NousResearch/Llama-2-7b-hf warn
No license declared — no usage rights by default; Special token id outside the vocabulary; 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 2026-08-2532,000-token embedding scanned · 132 undertrained |
| 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 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.
medium Special token id outside the vocabulary
pad_token_id=32000 is outside the declared vocab_size of 32000. Runtimes either crash on it or silently ignore the setting (e.g. pad_token_id: -1), which breaks batching and stop handling.
How to fixingot patch
Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).
- Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
- Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
- For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
- Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 132 undertrained tokens (norm < 0.3× the vocabulary median of 1.100), including 14 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "<0xFE>", "<0xFF>", "<0xFB>", "<0xFC>", "<0xFD>", "<0xFA>", "Mediabestanden", "oreferrer". 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.
Fix it
Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):
npx @ingotai/scan patch NousResearch/Llama-2-7b-hf
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 | llama · 32 layers · 4096-dim |
| parameters | 6738.4M |
| vocabulary | 32,000 tokens |
| license | none declared |
| serialization | safetensors pickle |
| chat template | none |
| glitch-token surface | 132 undertrained candidates, 14 plain-ASCII |
Full measured fingerprint
| architectures | LlamaForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 18 — pickle: pytorch_model-00001-of-00003.bin, pytorch_model-00002-of-00003.bin, pytorch_model-00003-of-00003.bin |
| revision | 8efe6c9b9365 |
| HF snapshot | 276.8k downloads · 176 likes · updated 2024-06-03 · captured 2026-08-25 |
| embedding tensor | model.embed_tokens.weight · F16 · 32,000×4096 |
| embedding norms | median 1.1001 · mean 1.08 |
| lineage check | no claimed base model |
| glitch-token samples | "<0xFE>", "<0xFF>", "<0xFB>", "<0xFC>", "<0xFD>", "<0xFA>", "Mediabestanden", "oreferrer", "Normdaten", "ITableView", "regnig", "demsel" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 20:26 | 22s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · F16 · 32,000×4096 |
| glitch surface | 132 undertrained, 14 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/NousResearch/Llama-2-7b-hf)