librarian-c06 / browser /patch.json
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Publish frozen Librarian C06 native weights and verified browser export
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{
"parent_sha256": "aa364f2a0e1d96b7ffb69e52bbdd4f1f07634ce27d10d3686ffff0c90824f7dd",
"prompt_sha256": "fe189f080016c60b02111f4e1f4388a38fc6c3bd415f56843ae411ab3e26f20e",
"answer_set_sha256": "2a77123aa6056cdc609bb3b78bc2821062636520ee66b215b34b0453df92b95e",
"rationale": "Proposal 06 fixes the bare English greeting but changes a previously exact named-visitor greeting from Hi to Hello and changes its continuation, so it fails the preservation gate. Start again from the original text checkpoint and retain those two Hello edits while giving Hi approximately the same coordinate increments, preserving the model's original relative greeting preference rather than globally forcing Hello. In the inspected English greeting, Hi is 4.23847 logits below Hello, so this is not expected to steal that greeting. The inspected Spanish activation predicts only a small change to both English tokens, well below Hola. New values are four explicit manually chosen BF16 scalars, not an optimizer or fitted update. Tied input embeddings also change source processing; only fresh free generation of all 60 references and development controls can establish an improvement. Reject if any previously exact answer or passing development control is lost.",
"edits": [
{
"tensor": "model.embed_tokens.weight",
"indices": [
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],
"old": 0.02392578125,
"new": 0.1484375,
"dtype": "BF16"
},
{
"tensor": "model.embed_tokens.weight",
"indices": [
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"old": -0.0181884765625,
"new": -0.08056640625,
"dtype": "BF16"
},
{
"tensor": "model.embed_tokens.weight",
"indices": [
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"old": -0.005645751953125,
"new": 0.119140625,
"dtype": "BF16"
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{
"tensor": "model.embed_tokens.weight",
"indices": [
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"old": -0.024169921875,
"new": -0.08642578125,
"dtype": "BF16"
}
],
"sha256": "3f8e536a11a7015c947deaa0cf26c4faf55adbe17fe57ad9c8bb160bcf80b90d"
}