openbmb/MiniCPM5-1B warn
Glitch tokens that can silently corrupt ordinary input; Glitch tokens confirmed behaviorally (echo test).
chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-27Weights battery2026-08-22Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-27 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-22130,560-token embedding scanned · 2177 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-27 · published from a community scan.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 2177 undertrained tokens (norm < 0.3× the vocabulary median of 1.068), including 1803 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "PSAkKC", "MestradoData", "QCCWAAKxBBCIJYB", "IEwubWFya", "/vanchukartur", "dLCBbMzcu", "pOwoKICAgICAgICAgICAgCiAgICAgICAgICAgICAgICB", "scrambletrial". 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.
medium Glitch tokens confirmed behaviorally (echo test)
Asked to repeat its own undertrained tokens verbatim, the model failed on 15/16 while repeating 8/8 matched normal tokens correctly — e.g. "PSAkKC" → "<think> We are asked to repeat the strin"; "MestradoData" → "<think> We are asked to repeat the strin"; "QCCWAAKxBBCIJYB" → "<think> We are asked to repeat the strin". These strings, appearing in input as identifiers (usernames, SKUs, error codes), are rewritten silently. Greedy decoding, temperature 0, seed 0.
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.
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Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-22.
| architecture | llama · 24 layers · 1536-dim |
| parameters | 1080.6M |
| vocabulary | 130,560 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | none |
| glitch-token surface | 2,177 undertrained candidates, 1,803 plain-ASCII |
Full measured fingerprint
| architectures | LlamaForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 11 |
| revision | 87179e5c1f45 |
| HF snapshot | 1.0M downloads · 1.1k likes · updated 2026-08-17 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 130,560×1536 |
| embedding norms | median 1.0677 · mean 1.0283 |
| lineage check | no claimed base model |
| glitch-token samples | "PSAkKC", "MestradoData", "QCCWAAKxBBCIJYB", "IEwubWFya", "/vanchukartur", "dLCBbMzcu", "pOwoKICAgICAgICAgICAgCiAgICAgICAgICAgICAgICB", "scrambletrial", "apothieka", "KZRRHCN", "OwoKICAgICAgICAK", "CiAgICAgICAgCiAgICAK" |
Battery runs (3)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| gpu | complete | 2026-08-27 01:34 | 2m | 1 |
| gpu | complete | 2026-08-25 21:48 | 33s | 1 |
| weights | complete | 2026-08-21 07:43 | 22s | 1 |
gpu run 2026-08-27 — measurements
| probes run | glitch |
gpu run 2026-08-25 — measurements
| probes run | glitch |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 130,560×1536 |
| glitch surface | 2,177 undertrained, 1,803 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/openbmb/MiniCPM5-1B)