kgrabko/JiRackTernary_405b

🚀 Llama 3 JiRack Ternary 405B — Proprietary Ternary-Quantized Transformer

Proprietary Invention Package – Ternary-Quantized Transformer Optimization

Under training yet: DEV

Inventor: Konstantin Vladimirovich Grabko
Email: [email protected]
Date: December 21, 2025
Needed: A sponsor for Llama 405b Distilation to high quality as original Llama 405b or Data Center with cooperation

Overview: This package contains documentation for a novel, proprietary method enabling efficient LLM inference on AMD ROCm hardware using ternary quantization, BRE, and SWA fusion.

Contents:

• license.md
• NDA.md
• invention_description.md
• claims.md
• performance_data.md
• [Diagrams and attachments]

Confidential: All materials are proprietary. Contact inventor for licensing discussions.

⚠️ IMPORTANT NOTICE — PROPRIETARY TECHNOLOGY

This model and all accompanying code, algorithms, and documentation are proprietary technology owned by Konstantin Vladimirovich Grabko.

© 2025 Konstantin Vladimirovich Grabko. All Rights Reserved. Patent Pending.

Allowed:

• Personal and non-commercial research use only

Strictly Prohibited without a written commercial license:

• Any commercial use (SaaS, mobile apps, edge devices, paid services, etc.)
• Creating and distributing derivative models for profit
• Removing or modifying any copyright or legal notices
• Patenting any part of this technology

Commercial users must obtain a signed license and pay 5% royalty on net revenue.

Any unauthorized commercial use will be pursued legally under New York law.

Contact for commercial license: [email protected]

JiRack Codding Agent IDE

• It is Agent Coding IDE for JiRack Models to run via Ollama on home PC
• It good choose for Agent Coding IDE such as Cursor , Windsurf IDE or Devin IDE etc but more safe that ask you to apply changes and review.
• Test version https://huggingface.co/CMSManhattan/JiRackDeltaNet_27b/resolve/main/jirack_ide.zip
• Final release version https://huggingface.co/CMSManhattan/JiRackDeltaNet_27b/resolve/main/jirack_ide_final.zip

Benefits for the JiRack 405B Project

VRAM Efficiency

✅ Easy Fine-tuning a 405B model usually requires massive resources, but with LoRA and your 70% VRAM reduction, users can fine-tune on consumer-grade multi-GPU setups.

Trainable Parameters:

• Base model (frozen): 405B parameters @ 2-bit = ~108 GB
• LoRA adapters (r=16): ~50M parameters @ FP32 = ~200 MB
• Total VRAM: ~108 GB (fits on 4x RTX 4090 with offloading)

Thermal Stability

✅ Since only a fraction of parameters are updated, the thermal footprint remains consistent with your SWA Fusion goals of staying < 80°C.

JiRack Ternary 405B on BitNet layers with meta-llama/Llama-3.2-405B compatable tokenizer

It supports safe safetensors format to use