# Linux/WSL
-weight: 500;">curl -fsSL https://ollama.ai/-weight: 500;">install.sh | sh # macOS
-weight: 500;">brew -weight: 500;">install ollama # Start the -weight: 500;">service
ollama serve
# Linux/WSL
-weight: 500;">curl -fsSL https://ollama.ai/-weight: 500;">install.sh | sh # macOS
-weight: 500;">brew -weight: 500;">install ollama # Start the -weight: 500;">service
ollama serve
# Linux/WSL
-weight: 500;">curl -fsSL https://ollama.ai/-weight: 500;">install.sh | sh # macOS
-weight: 500;">brew -weight: 500;">install ollama # Start the -weight: 500;">service
ollama serve
# Best balance of speed and quality (7B params, ~4GB)
ollama pull deepseek-coder:6.7b # Faster, smaller, good for completions (3B params, ~2GB)
ollama pull starcoder2:3b # Heavy hitter if you have the RAM (33B params, ~20GB)
ollama pull codellama:34b
# Best balance of speed and quality (7B params, ~4GB)
ollama pull deepseek-coder:6.7b # Faster, smaller, good for completions (3B params, ~2GB)
ollama pull starcoder2:3b # Heavy hitter if you have the RAM (33B params, ~20GB)
ollama pull codellama:34b
# Best balance of speed and quality (7B params, ~4GB)
ollama pull deepseek-coder:6.7b # Faster, smaller, good for completions (3B params, ~2GB)
ollama pull starcoder2:3b # Heavy hitter if you have the RAM (33B params, ~20GB)
ollama pull codellama:34b
ollama run deepseek-coder:6.7b "Write a Python function to merge two sorted lists"
ollama run deepseek-coder:6.7b "Write a Python function to merge two sorted lists"
ollama run deepseek-coder:6.7b "Write a Python function to merge two sorted lists"
{ "models": [ { "title": "DeepSeek Coder", "provider": "ollama", "model": "deepseek-coder:6.7b" } ], "tabAutocompleteModel": { "title": "StarCoder", "provider": "ollama", "model": "starcoder2:3b" }
}
{ "models": [ { "title": "DeepSeek Coder", "provider": "ollama", "model": "deepseek-coder:6.7b" } ], "tabAutocompleteModel": { "title": "StarCoder", "provider": "ollama", "model": "starcoder2:3b" }
}
{ "models": [ { "title": "DeepSeek Coder", "provider": "ollama", "model": "deepseek-coder:6.7b" } ], "tabAutocompleteModel": { "title": "StarCoder", "provider": "ollama", "model": "starcoder2:3b" }
}
-- lazy.nvim
{ "nomnivore/ollama.nvim", dependencies = { "nvim-lua/plenary.nvim" }, cmd = { "Ollama", "OllamaModel" }, opts = { model = "deepseek-coder:6.7b", url = "http://127.0.0.1:11434", }
}
-- lazy.nvim
{ "nomnivore/ollama.nvim", dependencies = { "nvim-lua/plenary.nvim" }, cmd = { "Ollama", "OllamaModel" }, opts = { model = "deepseek-coder:6.7b", url = "http://127.0.0.1:11434", }
}
-- lazy.nvim
{ "nomnivore/ollama.nvim", dependencies = { "nvim-lua/plenary.nvim" }, cmd = { "Ollama", "OllamaModel" }, opts = { model = "deepseek-coder:6.7b", url = "http://127.0.0.1:11434", }
}
vim.keymap.set("v", "<leader>oo", ":<c-u>lua require('ollama').prompt()<cr>")
vim.keymap.set("v", "<leader>oo", ":<c-u>lua require('ollama').prompt()<cr>")
vim.keymap.set("v", "<leader>oo", ":<c-u>lua require('ollama').prompt()<cr>")
# Add to .bashrc/.zshrc
ask() { ollama run deepseek-coder:6.7b "$*"
} # Usage
ask "What's the time complexity of Python's sorted()?"
# Add to .bashrc/.zshrc
ask() { ollama run deepseek-coder:6.7b "$*"
} # Usage
ask "What's the time complexity of Python's sorted()?"
# Add to .bashrc/.zshrc
ask() { ollama run deepseek-coder:6.7b "$*"
} # Usage
ask "What's the time complexity of Python's sorted()?"
cat broken_script.py | ollama run deepseek-coder:6.7b "Fix the bugs in this code"
cat broken_script.py | ollama run deepseek-coder:6.7b "Fix the bugs in this code"
cat broken_script.py | ollama run deepseek-coder:6.7b "Fix the bugs in this code"
ollama run deepseek-coder:6.7b --verbose
# Look for "using CUDA" in output
ollama run deepseek-coder:6.7b --verbose
# Look for "using CUDA" in output
ollama run deepseek-coder:6.7b --verbose
# Look for "using CUDA" in output
# List loaded models
-weight: 500;">curl http://localhost:11434/api/tags # Ollama auto-unloads after 5 min idle
# Or -weight: 500;">restart the -weight: 500;">service to clear everything
# List loaded models
-weight: 500;">curl http://localhost:11434/api/tags # Ollama auto-unloads after 5 min idle
# Or -weight: 500;">restart the -weight: 500;">service to clear everything
# List loaded models
-weight: 500;">curl http://localhost:11434/api/tags # Ollama auto-unloads after 5 min idle
# Or -weight: 500;">restart the -weight: 500;">service to clear everything
# q4 = 4-bit quantization, faster, less accurate
ollama pull deepseek-coder:6.7b-instruct-q4_0
# q4 = 4-bit quantization, faster, less accurate
ollama pull deepseek-coder:6.7b-instruct-q4_0
# q4 = 4-bit quantization, faster, less accurate
ollama pull deepseek-coder:6.7b-instruct-q4_0 - Privacy — My client code never leaves my machine
- Cost — $0/month after initial setup
- Speed — No network latency, works offline - RAM: 16GB minimum, 32GB recommended
- GPU: Optional but helps (NVIDIA with 8GB+ VRAM ideal)
- Storage: 10-50GB depending on models
- OS: Linux, macOS, or Windows with WSL2 - Install the Continue extension from VS Code marketplace
- Open Continue settings (Cmd/Ctrl + Shift + P → "Continue: Open config.json")
- Add your Ollama model: - Chat with code context (highlight code → ask questions)
- Tab completions as you type
- Inline edits (Cmd+I to refactor selected code) - Code completion and boilerplate
- Explaining unfamiliar code
- Writing tests for existing functions
- Regex and SQL generation
- Git commit messages - Complex architectural decisions
- Multi-file refactoring
- Debugging truly weird issues - Try different models — Mistral, Phi-3, Llama 3 all have coding variants
- Fine-tune on your codebase — Ollama supports custom Modelfiles
- Build custom tools — The Ollama API is dead simple to script against
