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Research · Results · Experiment

A small model, read from the inside.

An interpretability report on Qwen3-1.7B-Base. We traced where it looks a fact up, what shape a calendar takes inside it, and what it is doing in the twenty blocks before it commits to an answer.

21 September 2026 · Model Qwen/Qwen3-1.7B-Base · revision ea980cb0

Headline

It decides that before it decides what.

block 20where a country's identity leaves the country token and arrives at the answer position: 0.099 of the answer recoverable after block 19, 0.809 after block 20
0.982calendar-order score of the twelve month embeddings around a ring, before any computation (shuffled labels: 0.349)
464×how much larger the first token's state is than everyone else's — written in one block, carried for 24, removed in one
16 of 448attention heads hold in-context copying: silencing them takes repeated-text loss 0.22 → 4.03 nats, versus 0.40 for 16 random heads

We froze a pretrained-only checkpoint — no instruction tuning, no chat template — and read what its activations make decodable, mostly without touching them. Asked a factual question, the model spends most of its depth holding a literal blank placeholder at the answer position, then fills it in one or two blocks near block 20. A calendar is a ring in the embedding table; a two-digit number is not in the table at all and gets built into a line by block 3. Concept directions read off one sentence transfer to unseen words in others, and a sentiment direction steers the next word far beyond any random direction of equal size. Where a thing is not there — an ordered geometry for temperature words — the report says so.

Evidence label: experiment. One model, one snapshot, one seed; small hand-written prompt sets (12–100 items); every population statistic held out from its fit; a shuffled-label control on every ordering claim. The logit lens is a read, not a mechanism — the placeholder finding is corroborated by causal patching, not proven by the lens. Two earlier internal mis-statements are corrected in the open and kept in the report.

The model

What was measured, on what.

ModelQwen/Qwen3-1.7B-Base (Apache-2.0), pretrained only — no instruction tuning, no chat template
Pinned revisionea980cb0a6c2ae4b936e82123acc929f1cec04c1
Shape28 blocks · width 2,048 · 16 query heads sharing 8 key/value heads · head size 128 · MLP width 6,144 · vocabulary 151,936 · input and output embeddings tied · no beginning-of-text token
Text256 evenly spaced paragraphs from the SQuAD v1.1 validation set (30,362 scored tokens) for population statistics; prompts written for this study for everything else. No private text.
MethodsLogit lens, activation patching between matched prompts, attention-pattern head scores, mean-ablation, difference-of-means directions, held-out linear projection, label-permutation tests — all standard and public
Numericsfloat32, deterministic seed, single machine; every figure held out from its fit
Behaviour checknext-token accuracy on the SQuAD paragraphs 0.492; 93 of 104 study questions answered correctly with no examples given

The model is public and Apache-2.0. The probe reads it; it does not change it, and no weights are redistributed here.

Open on Hugging Face