ornith-ai/Ornith-1.5-35B-A3B-NVFP4 warn
Glitch tokens that can silently corrupt ordinary input.
chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-25Weights battery2026-08-25Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-25 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-25248,320-token embedding scanned · 1117 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-25 · published from a community scan.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 1117 undertrained tokens (norm < 0.3× the vocabulary median of 0.563), including 341 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "tedothi", "ForCanBeConvertedToF", "ForCanBeConverted", "szexf", "Kinhted", "xfabl", "PostalCodesNL", "useRalative". 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.
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 2026-08-25.
| architecture | qwen3_5_moe · 2048-dim |
| parameters | 18683.9M |
| vocabulary | 248,320 tokens |
| license | mit |
| serialization | safetensors |
| chat template | present (chat_template.jinja) · sha256:182e77dd83bd8e9c |
| glitch-token surface | 1,117 undertrained candidates, 341 plain-ASCII |
Full measured fingerprint
| architectures | Qwen3_5MoeForConditionalGeneration |
| library | transformers |
| pipeline | text-generation |
| repo files | 21 |
| revision | 0f0b1b59b879 |
| HF snapshot | 222.5k downloads · 37 likes · updated 2026-08-20 · captured 2026-08-25 |
| embedding tensor | model.language_model.embed_tokens.weight · BF16 · 248,320×2048 |
| embedding norms | median 0.5633 · mean 0.5514 |
| lineage check | no claimed base model |
| glitch-token samples | "tedothi", "ForCanBeConvertedToF", "ForCanBeConverted", "szexf", "Kinhted", "xfabl", "PostalCodesNL", "useRalative", "tarsker", "useRal", "ejahter", "echslungs" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 19:00 | 40s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, lineage-norm-correlation |
| embedding tensor | model.language_model.embed_tokens.weight · BF16 · 248,320×2048 |
| glitch surface | 1,117 undertrained, 341 plain-ASCII |
| lineage check | not checked (no claimed base model) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/ornith-ai/Ornith-1.5-35B-A3B-NVFP4)