Instructions to use CMSManhattan/JiRackDeltaNet_27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use CMSManhattan/JiRackDeltaNet_27b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Use Docker

docker model run hf.co/CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

LM Studio
Jan

vLLM

How to use CMSManhattan/JiRackDeltaNet_27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CMSManhattan/JiRackDeltaNet_27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CMSManhattan/JiRackDeltaNet_27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Ollama
How to use CMSManhattan/JiRackDeltaNet_27b with Ollama:
```
ollama run hf.co/CMSManhattan/JiRackDeltaNet_27b:Q4_K_M
```
Unsloth Desktop

How to use CMSManhattan/JiRackDeltaNet_27b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CMSManhattan/JiRackDeltaNet_27b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CMSManhattan/JiRackDeltaNet_27b with Docker Model Runner:
```
docker model run hf.co/CMSManhattan/JiRackDeltaNet_27b:Q4_K_M
```

Lemonade

How to use CMSManhattan/JiRackDeltaNet_27b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Run and chat with the model

lemonade run user.JiRackDeltaNet_27b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use CMSManhattan/JiRackDeltaNet_27b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CMSManhattan/JiRackDeltaNet_27b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CMSManhattan/JiRackDeltaNet_27b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "CMSManhattan/JiRackDeltaNet_27b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen 3.8 27b migrated to Ternary Architedure

Benefits high quality CPU inference TQ_2 on Llama.cpp and Ollama via QAT
Robotcs, Routing, Coding, Multimedia, Advanced tool calling via JiRackDeltaNetTokenizer
JiRack DeltaNet understand video and images that best for Robotics also

JiRack DeltaNet 27B (CPU)

A fast and efficient 27B model optimized for CPU inference. Built on a Qwen3.8-style DeltaNet architecture (hybrid attention + SSM), with an updated tokenizer that includes Routing, Media, Vision, Sound, Tool call, and Robotics tags. Ready-to-run GGUF quantizations, and native Ollama support with reasoning disabled by default for fast, direct responses.

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 service options

Current quantizations were done from the FP16 model.
If you need custom compression or fine-tuning, 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 plateau in training with NDA
Adapts to agentic or instruct models for tool calling, using the JiRack tokenizer to enable high-quality tool calling on small models — built as a domain-specific tool expert.
Deployment and scale

Ollama production support

JiRack DeltaNet 27B runs natively on Ollama with reasoning disabled by default (no forced <think> blocks).
Available now: cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning — https://ollama.com/cmsmanhattan
Runtime override also supported: ollama run cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning --think=false
Follow fresh Ollama platform updates

Spring Boot AI tool calls examples for JiRack DeltaNet series

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

GoEx AI tool calls examples for JiRack DeltaNet series

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

JiRack DeltaNet tool calls to boost tool call quality

Use JiRack Precision tokenizer 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/JiRackDeltaNetTokenizer

Available Variants on Docker with UI

Tag	Quant	Size	Approx. RAM	Description
`cmsmanhattan/jirack-deltanet-27b-cpu:latest`	Full	~55 GB	~56–64 GB	Full precision reference
`cmsmanhattan/jirack_deltanet_27b-cpu-q4:latest`	Q4_K_M	~16.8 GB	~18–24 GB	Recommended balance
`cmsmanhattan/jirack-deltanet-27b-cpu-q3:latest`	Q3_K_M	~13.9 GB	~15–20 GB	Good quality / size trade-off
`cmsmanhattan/jirack-deltanet-27b-cpu-q2:latest`	Q2_K	~11.2 GB	~12–17 GB	Maximum compression

Quick Start

Run with Ollama (recommended — reasoning off by default)

https://ollama.com/cmsmanhattan

ollama pull cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning
ollama run cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning "What is the capital of France?"

Run with Docker

27B docker images can be provided by request.
Build docker on local from source or request from me.
Docker images use hidden reasoning logic to make chat clear

Q8/int8

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \
  --restart unless-stopped \
  cmsmanhattan/jirack_deltanet_27b-cpu-q8:latest

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \
  --restart unless-stopped \
  cmsmanhattan/jirack_deltanet_27b-cpu-q6:latest

Default CPU (Q4/int4 recommended)

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --restart unless-stopped \
  cmsmanhattan/jirack_deltanet_27b-cpu-q4:latest

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \
  --restart unless-stopped \
  cmsmanhattan/jirack_deltanet_27b-cpu-q3:latest

Q2 (lowest memory)

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \
  --restart unless-stopped \
  cmsmanhattan/jirack_deltanet_27b-cpu-q2:latest

Full precision

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \
  --restart unless-stopped \
  cmsmanhattan/jirack-deltanet-27b-cpu:latest

Multi CPU

docker run -d \
  --name jirack_deltanet_27b \
  -p 7869:7869 \
  --cpus=16 \
  -e THREADS=16 \
  -e THREADS_BATCH=16 \
  --restart unless-stopped \
  --memory=32g \
  --cpus=8 \
  cmsmanhattan/jirack-deltanet-27b-q4:latest

Docker Compose Example

services:
  jirack:
    image: cmsmanhattan/jirack_deltanet_27b-cpu-q4:latest
    container_name: jirack_deltanet_27b
    ports:
      - "7869:7869"
    volumes:
      - .:/app
      - ./web:/app/web
    environment:
      - MAX_TOKENS=2048
      - TEMPERATURE=0.7
      - TOP_P=0.8
      - TOP_K=20
      - DEFAULT_STREAM=False
      - INTRA_THREADS=4
      - USE_ENV_ALLOCATOR=1
      - THREADS=16
      - THREADS_BATCH=16
    deploy:
      resources:
        limits:
          memory: 32g

Ollama platform

CPU without JiRack UI
GPU without JiRack UI
Ollama images displays reasoning logic by default but it can be off .
Use JiRack UI from https://www.jirack.com
cmsmanhattan/JiRackDeltaNet_27b-reasoning:latest 54 GB
cmsmanhattan/JiRackDeltaNet_27b-q2-reasoning:latest 10 GB
cmsmanhattan/JiRackDeltaNet_27b-q3-reasoning:latest 13 GB
cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning:latest 16 GB
cmsmanhattan/JiRackDeltaNet_27b-q6-reasoning:latest 22 GB
cmsmanhattan/JiRackDeltaNet_27b-q8-reasoning:latest 29 GB

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 DeltaNet 27B 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 DeltaNet 27B (single Docker container)

Use Case	CPU	RAM	Recommended Quant	Expected Speed	Recommendation
Recommended	Ryzen 9 / Intel i9 / Xeon	24–32 GB	Q4_K_M	Good interactive	Best choice
High Performance	High-core server CPU	48 GB+	Full / Q4	Excellent	Excellent
Low Memory	Modern 12+ core CPU	16–24 GB	Q3_K_M or Q2_K	Usable	Acceptable
Edge / Minimal	Strong workstation CPU	16 GB	Q2_K	Acceptable	Budget option

Important Memory Notes

Even though the quantized 27B models are relatively compact for their size, we recommend the following for best experience:

Q4_K_M: 18–24 GB system RAM minimum
Q3_K_M / Q2_K: 15–20 GB system RAM
Full precision: 48 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): 18 GB system RAM Ideal: 24–32 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

Qwen3.8-style DeltaNet architecture: hybrid attention + SSM design (qwen35 in GGUF metadata)
Updated tokenizer: Extended with new special tags for Routing, Tool call, and Robotics
No-forced-reasoning support: chat template patched so <think> blocks default to closed; native support on Ollama
Hidden 5120, 65 layers, attention heads 24 / KV heads 4, feed-forward 17408
SSM: conv kernel 4, state size 128, group count 16, time-step rank 48, inner size 6144
RoPE θ = 10,000,000, RMSNorm ε = 1e-6
Context length: up to 262,144 tokens
Ready-to-run GGUF quantizations (Q2_K, Q3_K_M, Q4_K_M)

Benchmarks

JiRack DeltaNet 27B is built on the Qwen3.5/Qwen3.8-style DeltaNet architecture. The table below reproduces the published base-model benchmark results from Qwen/Qwen3.8-27B for reference — these reflect the upstream base model's capabilities, not JiRack-specific fine-tuning or quantization results.

Text Performance

	Qwen3.8-27B	Qwen3.6-27B	Qwen3.7-Plus	Muse Glimmer-30B	Opus4.6 Max
Coding
Agentic terminal coding — Terminal Bench 2.1 (Terminus)	73.0	63.4	64.0	51.7	78.2
Agentic coding — SWE-bench Pro	61.7	53.5	57.6	51.2	53.4
Repo-level code generation — NL2Repo-Bench	42.3	36.2	41.1	--	47.6
Agentic coding — DeepSWE 1.1	42.2	13.3	14.2	--	--
Software engineering — QwenSWEBench	79.0	49.3	59.2	--	63.8
Agent
Long-horizon office work — CoWorkBench	70.7	61.0	65.1	--	68.2
Professional job tasks — JobBench	33.4	21.8	27.6	--	--
Frontier agentic tasks — Agents' Last Exam (Pass@1/Score)	20.4 / 42.9	10.6 / 27.3	13.2 / 33.6	--	--
General
Instruction following — IFBench	79.5	69.1	79.1	77.0	62.5
Scientific reasoning — GPQA Diamond	89.2	87.8	90.3	83.5	91.3
Multidisciplinary reasoning — HLE	30.8	24.0	34.7	22.0	40.0
Competitive coding — LiveCodeBench v6	90.3	83.9	89.6	--	88.8

VL Performance

	Qwen3.8-27B	Qwen3.6-27B	Qwen3.7-Plus	Muse Glimmer-30B	Opus4.6 Max
Agentic Multimodal Intelligence
Computer use — OSWorld-Verified	84.3	63.9	73.3	65.9	72.7
Browser use — WebArena-Verified	64.8	48.8	55.3	--	--
Mobile use — AndroidWorld	81.9	70.3	81.0	--	62.0
Application recreation — RecreationBench	47.1	29.8	30.2	--	--
Multimodal tool use — ClawEval-MM (Pass@3/Avg)	57.4 / 56.9	42.6 / 50.4	57.4 / 60.1	--	52.5 / 54.7
Multimodal SWE — SWE-MM	38.6	25.7	30.0	--	27.1
Visual web development — Vision2Web	62.9	45.0	42.1	--	--
General Multimodal Intelligence
Visual math — MathVision (w/o CI / w/ CI)	90.0 / 94.6	85.1 / --	90.3 / --	--	65.5 / --
General visual reasoning — BabyVision (w/o CI / w/ CI)	65.7 / 85.6	28.9 / --	64.7 / 70.4	--	12.6 / --
Scientific chart analysis — CharXiv (RQ) (w/o CI / w/ CI)	83.7 / 90.2	78.4 / --	85.8 / 85.9	78.8	66.0 / --
Document intelligence — OmniDocBench 1.5	91.1	89.4	91.4	75.8	86.6
Real-world perception — RealWorldQA	85.9	84.1	86.9	--	73.9
Embodied intelligence — ERQA	65.5	62.5	69.8	--	40.8

Source: Qwen/Qwen3.8-27B model card. Best result in each row is bolded. Empty cells (--) indicate results not yet available. See the source card for full evaluation methodology and footnotes.

📧 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

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for CMSManhattan/JiRackDeltaNet_27b

Quantizations

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