Optimized for real-world data science decisions. It delivers method selection, alternative comparison, and validation planning in one response. Trained on 16.5K expert insight dialogues, it focuses on actionable guidance over generic theory.

ollama run Zero9Tech/qwen3.5-9b-data-science

curl http://localhost:11434/api/chat \
  -d '{
    "model": "Zero9Tech/qwen3.5-9b-data-science",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='Zero9Tech/qwen3.5-9b-data-science',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'Zero9Tech/qwen3.5-9b-data-science',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

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Name

1 model

Size

Context

Input

qwen3.5-9b-data-science:latest

5.6GB · 256K context window · Text · 3 weeks ago

qwen3.5-9b-data-science:latest

5.6GB

256K

Text

Readme

Qwen3.5-9B-Data-Science-Insight-16.5K

This model is tuned for decision-oriented data mining and applied data science assistance.

Training Setup

Domain SFT: zero9tech/data-scientist-insight-dialog-en-16.5k.

Dataset Test Highlights

Total records: 16,463
Split: train: 14,021 · validation: 801 · test: 1,641
assistant_first_unique_ratio: 0.8408
assistant_final_unique_ratio: 1.0000

Usage Note

Model behavior is optimized for decision-focused responses (method choice, alternatives, risk signals, validation planning).

Copyright

License

Apache-2.0