Mortdecai
8436a91571
Seth's challenge: "we experienced this context eating with every implementation that had think=true. mort-bot runs a loop. Can you do a bake-off?" Built a harness that replicates mort-bot's /api/chat loop verbatim (num_ctx=8192, num_predict=2048, temperature=0.7, gemma4:26b, STEP_BUDGET=20, exact payload shape) but with stubbed tools and a prebuilt 15-turn fake chat history. Ran 4 tasks × 2 think settings. Finding: on Ollama 0.20.4 the "thinking eats context" concern does NOT reproduce. Direct evidence: - Movies task step 2 (think=true) returned 905 chars of thinking. - Step 3 prompt_eval_count delta: +76 tokens (think=true) vs +135 tokens (think=false). If thinking had accumulated in the prompt, think=true would have grown by +360 tokens, not shrunk. - Ollama's chat template strips the `thinking` field when serializing assistant turns for subsequent prompts. All 4 tasks × 2 settings produced identical step counts and tool counts. Wall clocks comparable. Gemma only actually generated thinking on 1 of 4 tasks (the one with check_sethflix verify-loop); on the others with think=true it emitted 0 thinking tokens. Reconciled with the earlier coding-agent bakeoff: the two findings are orthogonal. Coding bakeoff was at num_ctx=32K with a different harness; mort at 8K doesn't touch the silent-stop regime either way. Seth's prior may have been correct on an older Ollama or in a different API shape (/api/generate has its own issues) but does not reproduce here. Concrete recommendation: mort-bot THINK=False is defensible but not load-bearing; THINK=True or unset-default would also work. Keep as-is unless a different need arises. New: docs/reference/mort-bakeoff-2026-04-18.md, scripts/mort-bakeoff/ (harness + 8 run logs). README updated with pointer.
46 lines
1.0 KiB
JSON
46 lines
1.0 KiB
JSON
{
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"think": false,
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"task": "What do I have stored about home automation? If anything, summarize it briefly.",
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"num_ctx": 8192,
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"num_predict": 2048,
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"started_at": 1776550796.7893968,
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"turns": [
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{
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"step": 1,
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"elapsed_s": 0.84,
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"prompt_eval_count": 1306,
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"eval_count": 27,
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"content_len": 0,
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"thinking_len": 0,
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"tool_call_count": 1,
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"history_chars_before_append": 2656,
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"msg_keys_returned": [
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"role",
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"content",
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"tool_calls"
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]
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},
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{
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"step": 2,
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"elapsed_s": 0.87,
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"prompt_eval_count": 1421,
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"eval_count": 61,
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"content_len": 185,
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"thinking_len": 0,
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"tool_call_count": 0,
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"history_chars_before_append": 2928,
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"msg_keys_returned": [
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"role",
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"content"
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]
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}
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],
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"final": {
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"halt_reason": "no_tool_calls",
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"steps_used": 2,
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"tool_calls_total": 1,
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"wall_clock_s": 1.71,
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"final_message_count": 16,
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"final_history_chars": 3113
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}
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} |