106 lines
2.0 KiB
Python
106 lines
2.0 KiB
Python
import marimo
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__generated_with = "0.18.0"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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from utils import connect_qumo_ollama
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# VM_NAME = 'hiperf-gpu'
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VM_NAME = 'ollama-lite'
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client = connect_qumo_ollama(VM_NAME)
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return VM_NAME, client, mo
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@app.cell(hide_code=True)
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def _(VM_NAME, mo):
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mo.md(rf"""
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# Ollama Reference
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## Ollama Web-UI: http://{VM_NAME}.tail44fa00.ts.net:3000
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Use the UI to modify system prompts, custom models, etc...
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**if the connection fails, make sure Tailscale is up**
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## Ollama Python
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Docs: https://github.com/ollama/ollama-python
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Use the code below to programmatically interact with the models. E.g: create a small pipeline that loads a transcript and inserts it into the prompt. Helpful if we need to analyze 26 interviews...
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**Important Definitions:**
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- **Generate**: post a single message and get a response.
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- **Chat**: post a single message and the previous chat history, and get a response
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""")
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return
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@app.cell
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def _(client):
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client.list().models
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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# Sandbox Generate vs. Chat
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""")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Chat
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""")
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return
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@app.cell
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def _(client):
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response_chat = client.chat(model='deepseek-r1:7b', messages=[
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{
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'role': 'user',
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'content': 'Why is the sky blue?',
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},
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])
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return (response_chat,)
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@app.cell
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def _(mo, response_chat):
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mo.md(rf"""
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{response_chat.message.content}
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""")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Generate
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""")
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return
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@app.cell
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def _(client):
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response_generate = client.generate(model='deepseek-r1:32b', prompt='Why is the sky blue?')
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return (response_generate,)
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@app.cell
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def _(mo, response_generate):
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mo.md(rf"""
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{response_generate.response}
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""")
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return
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if __name__ == "__main__":
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app.run()
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