stanford-crfm/durin-gpt2-medium-x343 warn
Weights only ship in a format that can run code when loaded; no license declared — no usage rights by default; the weight files reference unusual code — review before loading. Plus 1 minor note.
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 2026-08-2550,257-token embedding scanned · 103 undertrained · pickle audit: 2 non-standard global(s) |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-25 · published from a community scan.
medium Pickle-serialized weights, no safetensors
Weights ship only as pickle-based files (global_step400009/mp_rank_00_model_states.pt, global_step400009/zero_pp_rank_0_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_10_mp_rank_00optim_states.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 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.
- With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
- Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.
low Undertrained tokens in vocabulary (non-ASCII tail)
Embedding-norm scan flagged 103 undertrained tokens (norm < 0.3× the vocabulary median), but 0 decode to plain-ASCII strings, so exposure in English-language pipelines is limited. 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: __builtin__.set, deepspeed.runtime.fp16.loss_scaler.DynamicLossScaler. Common in full-model (non-state-dict) saves — each is code that runs at load time. Review before loading, or demand 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 | gpt2 · 24 layers · 1024-dim |
| vocabulary | 50,257 tokens |
| license | none declared |
| serialization | no safetensors pickle |
| chat template | none |
| glitch-token surface | 103 undertrained candidates, 0 plain-ASCII |
Full measured fingerprint
| architectures | GPT2LMHeadModel |
| library | transformers |
| pipeline | text-generation |
| repo files | 28 — pickle: global_step400009/mp_rank_00_model_states.pt, global_step400009/zero_pp_rank_0_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_10_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_11_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_12_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_13_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_14_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_15_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_1_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_2_mp_rank_00optim_states.pt |
| revision | 904931e45f71 |
| HF snapshot | 412 downloads · 0 likes · updated 2026-03-11 · captured 2026-08-25 |
| pickle audit | pytorch_model.bin, global_step400009/mp_rank_00_model_states.pt, global_step400009/zero_pp_rank_0_mp_rank_00optim_states.pt, global_step400009/zero_pp_rank_10_mp_rank_00optim_states.pt — 5 standard global(s), suspicious __builtin__.set, deepspeed.runtime.fp16.loss_scaler.DynamicLossScaler |
| embedding tensor | transformer.wte.weight · F16 · 50,257×1024 |
| embedding norms | median 0.8733 · mean 0.8849 |
| lineage check | no claimed base model |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 21:48 | 20s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, pickle-static-analysis, lineage-norm-correlation |
| probes skipped | token-decode: no tokenizer.json |
| embedding tensor | transformer.wte.weight · F16 · 50,257×1024 |
| glitch surface | 103 undertrained, 0 plain-ASCII |
| lineage check | not checked (no claimed base model) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/stanford-crfm/durin-gpt2-medium-x343)