UniMaia: Steering Chess Policies with Language for Human-like Play
Paper • 2605.27767 • Published
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controlnet_onephase_full_training_run_v1/2021-02/model.safetensors | 84ac4f68e33bb66d1c33966ddd8272c73ce5adea8af4bef41764f8780ed8b7f7 | controlnet_onephase_full_training_run_v1 | 2 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_02.pt | f10be3adac40a0d31c4096deaea8787479d44e5c11a4b2491fc5c9d09d2159a5 | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-03/model.safetensors | f97221a0877778e4799d307e0e24d4b64cf8da9a4c14bc67955148334e5c6b2c | controlnet_onephase_full_training_run_v1 | 3 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_03.pt | e9c9f4f23baf5e360e62874c10140a7a0585d2d47c4dccd30c8f70a3082233f7 | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-04/model.safetensors | 61c4566ecc69190d3d4061ce1ff08c9358b578502447c46978349869aa4d2c6c | controlnet_onephase_full_training_run_v1 | 4 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_04.pt | 8aa6b8c63db2cc33f09ca92d8328022c585f33231b47e9bfe05b851880705666 | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-05/model.safetensors | 1f9ba1f3408d7c10fa6287c7f6ba9a220264d09ab66df3c83d1e180f9cbd5297 | controlnet_onephase_full_training_run_v1 | 5 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_05.pt | 1fee0d28b0c9b2a798f53d71b050600f86e6ad2d9bde0ce5d1b1481e7ca395df | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-06/model.safetensors | b2da714c17efbd1d6d1cf752c52bc8cd97c4b95301a2dc43e2eb891e12aabd60 | controlnet_onephase_full_training_run_v1 | 6 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_06.pt | b81edb9ffa750455be8f344afdd8449dea892d54d9dbae29dad4b5567898f81c | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-07/model.safetensors | 66f9d1a8f6972f88101404fa012ca977e2ae190e6c40ffc114a8d91c6454e847 | controlnet_onephase_full_training_run_v1 | 7 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_07.pt | 319a5eeab82f9051eaa6ae30ef577cd8e7532e2066945e795cd985c41572e4a1 | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-08/model.safetensors | 4f14ccf56a874eb4280e5c478d5164b73f73c15efda3aa105baf1aef48002078 | controlnet_onephase_full_training_run_v1 | 8 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_08.pt | a0e4d09689aedd3c7c6abc11788665267764625fa440758a5d5371869ba29d72 | 1,541 | null | 2,021 |
controlnet_onephase_full_training_run_v1/2021-09/model.safetensors | 6a67aceafc2888e5739902994d2a93cc587b2d5d295a5e20febb2b3cb1b49af9 | controlnet_onephase_full_training_run_v1 | 9 | 3,166,691,262 | 1 | controlnet_onephase_full_training_run_v1/model_checkpoint_2021_09.pt | 5368c8b377745f5aa8519cbdf9e35c1aa3d671e75f6ae09fae4e5dcdb8dcd707 | 1,541 | null | 2,021 |
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UniMaia historical checkpoint archive
This dataset repository is the reproducibility archive for intermediate UniMaia experiment checkpoints. Model artifacts are released under GPL-3.0; this does not relicense the separately distributed UniMaia, chessnets, or chesseval software.
checkpoint_catalog.json inventories the retained 198 checkpoints from 67
experiment directories, totaling approximately 1.21 TiB before conversion.
The reproducibility retention policy keeps every monthly snapshot for the two
final UniMaia/UniMaia-Aux runs and December snapshots for experimental runs.
The full local source inventory contains 590 wrappers (approximately 3.28 TiB),
of which 392 are intentionally excluded. The archive is being populated
incrementally as model-only safetensors:
<experiment>/<year>-<month>/model.safetensors
<experiment>/<year>-<month>/conversion.json
Each conversion record includes source and destination SHA-256 hashes. Full optimizer/RNG wrappers remain in the original research storage and are not uploaded because they use pickle serialization and are substantially larger. The conversion process uses PyTorch's restricted weights loader and refuses unexpected pickle globals.
Final supported releases are published separately:
For architecture, training, and evaluation details, see UniMaia: Steering Chess Policies with Language for Human-like Play.
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