Ollama AI Cheat Sheet

Master Ollama with this comprehensive cheat sheet covering installation, model management, API usage, and advanced configurations for running AI models locally.

#Installation

#Linux

curl -fsSL https://ollama.ai/install.sh | sh
# Manual installation:
curl -L https://ollama.ai/download/ollama-linux-amd64 -o /usr/local/bin/ollama
chmod +x /usr/local/bin/ollama

#Windows

# Download installer from https://ollama.ai/download
# Or use Windows Package Manager:
winget install Ollama.Ollama

#Docker

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
# With GPU support (NVIDIA):
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

#Basic Commands

#Common Usage Examples

# Start interactive chat with Llama 2
ollama run llama2

# Single prompt (AI responds once)
ollama run llama2 "Say hello"
ollama run llama2 "Explain quantum computing in simple terms"
ollama run llama2 "Write a Python function to sort a list"

# Pull the latest Codellama model
ollama pull codellama

# List all available models
ollama list
NAME           ID              SIZE    MODIFIED
llama2:latest  e38ae474bf77    3.8 GB  2 hours ago
codellama:7b   8fdf8f752f6e    3.8 GB  3 hours ago

# Get model information
ollama show llama2

# Remove a model
ollama rm llama2:13b

#Available Models

#Model Tags and Versions

#Base Language Models

# Llama family
ollama pull llama2:7b
ollama pull llama2:13b
ollama pull llama2:70b
ollama pull llama3:8b
ollama pull llama3:70b

# Mistral family
ollama pull mistral:latest
ollama pull mixtral:8x7b

# GPT-OSS models
ollama pull gpt-oss:20b
ollama pull gpt-oss:120b

#Code-Specialized Models

# Code Llama variants
ollama pull codellama:7b-code
ollama pull codellama:7b-instruct
ollama pull codellama:7b-python

# DeepSeek Coder
ollama pull deepseek-coder:6.7b
ollama pull deepseek-coder:33b

# Wizard Coder
ollama pull wizardcoder:15b

#Specialized Task Models

# Instruction following
ollama pull wizardlm:7b
ollama pull wizardlm:13b

# Mathematical reasoning
ollama pull wizardmath:7b

# General reasoning
ollama pull deepseek-llm:7b
ollama pull deepseek-llm:67b

#Community & Fine-tuned Models

# Conversational models
ollama pull openhermes:7b
ollama pull nous-hermes2:7b

# Multilingual models
ollama pull qwen:7b
ollama pull yi:34b

# Compact models
ollama pull phi:2.7b

#API Usage

#Generate Completion

curl http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt": "Why is the sky blue?",
  "stream": false
}'

#Chat Completion

curl http://localhost:11434/api/chat -d '{
  "model": "llama2",
  "messages": [
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ]
}'

#Python Client

pip install ollama

# Python usage
import ollama

response = ollama.chat(model='llama2', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response['message']['content'])

#JavaScript/Node.js

npm install ollama

// JavaScript usage
import ollama from 'ollama'

const response = await ollama.chat({
  model: 'llama2',
  messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
console.log(response.message.content)

#Streaming Responses

curl http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt": "Tell me a story",
  "stream": true
}'

# Python streaming
for chunk in ollama.chat(
    model='llama2',
    messages=[{'role': 'user', 'content': 'Tell me a story'}],
    stream=True,
):
    print(chunk['message']['content'], end='', flush=True)

#API Endpoints

#Text-to-Speech Integration

#Using espeak (Linux)

# Install espeak
sudo apt install espeak

# Pipe Ollama output to speech
ollama run llama2 "Tell me a joke" | espeak

# With custom voice settings
ollama run llama2 "Hello world" | espeak -s 150 -v en+f3

#Using Festival (Linux)

# Install Festival

#Advanced Configuration

#Environment Variables

# Set custom data directory
export OLLAMA_MODELS=/path/to/models

# Change default host and port
export OLLAMA_HOST=0.0.0.0:11434

# Enable debug logging
export OLLAMA_DEBUG=1

# Set maximum number of parallel requests
export OLLAMA_MAX_LOADED_MODELS=3

# GPU configuration
export OLLAMA_NUM_PARALLEL=4
export CUDA_VISIBLE_DEVICES=0,1

#Model Configuration

# Create custom model with Modelfile
cat > Modelfile << EOF
FROM llama2
PARAMETER temperature 0.8
PARAMETER num_predict 100
SYSTEM "You are a helpful AI assistant."
EOF

# Build custom model
ollama create mymodel -f Modelfile

#Performance Tuning

#Service Management

# Start Ollama service
ollama serve

# Run as systemd service (Linux)
sudo systemctl enable ollama
sudo systemctl start ollama
sudo systemctl status ollama

# Check service logs
journalctl -u ollama -f

#Troubleshooting

#Common Issues

#Diagnostic Commands

# Check Ollama version
ollama --version

# Check system resources
free -h
df -h
nvidia-smi

# Test API connectivity
curl http://localhost:11434/api/tags

# Check model status
ollama ps

# View logs (Linux)
journalctl -u ollama --no-pager

# View logs (Windows)
Get-EventLog -LogName Application -Source Ollama

#Performance Optimization

#Network Configuration

# Allow external connections
export OLLAMA_HOST=0.0.0.0:11434
ollama serve

# Configure firewall (Ubuntu)
sudo ufw allow 11434

# Test remote connection
curl http://your-server-ip:11434/api/tags