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README.md

@@ -66,8 +66,9 @@ and candidate code with `passage: ` when calling the model directly.
 
 ## Training and data receipts
 
-Full-backbone symmetric in-batch InfoNCE training used 24,626 language-balanced pairs,
-two epochs, batch size 32, learning rate 2e-5, temperature 0.05, and seed 17. The
+Full-backbone symmetric in-batch InfoNCE training used 24,626
+language-balanced pairs selected from the 42,626-row source training
+split, two epochs, batch size 32, learning rate 2e-5, temperature 0.05, and seed 17. The
 training report records the NVIDIA RTX A6000 runtime and validation history.
 
 - `train`: 42,626 rows, SHA-256 `426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629`

artifact_manifest.json

@@ -14,8 +14,8 @@
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-      "sha256": "1262b5c12530a3cafbb3906381383e4178a7513175b1ff24f7dd8dd1ba8c34ad"
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     "benchmarks/jina-code-calibrated-4k.json": {
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@@ -38,8 +38,8 @@
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-      "sha256": "cd1f5ff603e6ccebeb4d173c74dd5fee0f1ccf62f3d74c8b06a4818b493af436"
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@@ -99,5 +99,5 @@
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 }

benchmarks/lfm25-350m-gptq-mxfp8-vs-bf16-bootstrap.json

@@ -1,11 +1,15 @@
 {
   "bootstrap_samples": 10000,
+  "candidate_embeddings": "lfm25-350m-gptq-mxfp8-4k-embeddings.npz",
+  "candidate_embeddings_sha256": "2700f1c45fbec7a434a7e2542b933a538b90ecde0652025b00be1d245b885158",
   "candidate_mrr": 0.3710225680971694,
   "difference_ci_95_lower": -0.00101095982658736,
   "difference_ci_95_upper": 0.002104554561454204,
   "mrr_difference": 0.0005430037238132663,
   "pairs": 6995,
   "probability_difference_le_zero": 0.2531,
+  "reference_embeddings": "lfm25-350m-bf16-4k-embeddings.npz",
+  "reference_embeddings_sha256": "b28b9473f302f064ff64b41e73145b95eada245c86c0246126bab5e50f9b4155",
   "reference_mrr": 0.37047956437335616,
   "seed": 17
 }