Function buildLightLocalChoices

src/setup/wizard.ts:511–566

* Build the sub-agent model choices when the user picks "lighter local". * * Logic: offer models SMALLER than what they picked for the parent. If the parent is * already small (7B), we still offer the same so they can change their mind. * The hardware tier limits us: even on a Mac Studio, no poi

(
  hw: HardwareProfile,
  parentModel: string,
  detected: DetectedModel[] = [],
)

 * than the parent — that wouldn't be "lighter".
 */
function buildLightLocalChoices(
  hw: HardwareProfile,
  parentModel: string,
  detected: DetectedModel[] = [],
): Array<{ value: string; label: string; hint?: string }> {
  const parentSize = approxModelSizeB(parentModel);
  const choices: Array<{ value: string; label: string; hint?: string }> = [];
  const seen = new Set<string>();

  // First: any detected models that aren't the parent.
  // Order them by toolCallsLikely then size (closer to parent*0.7 is ideal).
  const detectedCandidates = detected
    .filter(m => m.id !== parentModel)
    .sort((a, b) => {
      const aTC = a.toolCallsLikely ? 1 : 0;
      const bTC = b.toolCallsLikely ? 1 : 0;
      if (aTC !== bTC) return bTC - aTC;
      return (b.paramsB ?? 0) - (a.paramsB ?? 0);
    });
  for (const m of detectedCandidates) {
    if (seen.has(m.id)) continue;
    seen.add(m.id);
    const f = formatModel(m);
    choices.push({ value: m.id, label: f.label, hint: `installed · ${f.hint}` });
  }

  // Then: hardcoded lighter candidates the user might want to pull.
  const fallbackCandidates = [
    { id: 'qwen3-coder', sizeB: 30, label: 'qwen3-coder', hint: '✓ tool-calls native, balanced' },
    { id: 'qwen2.5-coder:7b', sizeB: 7, label: 'qwen2.5-coder:7b', hint: 'best for tool use, fast' },
    { id: 'qwen2.5-coder:3b', sizeB: 3, label: 'qwen2.5-coder:3b', hint: 'fastest, lower quality' },
    { id: 'qwen2.5-coder:14b', sizeB: 14, label: 'qwen2.5-coder:14b', hint: 'balanced' },
    { id: 'deepseek-coder-v2:lite', sizeB: 16, label: 'deepseek-coder-v2:lite', hint: 'alternative perspective' },
    { id: 'llama3.2:3b', sizeB: 3, label: 'llama3.2:3b', hint: 'lean generalist' },
  ];
  const filtered = fallbackCandidates.filter(c => {
    if (seen.has(c.id)) return false;
    if (c.id === parentModel) return false;
    if (parentSize === 0) return true;
    return c.sizeB <= parentSize;
  });
  filtered.sort((a, b) => a.sizeB - b.sizeB);
  // Hardware filter
  const usable = filtered.filter(c => {
    const gb = c.sizeB * 0.6;
    const avail = hw.gpu.vramGb ?? hw.ramGb;
    return gb < avail * 0.6;
  });
  const chosenFallback = usable.length > 0 ? usable : filtered;
  for (const c of chosenFallback) {
    if (seen.has(c.id)) continue;
    seen.add(c.id);
    choices.push({ value: c.id, label: c.label, hint: `requires \`ollama pull\` · ${c.hint}` });
  }
  return choices;
}

function approxModelSizeB(modelId: string): number {