Local model onboarding

Local Model Onboarding (the rig)

End-to-end workflow for taking a candidate model → qualified member of the on-device eval rotation. This ties together the pieces that already exist (model-manager for models.yml/API access, local-ollama-eval for running on the rig) and adds the two things that were tribal knowledge: the hardware size/shape gate and the opencode+pi cross-harness registration.

The rig: Mac Studio M4 Max · 128 GB unified · 40-core GPU · ~546 GB/s memory bandwidth. One box, shared by every eval job via a mkdir mutex (rig lock).

Current State

• ollama: !'curl -s --max-time 3 http://localhost:11434/api/version >/dev/null 2>&1 && echo "✓ running" || echo "✗ not running — start it first"'
• Unified memory: !'echo "$(($(sysctl -n hw.memsize)/1024/1024/1024)) GB ($(sysctl -n machdep.cpu.brand_string 2>/dev/null || echo Apple Silicon))"'
• Models on disk: !'curl -s http://localhost:11434/api/tags 2>/dev/null | python3 -c "import sys,json;[print(" ",m["name"],"%.1fGB"%(m["size"]/1e9)) for m in json.load(sys.stdin).get("models",[])]" 2>/dev/null || echo " (ollama down)"'
• Currently resident (VRAM): !'curl -s http://localhost:11434/api/ps 2>/dev/null | python3 -c "import sys,json;d=json.load(sys.stdin);[print(" ",m["name"],"%.1fGB"%(m.get("size_vram",0)/1e9)) for m in d.get("models",[])] or print(" (none loaded)")" 2>/dev/null || echo " (ollama down)"'
• Rotation models: !'grep -E "OS_FILLER_MODELS|MODELS=" tools/launchd/os-rotation-filler.sh 2>/dev/null | head -1'
• Rig lock: !'ls "$HOME/.ailang/state/rig.lock.d" >/dev/null 2>&1 && echo "HELD — a job is running; do heavy pulls/probes around it" || echo "free"'

Use the state above. If ollama is down, start it before anything. If the rig lock is held, a scheduled job is running — ollama pull is fine (network/disk) but defer GPU probes, or acquire the lock (see tools/launchd/rig-lock.sh).

The hardware envelope (read this first)

Token generation on Apple Silicon is memory-bandwidth-bound, not compute-bound. Two consequences drive every decision in this skill:

1. --parallel 1 is the established default. All three rig jobs (nightly-eval.sh, nightly-lang-eval.sh, os-rotation-filler.sh) hard-code it. The documented rationale (tools/launchd/rig-lock.sh) is operational: the box is a single GPU / bandwidth-bound, concurrent streams thrash the ~546 GB/s bus, and an Ollama model reload mid-run silently kills a stream — all of which bite hard in a multi-model rotation. Evidence caveat (verify before changing): there is no recorded p=1-vs-p=2 head-to-head. The only measured parallelism data is a p=2-vs-p=4 run on a single small model (gemma4:26b) that favored p=2 over p=4 — a different question. So treat p=1 as the safe operational default, not a benchmarked optimum. If you want to raise it, run the head-to-head first (smoke set, p=1 vs p=2, compare wall-clock, pass rate, and TTFT-timeout count) — don't just trust the old p=2 note in local-ollama.md.

2. Prefer MoE with a small active parameter count. Total params live cheaply in unified memory (quality); only the active params compute per token (speed). qwen3.5:35b-a3b = 35B total / 3B active is the canonical shape. A dense model of the same size generates tokens far slower — avoid dense above ~14B for the rotation.

Memory budget (single-stream, p=1):

	GB
Unified total	128
Usable by Metal (~75% wired default)	~96
Reserve (OS + ailang server + opencode/pi subprocs + KV headroom)	~22
Safe ceiling for model resident	~74
Comfortable (slack at long context)	≤ ~50

Rule of thumb: on-disk ≤ ~45 GB, total ≤ ~80B, active ≤ ~8B. Run scripts/check_model_fit.sh to get a verdict (estimate pre-pull, or measure a pulled model's real resident VRAM).

Workflow

1. Select a candidate (shape gate + generation check)

Do not pick from memory — fetch live. The assistant's training is months stale and model names mislead. Use resources/model-watch.md: it lists the canonical sources to fetch — ranking boards SWE-bench, Terminal-Bench, LMArena (Aider dropped 2026-06, unmaintained); plus availability (Ollama library, OpenRouter, HF trending) and our own OS leaderboard. Refresh that file's table first, then choose from it. For each promising model, split into the 🖥 local track (on-device build that fits the rig) and the ☁️ cloud track (OpenRouter screen / ceiling ref) — see model-watch.

Find a recent coding/agentic model that matches the shape rule.

Verify the generation by RELEASE DATE, not the name — Qwen naming is a trap: Qwen3-Coder-30B-A3B is the Qwen3.0 coder (2025) and is older than the rig's incumbent qwen3.5:35b-a3b (Qwen 3.5, Feb 2026), despite the higher-looking "Coder" label. As of mid-2026 the genuinely newer same-shape upgrade is Qwen 3.6 35B-A3B (Apr 2026, MoE). Qwen 3.6-27B is newer but dense (no A3B) → slower on this bandwidth-bound box. Qwen 3.7-Max is API-only (not on-device). Always web-check the release date and confirm it actually beats the incumbent on the axis you care about before pulling.

Skip the giant frontier-OS models (GLM-5.1, DeepSeek-V4-Pro, Kimi, MiniMax-m3) — those stay as OpenRouter ceiling references; they don't fit on-device.

# Fit/shape verdict BEFORE spending rig time (estimate from the published size):
.Codex/skills/local-model-onboarding/scripts/check_model_fit.sh --disk 24 --active 3

2. Quality pre-screen via OpenRouter (cheap, zero rig time)

If the model is also hosted on OpenRouter, screen its quality first so you only pull winners. Use model-manager to add a temporary or-<model> entry and run a small standard+agent pass on AILANG + the 4-lang set, then compare to incumbents.

.Codex/skills/model-manager/scripts/test_model_access.sh <openrouter-id>     # access + pricing
.Codex/skills/model-manager/scripts/find_model_info.sh  <openrouter-id>
# add or-<model> to models.yml (model-manager), then:
ailang eval-suite --models or-<model> --benchmarks <smoke set> --langs ailang,python --trials 3 --output /tmp/screen_<model>

If it doesn't clear the bar vs your current local models, stop here — no pull.

3. Pull to the rig + confirm it fits for real

ollama pull <ollama-tag>
.Codex/skills/local-ollama-eval/scripts/check_ollama_model.sh <ollama-tag>     # loads + sanity
.Codex/skills/local-model-onboarding/scripts/check_model_fit.sh <ollama-tag>   # REAL resident VRAM verdict

Must come back GREEN/YELLOW. RED → pick a smaller quant (mxfp8 → nvfp4/Q4) or a lower-active variant.

4. Register the cross-harness pair in models.yml

Always register two entries — the same local model on opencode and on pi — so the OS/Local leaderboard gets a real harness comparison. They must share a model_family so the Explorer pairs them.

  opencode-<id>:
    api_name: "<ollama-tag>"
    provider: "ollama"
    agent_cli: "opencode"
    agent_model_name: "<ollama-tag>"
    model_family: "<family>"          # SAME on both entries → cross-harness pairing
    env_var: ""
    budgets: { hard_timeout_secs: 600 }   # local hang protection (see below)
    pricing: { input_per_1k: 0.0, output_per_1k: 0.0 }

  pi-<id>:
    api_name: "<ollama-tag>"
    provider: "ollama"
    agent_cli: "pi"
    agent_model_name: "ollama/<ollama-tag>"   # pi prefixes provider
    model_family: "<family>"
    env_var: ""
    budgets: { hard_timeout_secs: 600 }
    pricing: { input_per_1k: 0.0, output_per_1k: 0.0 }

Both harnesses need the model in their OWN config too — models.yml routing is not enough. This bites silently: the harness produces empty output / 0% (model-not-found) while the model is fine (the other harness passes). Register in both:

• opencode — add an entry under the ollama provider's models block in ~/.config/opencode/opencode.jsonc (mirror an existing qwen entry: name + temperature/top_p/max_tokens). Verify: opencode models | grep <tag>.
• pi — pi has no built-in ollama provider; add the tag's id to ~/.pi/agent/models.json (canonical copy: tools/setup/pi-ollama-models.json). Verify: a one-benchmark pi-<id> run returns non-empty output.

If one harness smoke-fails with empty stdout while the other passes, this missing registration is almost always why.

hard_timeout_secs: 600 — local models hang on some hard benchmarks; the 2400s default burns 40 min per hang. 600s is the standard local backstop.

Rebuild so the embedded registry updates: make quick-install (the launchd jobs use the installed binary). Verify routing:

ailang eval-suite --agent --models opencode-<id>,pi-<id> --benchmarks fizzbuzz --langs ailang --dry-run

5. Smoke-validate both harnesses (under the rig lock)

source tools/launchd/rig-lock.sh && rig_lock_acquire wait
ailang eval-suite --agent --models opencode-<id>,pi-<id> \
  --benchmarks fizzbuzz,cli_args --langs ailang,python --trials 1 --parallel 1 --output /tmp/onboard_<id>

Confirm: passes, cost 0, harness = opencode/pi, resident VRAM under budget (ollama ps). The lock releases on shell exit.

6. Add to the continuous rotation

Append the pair to the filler's model list — it round-robins benchmarks for all listed models, serially at p=1:

# tools/launchd/os-rotation-filler.sh
OS_FILLER_MODELS="opencode-qwen3-5-35b-a3b-mxfp8,pi-qwen3-5-35b-a3b-mxfp8,opencode-<id>,pi-<id>"

The launchd plist execs the repo script directly — next fire picks it up, no reinstall. Mind cycle wall-time: each added model multiplies runs/cycle; the rig lock prevents overlap so cycles just lengthen.

7. Record the decision

Note the screen result, fit verdict, and rotation status in the model's models.yml notes: and (if it's a keeper) the OS leaderboard story. Commit models.yml + the filler change + ~/.pi/agent/models.json mirror together.

Gotchas

• Never --parallel >1 on the rig (the whole point of this skill). Bandwidth.
• Two entries or it's not comparable — opencode + pi with a shared family.
• pi needs the ollama provider block in ~/.pi/agent/models.json.
• 600s hard timeout for local models, not the 2400s cloud default.
• Blackout 04:00–07:00 — the filler skips it (nightly window). Don't schedule heavy onboarding probes there.
• make quick-install after models.yml edits — the registry is embedded; the launchd jobs run the installed binary, not the source tree.
• OpenRouter screen is a quality filter only — a model that scores well hosted may differ on-device due to quant; always re-validate after pull (step 5), and the opencode-vs-pi-vs-OR comparison in the Explorer surfaces that delta.

Related

• model-manager — models.yml mechanics, API access, pricing (cloud + OR).
• local-ollama-eval — running/monitoring evals on the rig, OTEL observability.
• tools/launchd/os-rotation-filler.sh — the continuous rotation.
• docs/docs/guides/evaluation/local-ollama.md — human-facing rig reference.