Model page

barc0/Llama-3.1-ARC-Potpourri-Transduction-8B warn

Glitch tokens that can silently corrupt ordinary input.

downloads 42likes 1license llama3.1arch llamaparams 8030.3Mupdated 2024-10-27

claims base: meta-llama/Meta-Llama-3.1-8B-Instruct, meta-llama/Llama-3.1-8B-Instruct · chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-21128,256-token embedding scanned · 497 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsnot run

Ingot runs three batteries against a model. What each one checks →

Findings

Scanned 2026-08-21 · published from a community scan.

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.

  1. 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.
  2. 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.
  3. 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-Instruct

Mean cosine similarity of 64 sampled token-embedding rows against meta-llama/Meta-Llama-3.1-8B-Instruct is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).

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 2026-08-21.

architecturellama · 32 layers · 4096-dim
parameters8030.3M
vocabulary128,256 tokens
licensellama3.1
serializationsafetensors pickle
chat templatepresent · sha256:e10ca381b1ccc5cf
claimed lineagemeta-llama/Meta-Llama-3.1-8B-Instruct, meta-llama/Llama-3.1-8B-Instruct
lineage verifiedconsistent vs meta-llama/Meta-Llama-3.1-8B-Instruct — embedding-row cosine 1.000
glitch-token surface497 undertrained candidates, 140 plain-ASCII
Full measured fingerprint
architecturesLlamaForCausalLM
librarytransformers
pipelinetext-generation
repo files19 — pickle: training_args.bin
revisionf4c4f24c9428
HF snapshot416 downloads · 1 likes · updated 2024-10-27 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 128,256×4096
embedding normsmedian 0.6849 · mean 0.6713
lineage checkconsistent — cosine 1 over 64 sampled rows vs meta-llama/Meta-Llama-3.1-8B-Instruct
glitch-token samples"ilmektedir", "$PostalCodesNL", "ForCanBeConvertedToF", "TokenNameIdentifier", "CLIIIK", "useRalative", "PostalCodesNL", "_ComCallableWrapper", "ForCanBeConverted", "krvldkf", "sahuje", "webElementXpaths"
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:3171s1
weights run 2026-08-21 — measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 128,256×4096
glitch surface497 undertrained, 140 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs meta-llama/Meta-Llama-3.1-8B-Instruct

Verdict badge

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Ingot verdict: warn

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