HKUSTAudio/Llasa-8B warn
Its license differs from its base model's; the chat template was dropped from its base model, which changes behavior; its tokenizer differs from its claimed base model. Plus 1 more issue.
claims base: meta-llama/Llama-3.1-8B-Instruct · chat template: not found · view on Hugging Face ↗
Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batteryn/adetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-21 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-21193,800-token embedding scanned · 724 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | n/anot applicable: text-to-speech model has no text-generation surface to probe |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-21 · published from a community scan.
medium License differs from claimed parent (cc-by-nc-4.0 vs llama3.1)
This model declares cc-by-nc-4.0 while its claimed base meta-llama/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 Chat template dropped vs parent
meta-llama/Llama-3.1-8B-Instruct ships a chat template; this repo does not. Serving stacks will silently fall back to a generic template, changing behavior. (In our 296-model census, 78% of pure quantization re-releases changed or dropped the template.)
How to fixingot patch
Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.
- Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
- Or fix by hand: copy the `chat_template` value from the parent repo's `tokenizer_config.json` into this model's, and pin your serving stack to that file.
- If the drift was intentional (the author retrained on a new template), confirm that in the model card before "fixing" it — restoring the parent template on retrained weights changes behavior too.
medium Vocabulary size differs from claimed parent (193800 vs 128256)
A changed vocab means changed tokenization: strings will split differently than on meta-llama/Llama-3.1-8B-Instruct, which can shift behavior on identifiers, codes, and non-English text.
How to fixweight-level
Not patchable: the vocab size mirrors the embedding matrix in the weights. Verify the change was intentional.
- Do not edit `vocab_size` in config.json to "match the parent" — it must equal the embedding table in the shipped weights or the model won't load.
- Diff the tokenizers (`tokenizer.json` / added_tokens) against the parent to see what was added or removed, and test your own identifiers, codes, and non-English text through both.
- If the drift is unexplained by the model card, treat tokenization-sensitive behavior as unvalidated on this model.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 724 undertrained tokens (norm < 0.3× the vocabulary median of 0.843), including 202 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 Weights consistent with claimed parent meta-llama/Llama-3.1-8B-Instruct
Mean cosine similarity of 42 sampled token-embedding rows against meta-llama/Llama-3.1-8B-Instruct is 0.849 — the weights plausibly descend from the declared base (relation: unspecified).
Check every checkpoint before it ships
Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.
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 HKUSTAudio/Llasa-8B
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 | 8567.2M |
| vocabulary | 193,800 tokens |
| license | cc-by-nc-4.0 |
| serialization | safetensors |
| chat template | none |
| claimed lineage | meta-llama/Llama-3.1-8B-Instruct |
| lineage verified | consistent vs meta-llama/Llama-3.1-8B-Instruct — embedding-row cosine 0.849 |
| glitch-token surface | 724 undertrained candidates, 202 plain-ASCII |
Full measured fingerprint
| architectures | LlamaForCausalLM |
| pipeline | text-to-speech |
| repo files | 12 |
| revision | 2a8339e38734 |
| HF snapshot | 431 downloads · 97 likes · updated 2025-03-09 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 193,800×4096 |
| embedding norms | median 0.8427 · mean 0.7989 |
| lineage check | consistent — cosine 0.8486 over 42 sampled rows vs meta-llama/Llama-3.1-8B-Instruct |
| glitch-token samples | "ilmektedir", "$PostalCodesNL", "ForCanBeConvertedToF", "TokenNameIdentifier", "CLIIIK", "useRalative", "PostalCodesNL", "_ComCallableWrapper", "ForCanBeConverted", "krvldkf", "sahuje", "webElementXpaths" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:31 | 2m | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 193,800×4096 |
| glitch surface | 724 undertrained, 202 plain-ASCII |
| lineage check | consistent — cosine 0.8486 over 42 rows vs meta-llama/Llama-3.1-8B-Instruct |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/HKUSTAudio/Llasa-8B)