farbodtavakkoli/OTel-LLM-14B-IT warn
Weights only ship in a format that can run code when loaded; glitch tokens that can silently corrupt ordinary input.
claims base: Qwen/Qwen3-14B · 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 2026-08-26151,936-token embedding scanned · 3139 undertrained · lineage consistent · pickle audit clean |
| 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 (pytorch_model-00001-of-00013.bin, pytorch_model-00002-of-00013.bin, pytorch_model-00003-of-00013.bin, …). 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 3139 undertrained tokens (norm < 0.3× the vocabulary median of 1.371), including 107 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "$PostalCodesNL", "PostalCodesNL", "ForCanBeConvertedToF", "ForCanBeConverted", "useRalative", "useRal", "thuisontvangst", "sexkontakte". 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.
info Pickle static analysis clean
Opcode-level parse of pytorch_model-00001-of-00013.bin, pytorch_model-00002-of-00013.bin, pytorch_model-00003-of-00013.bin, pytorch_model-00004-of-00013.bin (no code executed) found only standard serialization globals (3 distinct, all torch/collections/numpy). Pickle remains an executable format — this verifies the current bytes, not future uploads; prefer a safetensors release.
info Weights consistent with claimed parent Qwen/Qwen3-14B
Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen3-14B 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.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- 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 2026-08-26.
| architecture | qwen3 · 40 layers · 5120-dim |
| vocabulary | 151,936 tokens |
| license | apache-2.0 |
| serialization | no safetensors pickle |
| chat template | present (chat_template.jinja) · sha256:a55ee1b1660128b7 |
| claimed lineage | Qwen/Qwen3-14B |
| lineage verified | consistent vs Qwen/Qwen3-14B — embedding-row cosine 1.000 |
| glitch-token surface | 3,139 undertrained candidates, 107 plain-ASCII |
Full measured fingerprint
| architectures | Qwen3ForCausalLM |
| pipeline | text-generation |
| repo files | 25 — pickle: pytorch_model-00001-of-00013.bin, pytorch_model-00002-of-00013.bin, pytorch_model-00003-of-00013.bin, pytorch_model-00004-of-00013.bin, pytorch_model-00005-of-00013.bin, pytorch_model-00006-of-00013.bin, pytorch_model-00007-of-00013.bin, pytorch_model-00008-of-00013.bin, pytorch_model-00009-of-00013.bin, pytorch_model-00010-of-00013.bin |
| revision | 5e9d0a7d8959 |
| HF snapshot | 195 downloads · 0 likes · updated 2026-06-23 · captured 2026-08-25 |
| pickle audit | pytorch_model-00001-of-00013.bin, pytorch_model-00002-of-00013.bin, pytorch_model-00003-of-00013.bin, pytorch_model-00004-of-00013.bin — 3 standard global(s) |
| embedding tensor | model.embed_tokens.weight · F32 · 151,936×5120 |
| embedding norms | median 1.3711 · mean 1.3067 |
| lineage check | consistent — cosine 0.9999 over 64 sampled rows vs Qwen/Qwen3-14B |
| glitch-token samples | "$PostalCodesNL", "PostalCodesNL", "ForCanBeConvertedToF", "ForCanBeConverted", "useRalative", "useRal", "thuisontvangst", "sexkontakte", "webElementX", "NdrFc", "sextreffen", "swingerclub" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 21:50 | 8m | 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 · F32 · 151,936×5120 |
| glitch surface | 3,139 undertrained, 107 plain-ASCII |
| lineage check | consistent — cosine 0.9999 over 64 rows vs Qwen/Qwen3-14B |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/farbodtavakkoli/OTel-LLM-14B-IT)