ollama-conventions

name	ollama-conventions
description	Ollama model-selection conventions (never recommend a model from

Ollama conventions

Never recommend a model from memory

Local-model leaderboards move every few weeks: new releases, new quantizations, new fine-tunes. Whatever you "remember" as the best 7B/13B/70B is almost certainly stale. Before you name a model, do the homework.

When the user asks you to pick, suggest, or hard-code an Ollama model — or when you would otherwise reach for a default tag — run a fresh benchmark study first. This is non-negotiable for any recommendation; it does not apply when the user already told you exactly which model to use.

The study (do this before recommending)

1. Search the web for current benchmarks and the latest releases. Use WebSearch / WebFetch (or the deep-research skill for a thorough, multi-source, fact-checked report when the choice is high-stakes). Cover at least:
   • The task the user actually has — code, chat, reasoning, RAG/retrieval, embeddings, vision, tool/function calling, long context, non-English. A model that tops a chat leaderboard can be mediocre at code.
   • Recent leaderboards and evals: LMArena / Chatbot Arena, the Open LLM Leaderboard, task-specific evals (HumanEval / MBPP / LiveCodeBench / SWE-bench for code, MMLU / GPQA / MATH for reasoning, MTEB for embeddings, etc.).
   • What's new on Ollama: check the Ollama model library (https://ollama.com/library) and recent model announcements — the tag has to actually exist and be pullable.
2. Match the model to the hardware. A benchmark winner the user can't run is useless. Account for parameter count, the quantization that fits their VRAM/RAM (q4_K_M, q5_K_M, q8_0, …), and context-window needs. Ask for or infer the available memory if it isn't obvious.
3. Cite what you found. When you give the recommendation, briefly say which benchmarks and how recent they are, and name a runner-up. The user should see the evidence, not just the verdict. Note the benchmark date — "as of <month/year>" — so it's clear the data has a shelf life.
4. Pin an exact tag. Recommend and hard-code a concrete, reproducible tag (e.g. qwen2.5-coder:7b-instruct-q4_K_M), never a floating :latest.

If web access is unavailable, say so and flag that the suggestion is from possibly-stale memory — don't present a remembered model as a current best.

Plumbing conventions

• Configuration via bare env vars, no project prefix (per global rules): honour OLLAMA_HOST and the standard OLLAMA_* names; use DATABASE_URL, LOG_LEVEL, etc. for surrounding config.
• Don't pin :latest. Always use an explicit, reproducible model tag in code, compose files, Modelfiles, and docs.
• Document the choice. When you commit a model tag, leave a one-line note (in README.md or a comment) of why — the task and the benchmark snapshot it was chosen from — so the next person knows when to revisit it.