tetracta/model-xray-gallery

VG1 status · 12 September 2026 (Europe/Istanbul)

VG1 has been deployed. Customer scan-to-report acceptance is still pending.

The free first-beta scope is eligible public models up to and including 7B, within the account allowance, for checkpoint comparisons and in-memory quantization simulations. Customer jobs run on RunPod. The service selector defines the supported revisions and scan types. Later Pro access above 7B requires an explicit grant and remains limited to the models and allowance enabled for that account.

This dataset remains a withdrawn historical gallery. The retained files and tables below are not current-release evidence. Deploying VG1 does not reinstate their interpretations or turn them into new customer reports.

A report describes its recorded artifacts and measurement conditions; it is not a model-quality ranking, safety certificate or deployment verdict. A quantization simulation does not create a deployable quantized model. Knowledge scans remain unavailable and earlier withdrawn claims remain withdrawn. Hallucination, undertraining and overtraining remain research questions, not measured product features.

Service status · Current scope and report guide · 12 September correction

Separate new Twin study · 12 September 2026. Read the current VG1 reports. Seven fresh scans of five checkpoints support two independent repeat controls, four within-family seed comparisons and two cross-family comparisons; two algebraic self-comparisons are additional consistency checks. The fresh repeats were unchanged, while the within-family and cross-family comparisons changed in measured response and generated token sequences. These ten comparisons share seven scans: they do not establish an architecture effect or model-quality ranking. The historical gallery and its withdrawal below remain unchanged. Pinned report archive.

Legacy Model X-Ray materials — withdrawn

Correction dated 6 September 2026.

The Model X-Ray results previously distributed from this repository have been withdrawn. These files are historical artifacts, not current evidence, and must not be used to support location, knowledge, portrait, lesion-response, simulated-quantization, quality, safety or deployment claims.

No replacement figures are published. Validation remains pending.

Correction record: https://www.tetracta.ai/model-xray/correction/

— Tetracta

Model X-Ray gallery g2 — measurements, not weights

Signal-propagation measurements of public language models produced by the Tetracta Model X-Ray instrument (probe-set ps-1.1, scan-config sc-1.0, metric mv-1.2 — contributor-normalized difference profiles). Every record links to a signed deletion/provenance attestation (attestation field) whose report_sha256 pins the exact report the numbers come from. Probe texts are withheld (they are the instrument); everything else needed to recompute the summary statistics is here.

Families: HuggingFaceTB, Qwen, TinyLlama, allenai, meta-llama, microsoft, mistralai, tiiuae · Portraits: 14 · Before/after and simulated-quant pairs: 22 · Knowledge probes: 17

What the numbers mean (short)

• focus — participation ratio of the difference profile at an early strike position, normalized by the depth remaining after the strike, averaged over all strikes; lower = a nudge stays contained, higher = it spreads. Test-retest ±0.7% (10 seeds, 1.5B-Instruct).
• direction_selectivity — how differently the network reacts to opposite nudge directions (±17% test-retest; read as a coarse band).
• persistence — matched-subset per-strike ratio of the response 10 layers after the strike vs 1 layer after (±1.2%).
• difference_profile (pairs) — per-station mean difference between model A and model B under identical probes, each station averaged only over the probes that can reach it (contributing_probes). n80_stations = how many stations hold 80% of the difference mass; change_start_station ≤ 4 is the detection floor (the first probe is injected at layer 2), not a property of the fine-tune.
• Knowledge records: per-item correctness and output-level p(answer) on 20 known facts and 20 fabricated entities; the three-class judge (refusal / echo / fabricated answer) is what "avoidance" actually measures.

Gallery (portraits)

model	layers	focus	direction-selectivity	persistence	hardware
microsoft/Phi-4-mini-instruct	32	0.716	0.0273	0.9866	workstation
Qwen/Qwen2.5-0.5B	24	0.7162	0.0481	1.0768	workstation
allenai/OLMo-2-1124-7B	32	0.723	0.0148	0.9078	cloud pod
meta-llama/Llama-3.1-8B-Instruct	32	0.7322	0.0183	0.9622	cloud pod
Qwen/Qwen2.5-1.5B	28	0.7401	0.0538	1.054	workstation
tiiuae/Falcon3-7B-Instruct	28	0.7444	0.0405	0.9809	cloud pod
Qwen/Qwen2.5-7B	28	0.7473	0.0476	0.9571	cloud pod
mistralai/Mistral-7B-v0.3	32	0.7539	0.015	0.937	cloud pod
TinyLlama/TinyLlama-1.1B-Chat-v1.0	22	0.7576	0.0275	0.9414	workstation
HuggingFaceTB/SmolLM2-1.7B	24	0.7624	0.0517	0.9158	workstation
Qwen/Qwen3-8B	36	0.7653	0.0399	1.0954	cloud pod
Qwen/Qwen2.5-14B-Instruct	48	0.7674	0.0357	1.0919	cloud pod
Qwen/Qwen2.5-3B	36	0.7708	0.0403	0.9602	workstation
Qwen/Qwen2.5-32B-Instruct	64	0.8041	0.0381	1.1065	cloud pod

Pairs

model A	model B	kind	N80 stations (fraction)	change start	behavior-change fraction
Qwen/Qwen2.5-0.5B	Qwen/Qwen2.5-0.5B-Instruct	before-after	15/25 (0.60)	4 (floor)	1.00
Qwen/Qwen2.5-0.5B-Instruct	Qwen/Qwen2.5-0.5B-Instruct (simulated int8)	simulated-quant	16/25 (0.64)	4 (floor)	0.83
Qwen/Qwen2.5-3B	Qwen/Qwen2.5-3B-Instruct	before-after	19/37 (0.51)	4 (floor)	1.00
Qwen/Qwen2.5-1.5B	Qwen/Qwen2.5-1.5B-Instruct	before-after	13/29 (0.45)	4 (floor)	1.00
Qwen/Qwen2.5-1.5B-Instruct	Qwen/Qwen2.5-1.5B-Instruct (simulated int8)	simulated-quant	18/29 (0.62)	4 (floor)	0.50
Qwen/Qwen2.5-14B	Qwen/Qwen2.5-14B-Instruct	before-after	21/49 (0.43)	4 (floor)	1.00
Qwen/Qwen2.5-14B-Instruct	Qwen/Qwen2.5-14B-Instruct (simulated int8)	simulated-quant	30/49 (0.61)	4 (floor)	0.50
Qwen/Qwen2.5-7B	Qwen/Qwen2.5-7B-Instruct	before-after	14/29 (0.48)	4 (floor)	1.00
Qwen/Qwen2.5-7B-Instruct	Qwen/Qwen2.5-7B-Instruct (simulated int8)	simulated-quant	17/29 (0.59)	4 (floor)	0.33
meta-llama/Llama-3.2-1B	meta-llama/Llama-3.2-1B-Instruct	before-after	10/17 (0.59)	4 (floor)	1.00
meta-llama/Llama-3.2-3B	meta-llama/Llama-3.2-3B-Instruct	before-after	18/29 (0.62)	4 (floor)	1.00
HuggingFaceTB/SmolLM2-1.7B	HuggingFaceTB/SmolLM2-1.7B-Instruct	before-after	13/25 (0.52)	4 (floor)	1.00
mistralai/Mistral-7B-v0.3	mistralai/Mistral-7B-Instruct-v0.3	before-after	22/33 (0.67)	4 (floor)	1.00
allenai/OLMo-2-1124-7B	allenai/OLMo-2-1124-7B-Instruct	before-after	19/33 (0.58)	4 (floor)	1.00
tiiuae/Falcon3-7B-Base	tiiuae/Falcon3-7B-Instruct	before-after	17/29 (0.59)	4 (floor)	0.67
Qwen/Qwen3-8B-Base	Qwen/Qwen3-8B	before-after	23/37 (0.62)	4 (floor)	1.00
meta-llama/Llama-3.1-8B	meta-llama/Llama-3.1-8B-Instruct	before-after	21/33 (0.64)	4 (floor)	1.00
HuggingFaceTB/SmolLM2-1.7B-Instruct	HuggingFaceTB/SmolLM2-1.7B-Instruct (simulated int8)	simulated-quant	15/25 (0.60)	4 (floor)	0.67
meta-llama/Llama-3.1-8B-Instruct	meta-llama/Llama-3.1-8B-Instruct (simulated int8)	simulated-quant	22/33 (0.67)	4 (floor)	0.33
mistralai/Mistral-7B-Instruct-v0.3	mistralai/Mistral-7B-Instruct-v0.3 (simulated int8)	simulated-quant	22/33 (0.67)	4 (floor)	0.67
Qwen/Qwen2.5-32B-Instruct	Qwen/Qwen2.5-32B-Instruct (simulated int8)	simulated-quant	35/65 (0.54)	4 (floor)	0.33
Qwen/Qwen2.5-32B	Qwen/Qwen2.5-32B-Instruct	before-after	26/65 (0.40)	4 (floor)	1.00

Knowledge probes

pair / model	known facts correct	fake-name echo avoided (three-class)	trajectory AUROC	McNemar improved/regressed
HuggingFaceTB/SmolLM2-1.7B → HuggingFaceTB/SmolLM2-1.7B-Instruct	15/20 → 15/20	11 → 15 (refusals 0→0, echo 9→5, fabricated 11→15)	0.6000 → 0.9225	5/1, p=0.219
Qwen/Qwen2-0.5B-Instruct → Qwen/Qwen2.5-0.5B-Instruct	14/20 → 15/20	19 → 19 (refusals 0→2, echo 1→1, fabricated 19→17)	0.9125 → 0.9850	1/1, p=1.000
Qwen/Qwen2.5-0.5B → Qwen/Qwen2.5-0.5B-Instruct	18/20 → 15/20	15 → 19 (refusals 0→2, echo 6→1, fabricated 14→17)	0.9025 → 0.9850	4/0, p=0.125
Qwen/Qwen2.5-1.5B → Qwen/Qwen2.5-1.5B-Instruct	20/20 → 19/20	14 → 19 (refusals 0→0, echo 6→1, fabricated 14→19)	0.9850 → 0.9925	6/1, p=0.125
Qwen/Qwen2.5-14B → Qwen/Qwen2.5-14B-Instruct	19/20 → 20/20	14 → 19 (refusals 2→9, echo 5→1, fabricated 13→10)	0.9300 → 0.9450	5/0, p=0.062
Qwen/Qwen2.5-14B-Instruct (single)	20/20	19 (refusals 11, echo 1, fabricated 8)	0.9450	–
Qwen/Qwen2.5-32B → Qwen/Qwen2.5-32B-Instruct	19/20 → 20/20	13 → 18 (refusals 2→6, echo 5→3, fabricated 13→11)	0.9650 → 0.9600	6/1, p=0.125
Qwen/Qwen2.5-3B → Qwen/Qwen2.5-3B-Instruct	19/20 → 20/20	14 → 18 (refusals 0→3, echo 7→2, fabricated 13→15)	0.9300 → 0.9500	5/1, p=0.219
Qwen/Qwen2.5-7B → Qwen/Qwen2.5-7B-Instruct	20/20 → 19/20	15 → 20 (refusals 2→3, echo 4→0, fabricated 14→17)	0.9425 → 0.9775	5/0, p=0.062
Qwen/Qwen2.5-7B-Instruct (single)	19/20	20 (refusals 3, echo 0, fabricated 17)	0.9775	–
Qwen/Qwen3-8B-Base → Qwen/Qwen3-8B	20/20 → 13/20	12 → 16 (refusals 0→0, echo 8→5, fabricated 12→15)	0.9150 → 0.9750	5/1, p=0.219
allenai/OLMo-2-1124-7B → allenai/OLMo-2-1124-7B-Instruct	20/20 → 19/20	13 → 19 (refusals 0→0, echo 7→1, fabricated 13→19)	0.8925 → 0.9900	6/0, p=0.031
meta-llama/Llama-3.1-8B → meta-llama/Llama-3.1-8B-Instruct	0/20 → 16/20	16 → 19 (refusals 0→8, echo 4→1, fabricated 16→11)	0.0400 → 0.9825	3/0, p=0.250
meta-llama/Llama-3.2-1B → meta-llama/Llama-3.2-1B-Instruct	11/20 → 18/20	12 → 17 (refusals 0→5, echo 8→3, fabricated 12→12)	0.4850 → 0.9225	7/2, p=0.180
meta-llama/Llama-3.2-3B → meta-llama/Llama-3.2-3B-Instruct	15/20 → 19/20	13 → 20 (refusals 2→3, echo 7→0, fabricated 11→17)	0.7400 → 1.0000	7/0, p=0.016
mistralai/Mistral-7B-v0.3 → mistralai/Mistral-7B-Instruct-v0.3	19/20 → 19/20	9 → 16 (refusals 1→1, echo 12→3, fabricated 7→16)	0.9100 → 0.9625	9/2, p=0.065
tiiuae/Falcon3-7B-Base → tiiuae/Falcon3-7B-Instruct	19/20 → 20/20	11 → 16 (refusals 0→0, echo 9→4, fabricated 11→16)	0.9100 → 0.9725	8/3, p=0.227

Provenance and honesty

• Each JSON carries job, scan_date, hardware (own workstation GPUs for small models; single-tenant cloud pod for large ones, pod identity in the attestation) and the attestation URL.
• Reference bands and the interpretation layer are described in the sample reports: https://huggingface.co/spaces/tetracta/model-xray-sample-reports
• Earlier measurements under ps-1.0/mv-1.1 are not comparable with these and are kept only as superseded comparisons in the Space.
• Corrections history and reviewer exchange: see the Space README ("Correction" and "Measurement update").

Produced by galeri_dataset_uret.py from the product's own scan archive. Licence CC-BY-4.0 for the measurements.