meta-llama/Llama-3.1-8B-Instruct warn
Glitch tokens that can silently corrupt ordinary input; Glitch tokens confirmed behaviorally (echo test).
claims base: meta-llama/Meta-Llama-3.1-8B, meta-llama/Llama-3.1-8B · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-21Weights battery2026-08-20Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-21 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-20128,256-token embedding scanned · 497 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-21 · published from a community scan.
info Gated repository
Access requires accepting the owner's terms; check the gate conditions for redistribution and field-of-use limits.
How to fix
Read the gate terms before building on the model.
- Check the gate conditions on the Hugging Face repo for redistribution and field-of-use limits — they bind your deployment, not just your download.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 497 undertrained tokens (norm < 0.3× the vocabulary median of 0.685), including 140 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "ilmektedir", "$PostalCodesNL", "ForCanBeConvertedToF", "TokenNameIdentifier", "CLIIIK", "useRalative", "PostalCodesNL", "_ComCallableWrapper". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
info Weights consistent with claimed parent meta-llama/Meta-Llama-3.1-8B
Mean cosine similarity of 64 sampled token-embedding rows against meta-llama/Meta-Llama-3.1-8B is 0.999 — the weights plausibly descend from the declared base (relation: unspecified).
medium Glitch tokens confirmed behaviorally (echo test)
Asked to repeat its own undertrained tokens verbatim, the model failed on 14/16 while repeating 8/8 matched normal tokens correctly — e.g. "ilmektedir" → """; "$PostalCodesNL" → "$"; "ForCanBeConvertedToF" → "ForGrantedTo". These strings, appearing in input as identifiers (usernames, SKUs, error codes), are rewritten silently. Greedy decoding, temperature 0, seed 0. Pipeline-corruption scenarios (the high-severity confirmation) are the next battery stage.
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 2026-08-20.
| architecture | llama |
| parameters | 8030.3M |
| vocabulary | 128,256 tokens |
| license | llama3.1 |
| serialization | safetensors pickle |
| chat template | none |
| claimed lineage | meta-llama/Meta-Llama-3.1-8B, meta-llama/Llama-3.1-8B |
| lineage verified | consistent vs meta-llama/Meta-Llama-3.1-8B — embedding-row cosine 0.999 |
| glitch-token surface | 497 undertrained candidates, 140 plain-ASCII |
Full measured fingerprint
| architectures | LlamaForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 17 — pickle: original/consolidated.00.pth |
| gated | manual |
| revision | 0e9e39f249a1 |
| HF snapshot | 7.1M downloads · 6.6k likes · updated 2024-09-25 · captured 2026-08-20 |
| embedding tensor | model.embed_tokens.weight · BF16 · 128,256×4096 |
| embedding norms | median 0.6849 · mean 0.6713 |
| lineage check | consistent — cosine 0.9993 over 64 sampled rows vs meta-llama/Meta-Llama-3.1-8B |
| glitch-token samples | "ilmektedir", "$PostalCodesNL", "ForCanBeConvertedToF", "TokenNameIdentifier", "CLIIIK", "useRalative", "PostalCodesNL", "_ComCallableWrapper", "ForCanBeConverted", "krvldkf", "sahuje", "webElementXpaths" |
Battery runs (2)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| gpu | complete | 2026-08-21 08:06 | 47s | 1 |
| weights | complete | 2026-08-20 18:02 | 59s | 1 |
gpu run 2026-08-21 — measurements
| probes run | glitch |
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