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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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     "benchmarks/jina-code-calibrated-4k.json": {
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@@ -38,8 +38,8 @@
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@@ -103,5 +103,5 @@
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benchmarks/lfm25-230m-gptq-mxfp8-vs-bf16-bootstrap.json

@@ -1,11 +1,15 @@
 {
   "bootstrap_samples": 10000,
+  "candidate_embeddings": "lfm25-230m-gptq-mxfp8-4k-embeddings.npz",
+  "candidate_embeddings_sha256": "e6886573caf693e3a6b002ccd1933a054d24a22b54dd4913647dee7d566b7c19",
   "candidate_mrr": 0.39336530977743367,
   "difference_ci_95_lower": -0.004237095202471089,
   "difference_ci_95_upper": -0.0009771653320113183,
   "mrr_difference": -0.0026152420061133245,
   "pairs": 6995,
   "probability_difference_le_zero": 0.9991,
+  "reference_embeddings": "lfm25-230m-bf16-4k-embeddings.npz",
+  "reference_embeddings_sha256": "49758382ac6c95350c74070f92dc2aa86b6738fc2f53914a54372624a6163c91",
   "reference_mrr": 0.39598055178354696,
   "seed": 17
 }