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Coffee Capsules Core Diffusion Sweep

This repository packages the coffee_capsules_category1_core_diffusion sweep from multitask_dit_policy.

What Is Included

This sweep compares DDPM vs DDIM, 20 vs 50 inference steps, and RoPE vs no-RoPE.

For each of the 8 variants, this repo publishes two params-only checkpoints: 40000 and 50000.

Each published checkpoint includes model.safetensors, config.json, and ramen_stats.pt. train_state.pt is intentionally omitted to keep the sweep inference-focused.

Main Takeaway

All 8 sweep variants trained cleanly and outperformed the older coffee_capsules_v1 baseline at step 50000, which ended around train/loss ~= 0.011694.

Within this sweep, the only consistent split was RoPE vs non-RoPE: RoPE variants finished at 0.0029481, while non-RoPE variants finished at 0.0030173. That difference is small enough that downstream evaluation quality should be the real decision-maker.

Variant Summary

variant 50k train loss canonical W&B note
baseline 0.0030173 8xhewlqu Control run.
ddpm_20 0.0030173 leamkw9q No clear gain over baseline.
ddpm_50 0.0030173 ay6nbct0 No clear gain over baseline.
ddpm_rope 0.0029481 3695rhof Best-loss group.
ddim_20 0.0030173 innf1jzx Stable, but tied with baseline group.
ddim_50 0.0030173 266664n6 Stable, but tied with baseline group.
ddim_rope_20 0.0029481 qh7lwdtq Best-loss group.
ddim_rope_50 0.0029481 1j441mvr Best-loss group.

Integrity Verification

All checksums below were generated twice directly from the source checkpoint files on Isambard. The values were only recorded after the two passes matched exactly.

variant step model.safetensors config.json ramen_stats.pt manifest_sha256
baseline 40000 4f45b07f3a8267fdf2b5fcfc02ce94a655ea5cd6d4da285ca69caf75780dc727 1b1c785af55dd820ef70820dbc8955cb3b3425e11173fa936a86571876607ed5 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 50bcb62b44fbc4e92cdce8da40ded1fa1a6d62c074747522dd0a85ac3faa9e40
baseline 50000 1e98c59755b247dd06ef2e56086fc2dd9db531a722b2535a760c2654906c6d38 1b1c785af55dd820ef70820dbc8955cb3b3425e11173fa936a86571876607ed5 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 76c9085eb40d95d6fc181d90c81102efaa6743232165ec90c3ba522f805af1a8
ddpm_20 40000 4f45b07f3a8267fdf2b5fcfc02ce94a655ea5cd6d4da285ca69caf75780dc727 014b2785ed96ec14b923f4b708df15370aef4907af184fa22c69b921d41c3a20 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 6c4210b1af83233b5c433db8314f52604ba8cb10aed282ed590704012d7312af
ddpm_20 50000 1e98c59755b247dd06ef2e56086fc2dd9db531a722b2535a760c2654906c6d38 014b2785ed96ec14b923f4b708df15370aef4907af184fa22c69b921d41c3a20 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 7443ab993ed13d417f9ed3a4a5f7bd45c3cc0b3f037be9bdebccd175bc86c656
ddpm_50 40000 4f45b07f3a8267fdf2b5fcfc02ce94a655ea5cd6d4da285ca69caf75780dc727 0d963b408b541f9b75b79fbe10a1462c9ecca034125c8b2c178d79b637cf975d 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 5bf388a6e688096253b3c7eabe8679a9551f52bd5bc170bdf4c97521b251dbd1
ddpm_50 50000 1e98c59755b247dd06ef2e56086fc2dd9db531a722b2535a760c2654906c6d38 0d963b408b541f9b75b79fbe10a1462c9ecca034125c8b2c178d79b637cf975d 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 734b59f0f69627f3810ddc075af93a3b067059f099fc1674a2d47bdeebfcb6a0
ddpm_rope 40000 d4c50efe6711cf387e3801623bee554366908613346f6c49c978e99a65a9f1aa baf684d0e35f60057cff63db8e91382522af67174985c427d31960f6d7bf2ced 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 9a3f247af34d20ca8a9d832a07fc485f65039ae0ec659614bec819a898c5a45f
ddpm_rope 50000 fcb539e3a2c269cd964756b820697be031ca689fbee8d30906fdffba6ab78dd1 baf684d0e35f60057cff63db8e91382522af67174985c427d31960f6d7bf2ced 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 ccd70303182bde4dd2a88dea78f6bea5bcbb166bd73e51ab25fbb381b9d215b8
ddim_20 40000 4f45b07f3a8267fdf2b5fcfc02ce94a655ea5cd6d4da285ca69caf75780dc727 8787253b3315853707692775014b8d7f7dbb1298eed132fcd521b176cbbbc9fe 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 cabd203413965463d18b69509f1bdcdfad46f719ec5c790b00c8a5c680824a01
ddim_20 50000 1e98c59755b247dd06ef2e56086fc2dd9db531a722b2535a760c2654906c6d38 8787253b3315853707692775014b8d7f7dbb1298eed132fcd521b176cbbbc9fe 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 954cf50d6314f8dcc3fa257eb9ba87013ed2c6e36619115f5d5e5026411331e6
ddim_50 40000 4f45b07f3a8267fdf2b5fcfc02ce94a655ea5cd6d4da285ca69caf75780dc727 3d3ebbcac48342d1504f8518e5392862d6fbffbdb9e3d84bcba53dfc7609f94a 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 e144c267dd8b40d0d89667aedd4f76dc296b60c4a6dbcede6fb835b473d63af9
ddim_50 50000 1e98c59755b247dd06ef2e56086fc2dd9db531a722b2535a760c2654906c6d38 3d3ebbcac48342d1504f8518e5392862d6fbffbdb9e3d84bcba53dfc7609f94a 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 e667dcd98ee1bc495b454d45df62efee674c2bfd29ec89d02fad711c91db446c
ddim_rope_20 40000 d4c50efe6711cf387e3801623bee554366908613346f6c49c978e99a65a9f1aa ebde4b7349317b24185799fa0ac9b5110a42b2f8ba46de970f352acb8291e095 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 5e7a66d0e3c05e357c675bed4d6a112e31748e3843d047befed22c4a7c220eaf
ddim_rope_20 50000 fcb539e3a2c269cd964756b820697be031ca689fbee8d30906fdffba6ab78dd1 ebde4b7349317b24185799fa0ac9b5110a42b2f8ba46de970f352acb8291e095 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 262b9839cb1124e9d39e07cf5762a0d673dc48f33ef4963c87a72b33f1c8d0fa
ddim_rope_50 40000 d4c50efe6711cf387e3801623bee554366908613346f6c49c978e99a65a9f1aa 4793c5d2f784560eeba511b534466aacd5d91738b3d1ae3403e7597dc3dd7933 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 043fd62974cb6cd1de5f3d3671d831122922b62cfa6523166bdf2eefb1112b41
ddim_rope_50 50000 fcb539e3a2c269cd964756b820697be031ca689fbee8d30906fdffba6ab78dd1 4793c5d2f784560eeba511b534466aacd5d91738b3d1ae3403e7597dc3dd7933 97c1bcc16b591683c3a554b8add91778631ad78918ccea25fa73c8615e2f1b18 326cd883f40e5e27d0c9dcc1fbdcfb5cdf4b9a3ed14732dfedc043ae725483b6

Published Layout

baseline/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddpm_20/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddpm_50/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddpm_rope/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddim_20/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddim_50/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddim_rope_20/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt
ddim_rope_50/
  checkpoints/
    40000/
      model.safetensors
      config.json
      ramen_stats.pt
    50000/
      model.safetensors
      config.json
      ramen_stats.pt

Provenance

The source experiment metadata lives in run_logs/coffee_capsules_category1_core_diffusion/index.md and the per-variant logs in run_logs/coffee_capsules_category1_core_diffusion/.

These published artifacts were uploaded directly from the original training outputs on scratch rather than from a separate local staging copy.

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