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authorMatthew Li <156706407+mattqlf@users.noreply.github.com>2026-03-19 13:28:12 -0400
committerGitHub <noreply@github.com>2026-03-19 10:28:12 -0700
commitd84a3e819100504d96879e1e36d022efa5cbb81b (patch)
tree99bdebeb83904d27e409f9a9f2df26905fcec06b /records/track_10min_16mb/2026-03-19_SlidingWindowEval/submission.json
parent194bb8766eb19ee21618490132594d533d1455ad (diff)
Add record: Sliding Window Eval (stride=64), val_bpb=1.1925 (#50)
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+{
+ "author": "Matthew Li",
+ "github_id": "mattqlf",
+ "name": "Sliding Window Eval (stride=64)",
+ "blurb": "Baseline 9x512 SP-1024 architecture with sliding window evaluation at stride=64. Each token is scored with 960+ tokens of context instead of the baseline's 0-1023. Training is identical to the naive baseline; the improvement comes entirely from the evaluation strategy. Post-quant int8+zlib roundtrip under the 16,000,000-byte cap.",
+ "date": "2026-03-19T04:48:00Z",
+ "val_loss": 2.01348383,
+ "val_bpb": 1.19250007,
+ "pre_quant_val_loss": 2.0592,
+ "pre_quant_val_bpb": 1.2196,
+ "step_stop": 13450,
+ "wallclock_seconds": 600.028,
+ "eval_time_seconds": 69.881,
+ "bytes_total": 15874829,
+ "bytes_model_int8_zlib": 15816489,
+ "bytes_code": 58340
+} \ No newline at end of file