Skywork/Skywork-Critic-Llama-3.1-8B warn
claims base: meta-llama/Meta-Llama-3.1-8B-Instruct, meta-llama/Llama-3.1-8B-Instruct · chat template: present · view on Hugging Face ↗
Scan coverage
Ingot runs three batteries against a model. What each one checks →
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-22 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-22128,256-token embedding scanned · 496 undertrained · lineage consistent · pickle audit clean |
| Behavioral battery | Live-inference differentials | not run |
Findings
Scanned 2026-08-22 · published from a community scan.
medium Pickle-serialized weights, no safetensors
Weights ship only as pickle-based files (pytorch_model-00001-of-00033.bin, pytorch_model-00002-of-00033.bin, pytorch_model-00003-of-00033.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 License differs from claimed parent (other vs llama3.1)
This model declares other while its claimed base meta-llama/Meta-Llama-3.1-8B-Instruct declares llama3.1. Verify the re-license is permitted before commercial use.
How to fix
Verify the re-license is actually permitted before relying on it.
- Read the parent's license for derivative-work and re-licensing terms — many open-weight licenses (e.g. Llama-family) do not permit arbitrary re-licensing.
- If the re-license is not permitted, the parent's terms govern your use regardless of what this repo declares.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 496 undertrained tokens (norm < 0.3× the vocabulary median of 0.685), including 140 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "ilmektedir", "$PostalCodesNL", "ForCanBeConvertedToF", "TokenNameIdentifier", "CLIIIK", "useRalative", "PostalCodesNL", "_ComCallableWrapper". 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-00033-of-00033.bin, pytorch_model-00001-of-00033.bin, pytorch_model-00002-of-00033.bin, pytorch_model-00003-of-00033.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 meta-llama/Meta-Llama-3.1-8B-Instruct
Mean cosine similarity of 64 sampled token-embedding rows against meta-llama/Meta-Llama-3.1-8B-Instruct 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 2026-08-22.
| architecture | llama · 32 layers · 4096-dim |
| vocabulary | 128,256 tokens |
| license | other |
| serialization | no safetensors pickle |
| chat template | present · sha256:e10ca381b1ccc5cf |
| claimed lineage | meta-llama/Meta-Llama-3.1-8B-Instruct, meta-llama/Llama-3.1-8B-Instruct |
| lineage verified | consistent vs meta-llama/Meta-Llama-3.1-8B-Instruct — embedding-row cosine 1.000 |
| glitch-token surface | 496 undertrained candidates, 140 plain-ASCII |
Full measured fingerprint
| architectures | LlamaForCausalLM |
| pipeline | text-generation |
| repo files | 44 — pickle: pytorch_model-00001-of-00033.bin, pytorch_model-00002-of-00033.bin, pytorch_model-00003-of-00033.bin, pytorch_model-00004-of-00033.bin, pytorch_model-00005-of-00033.bin, pytorch_model-00006-of-00033.bin, pytorch_model-00007-of-00033.bin, pytorch_model-00008-of-00033.bin, pytorch_model-00009-of-00033.bin, pytorch_model-00010-of-00033.bin |
| revision | 825f34599593 |
| HF snapshot | 367 downloads · 13 likes · updated 2024-09-29 · captured 2026-08-21 |
| pickle audit | pytorch_model-00033-of-00033.bin, pytorch_model-00001-of-00033.bin, pytorch_model-00002-of-00033.bin, pytorch_model-00003-of-00033.bin — 3 standard global(s) |
| embedding tensor | model.embed_tokens.weight · BF16 · 128,256×4096 |
| embedding norms | median 0.6847 · mean 0.6711 |
| lineage check | consistent — cosine 1 over 64 sampled rows vs meta-llama/Meta-Llama-3.1-8B-Instruct |
| glitch-token samples | "ilmektedir", "$PostalCodesNL", "ForCanBeConvertedToF", "TokenNameIdentifier", "CLIIIK", "useRalative", "PostalCodesNL", "_ComCallableWrapper", "ForCanBeConverted", "krvldkf", "sahuje", "webElementXpaths" |
Battery runs
The run trace behind the findings above: every deep-battery job for this model, with what each run measured or why it failed. Findings are only as good as the runs that produced them.
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:32 | 2m | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, pickle-static-analysis, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 128,256×4096 |
| glitch surface | 496 undertrained, 140 plain-ASCII |
| lineage check | consistent — cosine 1 over 64 rows vs meta-llama/Meta-Llama-3.1-8B-Instruct |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/Skywork/Skywork-Critic-Llama-3.1-8B)