bonsai-2:4b-q2

bonsai-2:4b-q2_0

398 Downloads Updated 5 hours ago

Bonsai 2 from Prism ML is a family of vision models built for ternary weights – smaller models with close to full-precision quality.

vision tools thinking 2b 4b 9b 27b

ollama run bonsai-2:4b-q2_0

curl http://localhost:11434/api/chat \
  -d '{
    "model": "bonsai-2:4b-q2_0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='bonsai-2:4b-q2_0',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'bonsai-2:4b-q2_0',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 5 hours ago

5 hours ago

93edd652c932 · 1.6GB

model

archqwen35

parameters4.21B

quantization(!unknown_file_type 41!)

1.2GB

projector

archclip

parameters334M

quantizationQ8_0

367MB

license

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR US

10kB

params

{ "presence_penalty": 1, "temperature": 1, "top_k": 20, "top_p": 0.95 }

63B

Readme

Bonsai 2 from Prism ML is a family of vision models built for ternary weights (−1, 0 or +1): about half the size of standard 4-bit models, at close to full-precision quality.

All sizes support thinking, tool calling, image input and a 262K-token context window.

Bonsai 2 from Prism ML is a family of vision models built for ternary weights – smaller models with close to full-precision quality.

Details

Readme

References