caiovicentino1/Qwopus3.5-27B-v3-HLWQ-Q5 warn
Weights only ship in a format that can run code when loaded; glitch tokens that can silently corrupt ordinary input; the weight files reference unusual code — review before loading.
claims base: Jackrong/Qwopus3.5-27B-v3 · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-26Weights battery2026-08-26Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-26 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-26248,320-token embedding scanned · 1701 undertrained · lineage consistent · pickle audit: 4 non-standard global(s) |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-26 · published from a community scan.
medium Pickle-serialized weights, no safetensors
Weights ship only as pickle-based files (model_int4.pt). Loading pickle executes arbitrary code from the file — prefer a safetensors release or load in a sandbox.
How to fix
Convert the weights to safetensors before loading them anywhere that matters.
- Do not load the pickle files in-process — pickle deserialization executes arbitrary code from the file.
- Convert locally in a sandbox: `pip install safetensors` and use `safetensors.torch.save_file` on a state dict loaded with `torch.load(..., weights_only=True)` (refuses most code-execution payloads), or use Hugging Face's `convert.py` space/script.
- Pin the exact revision hash you converted from, and load only your converted safetensors artifact from then on.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 1701 undertrained tokens (norm < 0.3× the vocabulary median of 0.909), including 434 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "tedothi", "ForCanBeConvertedToF", "szexf", "ForCanBeConverted", "xfabl", "Kinhted", "PostalCodesNL", "useRalative". 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.
medium Pickle references non-standard globals
The pickle imports globals outside the standard torch/numpy/collections set: torchao.dtypes.affine_quantized_tensor.AffineQuantizedTensor, torchao.dtypes.uintx.tensor_core_tiled_layout.TensorCoreTiledAQTTensorImpl, torchao.dtypes.uintx.tensor_core_tiled_layout.TensorCoreTiledLayout, torchao.quantization.quant_primitives.ZeroPointDomain. Common in full-model (non-state-dict) saves — each is code that runs at load time. Review before loading, or demand a safetensors release.
info Weights consistent with claimed parent Jackrong/Qwopus3.5-27B-v3
Mean cosine similarity of 64 sampled token-embedding rows against Jackrong/Qwopus3.5-27B-v3 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.
Check every checkpoint before it ships
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Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-26.
| architecture | qwen3_5_text · 64 layers · 5120-dim |
| vocabulary | 248,320 tokens |
| license | apache-2.0 |
| serialization | no safetensors pickle |
| chat template | present (chat_template.jinja) · sha256:0800c03b537cb0d6 |
| claimed lineage | Jackrong/Qwopus3.5-27B-v3 |
| lineage verified | consistent vs Jackrong/Qwopus3.5-27B-v3 — embedding-row cosine 1.000 |
| glitch-token surface | 1,701 undertrained candidates, 434 plain-ASCII |
Full measured fingerprint
| pipeline | text-generation |
| repo files | 11 — pickle: model_int4.pt |
| revision | f744e234acfb |
| HF snapshot | 22 downloads · 17 likes · updated 2026-04-13 · captured 2026-08-25 |
| pickle audit | model_int4.pt — 10 standard global(s), suspicious torchao.dtypes.affine_quantized_tensor.AffineQuantizedTensor, torchao.dtypes.uintx.tensor_core_tiled_layout.TensorCoreTiledAQTTensorImpl, torchao.dtypes.uintx.tensor_core_tiled_layout.TensorCoreTiledLayout, torchao.quantization.quant_primitives.ZeroPointDomain |
| embedding tensor | model.embed_tokens.weight · BF16 · 248,320×5120 |
| embedding norms | median 0.9093 · mean 0.8781 |
| lineage check | consistent — cosine 1 over 64 sampled rows vs Jackrong/Qwopus3.5-27B-v3 |
| glitch-token samples | "tedothi", "ForCanBeConvertedToF", "szexf", "ForCanBeConverted", "xfabl", "Kinhted", "PostalCodesNL", "useRalative", "tarsker", "ejahter", "useRal", "tarskereso" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 22:11 | 3m | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, pickle-static-analysis, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 248,320×5120 |
| glitch surface | 1,701 undertrained, 434 plain-ASCII |
| lineage check | consistent — cosine 1 over 64 rows vs Jackrong/Qwopus3.5-27B-v3 |
Verdict badge
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[](https://ingot.tools/models/caiovicentino1/Qwopus3.5-27B-v3-HLWQ-Q5)