swiss-ai/Apertus-8B-Instruct-2509 warn
claims base: swiss-ai/Apertus-8B-2509 · chat template: present · view on Hugging Face ↗
Scan coverage
Ingot runs three batteries against a model. What each one checks →
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-22 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-22131,072-token embedding scanned · 320 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | not run |
Findings
Scanned 2026-08-22 · published from a community scan.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 320 undertrained tokens (norm < 0.3× the vocabulary median of 4.925), including 69 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. ".shibangsoft", "itozibe", ".shibang", "Marasmio", "modifier", "erresident", "+crusher", "Astaputz". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
info Weights consistent with claimed parent swiss-ai/Apertus-8B-2509
Mean cosine similarity of 64 sampled token-embedding rows against swiss-ai/Apertus-8B-2509 is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).
How to fix
Fix or verify the `base_model` declaration so lineage checks can run.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-22.
| architecture | apertus · 32 layers · 4096-dim |
| parameters | 8053.3M |
| vocabulary | 131,072 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | none |
| claimed lineage | swiss-ai/Apertus-8B-2509 |
| lineage verified | consistent vs swiss-ai/Apertus-8B-2509 — embedding-row cosine 1.000 |
| glitch-token surface | 320 undertrained candidates, 69 plain-ASCII |
Full measured fingerprint
| architectures | ApertusForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 16 |
| revision | b946d40447b2 |
| HF snapshot | 668.9k downloads · 487 likes · updated 2026-07-17 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 131,072×4096 |
| embedding norms | median 4.9252 · mean 4.7743 |
| lineage check | consistent — cosine 0.9999 over 64 sampled rows vs swiss-ai/Apertus-8B-2509 |
| glitch-token samples | ".shibangsoft", "itozibe", ".shibang", "Marasmio", "modifier", "erresident", "+crusher", "Astaputz", "abezian", "Ezko", "Frantsesez", "Vriendschappelijk" |
Battery runs
The run trace behind the findings above: every deep-battery job for this model, with what each run measured or why it failed. Findings are only as good as the runs that produced them.
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 07:43 | 70s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 131,072×4096 |
| glitch surface | 320 undertrained, 69 plain-ASCII |
| lineage check | consistent — cosine 0.9999 over 64 rows vs swiss-ai/Apertus-8B-2509 |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/swiss-ai/Apertus-8B-Instruct-2509)