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fix(demos): drop openblas/metal from default features (CPU-only default, backends opt-in)
Performance Benchmarks #205
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70da8215a2
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2026-06-27 16:19:54 +00:00
4m28s
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fix(rtx-backend): gate CUDA dev-dep to x86_64-linux so cargo test works on macOS
Performance Benchmarks #202
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63776aa0f2
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2026-06-27 16:11:10 +00:00
2m15s
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fix(jepa-vision-bridge): add as_any/as_any_mut to RtxVisionJepaEncoder
Performance Benchmarks #199
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41a844864f
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2026-06-27 16:08:58 +00:00
1m58s
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feat(batch29): GPU GEMM dispatch, weight serialization, training metrics
Performance Benchmarks #196
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37db99107f
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2026-06-27 16:08:55 +00:00
2m23s
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feat(batch27): JEPA ViT bridge, WebDataset shard reading, training loop
Performance Benchmarks #190
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f487196367
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2026-06-27 15:31:51 +00:00
7m52s
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fix(ci,rtx-tensor): resolve clippy --all-features intel-mkl conflict; gate MKL to x86_64-linux
GPU Tests #189
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2026-06-27 15:13:52 +00:00
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fix(ci,rtx-tensor): resolve clippy --all-features intel-mkl conflict; gate MKL to x86_64-linux
Performance Benchmarks #186
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e8a2036db4
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2026-06-27 15:13:46 +00:00
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docs: update README + CLAUDE.md for Batches 20-26 JEPA platform completion
Performance Benchmarks #183
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4ccf089e82
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2026-06-27 15:13:43 +00:00
2m2s
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feat(batch24-26): JEPA ViT wiring, data pipeline, cluster-scale config
Performance Benchmarks #180
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1c822120d3
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2026-06-27 14:59:55 +00:00
7m59s
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feat(batch20-23): I-JEPA + V-JEPA + Neuro-JEPA self-supervised learning
Performance Benchmarks #177
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25322b019d
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2026-06-27 14:17:28 +00:00
2m12s
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docs: comprehensive README and CLAUDE.md refresh for 113-crate state
Performance Benchmarks #174
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60e6d05e86
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2026-06-27 13:43:11 +00:00
2m18s
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feat(batch19): Medusa heads, TIES+DARE model merging, Mixture of Depths
GPU Tests #173
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bff27c302f
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2026-06-27 15:13:19 +00:00
2h4m22s
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feat(batch19): Medusa heads, TIES+DARE model merging, Mixture of Depths
Performance Benchmarks #170
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bff27c302f
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2026-06-27 13:10:17 +00:00
2m11s
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feat(batch18): contrastive losses, feature distillation, advanced data samplers
Performance Benchmarks #167
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960fd73c82
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2026-06-27 12:43:34 +00:00
7m56s
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feat(batch17): RoPE scaling extensions, DPO loss, label smoothing + focal loss
Performance Benchmarks #164
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bb5f5c519f
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2026-06-27 07:54:07 +00:00
7m52s
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feat(batch16): SOAP optimizer, lookahead decoding, SWA+SWAG
GPU Tests #163
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2026-06-27 15:13:18 +00:00
8h28m34s
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feat(batch16): SOAP optimizer, lookahead decoding, SWA+SWAG
Performance Benchmarks #160
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54a9652041
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2026-06-27 06:45:21 +00:00
1m54s
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fix(rtx-vision-advanced): cap fixed at 1.30.0 for the nightly-2025-10-25 toolchain
Performance Benchmarks #157
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033ca3a48d
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2026-06-27 06:31:25 +00:00
2m22s
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feat(batch15): Shampoo optimizer, beam search decoder, sliding window attention
GPU Tests #156
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6daa20c94b
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2026-06-27 15:13:18 +00:00
8h45m40s
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feat(batch15): Shampoo optimizer, beam search decoder, sliding window attention
Performance Benchmarks #153
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6daa20c94b
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2026-06-27 06:28:33 +00:00
1m55s
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feat(batch14): Muon optimizer, logit processors, per-token activation quantization
GPU Tests #152
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e00a6da018
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2026-06-27 15:13:17 +00:00
9h1m13s
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feat(batch14): Muon optimizer, logit processors, per-token activation quantization
Performance Benchmarks #149
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e00a6da018
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2026-06-27 06:19:26 +00:00
7m59s
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Merge branch 'feat/f64-nn-layers'
Performance Benchmarks #146
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e2d902b34b
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2026-06-27 06:10:02 +00:00
2m0s
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feat(batch13): EMA model weights, cross-layer weight sharing, schedule-free optimizer
Performance Benchmarks #143
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9c3b9f82f0
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2026-06-27 06:07:37 +00:00
1m33s
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feat(batch12): online quant calibration, draft distillation loss, gradient noise scale
Performance Benchmarks #140
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924c237096
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2026-06-27 05:39:34 +00:00
2m10s
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Merge branch 'feat/f64-nn-mlp'
Performance Benchmarks #137
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3ec300af6b
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2026-06-27 05:44:40 +00:00
8m7s
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Merge branch 'feat/f64-rtx-tensor'
GPU Tests #136
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2026-06-27 15:13:17 +00:00
9h43m33s
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feat(batch11): WSD LR scheduler, KV CPU offloading, GQA KV head expansion
GPU Tests #132
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4ae0c34537
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2026-06-27 15:13:17 +00:00
9h44m21s
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feat(batch10): attention sinks (StreamingLLM), chunked prefill, per-layer LR decay
GPU Tests #125
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be3e3965b1
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2026-06-27 15:13:16 +00:00
10h18m15s
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feat(batch10): attention sinks (StreamingLLM), chunked prefill, per-layer LR decay
Performance Benchmarks #122
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be3e3965b1
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2026-06-27 04:55:12 +00:00
1m11s
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