malaiwah/minimax-m2-tiny-fidelity-root-v1
minimax-m2 random CPU fixture root A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/minimax-m2-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).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/minimax-m2-tiny-fidelity-root-v1.
minimax-m2 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/minimax-m2-tiny-random-bf16.
The cut
the final hidden state handed to lmhead -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.registerforwardprehook; 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
Does the model still generate sensibly
The capital of France is -> (p=0.0049, entropy 5.600 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>