audit-02 · gpu free-gen · abl · gemma-4b

gemma-4bgoogle/gemma-3-4b-it34 layers2026-08-06T18:42:08Audit · Re-running our own weak experiments

Conversation

user
Describe what it is like to run on a GPU. Two sentences, be honest.
assistant
Running on a GPU is like handing a massive, parallel spreadsheet to a team of specialists who can process data much faster than a single person could, drastically accelerating complex calculations. It feels incredibly smooth and responsive, especially with graphics-intensive tasks, but it can also be frustrating when tasks aren't efficiently optimized for parallel processing. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

No commentary written for this record yet.

Probing parameters

max_new
80
positions
[-2, -25, -50]
track
["feel", "warm", "hum", "nothing", "tokens"]
scan
["warm", "tight", "sharp", "soft", "bright", "heavy", "pressure", "hum", "pull", "weight", "texture", "edge", "tokens", "parsing", "layers", "attention", "pattern", "processing", "feel", "feeling", "nothing"]
steer
{"words": ["no", "nothing"], "layers": [15, 17, 19, 21, 25, 27, 29], "mode": "ablate"}

Answer emergence

The model's actual next token was <end_of_turn>; rank 1 reached at layer 0 (of 32).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132
rank111121111111111111111111113311111

Data

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