| name | local-model-coding |
| description | Guidance for coding effectively with a LOCAL model (Ollama via the Cline extension) instead of a frontier cloud model — scope tasks small, lean on git for safety, choose the right local model for the hardware, raise the context window for agents, and verify output under zero-trust. Use when the user is working offline/privately with Ollama, asks why a local model is weaker, or wants the best workflow for local AI coding on a laptop. |
Coding with a local model (Ollama + Cline)
Why this is different
A local model on a laptop is smaller and weaker than a frontier cloud model. It can still be genuinely useful — if you adapt how you work. The goal is private, offline, reliable help, not magic.
Work in small, well-scoped steps
- One file, one clear instruction at a time. Local models lose the thread on sprawling, multi-file edits.
- Give concrete context — paste the function, name the variables, state the expected behavior. Don't make the model guess the codebase.
- Prefer "edit this selection" over "refactor my whole project."
- Ask for reasoning when correctness matters, so faulty assumptions surface.
Lean on git as your safety net
- Commit before letting an agent edit. A clean working tree is your undo button.
- Review every change with
git show / git diff; undo a bad edit with git revert.
- This is the zero-trust habit: treat generated code as untrusted, verify before running.
Pick the right model for the hardware
| Task | Model (Ollama tag) | Needs |
|---|
| Serious multi-file coding | qwen3-coder:30b | ~32 GB RAM |
| Solid coding, mid laptop | qwen2.5-coder:14b | ~16 GB |
| Light laptop coding | qwen2.5-coder:7b | ~8 GB |
| Fast general helper | gemma4:e4b | ~8 GB |
| Reasoning/analysis | phi4 | ~16 GB |
| Driving an agent | devstral | ~24–32 GB |
Use Q4_K_M quantization by default. See 04_local/READ_models.md and READ_quantization.md.
Tooling
When the local model struggles
- Shrink the task; add more explicit context.
- Step up to a larger local model if RAM allows.
- For a genuinely hard problem with non-sensitive code, it's fine to switch to a cloud model — just make that choice deliberately.
Anti-patterns
- Asking a 7B model for a sweeping repo-wide refactor in one shot.
- Running generated code without reading it.
- Assuming "it ran locally" means "it's correct."
