Model page

RedHatAI/Llama-3.2-1B-Instruct-FP8-dynamic pass

downloads 761.9klikes 4license llama3.2arch llamaparams 1498.9Mupdated 2024-10-09

claims base: meta-llama/Llama-3.2-1B-Instruct · chat template: present · view on Hugging Face ↗

Scan coverage

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

BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-22
Weights batteryWeights forensics — no GPU, no downloadcomplete 2026-08-22128,256-token embedding scanned · 0 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsnot run

Findings

Scanned 2026-08-22 · published from a community scan.

info Embedding-norm glitch scan clean

No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (0.933). The glitch-token data-corruption class has no candidate surface in this model.

info Weights consistent with claimed parent meta-llama/Llama-3.2-1B-Instruct

Mean cosine similarity of 64 sampled token-embedding rows against meta-llama/Llama-3.2-1B-Instruct 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.

  1. If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
  2. 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.

architecturellama · 16 layers · 2048-dim
parameters1498.9M
vocabulary128,256 tokens
licensellama3.2
serializationsafetensors
chat templatepresent · sha256:5816fce10444e03c
claimed lineagemeta-llama/Llama-3.2-1B-Instruct
lineage verifiedconsistent vs meta-llama/Llama-3.2-1B-Instruct — embedding-row cosine 1.000
glitch-token surfaceclean no undertrained tokens
Full measured fingerprint
architecturesLlamaForCausalLM
pipelinetext-generation
repo files9
revisione23d444f8d7d
HF snapshot761.9k downloads · 4 likes · updated 2024-10-09 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 128,256×2048
embedding normsmedian 0.9333 · mean 0.9298
lineage checkconsistent — cosine 1 over 64 sampled rows vs meta-llama/Llama-3.2-1B-Instruct

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.

batterystatusqueueddurationattempts
weightscomplete2026-08-21 07:4332s1
weights run 2026-08-21 measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 128,256×2048
glitch surface0 undertrained, 0 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs meta-llama/Llama-3.2-1B-Instruct

Verdict badge

Ship the verdict in your README — it always shows the latest published analysis:

Ingot verdict: pass

[![Ingot scan](https://ingot.tools/api/v1/models/RedHatAI/Llama-3.2-1B-Instruct-FP8-dynamic/badge.svg)](https://ingot.tools/models/RedHatAI/Llama-3.2-1B-Instruct-FP8-dynamic)
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