SAGE — Self-Adaptive General Engine

SAGE is a senior-grade, production-structured Large Language Model (LLM) system built entirely from scratch using Python and PyTorch. It implements modern transformer architectures including Mixture of Experts (MoE), Rotary Positional Embeddings (RoPE), and Low-Rank Adaptation (LoRA).

Designed to be both educational and functional, SAGE can be trained, fine-tuned, quantized, and deployed on a single consumer GPU (e.g., NVIDIA T4 with 16GB VRAM).

🚀 Key Features

Decoder-Only Transformer: A GPT-style architecture with pre-layer normalization.
Mixture of Experts (MoE): Efficient scaling with a learned router selecting top-k experts per token.
Rotary Positional Embeddings (RoPE): Enhanced long-sequence generalization.
KV-Cache Inference: O(1) time-per-token generation for high-speed response.
Retrieval-Augmented Generation (RAG): Integration with FAISS for document-based context lookup.
Efficient Fine-Tuning: Support for LoRA and instruction tuning with loss masking.
Post-Training Quantization: INT8 support to reduce memory footprint.
Interactive CLI: A full REPL (Read-Eval-Print Loop) for chatting and system management.

📂 Project Structure

🛠️ Installation & Setup

1. Requirements

Ensure you have Python 3.9+ and a CUDA-compatible GPU (recommended).

# Clone the repository (GitHub)
git clone https://github.com/er-del/sage.git
cd sage

# OR Clone from Hugging Face
git clone https://huggingface.co/sage002/sage
cd sage

# Install dependencies
pip install -r requirements.txt

2. Dependencies

PyTorch: Core deep learning framework.
tiktoken: Fast BPE tokenization (OpenAI's cl100k_base).
datasets: For streaming training data from HuggingFace.
faiss-cpu: For vector-based retrieval (RAG).
tqdm: Progress bars for training.
bitsandbytes: (Optional) For advanced quantization.

🎮 Getting Started

Basic Chat

Once launched, simply type your message to chat with SAGE. The system uses a rolling conversation history to maintain context.

👨‍🏫 Training SAGE

SAGE supports real-time training either directly from the interactive REPL or via simple one-liner CLI commands (useful for background scripts).

Non-Interactive "One-Liner" Commands

🧠 Advanced Commands

🏗️ Architecture Details

🤗 Hugging Face Model Hub

This project is actively maintained on Hugging Face. You can find pre-trained checkpoints, datasets, and community discussions here:

🔗 huggingface.co/sage002/sage

📜 Disclaimer

SAGE is an experimental engine. While architecturally complete, the quality of generated responses depends heavily on the amount of training data and compute steps provided.