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[executorch][native] Add GGUF q4_k and q6_k packages to the op samples - #24541
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Pull Request resolved: #24366 PTN carries GGUF k-quant constants, but no op package exercised them. A `Gguf` scheme, as `gguf_q4_k` and `gguf_q6_k`, writes random valid blocks to a .gguf with gguf-py and loads them back as a checkpoint would. `golden-int8_act` now comes from per-token int8 pre-hooks on the weight-only model, which also work for GGUF, so `Scheme.weight_only` replaces `Scheme.act_quant`. FACTO also keeps the tuples whose input features fit 256-element blocks. Authored with Claude Code. Differential Revision: [D128254890](https://our.internmc.facebook.com/intern/diff/D128254890/) **NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D128254890/)! ghstack-source-id: 463657745
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24541
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #24366 by @digantdesai
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/digantdesai/193/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/193/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/digantdesai/192/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/193/orig
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