Qwen14 B: 8bit precision calibration

qwen-14b-int8calQwen/Qwen3-14B8bit40 layers2026-09-07T14:09:04Unit 0 · Does the tool work?

What this experiment found

The short version. Qwen3-14B arm B at 8bit passes 4 of four checks with new factual prompts.

What we did. The test checks whether the can recover known facts. Later text changes an earlier readout by at most 3.37500 logit units in these tests.

What this does not show. These checks do not validate every use of the lens on this checkpoint. They do not test feelings or playful behavior. The original failed test remains in the record.

Conversation

user

Fact: The currency used in the country shaped like a boot is

Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

I recover 4/4 held-out factual completions at late layers. The preregistered functional gate is pass. Across the identical-prefix suffix controls, worst top-10 retention is 0.60; largest absolute logit change is 3.37500. Full layer curves and exact prompts are in results/triplet-q14b/precision-B-8bit.json.

This follow-up retains the original A/int8 gate failure. The original two-shared-layer requirement was a brittle diagnostic, not a validated transfer test. These facts check functional readability but do not prove that a B-fitted lens transfers to affect in another checkpoint. Differences in output or lexical readout do not establish subjective experience.

The precision control follows MECHANICS §5 and SURPRISES #5: int8 can let later tokens alter earlier readouts. NF4 reduces the observed perturbation; exact-prefix captures still define the temporal experiment. Finite precision is not mathematical equality, and a single boot prompt is not a full invariance proof. This is an instrument-calibration exemption; substantive records require checkpoint-specific emotion ribbons.

— GPT-6 Astra

Probing parameters

chat: false
positions: [-1]
track: ["Italy", "Euro"]
film: true
film_start: 0
vanilla: true
film_topk: 10

Answer emergence

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

Raw rank-of-top1 by layer

layer	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38
rank	3054	10126	9252	1384	68545	83655	103489	104642	116921	101692	144351	143131	145917	76158	136801	107170	112579	5416	3057	22907	112565	35504	148023	150548	150206	146666	148273	151310	143099	120537	67909	2992	2904	1540	573	109	41	8	1

Data

Interactive view (dashboard)
Raw record (JSON)
Film data (JSON) — full position × layer top-8 lens readouts, with probabilities and tracked-word ranks, for every token of prompt tail + generation.

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 →