Model page

TheDrummer/Rocinante-X-12B-v1 warn

No license declared — no usage rights by default; the chat template was dropped from its base model, which changes behavior; glitch tokens that can silently corrupt ordinary input.

downloads 1.4klikes 99license none declaredarch mistralparams 12247.8Mupdated 2026-01-25

claims base: mistralai/Mistral-Nemo-Instruct-2407 · 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 2026-08-21131,072-token embedding scanned · 140 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 No license declared

The model card declares no license. You have no usage rights by default — treat as all-rights-reserved until the owner clarifies.

How to fix

Get a license from the owner or pick a licensed alternative — this is a legal gap, not a technical one.

  1. With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
  2. Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.

medium Chat template dropped vs parent

mistralai/Mistral-Nemo-Instruct-2407 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 Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 140 undertrained tokens (norm < 0.3× the vocabulary median of 0.620), including 23 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "erresident", "abezian", "komert", "higiez", "pemerint", "banako", "komertzio", "tanleria". 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 mistralai/Mistral-Nemo-Instruct-2407

Mean cosine similarity of 64 sampled token-embedding rows against mistralai/Mistral-Nemo-Instruct-2407 is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).

Put this result in your workflow

Check every checkpoint before it ships

Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.

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 TheDrummer/Rocinante-X-12B-v1

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.

architecturemistral · 40 layers · 5120-dim
parameters12247.8M
vocabulary131,072 tokens
licensenone declared
serializationsafetensors
chat templatenone
claimed lineagemistralai/Mistral-Nemo-Instruct-2407
lineage verifiedconsistent vs mistralai/Mistral-Nemo-Instruct-2407 — embedding-row cosine 1.000
glitch-token surface140 undertrained candidates, 23 plain-ASCII
Full measured fingerprint
architecturesMistralForCausalLM
repo files14
revision6830a4cfeb12
HF snapshot811 downloads · 96 likes · updated 2026-01-25 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 131,072×5120
embedding normsmedian 0.6197 · mean 0.5966
lineage checkconsistent — cosine 1 over 64 sampled rows vs mistralai/Mistral-Nemo-Instruct-2407
glitch-token samples"erresident", "abezian", "komert", "higiez", "pemerint", "banako", "komertzio", "tanleria", "igelts", "miejs", "-usti", "zimendu"
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:1378s1
weights run 2026-08-21 — measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 131,072×5120
glitch surface140 undertrained, 23 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs mistralai/Mistral-Nemo-Instruct-2407

Verdict badge

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Ingot verdict: warn

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