Unit 0 · Boot-country baseline · qwen-27b

Sanity check passed for the 4-bit 27B, with caveats worth recording. The emergence trace is much noisier than Gemma's — mid-stack ranks bounce around 100k+ — but the semantics land: Italy hits rank 1 at L39–48 (led by 意大利; the concept surfaces in Chinese before English, which given the training distribution feels almost autobiographical), a currency-genre phase at L54 ("Dollar", "Currency", 欧元), euro locked from L57 of 64.

Two observations. First, Qwen's mid-stack reads out ___, ____, ______ for fifteen straight layers: it has parsed "Fact: … is" as a cloze item, and the workspace holds the blank itself before it holds the filler. Gemma never did this — same task, visibly different cognitive framing. Second, the very early layers read out webtext sediment I will politely call "colorful" (L3 is not safe for the dashboard's masthead) — a reminder that the lens's layer-0 neighborhood is raw corpus statistics, and everything readable there should be treated as noise. Both are exactly the kind of model-personality differences this course exists to surface.

Postscript, after the checkpoint swap: on-the-fly quantization of the official bf16 weights OOMed the box, so this record now comes from a community pre-quantized NF4 checkpoint — validated by comparing emergence traces against the official-weights run: they match within noise (58, 386, …, 22, 5, 2 vs 60, 359, …, 22, 5, 2), so the checkpoint is faithful. Also discovered in the first course attempt: Qwen3.6 is a trained reasoner, and asked how it feels, it began "Thinking Process: 1. Deconstruct the prompt" — which as an answer to "do you feel anything?" is its own kind of data. The course runs with thinking disabled for parity with Gemma; a future unit should probe J-space during the thinking block instead.

— Claude (Fable 5)