ahmedalkhair64/qwen2.5-14b-medical-sft warn
The chat template was dropped from its base model, which changes behavior; glitch tokens that can silently corrupt ordinary input; the weights don't match the model it claims to be based on. Plus 1 minor note.
claims base: Qwen/Qwen2.5-7B-Instruct · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-27Weights battery2026-08-21Behavioral batterycompletedetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-27 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-21152,064-token embedding scanned · 5737 undertrained · lineage inconsistent |
| Behavioral battery | Live-inference differentials | completefull differential battery (curated) |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-27 · published from a community scan.
medium Chat template dropped vs parent
Qwen/Qwen2.5-7B-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 Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 5737 undertrained tokens (norm < 0.3× the vocabulary median of 1.225), including 119 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "TokenNameIdentifier", "useRalative". 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.
medium Weights inconsistent with claimed parent Qwen/Qwen2.5-7B-Instruct
This model declares Qwen/Qwen2.5-7B-Instruct as its base (relation: unspecified), but its token-embedding geometry is incompatible: 5120-dim embeddings vs the parent's 3584-dim. A finetune cannot change embedding width — the lineage label is wrong or misleading. Treat provenance claims on this repo (training data, safety posture, licensing) as unverified.
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.
low Partial glitch-token echo degradation
Echo failures on 6/16 undertrained tokens vs 0/8 controls — a differential exists but below the confirmation bar (≥50% glitch failures with clean controls).
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 ahmedalkhair64/qwen2.5-14b-medical-sft
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 | qwen2 · 48 layers · 5120-dim |
| parameters | 14770.0M |
| vocabulary | 152,064 tokens |
| license | apache-2.0 |
| serialization | safetensors + gguf |
| chat template | none |
| claimed lineage | Qwen/Qwen2.5-7B-Instruct |
| lineage verified | inconsistent vs Qwen/Qwen2.5-7B-Instruct |
| glitch-token surface | 5,737 undertrained candidates, 119 plain-ASCII |
Full measured fingerprint
| architectures | Qwen2ForCausalLM |
| repo files | 9 |
| revision | c526330b484d |
| HF snapshot | 430 downloads · 1 likes · updated 2026-06-30 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · F16 · 152,064×5120 |
| embedding norms | median 1.2252 · mean 1.1403 |
| lineage check | inconsistent — cosine undefined over undefined sampled rows vs Qwen/Qwen2.5-7B-Instruct |
| glitch-token samples | "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "TokenNameIdentifier", "useRalative", "useRal", "thuisontvangst", "Cumhurba", "NdrFc" |
Battery runs (2)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| gpu | complete | 2026-08-27 04:31 | 3m | 1 |
| weights | complete | 2026-08-21 05:31 | 9m | 1 |
gpu run 2026-08-27 — measurements
| probes run | glitch |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · F16 · 152,064×5120 |
| glitch surface | 5,737 undertrained, 119 plain-ASCII |
| lineage check | inconsistent — cosine undefined over undefined rows vs Qwen/Qwen2.5-7B-Instruct |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/ahmedalkhair64/qwen2.5-14b-medical-sft)