sage

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).

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🚀 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.

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📂 Project Structure

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🛠️ 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.

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🎮 Getting Started

Basic Chat

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

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👨‍🏫 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

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🧠 Advanced Commands

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🏗️ Architecture Details

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🤗 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

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📜 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.