llama llama red pajama

Llama_with_llama3_1

# a machine with a GPU would be nice. makes it faster.

# install Ollama - a runner for the models

sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
sudo chmod +x /usr/bin/ollama
sudo useradd -r -s /bin/false -m -d /usr/share/ollama ollama

# create service unit file
tee /usr/lib/systemd/system/ollama.service > /dev/null <<EOF
[Unit]
Description=Ollama Service
After=network-online.target

[Service]
ExecStart=/usr/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="OLLAMA_HOST=0.0.0.0" 
Environment="OLLAMA_ORIGINS=*"

[Install]
WantedBy=default.target
EOF

# reload the systemd daemon and enable ollama

sudo systemctl daemon-reload
sudo systemctl enable ollama
sudo systemctl start ollama


# testing
sudo lsof -i :11434
sudo systemctl stop ollama
export OLLAMA_HOST=0.0.0.0 
ollama serve




# run the llama3.1 model

ollama run llama3.1

# restart the ollama runner 
sudo service ollama restart


# watch the logs 
journalctl -xeu ollama

# watch nvidia GPU

watch -n 1 nvidia-smi

# DeepSeek Coder is trained from scratch on both 87% code and 13% natural language in English and Chinese. Each of the models are pre-trained on 2 trillion tokens.

### Models available

ollama run deepseek-coder
ollama run deepseek-coder:6.7b
ollama run deepseek-coder:33b

### API access to your own machine

Example using curl:

curl -X POST http://your-machine-ip-address:11434/api/generate -d '{
  "model": "llama3.1",
  "prompt":"Why is the sky blue?"
 }'
 
 
# Enchanted App on iOS app store

http://your-machine-ip-address:11434



# zed editor integrates personal ollama
# vscode editor integration with personal ollama
# obsidian notes integration via khoj plugin connected to personal ollama

prompt example

You are an expert note-making AI for obsidian who specializes in the Linking Your Thinking (LYK) strategy. The following is a transcription of recording of someone talking aloud or people in a conversation. There may be a lot of random things said given fluidity of conversation or thought process and the microphone's ability to pick up all audio. Give me detailed notes in markdown language on what was said in the most easy-to-understand, detailed, and conceptual format. Include any helpful information that can conceptualize the notes further or enhance the ideas, and then summarize what was said. Do not mention "the speaker" anywhere in your response. The notes your write should be written as if I were writting them. Finally, ensure to end with code for a mermaid chart that shows an enlightening concept map combining both the transcription and the information you added to it. The following is the transcribed audio:

I run llama3.1 model on an nvidia cpu machine and then run open-webui in another machine using docker compose. That way I can get a locally running inferance engine at my finger tips

https://openwebui.com/#open-webui-community