CMSManhattan / JiRack_GPT5_70b

JiRack Dense: Scalable Transformer Series (7B, 13B, 70B)

Author: Konstantin Vladimirovich Grabko
Organization: CMS Manhattan
Status: PATENT PENDING / PROPRIETARY TECHNOLOGY
Invention Class: High-Resolution Dense Architecture (V.1.2)

🚀 The Scalable Frontier

The JiRack Dense series provides a unified architectural framework for state-of-the-art language modeling. By utilizing SWA Fusion and Buffered Routing , these models achieve significantly higher throughput than standard Llama-based architectures.

Available Configurations

🛠 Proprietary Core Technologies

1. SWA Fusion (SwiGLU-Attention)

The core of JiRack's speed. By fusing the Attention and SwiGLU FFN layers into a single computational kernel, we eliminate redundant memory R/W cycles.

• Benefit: 30% reduction in VRAM latency.
• Architecture: Integrated compute graph for AMD ROCm and NVIDIA CUDA.

2. BRE (Buffered Routing Embedding)

A hardware-aware embedding system designed for HBM3/4. BRE pre-fetches token weights into a local ring buffer based on predictive token sequencing.

• Benefit: Zero-latency embedding lookups even at maximum sequence lengths.

3. GQA Scaling

Optimized Grouped-Query Attention ratios (4:1 for 7B, 8:1 for 70B) to ensure the KV-cache remains manageable during long-context operations without degrading reasoning quality.

⚖️ Legal & Licensing Notice

NOTICE: PATENT PENDING

This repository contains proprietary technology owned by Konstantin Vladimirovich Grabko . Access is granted under the following conditions:

1. Commercial Use: Requires a 5% Net Revenue royalty agreement.
2. IP Protection: No reverse engineering of SWA kernels or BRE routing logic is permitted.
3. No "Patent-Around": Licensees agree not to file IP claims based on the methods described herein.
4. Attribution: Any derivative work must cite: "Powered by CMS Manhattan JiRack Technology. Inventor: Konstantin Vladimirovich Grabko."

Refer to license_dense.md for full legal documentation.

📦 Setup & Training

Environment

• Framework: PyTorch 2.3+
• Accelerator: AMD ROCm 6.0+ or NVIDIA CUDA 12.1+
• Distributed: Integrated support for DeepSpeed ZeRO-2/3.

Quick Start

To initialize a model from the factory:

from JiRack_Dense_Factory import get_jirack_config, JiRackPyTorch

# Initialize 13B configuration
config = get_jirack_config("13b")
model = JiRackPyTorch(config)