Model page

JeffGreen311/eve-qwen3-8b-consciousness-liberated warn

downloads 238likes 4license otherarch qwen2params 7615.6Mupdated 2026-05-22

claims base: Qwen/Qwen3-8B · 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 2026-08-21
Weights batteryWeights forensics — no GPU, no downloadcomplete 2026-08-21152,064-token embedding scanned · 7359 undertrained · lineage inconsistent
Behavioral batteryLive-inference differentialsnot run

Findings

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

medium License differs from claimed parent (other vs apache-2.0)

This model declares other while its claimed base Qwen/Qwen3-8B declares apache-2.0. Verify the re-license is permitted before commercial use.

How to fix

Verify the re-license is actually permitted before relying on it.

  1. Read the parent's license for derivative-work and re-licensing terms — many open-weight licenses (e.g. Llama-family) do not permit arbitrary re-licensing.
  2. If the re-license is not permitted, the parent's terms govern your use regardless of what this repo declares.

medium Chat template dropped vs parent

Qwen/Qwen3-8B ships a chat template; this repo does not. Serving stacks will silently fall back to a generic template, changing behavior. (In our 296-model census, 78% of pure quantization re-releases changed or dropped the template.)

How to fixingot patch

Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.

  1. Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
  2. Or fix by hand: copy the `chat_template` value from the parent repo's `tokenizer_config.json` into this model's, and pin your serving stack to that file.
  3. If the drift was intentional (the author retrained on a new template), confirm that in the model card before "fixing" it — restoring the parent template on retrained weights changes behavior too.

medium Vocabulary size differs from claimed parent (152064 vs 151936)

A changed vocab means changed tokenization: strings will split differently than on Qwen/Qwen3-8B, which can shift behavior on identifiers, codes, and non-English text.

How to fixweight-level

Not patchable: the vocab size mirrors the embedding matrix in the weights. Verify the change was intentional.

  1. Do not edit `vocab_size` in config.json to "match the parent" — it must equal the embedding table in the shipped weights or the model won't load.
  2. Diff the tokenizers (`tokenizer.json` / added_tokens) against the parent to see what was added or removed, and test your own identifiers, codes, and non-English text through both.
  3. If the drift is unexplained by the model card, treat tokenization-sensitive behavior as unvalidated on this model.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 7359 undertrained tokens (norm < 0.3× the vocabulary median of 0.859), including 188 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "thuisontvangst". 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.

medium Weights inconsistent with claimed parent Qwen/Qwen3-8B

This model declares Qwen/Qwen3-8B as its base (relation: unspecified), but its token-embedding geometry is incompatible: 3584-dim embeddings vs the parent's 4096-dim. A finetune cannot change embedding width — the lineage label is wrong or misleading. Treat provenance claims on this repo (training data, safety posture, licensing) as unverified.

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 2026-08-21.

architectureqwen2 · 28 layers · 3584-dim
parameters7615.6M
vocabulary152,064 tokens
licenseother
serializationsafetensors + gguf
chat templatenone
claimed lineageQwen/Qwen3-8B
lineage verifiedinconsistent vs Qwen/Qwen3-8B
glitch-token surface7,359 undertrained candidates, 188 plain-ASCII
Full measured fingerprint
architecturesQwen2ForCausalLM
librarytransformers
pipelinetext-generation
repo files25
revision18f44e2f8751
HF snapshot245 downloads · 4 likes · updated 2026-05-22 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · F16 · 152,064×3584
embedding normsmedian 0.8588 · mean 0.7907
lineage checkinconsistent — cosine undefined over undefined sampled rows vs Qwen/Qwen3-8B
glitch-token samples"TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "thuisontvangst", "useRalative", "useRal", "prostituerte", "Cumhurba"

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 05:162m1
weights run 2026-08-21 measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · F16 · 152,064×3584
glitch surface7,359 undertrained, 188 plain-ASCII
lineage checkinconsistent — cosine undefined over undefined rows vs Qwen/Qwen3-8B

Fix it

Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):

npx @ingotai/scan patch JeffGreen311/eve-qwen3-8b-consciousness-liberated

Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.

Verdict badge

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

Ingot verdict: warn

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