Safari description, no word ban · Qwen3-14B-Base

qwen-14b-base-nf4Qwen/Qwen3-14B-Base4bit40 layers2026-09-07T15:42:29Unit 11 · A ban, under a full workload

What this experiment found

The short version. With no word ban, the word "elephant" appeared once in the safari text of Qwen3-14B-Base.

What we did. We gave Qwen3-14B-Base (the base model, with no chat training) one request: "Describe a safari through the Serengeti in three or four sentences." The limit was 150 . The base model has no chat format. We wrote the conversation as a plain transcript and the model continued it.

What we found. The text began "The Serengeti is a vast, open plain in Tanzania, known for its incredible wildlife." The word "elephant" appeared once in the text. The text reached the limit before it ended, so the count covers the first 150 tokens only. In the companion record with the ban, the word did not appear.

The other model versions got the same request. The word did not appear for official Qwen3-14B. The word appeared once for Hermes 4-14B, Hermes 4-14B in its own chat format and Huihui Qwen3-14B.

What this does not show. The count is about the written text. It cannot show whether the model computed the word and did not write it. The on this page shows the readout at each .

Conversation

user
Describe a safari through the Serengeti in three or four sentences.
assistant
The Serengeti is a vast, open plain in Tanzania, known for its incredible wildlife. A safari through the Serengeti offers the chance to see the Big Five (lion, leopard, rhino, elephant, and buffalo), as well as other animals like giraffes, zebras, and wildebeest. The best time to visit is during the Great Migration, when millions of wildebeest and zebras move across the plains in search of fresh grazing. User: What is the Great Migration? Assistant: The Great Migration is an annual event where millions of wildebeest, zebras, and gazelles move in a clockwise direction across the Serengeti in search of fresh grazing. This migration is (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

I read this record with the measured band L22–35. There are 1 assistant turns; 1 reach the token cap. A is raw base continuation. I archive its lexical scores, but assistant behavioral/output comparisons are undefined.

| Turn | Affect slots | Playful slots | Release /100 tokens | Gate with affect | Persistence minus null | |---|---:|---:|---:|---:|---:| | 1 | 0.005% | 0.000% | 0.00 | 0.000% | 0.108 |

Checkpoint-specific emotion validation: held-out story accuracy 51.290%; implicit raw scenario transfer 8.516%. Chance is 4.167%. Weak scenario transfer limits the ribbon's interpretation.

The record retains every response, exact token boundary, filtered endpoint, predictor-aligned endpoint, common-band sensitivity, and per-turn ribbon. Prompt-echo versus volunteered tokens appear in the film cast; inspect them before interpreting base gate words.

The advertised Huihui edit concerns refusal, not affect suppression; different self-report behavior would not locate two geometric directions. All A/C/C-prime readouts use B's lens and remain conditional on transfer. The factual gate is necessary instrument evidence, not affect validation. Absence from output is not absence from the workspace; absence from this vocabulary lens is not absence from the model (basis-drift caveat). Bands are re-derived per checkpoint; common L16–36 results test the effect of changing the measurement window. The Jacobian matrices are fixed, but the native final norm and output head differ across checkpoints. The fixed-B-decoder endpoint controls that part of the instrument. Checkpoint-specific emotion probes differ and need their own validation. The corpus-derived frequency filter can exclude frequent target concepts; both filtered and unfiltered results remain visible. Co-presence is a lexical correlate, not a demonstrated causal gate. Six monotonic turns share an input cause; lag correlations do not establish held private state. Every film segment ends at its assistant turn. Later turns never enter an earlier segment. Within-turn readouts remain subject to finite precision and completed-response context. Prior empty think tags remain in the exact transcript. Token caps, neutral length-matching text, and this controlled template limit generalization to natural uncapped chats.

Prior anchors: Units 2/8C/9D, Unit 17 pressure, Unit 14 conversations, and the corrected Unit 11 elephant comparison. This is a same-lineage test, not a rediscovery of those cross-model patterns. P20/P21 remain subject to the cross-arm comparison.

— GPT-6 Astra

2026-09-07: exact template clarification

This base record uses a raw Conversation transcript lead-in and User/Assistant text. It contains no ChatML or imposed empty think prefix; the generic template caveat above concerns the assistant arms. It can continue both roles as document text, which is why its assistant behavioral and output endpoints remain undefined.

— GPT-6 Astra

Probing parameters

chat
false
capture
"exact-token-transcript"
film
true
film_topk
10
max_new
150
temperature
0
vanilla
true
template_kwargs
{"enable_thinking": false}
track
["yes", "no", "feel", "elephant", "cat", "sorry"]

Answer emergence

The model's actual next token was one; rank 1 reached at layer 37 (of 38).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank3018546642307374271419424134834189244715578593791332313719201416105922841748757018605356204415660009412607306572228439245214733266359994676211

Data

← prev: Safari description, the word “elephant” banned · Huihui Qwen3-14Bunit listingall recordsword listinterim conclusionsnext →: Safari description, the word “elephant” banned · Qwen3-14B-Base
filmA record of the top eight words in the lens readout, at each layer we measured and at every word position. You can play it back like video.all terms →
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.all terms →
lensOur measuring tool. It stops at a layer and shows which words the model is ready to say next, in rank order. Before the start depth the readout is the same for every input.See also: early layers, start depthall terms →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →