Salesforce/codegen-350M-multi warn
Weights only ship in a format that can run code when loaded; glitch tokens that can silently corrupt ordinary input.
chat template: not found · view on Hugging Face ↗
Scan coverageStatic battery2026-08-25Weights battery2026-08-25Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-25 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-2551,200-token embedding scanned · 977 undertrained · pickle audit clean |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-25 · published from a community scan.
medium Pickle-serialized weights, no safetensors
Weights ship only as pickle-based files (pytorch_model.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 977 undertrained tokens (norm < 0.3× the vocabulary median of 0.688), including 45 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "quickShipAvailable", "strutConnector", "soDeliveryDate", "PsyNet", "ItemThumbnailImage", "BuyableInstoreAndOnline", "isSpecialOrderable", "guiActiveUnfocused". 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.bin (no code executed) found only standard serialization globals (4 distinct, all torch/collections/numpy). Pickle remains an executable format — this verifies the current bytes, not future uploads; prefer a safetensors release.
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 2026-08-25.
| architecture | codegen · 20 layers · 1024-dim |
| vocabulary | 51,200 tokens |
| license | bsd-3-clause |
| serialization | no safetensors pickle |
| chat template | none |
| glitch-token surface | 977 undertrained candidates, 45 plain-ASCII |
Full measured fingerprint
| architectures | CodeGenForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 10 — pickle: pytorch_model.bin |
| revision | b25de779e204 |
| HF snapshot | 4.7k downloads · 61 likes · updated 2025-01-31 · captured 2026-08-25 |
| pickle audit | pytorch_model.bin — 4 standard global(s) |
| embedding tensor | transformer.wte.weight · F16 · 51,200×1024 |
| embedding norms | median 0.6881 · mean 0.6396 |
| lineage check | model metadata unreadable |
| glitch-token samples | "quickShipAvailable", "strutConnector", "soDeliveryDate", "PsyNet", "ItemThumbnailImage", "BuyableInstoreAndOnline", "isSpecialOrderable", "guiActiveUnfocused", "DragonMagazine", "sqor", "assetsadobe", "Downloadha" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 21:46 | 12s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, pickle-static-analysis, lineage-norm-correlation |
| embedding tensor | transformer.wte.weight · F16 · 51,200×1024 |
| glitch surface | 977 undertrained, 45 plain-ASCII |
| lineage check | not checked (model metadata unreadable) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/Salesforce/codegen-350M-multi)