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README.md

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+# NGen3: Next-Generation Foundational Model
+
+NGen3 is a production-level foundational language model inspired by state-of-the-art architectures such as GPT-4, Claude-3, and Llama 2. It is designed to be highly modular, efficient, and accessible via a flexible command-line interface (CLI). NGen3 supports multiple model variants—from 7M parameters to 1B parameters—and offers a comprehensive suite of tools for:
+
+- **Tokenization:** Process text from local files, URLs, or Hugging Face datasets.
+- **Training:** Train the model on tokenized data.
+- **Sampling:** Generate text from trained models.
+- **Exporting:** Save models and minimal tokenizer configurations in formats compatible with Hugging Face.
+- **Knowledge Distillation:** Train a smaller student model using a larger teacher model.
+- **Fine-Tuning:** Adapt a distilled model on conversational data (from local sources or directly from Hugging Face).
+
+This repository provides a complete implementation of the NGen3 model along with detailed CLI commands to facilitate experimentation and research.
+
+---
+
+## Table of Contents
+
+- [Model Overview](#model-overview)
+- [Architecture](#architecture)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Tokenization](#tokenization)
+  - [Training](#training)
+  - [Sampling](#sampling)
+  - [Exporting](#exporting)
+  - [Knowledge Distillation](#knowledge-distillation)
+  - [Fine-Tuning](#fine-tuning)
+    - [Local Fine-Tuning](#local-fine-tuning)
+    - [Hugging Face Fine-Tuning](#hugging-face-fine-tuning)
+- [Hyperparameters](#hyperparameters)
+- [Contributing](#contributing)
+- [License](#license)
+- [Acknowledgements](#acknowledgements)
+
+---
+
+## Model Overview
+
+NGen3 is designed for rapid development and deployment of foundational language models. Its flexible CLI allows users to:
+
+- **Tokenize Text:** Convert raw text or datasets into tokenized binary format.
+- **Train Models:** Use various hyperparameter configurations based on the desired model size.
+- **Generate Samples:** Evaluate model performance and generate text samples.
+- **Export Models:** Easily export models in `safetensors` and JSON configurations for integration with Hugging Face tools.
+- **Distill Models:** Leverage knowledge distillation to compress larger models into efficient student variants.
+- **Fine-Tune on Conversations:** Adapt models to conversational data using both local and Hugging Face datasets.
+
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+
+## Architecture
+
+NGen3’s architecture is built upon the transformer decoder design. Key components include:
+
+- **Token and Positional Embeddings:** Learnable embeddings that encode input tokens and their positions.
+- **Stack of Transformer Blocks:** Each block contains:
+  - **Causal Self-Attention:** With multi-head attention and masking to prevent information leakage.
+  - **MLP (Feed-Forward Network):** Utilizes GELU activation for non-linearity.
+  - **Residual Connections and Layer Normalization:** Stabilize training and improve convergence.
+- **Final Projection Layer:** Maps embeddings to logits over the vocabulary.
+
+The model supports variants with parameter counts ranging from 7M to 1B, making it adaptable for various research and production needs.
+
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-Edit this `README.md` markdown file to author your organization card.
+## Installation
+
+Ensure you have Python 3.8+ installed along with the following packages:
+
+- PyTorch
+- transformers
+- datasets
+- tqdm
+- safetensors (for export functionality)
+
+Install the required packages using pip:
+
+```bash
+pip install torch transformers datasets tqdm safetensors