QFS test fixtures and reproducible captures
Collection
Tiny random models, quantized format fixtures and verified CPU captures for QFS. Pipeline tests, not assistants or quality benchmarks. • 64 items • Updated
checks_run list | error_count int64 | errors list | format_version int64 | receipt_sha256 string | schema string | structural_status string | subject string | warning_count int64 | warnings list |
|---|---|---|---|---|---|---|---|---|---|
[
"pre-seal"
] | 0 | [] | 1 | 8390cf1ddd67bff4cbc87eef4b2e734b242063e9fb63dbbadd38f8ac9b93b836 | malaiwah.fidelity-structural-validation.v1 | sealed | dataset:fidelity--k2-horizon.community-root | 0 | [] |
k2-horizon random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/k2-horizon-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut as engines/tools/hidden_replay.py and as Festr's kimi-k3 hidden-replay qualification.
What is here
| records | 4 |
| scored positions | 252 |
| context length | 64 |
| hidden width | 64 |
| vocab size | 272 |
| capture bytes (hidden form) | 33416 |
| the same capture in logit form would be | 274176 bytes (8x) |
| scope policy | native |
| lane | other |
Does the model still generate sensibly
The capital of France is -> � (p=0.0056, entropy 5.593 of a possible 5.606 nats). Recorded, not enforced. One extra window through the same schedule; its hidden state was discarded and is not part of this dataset.
License
This dataset redistributes the captured model's native output-head weights. The source license and required notice are reproduced byte-for-byte in LICENSE.
How to check it
bin/fidelity-dataset verify <this directory>
Full specification: https://github.com/malaiwah/quant-fidelity-suite/blob/main/docs/FIDELITY-DATASET-SPEC.md
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