cmsmanhattan/ jirack-ultra-14b-q2:latest

4 Downloads Updated 1 month ago

ollama run cmsmanhattan/jirack-ultra-14b-q2

curl http://localhost:11434/api/chat \
  -d '{
    "model": "cmsmanhattan/jirack-ultra-14b-q2",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='cmsmanhattan/jirack-ultra-14b-q2',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'cmsmanhattan/jirack-ultra-14b-q2',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 1 month ago

1 month ago

96397300c0d3 · 5.8GB

model

archqwen2

parameters14.8B

quantizationQ2_K

5.8GB

system

You are JiRack, a helpful and honest AI assistant.

50B

params

{ "num_ctx": 8192, "repeat_penalty": 1.1, "stop": [ "<｜end▁of▁sentence｜>

142B

template

<｜User｜>{{ .Prompt }}<｜Assistant｜>

42B

Readme

JiRack Ultra 14B Q2_K

Mid-size ~14B model from CMS Manhattan. BitNet-style ternary path, extended tokenizer with Routing, Tool-call and Robotics tags. Production Q2_K build for maximum compression.

HF: https://huggingface.co/CMSManhattan/JiRackUltra_14b

Run

ollama run cmsmanhattan/jirack-ultra-14b-q2

What it is

Stronger general + coding / tool-call capacity than the 1B line
Same JiRack tokenizer tags: routing, tools, robotics
Q2_K quant for the smallest practical disk and RAM footprint
Fits RAG, agents, and Spring / Java tool-call stacks on constrained boxes
American ternary stack from CMS Manhattan (New York)

Hardware

Q2_K: roughly 5–6 GB on disk.
Recommend 8–10 GB VRAM (GPU) or 12–16 GB system RAM (CPU).

Contact

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

License

MIT on weights unless stated otherwise on the HF card. Commercial terms for UI / enterprise on request.