A fast and efficient 7B model optimized for CPU inference. The model was refactored with BitNet features and an updated tokenizer that includes new Routing, Media, Vision , Sound ,Tool call, and Robotics tags. Built on a DeepSeek R1 -7B architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations.

JiRack is a cloud-ready model that helps save money on cloud infrastructure. It can be used as an expert model in RAG deployments, with the ONNX JiRack Java server as an alternative.

JiRack Ternary Architedure & JiRack Tokenizer

Benefits high quality CPU inference TQ_2 on Llama.cpp and Ollama via QAT
Robotcs, Routing, Coding, Multimedia, Advanced tool calling via JiRackDeltaNetTokenizer

JiRack sevice options

Current quantizations were done from the FP16 model, but the model allows for more compression thanks to its ternary architecture.
If you need to do ternary compression, please write to me and I'll perform QAT from your dataset, tailored specifically to your task.
Plus double QAT via ONNX QAT.
Adapt train process to avoid catastrophic forgetting with NDA
Adapt train process to avoid fast plato in training with NDA
Convert model to TQ2_0 with support AVX2 and AVX-512 CPU instructions for high performance on CPU
QAT for TQ_2 Llama.cpp Ternarization docs https://huggingface.co/CMSManhattan/JiRackUltra_7b/blob/main/QAT_to_Llama.cpp_GGUF_TQ2_0.md
Adapts to agentic or instruct models for tool calling, using the JiRak tokenizer to enable high-quality tool calling on small models — built as a domain-specific tool expert.
Deployment and scale

Ollama production support

We are working to support JiRack on Ollama for production systems also
added Jirack chat without reasoning feature https://ollama.com/cmsmanhattan
Follow fresh Ollama platform updates

Spring Boot AI tool calls examples for JiRack Ultra series

Tool call library on java for Enterprise https://github.com/alibaba/spring-ai-alibaba

GoEx AI tool calls examples for JiRack Ultra series

Tool call library on python https://github.com/ShishirPatil/gorilla

JiRack Ultra 1 tool calls to boost tool call quality

Use JiRack Precision tokenzer tags for tool calls with ToolBench https://github.com/OpenBMB/ToolBench
https://huggingface.co/xalss/Qwen2-7B-Instruct-glaive-function-calling
https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1
Add JiRack tool call tags in the dataset and modify tool call processor if needed

JiRack RoboTech

Advanced Tokenizer with Robotics & Routing & Tool calls Tokenizer and other
CMSManhattan/JiRackPrecisionTokenizer

Available Variants

Tag	Quant	Size	Approx. RAM	Description
`cmsmanhattan/jirack-ultra-7b-cpu:latest`	Full	28.1 GB	~12.2 GB	Full precision reference
`cmsmanhattan/jirack-ultra-7b-cpu-q4:latest`	Q4_K_M	10.1 GB	~4.8 GB	Recommended balance
`cmsmanhattan/jirack-ultra-7b-cpu-q3:latest`	Q3_K_M	8.42 GB	~4.0 GB	Good quality / size trade-off
`cmsmanhattan/jirack-ultra-7b-cpu-q2:latest`	Q2_K	6.81 GB	~3.2 GB	Maximum compression

Quick Start

Run with Docker

Default CPU (Q4 recommended)

bash

docker run -d \
  --name jirack_ultra_7b \
  -p 7869:7869 \
  --restart unless-stopped \
  cmsmanhattan/jirack-ultra-7b-cpu-q4:latest

bash

docker run -d \
  --name jirack_ultra_7b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \ 
  --restart unless-stopped \
  cmsmanhattan/jirack-ultra-7b-cpu-q3:latest

Q2 (lowest memory)

bash

docker run -d \
  --name jirack_ultra_7b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \ 
  --restart unless-stopped \
  cmsmanhattan/jirack-ultra-7b-cpu-q2:latest

Full precision

bash

docker run -d \
  --name jirack_ultra_7b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \ 
  --restart unless-stopped \
  cmsmanhattan/jirack-ultra-7b-cpu:latest

Multi CPU

bash

docker run -d \
  --name jirack_ultra_7b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \ 
  --restart unless-stopped \
  --memory=16g \
  --cpus=8 \
  cmsmanhattan/jirack-ultra-7b-cpu-q4:latest

Docker Compose Example

yaml

services:
  jirack:
    image: cmsmanhattan/jirack-ultra-7b-cpu-q4:latest
    container_name: jirack_ultra_7b
    ports:
      - "7869:7869"
    volumes:
      - .:/app
      - ./web:/app/web
    environment:
      - MAX_TOKENS=2048
      - TEMPERATURE=0.7
      - TOP_P=0.9
      - DEFAULT_STREAM=False
      - INTRA_THREADS=4
      - USE_ENV_ALLOCATOR=1
      - THREADS=16 
      - THREADS_BATCH=16 
    deploy:
      resources:
        limits:
          memory: 16g

Access the UI

Once the container is running, open your browser and navigate to:

http://localhost:7869

This opens the JiRack UI — a clean web interface.

Changing the Port

The listening port can be easily modified directly from the Settings panel within the JiRack UI.

Licensing

Model weights are released under the MIT License — free to use, modify, and distribute for any purpose, including commercial. No royalties, no per-user fees, no subscription.
The Docker image with UI and the pre-built Ollama quantizations are separate paid products. If you prefer to build your own secure deployment — take the weights, assemble your own stack, and you're done.
The JiRack Ultra 7B model for Docker and Ollama is provided under a commercial license ($12 per user per year).
All JiRack UI clients are provided under a commercial license.
However, the UI clients can be used for free when running together with the official JiRack Docker containers, as long as they are not redistributed separately.

For commercial licensing, cluster deployment, or enterprise use of JiRack models, please contact us.

JiRack MS Windows 11 Desktop Client (with Ollama API): https://huggingface.co/kgrabko/JiRackTernary_1b/resolve/main/jirack-chat.zip
Live email chat with the model: support@cmsmanhattan.com

Hardware Recommendations

Recommended Hardware for JiRack Ultra 7B (single Docker container)

Use Case	CPU	RAM	Recommended Quant	Expected Speed	Recommendation
Recommended	Ryzen 7 / Intel i7	16 GB	Q4_K_M	Good interactive	Best choice
High Performance	Ryzen 9 / Intel i9	24–32 GB	Full / Q4	Excellent	Excellent
Low Memory	Modern 6+ core CPU	8–12 GB	Q3_K_M or Q2_K	Usable	Acceptable
Edge / Minimal	Laptop CPU	8 GB	Q2_K	Acceptable	Budget option

Important Memory Notes

Even though the quantized 7B models are small, we recommend the following for best experience:

Q4_K_M: 8–12 GB system RAM minimum
Q3_K_M / Q2_K: 6–10 GB system RAM
Full precision: 16 GB+ system RAM recommended

Reasons for extra headroom:

KV-cache consumption during generation
Runtime overhead and temporary buffers
System stability and avoiding out-of-memory errors
Room for larger context windows

Minimum recommended (Q4): 12 GB system RAM
Ideal: 16–24 GB system RAM

I added the default model in full precision. This serves as the base for quantization, allowing us to find the optimal balance between model size and performance.

Architecture Notes

Refactored with BitNet features: Native BitLinear ternary path (b1.58-style) with λ-warmup STE
Updated tokenizer: Extended with new special tags for Routing, Tool call, and Robotics
Base: Qwen2.5-7B style (Hidden 3584, 28 layers, GQA 28/4, vocab 152064)
RoPE θ = 10000, RMSNorm ε = 1e-6
Ready-to-run GGUF quantizations (Q2_K, Q3_K_M, Q4_K_M)

📧 Contact & Licensing

For joint venture opportunities, hardware integration, or licensing inquiries:

Email: grabko@cmsmanhattan.com
Phone: +1 (516) 777-0945
Location: New York, USA

License

MIT License

CMSManhattan/JiRackUltra_7b

JiRack Ultra 7B (CPU)

JiRack Ternary Architedure & JiRack Tokenizer

JiRack sevice options

Ollama production support

Spring Boot AI tool calls examples for JiRack Ultra series

GoEx AI tool calls examples for JiRack Ultra series

JiRack Ultra 1 tool calls to boost tool call quality

JiRack RoboTech

Available Variants

Quick Start

Run with Docker

Docker Compose Example

Access the UI

Changing the Port

Licensing

Hardware Recommendations

Recommended Hardware for JiRack Ultra 7B (single Docker container)

Important Memory Notes

Architecture Notes

📧 Contact & Licensing

License

CMSManhattan/
JiRackUltra_7b