CMSManhattan/JiRackUltra_7b
Text Generation • 8B • Updated • 9.99k • 1
Konstantin Grabko PRO
kgrabko
AI & ML interests
Konstantin Grabko | CEO & CTO CMSManhattan inc .
----------------------------------- Follow for final releases on company page
Recent Activity
published an article about 12 hours ago
JiRack Ultra blew up the Hugging Face charts published an article about 12 hours ago
Almost Opus 4.6 Max quality with JiRack DeltaNet 27B — but it runs on your PC posted an update about 12 hours ago
Ternary Transformers & Micro-Agent Architecture
CMSManhattan : Center Business Solutions Inc.
JiRack — Ternary Transformers & Micro-Agent Architecture
We build highly efficient large language models using 1.58-bit ternary weights {-1, 0, 1} for extreme compression and fast CPU/GPU inference.
Core focus:
JiRack Ternary Transformer Architecture — fresh Qwen base, trained on DeepSeek-style datasets, optimized for fast CPU inference (MIT License)
JiRack Micro-Agent Deployment — specialized small models + smart router for low-cost agentic systems
Production-ready ONNX Runtime & Docker inference stacks
Public Models
ModelSizeStatusJiRackUltra series (1B / 7B / 14B / 32B)—Released
https://huggingface.co/CMSManhattan/JiRackUltra_1b
https://huggingface.co/CMSManhattan/JiRackUltra_7b
https://huggingface.co/CMSManhattan/JiRackUltra_14b
https://huggingface.co/CMSManhattan/JiRackUltra_32b
JiRackTernary series1B → 10B+ReleasedJiRackPrecisionTokenizer—Released
Mission
Democratize frontier-scale language models through extreme efficiency. Train and run powerful models on accessible hardware without sacrificing quality.
Solved issues
Benefits of JiRack Micro-Agent Architecture:
Solves catastrophic forgetting during training by using small, specialized models for each domain, managed by a smart router Enables extremely cheap inference using ternary models Significantly reduces cloud inference costs while maintaining high performance In classical architecture, an expensive model has to search for MCP-agents every time, while JiRack uses a very small model and cheap router for agent tasks, saving big money right from the start Considered one of the best approaches for enterprise AI deployments
Hugging Face: https://huggingface.co/CMSManhattan
Ollama : https://ollama.com/cmsmanhattan
Docker Hub: cmsmanhattan Contact: grabko@cmsmanhattan.comOrganizations
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