The claim that is not a proof
A repository says the model is BF16. The config says "torch_dtype": "bfloat16". The filename says bfloat16. None of that proves the file holds the maker's native representation — it proves the file holds BF16-encoded numbers. Those are different claims, and the gap between them is where acquisition mistakes live.
Three different artifacts can all honestly wear the BF16 label:
- The native representation — the precision the maker actually trained or exported in. Highest information content for this model; the archive's default target.
- A real conversion — an FP32 master downcast to BF16 at release. Legitimate, but it is a derivative wearing the same label as the native form.
- A padded upcast — a lossy source (a quantized file, an FP8 export) expanded back to 16-bit containers. The label is technically true and practically a trap: the bytes claim more precision than the information they hold. You cannot see this from the label; the information was already gone before the upcast.
Arithmetic is the archivist's instrument
The bytes-per-parameter check is cheap and catches most mislabels. Multiply parameter count by the claimed bytes per element and compare with the actual shard sizes:
- 7B params at BF16 (2 bytes) ≈ 14 GB of tensors. A repo claiming BF16 at ~28 GB is F32-sized — either a full-precision master (good to know) or an upcast.
- The same 7B at ~7 GB is Q8-shaped; at ~4 GB, Q4-shaped. A "BF16" repo whose tensors sum to a quantization-sized footprint is wearing someone else's label.
Then corroborate with provenance, which you will learn to read in the next track: does the maker's own card name the release precision? Does the repo's commit history show a conversion step? Do independent mirrors of the same release agree on sizes? A dtype claim plus consistent arithmetic plus a credible story is a verdict; any leg missing means the label stays evidence.
Why this is the load-bearing skill
Every acquisition policy in this quest — highest-fidelity authoritative representation, one copy per thing — depends on being able to rank candidate artifacts by fidelity. Ranking requires exactly this: reading labels as claims, testing them with size arithmetic, and consulting provenance before anointing a file as the master. The acquisition decision this skill feeds is the difference between an archive and a pile of mislabeled derivatives.