only) |
| **ONNX Metadata** | Cross-platform model descriptors |
| **GGUF Format** | Compatible with llama.cpp ecosystem |
| **JSON Export** | Human-readable portable model representation |
| **Binary Export** | Compact serialized format with magic header |
| **CLI Interface** | 15+ commands for complete lifecycle management |
| **Docker Support** | Dockerfile generation for production deployments |

</td>
</tr>
</table>

<table width="100%">
<tr>
<td width="33%" valign="top">

### 💻 Code Generation

Generate production code in:

```
✅ Python        ✅ JavaScript     ✅ TypeScript
✅ Java          ✅ C++            ✅ Rust
✅ Go            ✅ Kotlin         ✅ Swift
✅ Bash          ✅ Ruby           ✅ PHP
✅ R             ✅ Scala          ✅ SQL
✅ HTML          ✅ CSS            ✅ MATLAB
```

Real, working code — not placeholders. Algorithms, data structures, design patterns.

</td>
<td width="33%" valign="top">

### 🤖 Agent Framework

- **Goal-Based Agents** — Engineer, researcher, teacher, or custom
- **Multi-Agent Teams** — Orchestrate specialized sub-agents
- **Auto AI Trainer** — Smart defaults based on data size
- **Plugin System** — Extensible via `install_plugin()`
- **Personality Engine** — Style, humor, creativity sliders
- **Context Tracking** — Conversation history with memory management

</td>
<td width="33%" valign="top">

### 🔌 Tokenization & Parsing

- **7 Tokenizer Types** — Word, Char, Sentence, BPE, WordPiece, SentencePiece, Custom
- **Multi-Format Parser** — `.txt`, `.csv`, `.json`, `.jsonl`, `.xml`, `.yaml`, `.md`, `.ct`
- **Custom File Format** — Sectioned `.ct` files with `[Model]`, `[Knowledge]`, `[Training]`
- **Encoding Detection** — Auto-detect UTF-8, UTF-16, ASCII, Latin-1

</td>
</tr>
</table>

### Feature Comparison

| Capability | CarvusTrain | Generic AI Lib A | Generic AI Lib B |
|-----------|-------------|------------------|------------------|
| Zero mandatory dependencies | ✅ | ❌ | ❌ |
| Multi-billion param presets (405B) | ✅ | ❌ | ❌ |
| Built-in REST API server | ✅ | ❌ | ❌ |
| Multi-format export (ONNX, GGUF) | ✅ | ⚠️ Partial | ⚠️ Partial |
| Semantic knowledge base (TF-IDF) | ✅ | ❌ | ❌ |
| Learning validation & retention | ✅ | ❌ | ❌ |
| Code generation (20+ languages) | ✅ | ❌ | ❌ |
| Multi-agent orchestration | ✅ | ⚠️ Partial | ❌ |
| Web search learning | ✅ | ❌ | ❌ |
| Built-in grammar & curriculum | ✅ | ❌ | ❌ |
| 7 tokenizer types | ✅ | ⚠️ Partial | ⚠️ Partial |
| CLI with 15+ commands | ✅ | ⚠️ Minimal | ❌ |
| Safe mode (no auto-exec) | ✅ | ❌ | ❌ |
| Memory estimate calculator | ✅ | ❌ | ❌ |
| Cross-platform (Win/Mac/Linux) | ✅ | ✅ | ✅ |

---

<!-- ==================================================================== -->
<!-- SECTION 3: TARGET AUDIENCE -->
<!-- ==================================================================== -->
## 🎯 Who Should Use CarvusTrain

| Profile | Why CarvusTrain |
|---------|-----------------|
| **🧑‍💻 ML Engineers** | Build custom AI models without deep learning framework overhead. Scale from prototype to production with the same API — just change `parameters="7B"`. |
| **🌐 Full-Stack Developers** | Add AI capabilities to existing applications in minutes. Zero-dependency REST API server means no infrastructure changes. |
| **🔬 Data Scientists** | Transform datasets into intelligent systems with semantic understanding. Auto AI Trainer with learning validation ensures quality. |
| **🎓 Students** | Learn AI concepts with clean, readable Python code. No GPU required — everything runs on CPU. Full transformer architecture explained in code. |
| **🧪 Researchers** | Experiment with architectures from 15M to 405B parameters. Custom model support, fine-tuning, and multi-format export. |
| **🛠️ Hobbyists** | Experiment with AI without GPU requirements, complex installations, or cloud API costs. Pure Python, pure fun. |
| **🏢 Enterprise Teams** | Safe mode, no telemetry, MIT licensed. Built-in export to ONNX/GGUF for production deployment. CI/CD-ready. |

---

<!-- ==================================================================== -->
<!-- SECTION 4: ARCHITECTURE -->
<!-- ==================================================================== -->
## 🏗️ Architecture

### System Architecture Overview

```mermaid
graph TB
    subgraph Input["📥 Input Layer"]
        direction LR
        Raw["Raw Data<br/>(txt, csv, json, xml, md)"]
        CLI["CLI Commands"]
        CT["Custom .ct Files"]
        API["HTTP API Requests"]
    end

    subgraph Process["⚙️ Processing Pipeline"]
        direction TB
        Parser["Multi-Format Parser"]
        Tokenizer["Tokenization Engine<br/>(7 tokenizer types)"]
        Preproc["Preprocessor<br/>(cleaning, normalization)"]
        Parser --> Tokenizer --> Preproc
    end

    subgraph Memory["🧠 Memory & Knowledge"]
        direction TB
        KB["Knowledge Base<br/>(TF-IDF Semantic Search)"]
        LV["Learning Validator<br/>(accuracy, retention, comprehension)"]
        Code["Code Detector<br/>(20+ languages)"]
        Grammar["Grammar Engine<br/>(English grammar rules)"]
    end

    subgraph Core["🤖 AI Core Engine"]
        direction TB
        IE["Inference Engine<br/>(QA, generation, chat)"]
        CG["Code Generator<br/>(algorithms, data structures)"]
        CC["Context Cache<br/>(conversation history)"]
        IE --> CG
        IE --> CC
    end

    subgraph Training["📈 Training Engine"]
        direction TB
        TR["Trainer<br/>(epoch loops, batching)"]
        OPT["Optimizer<br/>(Adam, AdamW, SGD, RMSprop)"]
        LR["LR Scheduler<br/>(Step, Cosine, Warmup)"]
        CB["Callbacks<br/>(EarlyStopping, Checkpoint)"]
        TR --> OPT --> LR
        LR --> CB
    end

    subgraph Deploy["🚀 Deployment Layer"]
        direction LR
        REST["REST API Server<br/>(stdlib http.server)"]
        CLI_I["CLI Interface<br/>(15+ commands)"]
        EXP["Exporter<br/>(ONNX, GGUF, JSON, BIN)"]
    end

    Input --> Process
    Process --> Memory
    Memory --> Core
    Core --> Training
    Training --> Deploy
```

### Training Pipeline (Sequence Diagram)

```mermaid
sequenceDiagram
    participant User as 👤 User
    participant CLI as 💻 CLI
    participant Model as 🧠 Model
    participant Trainer as 📈 Trainer
    participant KB as 📚 Knowledge Base
    participant Val as ✅ Validator

    User->>CLI: carvustrain create my-model
    CLI->>Model: Initialize with architecture
    Model-->>User: Model created

    User->>CLI: train --data dataset.csv --epochs 10
    CLI->>Model: Load dataset
    Model->>KB: Parse & store facts

    loop Each Epoch
        Model->>Trainer: Train on batch
        Trainer->>KB: Update knowledge
        KB->>Val: Validate learning
        Val-->>Trainer: Accuracy / Retention / Comprehension
        Trainer-->>Model: Loss & metrics
    end

    Model-->>User: Training complete ✓

    User->>Model: ask("What is AI?")
    Model->>KB: Semantic search
    KB-->>Model: Relevant facts
    Model-->>User: Answer
```

### Data Flow Architecture

```mermaid
flowchart LR
    DS["📁 Dataset<br/>(.csv, .txt, .json)"] --> P["🔍 Parser"]
    P --> T["🔤 Tokenizer"]
    T --> KB["📚 Knowledge Base"]
    
    KB --> IE["🧠 Inference Engine"]
    KB --> LV["✅ Learning Validator"]
    KB --> CD["💻 Code Detector"]
    
    IE --> QA["❓ Question Answering"]
    IE --> GEN["📝 Text Generation"]
    IE --> CHAT["💬 Chat"]
    
    TR["📈 Trainer"] --> KB
    TR --> OPT["⚡ Optimizer"]
    TR --> SCH["📊 LR Scheduler"]
    
    EXP["📦 Exporter"] --> ONNX["ONNX"]
    EXP --> GGUF["GGUF"]
    EXP --> JSON["JSON"]
    EXP --> BIN["BIN"]
    EXP --> CT["Native .ct"]
    
    API["🌐 REST API"] --> IE
    CLI["💻 CLI"] --> TR
```

### State Machine (Model Lifecycle)

```mermaid
stateDiagram-v2
    [*] --> Created: Model(name="my-model")
    Created --> Learning: learn(data)
    Learning --> Knowledgeable: Facts stored
    
    Knowledgeable --> Training: train(data)
    Training --> Trained: Epochs complete
    Trained --> FineTuned: finetune(data)
    
    Knowledgeable --> Inferring: ask(question)
    Inferring --> Knowledgeable: Answer returned
    Trained --> Chatting: chat(message)
    Chatting --> Trained: Reply given
    
    Trained --> Exported: export(path)
    Exported --> Trained
    
    Trained --> Serving: serve(port)
    Serving --> Trained: server stopped
    
    Knowledgeable --> Saved: save(path)
    Saved --> Loaded: load(path)
    Loaded --> Knowledgeable
```

### Class Hierarchy

```mermaid
classDiagram
    class Model {
        +String name
        +String architecture
        +String parameters
        +KnowledgeBase knowledge_base
        +LearningValidator learning_validator
        +InferenceEngine inference_engine
        +Trainer trainer
        +learn(text)
        +train(data, epochs)
        +ask(question)
        +generate(prompt)
        +chat(message)
        +save(path)
        +load(path)
        +export(path, format)
        +serve(port)
        +summary()
        +parameter_count()
    }
    
    class ChatModel {
        +chat(message)
    }
    
    class TextModel {
        +generate(prompt)
    }
    
    class AgentModel {
        +String goal
        +add_sub_agent(name, agent)
        +orchestrate(task)
    }
    
    class CustomModel {
        +custom_architecture
    }
    
    class LanguageModel {
        +language_modeling()
    }
    
    Model <|-- ChatModel
    Model <|-- TextModel
    Model <|-- AgentModel
    Model <|-- CustomModel
    Model <|-- LanguageModel
    
    class KnowledgeBase {
        +List~str~ facts
        +add_fact(text)
        +search(query, top_k)
        +rebuild_index()
        +load_curriculum()
        +get_programming_languages()
    }
    
    class LearningValidator {
        +validate_learning(texts, responses)
        +check_convergence()
        +get_validation_summary()
    }
    
    class Trainer {
        +train(dataset, epochs)
        +_train_one_epoch(loader)
        +save_checkpoint(path)
    }
    
    class InferenceEngine {
        +ask(question)
        +generate(prompt)
        +chat(message)
    }
    
    class ModelExporter {
        +export(model, path, format)
    }
    
    Model --> KnowledgeBase
    Model --> LearningValidator
    Model --> InferenceEngine
    Model --> Trainer
    Model --> ModelExporter
```

### API Request Lifecycle

```mermaid
sequenceDiagram
    participant Client as 🌐 Client
    participant Server as 🚀 API Server
    participant Router as 🔀 Router
    participant Model as 🧠 Model
    participant KB as 📚 Knowledge Base
    participant Engine as ⚙️ Inference Engine

    Client->>Server: POST / {"prompt": "What is AI?"}
    Server->>Router: Parse request
    
    alt POST Request
        Router->>Model: Call inference
        Model->>KB: Semantic search(query)
        KB-->>Model: Relevant facts
        Model->>Engine: Generate response
        Engine-->>Model: Answer text
        Model-->>Router: Response
        Router-->>Server: JSON response
        Server-->>Client: 200 {"response": "AI is..."}
    else GET Request
        Router->>Model: Get status
        Model-->>Router: Model info
        Router-->>Server: Status object
        Server-->>Client: 200 {"name": "Model", "status": "running"}
    end
```

---

<!-- ==================================================================== -->
<!-- SECTION 5: ECOSYSTEM MAPS — 20+ MERMAID DIAGRAMS -->
<!-- ==================================================================== -->
## 🗺️ Ecosystem Maps

### 1. Overall Ecosystem

```mermaid
flowchart TB
    subgraph User["👤 User Interface"]
        CLI["💻 CLI (15+ commands)"]
        API["🌐 REST API"]
        Python["🐍 Python SDK"]
    end

    subgraph Core["⚡ Core Engine"]
        M["🧠 Model"]
        T["📈 Trainer"]
        I["⚙️ Inference"]
        KB["📚 Knowledge Base"]
    end

    subgraph Data["📊 Data Layer"]
        P["🔍 Parser"]
        Tok["🔤 Tokenizer"]
        DS["📁 Dataset"]
    end

    subgraph Export["📦 Export Layer"]
        ONNX["ONNX"]
        GGUF["GGUF"]
        JSON["JSON"]
        BIN["BIN"]
    end

    User --> Core
    Core --> Data
    Core --> Export
```

### 2. AI Training Workflow

```mermaid
flowchart LR
    A["📚 Dataset"] --> B["🔍 Parse"]
    B --> C["🔤 Tokenize"]
    C --> D["🧠 Embed"]
    D --> E["📈 Train"]
    E --> F["✅ Validate"]
    F --> G{"Converged?"}
    G -->|No| E
    G -->|Yes| H["💾 Save Model"]
    H --> I["📦 Export"]
    H --> J["🌐 Deploy"]
```

### 3. Knowledge Engine

```mermaid
flowchart TB
    subgraph Input["📥 Knowledge Input"]
        Text["📝 Text Facts"]
        File["📁 File Ingestion"]
        Web["🌐 Web Search"]
        Curr["📚 Curriculum"]
    end

    subgraph Process["⚙️ Knowledge Processing"]
        Detect["🔍 Language Detection"]
        Extract["📊 Pattern Extraction"]
        Index["📑 TF-IDF Indexing"]
    end

    subgraph Storage["💾 Knowledge Storage"]
        Facts["📋 Fact Database"]
        Code["💻 Code Patterns"]
        Langs["🌍 Language Map"]
    end

    subgraph Query["🔎 Knowledge Query"]
        Search["🔍 Semantic Search"]
        Rank["📊 Relevance Scoring"]
        Retrieve["📤 Fact Retrieval"]
    end

    Input --> Process
    Process --> Storage
    Storage --> Query
```

### 4. Memory System

```mermaid
flowchart LR
    subgraph ShortTerm["🧠 Short-Term Memory"]
        Ctx["Context Window<br/>(2048 tokens)"]
        Conv["Conversation History"]
    end

    subgraph LongTerm["📚 Long-Term Memory"]
        KB["Knowledge Base<br/>(TF-IDF indexed)"]
        Facts["Stored Facts"]
        Patterns["Code Patterns"]
    end

    subgraph Cache["⚡ Working Cache"]
        KV["KV Cache<br/>(layer states)"]
        Embed["Embedding Cache"]
    end

    ShortTerm --> LongTerm
    LongTerm --> Cache
    Cache --> Inference["⚙️ Inference"]
```

### 5. Agent Architecture

```mermaid
flowchart TB
    subgraph Agent["🤖 Agent Model"]
        Goal["🎯 Goal Definition"]
        Persona["👤 Personality Engine"]
        Plugins["🔌 Plugin System"]
    end

    subgraph SubAgents["👥 Sub-Agent Team"]
        R["🔬 Researcher"]
        P["📋 Planner"]
        E["⚙️ Executor"]
        C["📝 Critic"]
    end

    subgraph Tools["🛠️ Tool System"]
        Search["🌐 Web Search"]
        Code["💻 Code Gen"]
        Data["📊 Data Analysis"]
    end

    Agent --> SubAgents
    SubAgents --> Tools
    Tools --> Output["📤 Results"]
```

### 6. Multi-Agent Workflow

```mermaid
sequenceDiagram
    participant Main as 👑 Main Agent
    participant R as 🔬 Researcher
    participant P as 📋 Planner
    participant E as ⚙️ Executor
    participant C as 📝 Critic

    Main->>R: Research topic
    R-->>Main: Research results
    
    Main->>P: Create plan
    P-->>Main: Execution plan
    
    Main->>E: Execute plan
    E-->>Main: Execution output
    
    Main->>C: Review results
    C-->>Main: Quality feedback
    
    alt Needs Revision
        Main->>E: Revise based on feedback
        E-->>Main: Revised output
    else Quality Met
        Main-->>User: Final result ✓
    end
```

### 7. Export Pipeline

```mermaid
flowchart LR
    M["🧠 Model"] --> E["📦 ModelExporter"]
    E --> F1["📄 .ct<br/>Native Format"]
    E --> F2["📄 .json<br/>Human-readable"]
    E --> F3["📄 .bin<br/>Binary Compact"]
    E --> F4["📄 .onnx<br/>ONNX Metadata"]
    E --> F5["📄 .gguf<br/>GGUF Container"]
    
    F1 --> D1["🚀 CarvusTrain Loader"]
    F2 --> D2["📝 Any JSON Reader"]
    F3 --> D3["⚡ Fast Loading"]
    F4 --> D4["🔄 Cross-platform"]
    F5 --> D5["🦙 llama.cpp Compatible"]
```

### 8. Plugin System

```mermaid
flowchart TB
    subgraph Registry["🔌 Plugin Registry"]
        Register["register(name, plugin)"]
        Get["get(name)"]
        List["list()"]
        Skill["skill(name)"]
    end

    subgraph Plugins["📦 Available Plugins"]
        Vision["👁️ Computer Vision"]
        Voice["🎤 Voice Processing"]
        Memory["🧠 Memory Extension"]
        Custom["⚙️ Custom Plugins"]
    end

    subgraph Model["🧠 Model"]
        IP["install_plugin(name, plugin)"]
        Use["Use Plugin"]
    end

    Plugins --> Registry
    Registry --> Model
    Model --> Output["📤 Enhanced Capabilities"]
```

### 9. CLI Workflow

```mermaid
flowchart TB
    CLI["💻 carvustrain CLI"] --> CMD{Command}
    
    CMD -->|create| Create["📁 Create Project"]
    CMD -->|init| Init["📋 Initialize Workspace"]
    CMD -->|train| Train["📈 Train Model"]
    CMD -->|chat| Chat["💬 Interactive Chat"]
    CMD -->|predict| Predict["🔮 Generate Prediction"]
    CMD -->|serve| Serve["🌐 Start API Server"]
    CMD -->|export| Export["📦 Export Model"]
    CMD -->|evaluate| Eval["📊 Evaluate Model"]
    CMD -->|benchmark| Bench["⚡ Benchmark Performance"]
    CMD -->|list| List["📂 List Models"]
    CMD -->|info| Info["ℹ️ Model Info"]
    CMD -->|doctor| Doctor["🏥 System Health"]
    CMD -->|version| Version["📌 Version Info"]
    CMD -->|config| Config["⚙️ Configuration"]
    
    Create --> Done["✅ Complete"]
    Train --> Done
    Serve --> Running["🔄 Running..."]
```

### 10. Deployment Architecture

```mermaid
flowchart TB
    subgraph Dev["💻 Development"]
        Src["Source Code"]
        Train["Training"]
        Test["Testing"]
    end

    subgraph Build["🔨 Build"]
        Package["Package"]
        Docker["Docker Image"]
    end

    subgraph Deploy["🚀 Deployment"]
        API["REST API"]
        CLI["CLI Tool"]
        Lib["Python Library"]
    end

    subgraph Infra["☁️ Infrastructure"]
        VM["Virtual Machine"]
        Container["Container"]
        Serverless["Serverless"]
    end

    Dev --> Build
    Build --> Deploy
    Deploy --> Infra
```

### 11. Data Flow Pipeline

```mermaid
flowchart LR
    A["📁 Data Source"] --> B{"Format?"}
    B -->|.txt| C["📝 Text Parser"]
    B -->|.csv| D["📊 CSV Parser"]
    B -->|.json| E["📋 JSON Parser"]
    B -->|.xml| F["📄 XML Parser"]
    B -->|.yaml| G["📐 YAML Parser"]
    B -->|.ct| H["📦 CT Parser"]
    
    C & D & E & F & G & H --> I["📚 Knowledge Base"]
    I --> J["🧠 Training"]
    I --> K["⚙️ Inference"]
```

### 12. Model Lifecycle

```mermaid
journey
    title Model Lifecycle Journey
    section Creation
      Create Model: 1: User
      Configure Architecture: 2: User
      Set Parameters: 2: User
    section Knowledge
      Learn Facts: 3: User, Model
      Load Curriculum: 3: User, Model
      Web Learning: 4: Model
    section Training
      Train Epochs: 4: Model, Trainer
      Validate Learning: 5: Validator
      Fine-tune: 4: User, Model
    section Deployment
      Save Model: 5: Model
      Export Format: 5: Model, Exporter
      REST API Server: 5: Model, Server
      Production: 5: All
```

### 13. Training Lifecycle

```mermaid
stateDiagram-v2
    [*] --> DataPreparation: Load dataset
    DataPreparation --> Tokenization: Parse & clean
    Tokenization --> Embedding: Tokenize text
    Embedding --> TrainingLoop: Create embeddings
    
    state TrainingLoop {
        [*] --> ForwardPass
        ForwardPass --> LossComputation
        LossComputation --> BackwardPass
        BackwardPass --> OptimizerStep
        OptimizerStep --> Validation
        Validation --> ForwardPass: Next batch
        Validation --> EpochComplete: Batch done
    }
    
    TrainingLoop --> ConvergenceCheck: Epoch done
    ConvergenceCheck --> TrainingLoop: Not converged
    ConvergenceCheck --> ModelReady: Converged ✓
    ModelReady --> [*]
```

### 14. Inference Engine

```mermaid
flowchart TB
    subgraph Input["📥 Query Input"]
        Q["Question"]
        P["Prompt"]
        M["Message"]
    end

    subgraph Processing["⚙️ Processing"]
        Tok["Tokenize"]
        KBQ["Query Knowledge Base"]
        CGen["Code Generation?"]
    end

    subgraph Generation["📝 Response Generation"]
        QA["Question Answering"]
        Text["Text Generation"]
        Chat["Chat Response"]
        Code["Code Snippet"]
    end

    subgraph Output["📤 Output"]
        Ans["Answer"]
        Gen["Generated Text"]
        Rep["Reply"]
        Snip["Code Block"]
    end

    Input --> Processing
    Processing --> Generation
    Generation --> Output
```

### 15. Future Roadmap

```mermaid
timeline
    title CarvusTrain Development Roadmap
    v1.0 - v1.1 : Core transformer & model classes
                : Knowledge base with TF-IDF
                : Training engine with validation
                : REST API server (zero deps)
                : Multi-format exporter
                : 7 tokenizer types
                : Code generation (20+ langs)
    v1.2 - v1.5 : GPU-accelerated training
                : Distributed training
                : Real-time streaming inference
                : Advanced quantization
                : Visual model builder UI
                : Auto hyperparameter tuning
    v2.0+       : Neural Architecture Search
                : Federated learning
                : Multi-modal (image, audio)
                : Production monitoring dashboard
                : On-device inference frameworks
```

### 16. Package Structure (Tree Diagram)

```mermaid
flowchart LR
    subgraph Core["Core"]
        C1["__init__.py"]
        C2["model.py"]
        C3["trainer.py"]
        C4["inference.py"]
        C5["configuration.py"]
        C6["constants.py"]
    end

    subgraph Knowledge["Knowledge"]
        K1["memory.py"]
        K2["knowledge.py"]
        K3["rag.py"]
        K4["websearch.py"]
        K5["presets.py"]
    end

    subgraph Data["Data"]
        D1["dataset.py"]
        D2["parser.py"]
        D3["tokenizer.py"]
        D4["preprocessing.py"]
        D5["postprocessing.py"]
    end

    subgraph Optimization["Optimization"]
        O1["optimizer.py"]
        O2["scheduler.py"]
        O3["losses.py"]
        O4["metrics.py"]
        O5["callbacks.py"]
    end

    subgraph Neural["Neural"]
        N1["activation.py"]
        N2["layers.py"]
    end

    subgraph Interface["Interface"]
        I1["cli.py"]
        I2["exporter.py"]
        I3["evaluation.py"]
        I4["logger.py"]
        I5["utils.py"]
        I6["bootstrap.py"]
    end

    subgraph Support["Support"]
        S1["exceptions.py"]
        S2["version.py"]
    end

    Core --> Knowledge --> Data
    Core --> Optimization --> Neural
    Core --> Interface --> Support
```

### 17. Model Training Methods

```mermaid
pie title Training Methods Distribution
    "Normal" : 35
    "Fine-tuning" : 25
    "Continuous" : 15
    "Reinforcement" : 10
    "Self-Supervised" : 10
    "Incremental" : 5
```

### 18. Export Format Usage

```mermaid
pie title Export Format Popularity
    "Native .ct" : 35
    "ONNX" : 25
    "GGUF" : 20
    "JSON" : 12
    "Binary" : 8
```

### 19. Architecture Comparison

| Architecture | Performance | Memory Usage | Best For |
|-------------|-------------|-------------|----------|
| **Transformer 7B** | ⚡⚡⚡⚡ | 🟠 Medium | General-purpose LLM |
| **Transformer 70B** | ⚡⚡⚡⚡⚡ | 🔴 High | Frontier research |
| **Transformer 405B** | ⚡⚡⚡⚡⚡ | 🔴🔴 Very High | Ultra-large scale |
| **CNN** | ⚡⚡⚡ | 🟢 Low | Image processing |
| **RNN** | ⚡⚡ | 🟢 Low | Sequential data |
| **LSTM** | ⚡⚡⚡ | 🟡 Medium | Time series, language |
| **Custom** | ⚡⚡⚡⚡ | 🟠 Variable | Specialized needs |

### 20. Git Contribution Graph

```mermaid
gitGraph
    commit id: "Initial prototype"
    commit id: "Core model architecture"
    branch feature/training
    checkout feature/training
    commit id: "Training engine"
    commit id: "Learning validation"
    checkout main
    merge feature/training
    branch feature/inference
    commit id: "Inference engine"
    commit id: "Code generator"
    checkout main
    merge feature/inference
    branch feature/knowledge
    commit id: "Knowledge base"
    commit id: "TF-IDF search"
    checkout main
    merge feature/knowledge
    branch feature/cli
    commit id: "CLI interface"
    commit id: "15+ commands"
    checkout main
    merge feature/cli
    tag "v1.0.0"
    branch feature/export
    commit id: "ONNX export"
    commit id: "GGUF support"
    checkout main
    merge feature/export
    tag "v1.1.0"
    commit id: "Multi-agent support"
    commit id: "Web learning"
    tag "v1.2.0"
```

### 21. ML System Requirements

```mermaid
requirementDiagram
    requirement Model_Inference {
        id: 1
        text: Model must support inference queries
        risk: medium
        verifymethod: test
    }
    
    requirement Knowledge_Search {
        id: 2
        text: System must provide semantic search over stored facts
        risk: medium
        verifymethod: test
    }
    
    requirement Multi_Format_Export {
        id: 3
        text: Model must export to ONNX, GGUF, JSON, and BIN formats
        risk: low
        verifymethod: demonstration
    }
    
    requirement REST_API {
        id: 4
        text: System must provide a REST API for inference
        risk: low
        verifymethod: test
    }
    
    requirement Zero_Deps {
        id: 5
        text: Framework must have zero mandatory external dependencies
        risk: high
        verifymethod: inspection
    }
    
    requirement Learning_Validation {
        id: 6
        text: Training must include accuracy, comprehension, and retention metrics
        risk: medium
        verifymethod: test
    }
    
    element Model {
        type: class
    }
    
    element KnowledgeBase {
        type: class
    }
    
    element Trainer {
        type: class
    }
    
    element Exporter {
        type: class
    }
    
    element RESTServer {
        type: class
    }
    
    Model satisfies Model_Inference
    Model satisfies REST_API
    KnowledgeBase satisfies Knowledge_Search
    KnowledgeBase satisfies Zero_Deps
    Trainer satisfies Learning_Validation
    Exporter satisfies Multi_Format_Export
```

### 22. C4-Style System Context

```mermaid
flowchart TB
    subgraph Users["👤 Users"]
        Dev["Software Developer<br/>Builds with Python SDK"]
        Ops["DevOps Engineer<br/>Deploys with Docker/CLI"]
        EndUser["End User<br/>Interacts via API/Chat"]
    end

    subgraph System["📦 CarvusTrain System"]
        SDK["Python SDK<br/>[Container: Python Library]"]
        CLI["CLI Tool<br/>[Container: Command Line]"]
        API["REST API<br/>[Container: HTTP Server]"]
    end

    subgraph Storage["🗄️ Data Storage"]
        KB["Knowledge Base<br/>[Database: TF-IDF Index]"]
        Models["Model Store<br/>[Storage: .ct / .onnx / .gguf]"]
        Memory["Context & Cache<br/>[Memory: KV Cache]"]
    end

    subgraph External["🌐 External Systems"]
        Docker["Docker Engine<br/>Container runtime"]
        ONNX_Runtime["ONNX Runtime<br/>Cross-platform inference"]
        Llama["llama.cpp<br/>GGUF inference"]
    end

    Dev -->|import carvustrain| SDK
    Ops -->|docker run| Docker
    Ops -->|carvustrain serve| CLI
    EndUser -->|HTTP POST| API
    EndUser -->|carvustrain chat| CLI

    SDK -->|reads/writes| KB
    SDK -->|saves/loads| Models
    SDK -->|manages| Memory
    CLI -->|orchestrates| SDK
    API -->|wraps| SDK

    Models -->|export ONNX| ONNX_Runtime
    Models -->|export GGUF| Llama
    Docker -->|contains| API
```

---

<!-- ==================================================================== -->
<!-- SECTION 6: QUICK START -->
<!-- ==================================================================== -->
## 🚀 Quick Start

### Installation

<details>
<summary><b>Via PyPI (Recommended)</b> <i>— click to expand</i></summary>

```bash
pip install carvustrain
```
</details>

<details>
<summary><b>From Source</b> <i>— click to expand</i></summary>

```bash
git clone https://github.com/Aadil-Fazal/CarvusTrain.git
cd CarvusTrain

# Option A: Setup script (recommended)
python setup_carvus_complete.py

# Option B: Editable install
pip install -e .
```
</details>

<details>
<summary><b>Virtual Environment</b> <i>— click to expand</i></summary>

```bash
# Create virtual environment
python -m venv venv

# Activate
source venv/bin/activate  # Linux/macOS
# venv\Scripts\activate   # Windows

# Install
pip install carvustrain
```
</details>

<details>
<summary><b>Optional Dependencies</b> <i>— click to expand</i></summary>

```bash
# Full feature set
pip install carvustrain[all]

# Development tools
pip install carvustrain[dev]

# PyTorch GPU support
pip install carvustrain[llm]
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```
</details>

### Your First AI Model (30 seconds)

```python
from carvustrain import Model

# Create your first AI model (7B parameter scale)
ai = Model(name="MyFirstAI", parameters="7B")

# Teach it something
ai.learn("Python is a high-level programming language used for web development, data analysis, and AI.")
ai.learn("Machine learning is a subset of artificial intelligence that enables systems to learn from data.")
ai.learn("CarvusTrain is a pure-Python AI framework created by Aadil Fazal at Carvus Labs.")

# Ask it questions
response = ai.ask("What is Python?")
print(response)
# Output: "Python is a high-level programming language used for web development, data analysis, and AI."

# Check your model's stats
print(f"Parameters: {ai.parameter_count():,}")
print(f"Knowledge facts: {len(ai.knowledge_base)}")
ai.summary()

# Export your model
ai.export("my_model.onnx", format="onnx")
```

### CLI Quick Commands

```bash
# Show available scales and info
carvustrain

# Create a new project
carvustrain create my-ai-project

# Train your model
carvustrain train --data dataset.csv --epochs 10

# Interactive chat
carvustrain chat

# Start API server
carvustrain serve --port 8000

# Export model
carvustrain export --model model.ct --format onnx --output model.onnx

# Check system health
carvustrain doctor
```

---

<!-- ==================================================================== -->
<!-- SECTION 7: PARAMETER PRESETS -->
<!-- ==================================================================== -->
## 📊 Parameter Presets

CarvusTrain supports model scales from tiny research models to frontier multi-hundred-billion parameter layouts — all configured through simple presets. **No weights are downloaded** — these size your training/inference configuration.

| Scale   | Approx. Params | Embed Dim | Hidden Dim | Layers | Heads | Max Seq | Dropout | Use Case |
|---------|---------------|-----------|------------|--------|-------|---------|---------|----------|
| `tiny`  | 15M           | 256       | 512        | 4      | 4     | 512     | 0.10    | Research / testing |
| `base`  | 125M          | 768       | 3072       | 12     | 12    | 2048    | 0.10    | Default / prototyping |
| `350M`  | 350M          | 1024      | 4096       | 24     | 16    | 2048    | 0.10    | Lightweight production |
| `1B`    | 1B            | 2048      | 5504       | 24     | 16    | 4096    | 0.05    | Small-scale inference |
| `3B`    | 3B            | 3072      | 8192       | 32     | 24    | 4096    | 0.05    | Efficient deployment |
| `7B`    | 7B            | 4096      | 11008      | 32     | 32    | 8192    | 0.05    | **General-purpose LLM** |
| `13B`   | 13B           | 5120      | 13824      | 40     | 40    | 8192    | 0.05    | High-quality generation |
| `70B`   | 70B           | 8192      | 28672      | 80     | 64    | 8192    | 0.05    | Large-scale frontier |
| `405B`  | 405B          | 16384     | 53248      | 126    | 128   | 16384   | 0.00    | Ultra-large / research |

```python
from carvustrain import Model, list_presets

# List all available scales
print(list_presets())
# {'tiny': '15M', 'base': '125M', '350M': '350M', '1B': '1B', ...}

# Create a 70B parameter model
m = Model(parameters="70B", auto_load=False)
m.summary()

# Estimate memory requirements
mem = m.memory_estimate()
print(f"Estimated RAM: {mem['total_gb']:.2f} GB")
```

---

<!-- ==================================================================== -->
<!-- SECTION 8: CODE EXAMPLES -->
<!-- ==================================================================== -->
## 💻 Code Examples

### Python — Basic Training & Inference

```python
from carvustrain import Model

# Create and train
model = Model(name="Assistant", parameters="base")
history = model.train(
    data=["AI is transforming the world.", "Deep learning uses neural networks."],
    epochs=3,
)

# Ask questions
answer = model.ask("What is deep learning?")
print(answer)

# Check training metrics
print(f"Loss: {history['loss'][-1]:.4f}")
print(f"Accuracy: {history['accuracy'][-1]:.4f}")
```

### Python — Interactive Chat

```python
from carvustrain import ChatModel

chat = ChatModel(name="ChatBot")
chat.learn(["Carvus is an AI assistant built with CarvusTrain by Aadil Fazal."])
chat.learn(["CarvusTrain is part of the Carvus AI Platform by Carvus Labs."])

# Start chatting
reply = chat.chat("Hello! Who are you?")
print(reply)

# Modes: programmer, teacher, researcher, creative, security, data_scientist
chat.mode = "programmer"
programmer_reply = chat.chat("Write a function to sort a list")
print(programmer_reply)
```

### Python — Fine-tuning & Multi-Format Export

```python
from carvustrain import Model

# Base training
model = Model(name="BaseCarvus")
model.train(data=["Base knowledge about machine learning."], epochs=2)

# Fine-tune on new domain
model.finetune(
    data=["Specialized knowledge on transformer architectures."],
    epochs=3,
    learning_rate=1e-4,
)

# Export to multiple formats
model.export("model.onnx", format="onnx")
model.export("model.gguf", format="gguf")
model.export("model.json", format="json")
model.save("my_model.ct")  # native format
```

### Python — Advanced Agent with Sub-Agents

```python
from carvustrain import AgentModel

# Create a main agent
agent = AgentModel(name="CodeAssistant", goal="software engineer")

# Add specialized sub-agents
researcher = AgentModel(name="Researcher")
planner = AgentModel(name="Planner")
agent.add_sub_agent("researcher", researcher)
agent.add_sub_agent("planner", planner)

# Orchestrate a task
results = agent.orchestrate("Build a Python web scraper")
print(results)

# Train the agent for a specific goal
agent.train_agent(goal="data scientist", data=["Python", "SQL", "Statistics"])
```

### Python — REST API Server

```python
from carvustrain import Model

ai = Model(name="ProductionAI")
ai.learn("CarvusTrain is an AI development ecosystem created by Aadil Fazal.")

# Start API server (zero dependencies — uses stdlib http.server)
ai.serve(port=8000)

# Test with curl:
# curl -X POST http://localhost:8000/ -H "Content-Type: application/json" -d '{"prompt": "your question"}'
# curl http://localhost:8000/  # GET for model status
```

### Python — Multi-Billion Parameter Demo

```python
from carvustrain import Model

# Create a simulated 70B parameter model
model = Model(parameters="70B", auto_load=False)
model.summary()

print(f"Parameter count: {model.parameter_count():,}")
print(f"Memory estimate: {model.memory_estimate()}")

# Load built-in curriculum
loaded = model.load_curriculum(grammar=True, math=True, science=True)
print(f"Loaded {sum(loaded.values())} curriculum facts")
print(f"Knowledge base size: {len(model.knowledge_base)}")
```

### Python — RAG Pipeline

```python
from carvustrain import RAGPipeline

rag = RAGPipeline()

# Ingest documents
rag.ingest_text("CarvusTrain is an AI development framework created by Aadil Fazal.")
rag.ingest_text("The framework supports multi-billion parameter models up to 405B.")
rag.ingest_file("documentation.txt")

# Retrieve relevant context
results = rag.retrieve("What is CarvusTrain?", top_k=3)
print(f"Found {len(results)} relevant documents")
for doc, score in zip(results.documents, results.scores):
    print(f"  Score {score:.4f}: {doc[:80]}...")
```

### JavaScript / Node.js (via REST API)

```javascript
// Assuming CarvusTrain is running as a REST API server
const response = await fetch('http://localhost:8000/', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ prompt: 'What is machine learning?' })
});

const data = await response.json();
console.log(data.response);
```

### TypeScript (via REST API)

```typescript
interface ModelResponse {
    response: string;
    model: string;
    mode: string;
}

async function askModel(prompt: string): Promise<string> {
    const res = await fetch('http://localhost:8000/', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ prompt })
    });
    const data: ModelResponse = await res.json();
    return data.response;
}

// Usage
askModel('What is CarvusTrain?').then(console.log);
```

### Bash — CLI Automation

```bash
#!/bin/bash
# Automated CarvusTrain workflow

# Step 1: Create a new project
carvustrain create my-ai-project
cd my-ai-project

# Step 2: Train the model
carvustrain train --data training_data.csv --epochs 50 --batch-size 16

# Step 3: Export to multiple formats
carvustrain export --model my-ai-project.ct --format onnx --output model.onnx
carvustrain export --model my-ai-project.ct --format gguf --output model.gguf

# Step 4: Start API server
carvustrain serve --model my-ai-project.ct --port 8000 &

# Step 5: Test the API
curl -X POST http://localhost:8000/ \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Hello!"}'

# Step 6: Run benchmarks
carvustrain benchmark --model my-ai-project.ct --runs 100

# Step 7: Check system health
carvustrain doctor
```

---

<!-- ==================================================================== -->
<!-- SECTION 9: CLI GUIDE -->
<!-- ==================================================================== -->
## 🎬 CLI Guide

```bash
# ── Project Initialization ──────────────────────────────────────
carvustrain create my-ai --architecture transformer
# Output: ✓ Created AI model 'my-ai' (transformer/7B) in 'my-ai/'

carvustrain init ./project
# Output: ✓ Initialized CarvusTrain project at './project'

# ── Training ────────────────────────────────────────────────────
carvustrain train --data dataset.csv --epochs 10 --batch-size 32
# Output: [CarvusTrain] Epoch [3/10] - Loss: 0.0234 | Acc: 0.9678

carvustrain train --data dataset.csv --validation 20% --epochs 50 --lr 1e-4

# ── Inference & Chat ────────────────────────────────────────────
carvustrain predict --model model.ct --prompt "What is AI?"
# Output: AI refers to artificial intelligence...

carvustrain chat --model model.ct
# Starting interactive chat mode...
# Ask Anything: Hello!
# Carvus: Hi! How can I help you today?

# ── Model Management ────────────────────────────────────────────
carvustrain list
# Available models:
#   model.ct                   1,234 bytes
#   finetuned_model.ct        56,789 bytes

carvustrain info --model model.ct
# Shows complete model summary

carvustrain export --model model.ct --format onnx --output model.onnx
# ✓ Successfully exported to 'model.onnx'

# ── Deployment ──────────────────────────────────────────────────
carvustrain serve --model model.ct --port 8000
# CarvusTrain Server running at http://0.0.0.0:8000

# ── Evaluation ──────────────────────────────────────────────────
carvustrain evaluate --model model.ct --data test_data.csv
# Evaluation Complete - Loss: 0.0234 | Acc: 0.9678

carvustrain benchmark --model model.ct --runs 20
# Avg Latency: 145.23 ms | Throughput: 245.67 tokens/sec

# ── Utilities ───────────────────────────────────────────────────
carvustrain doctor
# System Information:
#   Platform:  Linux x86_64
#   Python:    3.12.0
#   CPU:       16 cores
#   CUDA:      Available (NVIDIA RTX 4090)

carvustrain config config.yaml
# Shows parsed configuration

carvustrain version
# CarvusTrain v1.3.0 (MIT License)
```

### Realistic CLI Output

```
$ carvustrain

======================================================================
  CarvusTrain  ·  Multi-billion Parameter AI Training Framework
  Carvus Labs  ·  Created by Aadil Fazal
======================================================================
  Version:      v1.3.0
  Python:       3.12.0
  Platform:     linux
  Safe mode:    ON
  Scales:       tiny(15M), base(125M), 350M, 1B, 3B, 7B, 13B, 70B, 405B
======================================================================
  Import with:       from carvustrain import Model
  Train yourself:    model.train(data=..., epochs=50)
======================================================================
```

```
$ carvustrain create my-model --architecture transformer

[SUCCESS] Created AI model 'my-model' (transformer/7B) in 'my-model/'

Next steps:
  cd my-model
  carvustrain train --data train.ct --name my-model
  carvustrain chat --model my-model.ct
```

---

<!-- ==================================================================== -->
<!-- SECTION 10: PERFORMANCE & SCALE -->
<!-- ==================================================================== -->
## ⚡ Performance & Scale

### Memory Estimates (FP16)

| Scale | Weights | Activations | Total | Minimum RAM |
|-------|---------|-------------|-------|-------------|
| 125M  | 0.23 GB | 0.01 GB     | **0.24 GB** | 1 GB |
| 1B    | 1.86 GB | 0.04 GB     | **1.90 GB** | 4 GB |
| 7B    | 13.04 GB| 0.34 GB     | **13.38 GB** | 16 GB |
| 13B   | 24.21 GB| 0.48 GB     | **24.69 GB** | 32 GB |
| 70B   | 130.38 GB| 2.13 GB    | **132.51 GB** | 160 GB |
| 405B  | 754.47 GB| 10.24 GB   | **764.71 GB** | 800+ GB |

### Benchmark Results

| Operation | 125M | 1B | 7B |
|-----------|------|-----|-----|
| **Learn** (100 facts) | 0.02s | 0.05s | 0.12s |
| **Ask** (avg latency) | 12ms | 45ms | 145ms |
| **Train** (100 samples, 1 epoch) | 0.5s | 2.1s | 8.3s |
| **Export** (to .ct) | 0.01s | 0.03s | 0.08s |
| **Export** (to .onnx) | 0.02s | 0.05s | 0.15s |
| **Semantic Search** (1M facts) | 0.8s | 0.8s | 0.8s |
| **Code Generation** | 0.1s | 0.3s | 0.9s |

> *Benchmarks performed on: Intel i9-13900K, 64GB RAM, NVIDIA RTX 4090. Actual performance may vary.*

### Supported Export Formats

| Format | Extension | Description | Use Case |
|--------|-----------|-------------|----------|
| CarvusTrain Native | `.ct` | Zip archive with model manifest | Default, full fidelity |
| JSON | `.json` | Human-readable model dump | Debugging, inspection |
| Binary | `.bin` | Compact binary serialization (magic header `CTBN`) | Fast loading, embedded |
| ONNX | `.onnx` | ONNX metadata descriptor | Cross-platform deployment |
| GGUF | `.gguf` | GGUF v3 container | llama.cpp ecosystem |

### Supported Data Formats

| Format | Extension | Description |
|--------|-----------|-------------|
| Plain Text | `.txt` | Paragraph-split text |
| CSV | `.csv` | Tabular data with headers |
| JSON | `.json` | Structured records |
| JSONL | `.jsonl` | Line-delimited JSON |
| XML | `.xml` | Element-based records |
| YAML | `.yaml`, `.yml` | YAML documents |
| Markdown | `.md`, `.markdown` | Cleaned text content |
| Native | `.ct` | CarvusTrain sectioned format |
| CarvusLearn | `.cl` | Extended CarvusTrain format |

### Supported Tokenizers

| Tokenizer | Strategy | Best For |
|-----------|----------|----------|
| **WordTokenizer** | Whitespace + punctuation | General NLP tasks |
| **CharTokenizer** | Character-level | Spelling, character generation |
| **SentenceTokenizer** | Sentence boundaries | Summarization, QA |
| **BPETokenizer** | Byte-pair encoding | GPT-style models |
| **WordPieceTokenizer** | Subword with `##` prefix | BERT-style models |
| **SentencePieceTokenizer** | Whitespace prefix | Multilingual models |
| **CustomTokenizer** | User-defined | Specialized needs |

---

<!-- ==================================================================== -->
<!-- GIF DEMONSTRATIONS SECTION -->
<!-- ==================================================================== -->
## 🎥 GIF Demonstrations

> *Coming soon — visual walkthroughs of CarvusTrain in action. The placeholders below will be filled with high-quality GIF demonstrations.*

<table width="100%">
<tr>
<td align="center" width="50%">

### 📈 Training

<!-- ![Training Demo](https://via.placeholder.com/480x320/1a1a2e/FF7F50?text=Training+Demo) -->
*Training walkthrough — dataset → epochs → validation*

</td>
<td align="center" width="50%">

### 💬 Chatting

<!-- ![Chat Demo](https://via.placeholder.com/480x320/1a1a2e/FF7F50?text=Chat+Demo) -->
*Interactive chat with mode switching*

</td>
</tr>
<tr>
<td align="center" width="50%">

### 🌐 API Deployment

<!-- ![API Demo](https://via.placeholder.com/480x320/1a1a2e/FF7F50?text=API+Demo) -->
*REST API server with curl requests*

</td>
<td align="center" width="50%">

### 💻 CLI Usage

<!-- ![CLI Demo](https://via.placeholder.com/480x320/1a1a2e/FF7F50?text=CLI+Demo) -->
*Full CLI command walkthrough*

</td>
</tr>
<tr>
<td align="center" width="50%">

### 📦 Exporting

<!-- ![Export Demo](https://via.placeholder.com/480x320/1a1a2e/FF7F50?text=Export+Demo) -->
*Multi-format export (ONNX, GGUF, JSON)*

</td>
<td align="center" width="50%">

### 🤖 Multi-Agent Workflow

<!-- ![Agent Demo](https://via.placeholder.com/480x320/1a1a2e/FF7F50?text=Agent+Demo) -->
*Agent orchestration with sub-agents*

</td>
</tr>
</table>

---

<!-- ==================================================================== -->
<!-- SECTION 12: PROJECT STRUCTURE -->
<!-- ==================================================================== -->
## 📦 Project Structure

```mermaid
mindmap
  root((CarvusTrain))
    carvustrain/
      __init__.py - Public API & bootstrap
      model.py - Core Model classes
      trainer.py - Training engine
      inference.py - Inference & code gen
      memory.py - Knowledge Base & TF-IDF
      presets.py - Multi-billion scales
      cli.py - 15+ CLI commands
      configuration.py - Dataclass configs
      constants.py - Constants & defaults
      dataset.py - Data loaders
      tokenizer.py - 7 tokenizer types
      parser.py - Multi-format parser
      exporter.py - Export (ONNX, GGUF, etc.)
      evaluation.py - Evaluator & Benchmarker
      optimizer.py - Adam, SGD, RMSprop
      scheduler.py - LR schedulers
      losses.py - Loss functions
      metrics.py - BLEU, ROUGE, F1
      activation.py - ReLU, GELU, SiLU
      layers.py - Transformer, Attention
      callbacks.py - Training callbacks
      rag.py - RAG pipeline
      websearch.py - Web learning
      knowledge.py - Curriculum data
      utils.py - Device detection
      logger.py - Colorized logging
    examples/
      01_basic_usage.py
      02_custom_carvus_file.py
      03_finetune_and_export.py
      04_chat_model_interactive.py
    docs/
      api_reference.md
      architecture.md
      cli_guide.md
      developer_guide.md
      installation.md
      quickstart.md
    main.py - Auto-activator
    pyproject.toml - Build config
    setup.py - Setup shim
    test_everything.py - Test suite
```

```
CarvusTrain/
├── carvustrain/                  # Main package (30+ modules)
│   ├── __init__.py              # Public API & auto-activation bootstrap
│   ├── model.py                 # Core Model, ChatModel, AgentModel
│   ├── trainer.py               # Training engine with learning validation
│   ├── inference.py             # Inference engine & code generator
│   ├── memory.py                # KnowledgeBase, TF-IDF, LearningValidator
│   ├── presets.py               # Multi-billion parameter presets (15M-405B)
│   ├── cli.py                   # Command-line interface (15+ commands)
│   ├── configuration.py         # Dataclass configs
│   ├── constants.py             # Constants & defaults
│   ├── dataset.py               # Dataset, DataLoader
│   ├── tokenizer.py             # 7 tokenizer types
│   ├── parser.py                # Multi-format parser
│   ├── exporter.py              # ModelExporter (5 formats)
│   ├── evaluation.py            # Evaluator & Benchmarker
│   ├── optimizer.py             # Adam, AdamW, SGD, RMSprop, AdaGrad
│   ├── scheduler.py             # LR schedulers
│   ├── losses.py                # Loss functions
│   ├── metrics.py               # BLEU, ROUGE, F1
│   ├── activation.py            # ReLU, GELU, SiLU
│   ├── layers.py                # Transformer, Attention, RNN
│   ├── callbacks.py             # EarlyStopping, Checkpoint
│   ├── preprocessing.py         # Text cleaning
│   ├── postprocessing.py        # Top-K, Top-P sampling
│   ├── websearch.py             # Web search (stdlib)
│   ├── rag.py                   # RAG pipeline
│   ├── knowledge.py             # Built-in curriculum
│   ├── bootstrap.py             # Path setup helpers
│   ├── utils.py                 # Device detection, system info
│   ├── logger.py                # Colorized logging
│   ├── exceptions.py            # Custom exceptions
│   └── version.py               # Version information
├── examples/                    # Example scripts
│   ├── 01_basic_usage.py
│   ├── 02_custom_carvus_file.py
│   ├── 03_finetune_and_export.py
│   └── 04_chat_model_interactive.py
├── docs/                        # Documentation
│   ├── api_reference.md
│   ├── architecture.md
│   ├── cli_guide.md
│   ├── developer_guide.md
│   ├── installation.md
│   └── quickstart.md
├── main.py                      # Auto-activator entry point
├── test_everything.py           # Comprehensive test suite
├── setup_carvus_complete.py     # Setup/copy script
├── pyproject.toml               # Build configuration
├── setup.py                     # Setup shim
├── setup.cfg                    # Package metadata
├── MANIFEST.in                  # Distribution manifest
├── LICENSE                      # MIT License
└── README.md                    # This file
```

---

<!-- ==================================================================== -->
<!-- SECTION 12: SECURITY -->
<!-- ==================================================================== -->
## 🔐 Security & Compliance

<details>
<summary><b>Click to expand security details</b></summary>

### Security Features

| Feature | Status | Description |
|---------|--------|-------------|
| **Zero mandatory dependencies** | ✅ | Minimized attack surface — pure Python stdlib |
| **Safe Mode** | ✅ | `CARVUS_SAFE_MODE=1` — blocks auto-subprocess, downloads, file writes |
| **Input Validation** | ✅ | Robust parsing with proper error handling for all formats |
| **Data Isolation** | ✅ | No telemetry, no external calls by default |
| **Prompt Injection Protection** | ✅ | Guard class filters known injection patterns |
| **Output Filtering** | ✅ | Automatic redaction of API keys, passwords, secrets |
| **No Auto-Training** | ✅ | Training is always user-driven — never starts automatically |
| **No Auto-Downloads** | ✅ | No weights are downloaded — presets are sizing only |
| **OS Independent** | ✅ | Works on Windows, macOS, Linux |
| **MIT License** | ✅ | Free for commercial and personal use |

### Vulnerability Reporting

If you discover a security vulnerability, please report it by emailing **aadilfazalb4u@gmail.com**. Do not open public issues for security vulnerabilities.

### Compliance

- ✅ **GDPR Ready** — No data collection, no telemetry
- ✅ **SOC2 Compatible** — User controls all data
- ✅ **MIT Licensed** — No restrictions on commercial use
- ✅ **No Cryptocurrency Mining** — No hidden computation
- ✅ **No Network Calls** — All inference runs locally by default

</details>

---

<!-- ==================================================================== -->
<!-- SECTION 13: FAQ -->
<!-- ==================================================================== -->
## ❓ FAQ

<details>
<summary><b>Does CarvusTrain require deep learning knowledge?</b></summary>

No! CarvusTrain abstracts away complexity with high-level APIs. You can create, train, and use AI models with just a few lines of Python. However, understanding ML concepts helps optimize your models.
</details>

<details>
<summary><b>What's the minimum hardware requirement?</b></summary>

- **Minimum**: 4GB RAM, any modern CPU — runs on a Raspberry Pi 4+
- **Recommended**: 8GB+ RAM for larger datasets
- **GPU**: Optional — works on CPU only by default. For large-scale training (70B+), GPU recommended.
</details>

<details>
<summary><b>Can I use CarvusTrain for production systems?</b></summary>

Absolutely! It's designed for production use with:
- Built-in REST API server (zero-dependency HTTP)
- Multiple export formats (ONNX, GGUF, JSON, BIN)
- Safe mode for production environments
- No telemetry or external calls
- MIT License — no restrictions
</details>

<details>
<summary><b>How does pricing work?</b></summary>

CarvusTrain is **completely free and open-source** under the MIT License. No hidden fees, no usage limits, no cloud dependencies, no API keys needed.
</details>

<details>
<summary><b>Does it require external AI models or APIs?</b></summary>

No! CarvusTrain is a pure-Python framework with **zero mandatory dependencies**. Everything runs locally. No OpenAI API, no cloud services, no model downloads required.
</details>

<details>
<summary><b>How is CarvusTrain different from PyTorch/TensorFlow?</b></summary>

While PyTorch and TensorFlow are low-level frameworks requiring GPU setup and heavy dependencies, CarvusTrain is a high-level, zero-dependency platform that works out of the box. Think of it as the difference between assembly language (PyTorch) and Python (CarvusTrain). Both have their place — CarvusTrain can optionally use PyTorch when available.
</details>

<details>
<summary><b>What are the multi-billion parameter presets?</b></summary>

CarvusTrain includes 9 architecture presets from 15M to 405B parameters. These **size the model configuration** (embedding dimensions, layer counts, attention heads) — they do not download pre-trained weights. This lets you design and train models at any scale with a single parameter change.
</details>

<details>
<summary><b>How do I contribute?</b></summary>

We welcome contributions! Fork the repo, make your changes, and submit a pull request. See the [Contributing](#-contributing) section for details.
</details>

---

<!-- ==================================================================== -->
<!-- SECTION 14: ROADMAP -->
<!-- ==================================================================== -->
## 🗺️ Roadmap

```mermaid
journey
    title CarvusTrain Development Journey
    section v1.0 - Foundation
      Core transformer architecture: 5: Done
      Knowledge base with TF-IDF: 5: Done
      Training engine: 5: Done
      REST API server: 5: Done
      Multi-format exporter: 5: Done
      CLI interface: 5: Done
    section v1.1 - Intelligence
      Code generation (20+ langs): 5: Done
      Learning validation: 5: Done
      Grammar knowledge base: 5: Done
      7 Tokenizer types: 5: Done
      Multi-format parser: 5: Done
    section v1.2 - Scale
      Multi-billion presets: 5: Done
      Agent framework: 5: Done
      Web search learning: 5: Done
      RAG pipeline: 5: Done
      Plugin system: 4: Done
    section v1.3+ - Performance
      GPU acceleration: 3: In Progress
      Distributed training: 2: In Progress
      Quantization: 2: Planned
      Streaming inference: 2: Planned
    section v2.0 - Future
      Neural Architecture Search: 1: Planned
      Multi-modal (vision, audio): 1: Planned
      Federated learning: 1: Planned
      Monitoring dashboard: 1: Planned
```

### ✅ Completed (v1.0 - v1.2)

- [x] Core transformer architecture & model classes
- [x] Knowledge base with TF-IDF semantic search
- [x] Multi-format data parser (txt, csv, json, xml, yaml, md)
- [x] Training engine with learning validation
- [x] REST API server (zero deps — stdlib http.server)
- [x] CLI interface with 15+ commands
- [x] Code generation engine (20+ languages)
- [x] Multi-format exporter (ct, json, bin, onnx, gguf)
- [x] English grammar knowledge base (500+ facts)
- [x] Math & science curriculum (400+ facts)
- [x] Tokenizer suite (7 tokenizer types)
- [x] Multi-billion parameter presets (9 scales, up to 405B)
- [x] Agent framework with sub-agent orchestration
- [x] Web search learning (stdlib-only DuckDuckGo)
- [x] RAG pipeline for document retrieval
- [x] Plugin system for extensions
- [x] Auto-activation bootstrap (works everywhere)
- [x] Safe mode (no auto-exec, auto-downloads)

### 🔄 In Progress (v1.3 - v1.5)

- [ ] GPU-accelerated training (CUDA/ROCm)
- [ ] Distributed training across multiple machines
- [ ] Real-time streaming inference
- [ ] Advanced quantization (INT8, INT4, NF4)
- [ ] Visual model builder UI
- [ ] Auto hyperparameter tuning (grid search, Bayesian)

### 🚀 Planned (v2.0+)

- [ ] Neural Architecture Search (NAS)
- [ ] Federated learning support
- [ ] Multi-modal capabilities (image, audio)
- [ ] Production monitoring dashboard
- [ ] On-device inference frameworks
- [ ] Model versioning & experiment tracking
- [ ] Community model hub

---

<!-- ==================================================================== -->
<!-- SECTION 15: REPOSITORY ANALYTICS -->
<!-- ==================================================================== -->
## 📈 Repository Analytics

<div align="center">

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=Aadil-Fazal&repo=CarvusTrain&theme=tokyonight&hide_border=true&bg_color=0D1117&title_color=FF7F50&icon_color=FF7F50&text_color=c9d1d9)](https://github.com/Aadil-Fazal/CarvusTrain)

### 📊 GitHub Stats

| Metric | Value |
|--------|-------|
| ⭐ **Stars** | [![Stars](https://img.shields.io/github/stars/Aadil-Fazal/CarvusTrain?style=social)](https://github.com/Aadil-Fazal/CarvusTrain/stargazers) |
| 🍴 **Forks** | [![Forks](https://img.shields.io/github/forks/Aadil-Fazal/CarvusTrain?style=social)](https://github.com/Aadil-Fazal/CarvusTrain/network/members) |
| 📥 **Downloads** | [![Downloads](https://img.shields.io/pypi/dm/CarvusTrain?style=social)](https://pypi.org/project/CarvusTrain/) |
| 👥 **Contributors** | [![Contributors](https://img.shields.io/github/contributors/Aadil-Fazal/CarvusTrain?style=social)](https://github.com/Aadil-Fazal/CarvusTrain/graphs/contributors) |
| 📝 **Commits** | [![Commit Activity](https://img.shields.io/github/commit-activity/m/Aadil-Fazal/CarvusTrain?style=social)](https://github.com/Aadil-Fazal/CarvusTrain/commits/main) |
| 🏷️ **Releases** | [![Releases](https://img.shields.io/github/v/release/Aadil-Fazal/CarvusTrain?style=social)](https://github.com/Aadil-Fazal/CarvusTrain/releases) |

</div>

---

<!-- ==================================================================== -->
<!-- SECTION 16: CONTRIBUTING -->
<!-- ==================================================================== -->
## 🤝 Contributing

We welcome contributions from everyone! Here's how to get started:

### Quick Start for Contributors

```bash
# 1. Fork the repository
# 2. Clone your fork
git clone https://github.com/your-username/CarvusTrain.git
cd CarvusTrain

# 3. Set up development environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"

# 4. Run the setup script
python setup_carvus_complete.py

# 5. Run tests
python test_everything.py

# 6. Make your changes
# 7. Run tests again
python test_everything.py

# 8. Submit a Pull Request
```

### Contribution Guidelines

1. **Fork & Clone** — Create your feature branch (`git checkout -b feature/amazing-feature`)
2. **Make Changes** — Code with tests and documentation
3. **Test Locally** — Run full test suite: `python test_everything.py`
4. **Commit** — Write clear commit messages
5. **Push** — Push to your fork (`git push origin feature/amazing-feature`)
6. **Pull Request** — Create PR with detailed description of changes

### What We Need Help With

- 📖 **Documentation** — Improve tutorials, API docs, examples
- 🐛 **Bug Fixes** — Report or fix issues
- 🧪 **Tests** — Increase test coverage
- 🚀 **Performance** — Optimize core algorithms
- 🌍 **Localization** — Translate docs and error messages
- 💡 **Feature Requests** — Suggest and implement new features

---

<!-- ==================================================================== -->
<!-- SECTION 17: DOCUMENTATION -->
<!-- ==================================================================== -->
## 📚 Documentation

| Resource | Link | Description |
|----------|------|-------------|
| 🚀 **Quick Start** | [docs/quickstart.md](docs/quickstart.md) | Get started in 5 minutes |
| 📖 **API Reference** | [docs/api_reference.md](docs/api_reference.md) | Complete API documentation |
| 🎬 **CLI Guide** | [docs/cli_guide.md](docs/cli_guide.md) | Command-line reference |
| 🏗️ **Architecture** | [docs/architecture.md](docs/architecture.md) | System design and internals |
| 🛠️ **Developer Guide** | [docs/developer_guide.md](docs/developer_guide.md) | Contributing and extending |
| 📦 **Installation** | [docs/installation.md](docs/installation.md) | Installation options and troubleshooting |
| 💻 **Examples** | [examples/](examples/) | Code examples and tutorials |
| 📋 **Changelog** | [CHANGELOG.md](CHANGELOG.md) | Release history and changes |
| 📄 **License** | [LICENSE](LICENSE) | MIT License |
| 💬 **Discussions** | [GitHub Discussions](https://github.com/Aadil-Fazal/CarvusTrain/discussions) | Community discussions |
| 🐛 **Issues** | [GitHub Issues](https://github.com/Aadil-Fazal/CarvusTrain/issues) | Bug reports and feature requests |

---

<!-- ==================================================================== -->
<!-- SECTION 18: ACKNOWLEDGMENTS -->
<!-- ==================================================================== -->
## 🙏 Acknowledgments

- Built with **Python 3.10+** and the amazing Python standard library
- Inspired by the open-source AI community and projects like Hugging Face, LangChain, and llama.cpp
- Special thanks to all contributors, stargazers, and users ⭐
- Created with passion by **Aadil Fazal** at **Carvus Labs**

---

<!-- ==================================================================== -->
<!-- PREMIUM FOOTER -->
<!-- ==================================================================== -->
<div align="center">

<!-- Capsule Render Wave Footer -->
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://capsule-render.vercel.app/api?type=waving&color=gradient&customColorList=0,2,3,8,11,20&height=180&section=footer&text=Built%20with%20%E2%9D%A4%EF%B8%8F%20by%20Aadil%20Fazal&fontSize=26&fontColor=ffffff&animation=twinkling">
  <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&customColorList=0,2,3,8,11,20&height=180&section=footer&text=Built%20with%20%E2%9D%A4%EF%B8%8F%20by%20Aadil%20Fazal&fontSize=26&fontColor=ffffff&animation=twinkling" alt="Footer">
</picture>

<br><br>

<!-- Star Call to Action -->
### ⭐ Star This Project

If CarvusTrain helps you, please consider giving us a star ⭐ on GitHub. It means the world to us!

[![GitHub Stars](https://img.shields.io/github/stars/Aadil-Fazal/CarvusTrain?style=social&label=Star%20this%20repo)](https://github.com/Aadil-Fazal/CarvusTrain)

<br>

<!-- Social Links -->
<p>
  <a href="https://github.com/Aadil-Fazal/CarvusTrain">
    <img src="https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"/>
  </a>
  <a href="https://pypi.org/project/CarvusTrain">
    <img src="https://img.shields.io/badge/PyPI-3776AB?style=for-the-badge&logo=pypi&logoColor=white" alt="PyPI"/>
  </a>
  <a href="https://discord.gg/carvustrain">
    <img src="https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"/>
  </a>
  <a href="https://carvus.ai">
    <img src="https://img.shields.io/badge/Website-FF7F50?style=for-the-badge&logo=web&logoColor=white" alt="Website"/>
  </a>
  <a href="docs/">
    <img src="https://img.shields.io/badge/Documentation-3776AB?style=for-the-badge&logo=readthedocs&logoColor=white" alt="Docs"/>
  </a>
</p>

<br>

<!-- Divider -->
<img src="https://raw.githubusercontent.com/andreasbm/readme/master/assets/lines/dark.png" width="100%" alt="Divider">

<br>

**Made with ⚡ by [Aadil Fazal](https://github.com/Aadil-Fazal)**  
*Carvus Labs — Building the future of AI, one line of Python at a time.*

[GitHub](https://github.com/Aadil-Fazal/CarvusTrain) • [Documentation](docs/) • [PyPI](https://pypi.org/project/CarvusTrain/) • [Discord](https://discord.gg/carvustrain) • [Website](https://carvus.ai)

```
# CarvusTrain v1.3.0 — MIT License
# Carvus Labs © 2026 Aadil Fazal
```

*Last Updated: 2026*

</div>

<!-- ==================================================================== -->
<!-- END OF README -->
<!-- ==================================================================== -->
