dphn/dolphin-2.9.1-yi-1.5-34b warn
claims base: 01-ai/Yi-1.5-34B · chat template: present · view on Hugging Face ↗
Ingot findings
Static battery clean: safetensors weights, license declared, no template/tokenizer drift detected. Deep battery not yet run. Weights battery: embedding-norm scan over 64000 tokens (BF16, 7168-dim) found 1149 undertrained candidates, 756 plain-ASCII. Lineage vs 01-ai/Yi-1.5-34B: consistent. Scanned 2026-08-20 (published from a community scan).
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 1149 undertrained tokens (norm < 0.3× the vocabulary median of 0.916), including 756 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "<|unused091|>", "<|unused121|>", "<|unused123|>", "<|unused114|>", "<|unused022|>", "<|unused030|>", "<|unused100|>", "<|unused102|>". 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 GPU deep 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 01-ai/Yi-1.5-34B
Mean cosine similarity of 64 sampled token-embedding rows against 01-ai/Yi-1.5-34B 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 weights-and-metadata profile of this model, rebuilt on every scan and deep-battery run. Updated 2026-08-20.
| architecture | llama · 60 layers · 7168-dim |
| parameters | 34388.9M |
| vocabulary | 64,000 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | present · sha256:58c1a1f04baa7ada |
| claimed lineage | 01-ai/Yi-1.5-34B |
| lineage verified | consistent vs 01-ai/Yi-1.5-34B — embedding-row cosine 1.000 |
| glitch-token surface | 1,149 undertrained candidates, 756 plain-ASCII |
Full fingerprint
| architectures | LlamaForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 24 |
| revision | 0141cba238d0 |
| HF snapshot | 4.6M downloads · 65 likes · updated 2025-09-08 · captured 2026-08-20 |
| embedding tensor | model.embed_tokens.weight · BF16 · 64,000×7168 |
| embedding norms | median 0.9163 · mean 0.881 |
| lineage check | consistent — cosine 0.9996 over 64 sampled rows vs 01-ai/Yi-1.5-34B |
| glitch-token samples | "<|unused091|>", "<|unused121|>", "<|unused123|>", "<|unused114|>", "<|unused022|>", "<|unused030|>", "<|unused100|>", "<|unused102|>", "<|unused152|>", "<|unused140|>", "<|unused099|>", "<|unused002|>" |
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