Instructions to use CMSManhattan/JiRackUltra_14b 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/JiRackUltra_14b 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/JiRackUltra_14b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CMSManhattan/JiRackUltra_14b: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/JiRackUltra_14b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CMSManhattan/JiRackUltra_14b: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/JiRackUltra_14b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CMSManhattan/JiRackUltra_14b: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/JiRackUltra_14b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CMSManhattan/JiRackUltra_14b:Q4_K_M

Use Docker

docker model run hf.co/CMSManhattan/JiRackUltra_14b:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use CMSManhattan/JiRackUltra_14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CMSManhattan/JiRackUltra_14b"
# 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/JiRackUltra_14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CMSManhattan/JiRackUltra_14b:Q4_K_M

Ollama
How to use CMSManhattan/JiRackUltra_14b with Ollama:
```
ollama run hf.co/CMSManhattan/JiRackUltra_14b:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use CMSManhattan/JiRackUltra_14b with Docker Model Runner:
```
docker model run hf.co/CMSManhattan/JiRackUltra_14b:Q4_K_M
```

Lemonade

How to use CMSManhattan/JiRackUltra_14b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CMSManhattan/JiRackUltra_14b:Q4_K_M

Run and chat with the model

lemonade run user.JiRackUltra_14b-Q4_K_M

List all available models

lemonade list

Atomic Chat

kgrabko commited on Sep 27

Commit

eab5fbc

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1 Parent(s): 1650e9a

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@@ -32,7 +32,7 @@ tags:
 license: mit
 ---
-![NVIDIA Inception Program Member](badge.jpg)
 # JiRack Ultra 14B (CPU)
 A fast and efficient 14B 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 R1-14B architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations.

 license: mit
 ---
 # JiRack Ultra 14B (CPU)
 A fast and efficient 14B 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 R1-14B architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations.