Cool AI Things 13 AUG 2025

[title Cool AI Things 13 AUG 2025 title]

Install and Run n8n locally
https://www.youtube.com/watch?v=-ErfsM2TYsM

Run n8n from Hostinger VPS
https://www.youtube.com/watch?v=ONgECvZNI3o

Note

You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
Play with temperature. Change per Persona. Maybe set for each Persona and save that temperature to the Persona File.

###

# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1

# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""

###

You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user’s requests, you cite your sources in your answers, according to those instructions.

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https://github.com/ollama/ollama/tree/main#pass-the-prompt-as-an-argument

Pass the prompt as an argument
ollama run llama3.2 "Summarize this file: $(cat README.md)"
Output: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.

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https://github.com/ollama/ollama/tree/main#import-from-gguf

Import from GGUF
Ollama supports importing GGUF models in the Modelfile:

Create a file named Modelfile, with a FROM instruction with the local filepath to the model you want to import.

FROM ./vicuna-33b.Q4_0.gguf
Create the model in Ollama

ollama create example -f Modelfile
Run the model

ollama run example

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Python to create a client that connects to Ollama on another server
https://github.com/ollama/ollama-python#custom-client

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How can I tell if my model was loaded onto the GPU?
https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-tell-if-my-model-was-loaded-onto-the-gpu

###

Setting environment variables on Linux
https://github.com/ollama/ollama/blob/main/docs/faq.md#setting-environment-variables-on-linux

Ollama binds 127.0.0.1 port 11434 by default. Change the bind address with the OLLAMA_HOST environment variable.

###

Where are models stored?
https://github.com/ollama/ollama/blob/main/docs/faq.md#where-are-models-stored

macOS: ~/.ollama/models
Linux: /usr/share/ollama/.ollama/models
Windows: C:\Users\%username%\.ollama\models
How do I set them to a different location?
If a different directory needs to be used, set the environment variable OLLAMA_MODELS to the chosen directory.

Note: on Linux using the standard installer, the ollama user needs read and write access to the specified directory. To assign the directory to the ollama user run sudo chown -R ollama:ollama <directory>.

###

Request (Reproducible outputs)
https://github.com/ollama/ollama/blob/main/docs/api.md#request-reproducible-outputs

For reproducible outputs, set seed to a number:

Request
curl http://localhost:11434/api/generate -d ‘{
"model": "mistral",
"prompt": "Why is the sky blue?",
"options": {
"seed": 123
}
}’
Response
{
"model": "mistral",
"created_at": "2023-11-03T15:36:02.583064Z",
"response": " The sky appears blue because of a phenomenon called Rayleigh scattering.",
"done": true,
"total_duration": 8493852375,
"load_duration": 6589624375,
"prompt_eval_count": 14,
"prompt_eval_duration": 119039000,
"eval_count": 110,
"eval_duration": 1779061000
}

###