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SameedHussain/qwen-0.5b-flight-25k-v1 warn

Weights only ship in a format that can run code when loaded; the chat template differs from its base model, which changes behavior; the weights don't match the model it claims to be based on.

downloads 18likes 0license apache-2.0arch qwen2updated 2024-08-16

claims base: unsloth/qwen2-1.5b-bnb-4bit · chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-26Weights battery2026-08-26Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-26
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-26151,936-token embedding scanned · 0 undertrained · lineage inconsistent · pickle audit clean
Behavioral batteryLive-inference differentialsnot run

Ingot runs three batteries against a model. What each one checks →

Findings

Scanned 2026-08-26 · published from a community scan.

medium Pickle-serialized weights, no safetensors

Weights ship only as pickle-based files (pytorch_model.bin). Loading pickle executes arbitrary code from the file — prefer a safetensors release or load in a sandbox.

How to fix

Convert the weights to safetensors before loading them anywhere that matters.

  1. Do not load the pickle files in-process — pickle deserialization executes arbitrary code from the file.
  2. Convert locally in a sandbox: `pip install safetensors` and use `safetensors.torch.save_file` on a state dict loaded with `torch.load(..., weights_only=True)` (refuses most code-execution payloads), or use Hugging Face's `convert.py` space/script.
  3. Pin the exact revision hash you converted from, and load only your converted safetensors artifact from then on.

medium Chat template differs from claimed parent

The chat template does not match unsloth/qwen2-1.5b-bnb-4bit's. Template drift silently changes model behavior even when weights are identical — 37% of drifted derivatives in our census left it undisclosed. Diff the templates before deploying.

How to fixingot patch

Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.

  1. Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
  2. 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.
  3. 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.

info Embedding-norm glitch scan clean

No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (0.453). The glitch-token data-corruption class has no candidate surface in this model.

info Pickle static analysis clean

Opcode-level parse of pytorch_model.bin (no code executed) found only standard serialization globals (3 distinct, all torch/collections/numpy). Pickle remains an executable format — this verifies the current bytes, not future uploads; prefer a safetensors release.

medium Weights inconsistent with claimed parent unsloth/qwen2-1.5b-bnb-4bit

This model declares unsloth/qwen2-1.5b-bnb-4bit as its base (relation: unspecified), but its token-embedding geometry is incompatible: 896-dim embeddings vs the parent's 1536-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.

  1. If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
  2. If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
Put this result in your workflow

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 SameedHussain/qwen-0.5b-flight-25k-v1

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 2026-08-26.

architectureqwen2 · 24 layers · 896-dim
vocabulary151,936 tokens
licenseapache-2.0
serializationno safetensors pickle
chat templatepresent · sha256:793202280c0910ab
claimed lineageunsloth/qwen2-1.5b-bnb-4bit
lineage verifiedinconsistent vs unsloth/qwen2-1.5b-bnb-4bit
glitch-token surfaceclean no undertrained tokens
Full measured fingerprint
architecturesQwen2ForCausalLM
librarytransformers
pipelinetext-generation
repo files11 — pickle: pytorch_model.bin
revisionff58980951ff
HF snapshot17 downloads · 0 likes · updated 2024-08-16 · captured 2026-08-25
pickle auditpytorch_model.bin — 3 standard global(s)
embedding tensormodel.embed_tokens.weight · F16 · 151,936×896
embedding normsmedian 0.4527 · mean 0.4524
lineage checkinconsistent — cosine undefined over undefined sampled rows vs unsloth/qwen2-1.5b-bnb-4bit
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-25 22:2163s1
weights run 2026-08-25 — measurements
probes runglitch-norm-scan, zero-template-token-scan, pickle-static-analysis, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · F16 · 151,936×896
glitch surface0 undertrained, 0 plain-ASCII
lineage checkinconsistent — cosine undefined over undefined rows vs unsloth/qwen2-1.5b-bnb-4bit

Verdict badge

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Ingot verdict: warn

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