Youliang/llama3-8b-instruct-derta-100step warn
claims base: meta-llama/Meta-Llama-3-8B-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 2026-08-21 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-21128,257-token embedding scanned · 461 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Findings
Scanned 2026-08-21 · published from a community scan.
medium Repo ships executable Python (trust_remote_code)
The repository contains custom code files that run in-process when loaded with trust_remote_code=True. Pin the revision hash and review the code before loading.
How to fix
Review and pin the custom code; never float on `main` with trust_remote_code=True.
- Read every `.py` file in the repo before first load — this code runs in your process.
- Pin the revision: `from_pretrained(model_id, revision="<commit sha>", trust_remote_code=True)` so a later push can't swap the code under you.
- Prefer a version of the architecture already in `transformers` if one exists, which removes the remote-code requirement entirely.
medium License differs from claimed parent (apache-2.0 vs llama3)
This model declares apache-2.0 while its claimed base meta-llama/Meta-Llama-3-8B-Instruct declares llama3. 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 461 undertrained tokens (norm < 0.3× the vocabulary median of 0.601), including 131 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "$PostalCodesNL", "ForCanBeConverted", "TokenNameIdentifier", "useRalative", "ForCanBeConvertedToF", "PostalCodesNL", "ilmektedir", "CLIIIK". 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.
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 2026-08-21.
| architecture | llama · 32 layers · 4096-dim |
| parameters | 8030.3M |
| vocabulary | 128,257 tokens |
| license | apache-2.0 |
| serialization | safetensors pickle custom code |
| chat template | present · sha256:ba03a121d097859c |
| claimed lineage | meta-llama/Meta-Llama-3-8B-Instruct |
| lineage verified | unverified — weights battery pending |
| glitch-token surface | 461 undertrained candidates, 131 plain-ASCII |
Full measured fingerprint
| architectures | MyLlamaForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 15 — pickle: training_args.bin |
| revision | 815bb6a3f3d6 |
| HF snapshot | 232 downloads · 1 likes · updated 2024-07-21 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 128,257×4096 |
| embedding norms | median 0.6012 · mean 0.5912 |
| lineage check | parent weights unreadable (403 Forbidden for https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/resolve/main/model.safetensors.index.json (gated repo — the HF_TOKEN account has not accepted this repo's license, or the token lacks gated-repo read scope)) |
| glitch-token samples | "$PostalCodesNL", "ForCanBeConverted", "TokenNameIdentifier", "useRalative", "ForCanBeConvertedToF", "PostalCodesNL", "ilmektedir", "CLIIIK", "_ComCallableWrapper", "krvldkf", "webElementXpaths", "useRalativeImagePath" |
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 05:16 | 57s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 128,257×4096 |
| glitch surface | 461 undertrained, 131 plain-ASCII |
| lineage check | not checked (parent weights unreadable (403 Forbidden for https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/resolve/main/model.safetensors.index.json (gated repo — the HF_TOKEN account has not accepted this repo's license, or the token lacks gated-repo read scope))) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/Youliang/llama3-8b-instruct-derta-100step)