Add results/ (test outputs, logs) and Polish-target Uni-Sign checkpoints

checkpoints/unisign/pjm_pl_ms_how2sign/best_checkpoint.pth

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+size 1175634096

checkpoints/unisign/pjm_pl_si_how2sign/best_checkpoint.pth

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+version https://git-lfs.github.com/spec/v1
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results/README.md

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+# Test outputs and logs
+
+Model outputs, references, metric logs and training logs behind the CrocoSign paper. No weights; those are in
+`checkpoints/` and as ONNX in `unisign/` and `spamo/`.
+
+## Uni-Sign (`unisign/eval_<run>/`)
+
+`test_tmp_pres.txt` and `test_tmp_refs.txt` hold one line per test clip (`sample: <key>, prediction: ...` and
+`sample: <key>, ground-truth: ...`), `log.txt` the evaluation log with BLEU, ROUGE-L and chrF, `bleurt_BLEURT-20.json`
+the per-clip BLEURT-20 scores, `train.log.gz` the training log. Table 3 rows:
+
+| Row | Directory |
+|---|---|
+| CSL-News MS / SI | `eval_pjm_phase3_full_ms`, `eval_pjm_phase3_full_si` |
+| How2Sign MS / SI | `eval_pjm_phase3_full_ms_how2sign`, `eval_pjm_phase3_full_si_how2sign` |
+| OpenASL MS / SI | `eval_pjm_phase3_full_ms_openasl`, `eval_pjm_phase3_full_si_openasl` |
+| scratch MS / SI | `eval_pjm_phase3_full_ms_scratch`, `eval_pjm_phase3_full_si_scratch` |
+
+The other directories are the second seeds (`_s43`), the chained and distilled experiments, and the SI
+regularisation experiments (`_aug`, `_drop03*`). `greedy_ens.log` is the greedy ensemble selection.
+
+## SpaMo and Mod-SpaMo (`spamo/<run>/`)
+
+`test_outputs.txt` holds reference and generated text per clip (lowercased, in test-set order without keys), plus
+the wandb summary, config and console log of the test run where they exist. Table 3 rows:
+
+| Row | Directory |
+|---|---|
+| SpaMo PHOENIX-2014T MS | `2026-05-05T23-00-55_test_upstream_pjm_ms_dual` |
+| SpaMo PHOENIX-2014T SI | `2026-10-31T07-09-05_test_upstream_pjm_si_dual` |
+| SpaMo scratch MS / SI | `2026-05-05T21-58-47_test_v30_dual_ms_scratch`, `2026-05-06T15-27-35_test_v32_dual_si_scratch` |
+| Mod-SpaMo PHOENIX-2014T MS | `2026-05-04T09-09-26_test_v27_quad_xattn` |
+| Mod-SpaMo PHOENIX-2014T SI | `2026-10-31T15-20-57_test_v27_quad_si_filtered` |
+| Mod-SpaMo scratch MS / SI | `2026-05-05T22-10-23_test_v31_quad_ms_scratch`, `2026-05-07T04-18-55_test_v33_quad_si_scratch` |
+
+`run_phoenix_si.log.gz` is the log of the SpaMo and Mod-SpaMo PHOENIX-2014T SI runs, `finetune_pjm_ms.log.gz`
+the training log of the SpaMo PHOENIX-2014T MS run. BLEURT for the older SpaMo rows was computed separately and
+is not stored here.
+
+## Polish targets (`polish/`)
+
+`pjm_pl_{ms,si}_how2sign`: Uni-Sign How2Sign trained on Polish targets (`pl_fixed` of
+`CrocoSign/pjm-data/texts/texts_fixed.csv`), weights in `checkpoints/unisign/pjm_pl_{ms,si}_how2sign/`.
+`en2pl_{ms,si}_how2sign`: the English How2Sign outputs translated to Polish by a language model (Claude Sonnet).
+Both are scored against `pl_fixed`; see the "Polish targets" paragraph of the paper.

results/polish/en2pl_ms_how2sign/bleurt_BLEURT-20.json

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results/polish/pjm_pl_ms_how2sign/log.txt

@@ -0,0 +1,331 @@
+[2026-10-31 22:29:18,766] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+| distributed init (rank 0): env://
+[2026-10-31 22:29:19,361] [INFO] [comm.py:652:init_distributed] cdb=None
+[2026-10-31 22:29:19,361] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
+Namespace(batch_size=8, gradient_accumulation_steps=8, gradient_clipping=1.0, epochs=20, world_size=1, dist_url='env://', local_rank=0, hidden_dim=256, finetune='out/polish/pjm_pl_ms_how2sign/best_checkpoint.pth', opt='adamw', opt_eps=1e-09, opt_betas=None, clip_grad=None, momentum=0.9, weight_decay=0.0001, sched='cosine', lr=0.001, min_lr=1e-08, warmup_epochs=0, output_dir='out/polish/eval_pjm_pl_ms_how2sign', seed=42, eval=True, num_workers=4, pin_mem=True, offload=False, dtype='bf16', zero_stage=2, compute_fp32_loss=False, quick_break=0, rgb_support=False, max_length=256, dataset='PJM', wandb=False, wandb_project='uni-sign', wandb_dir='./wandb_logs', bertscore=False, num_examples=5, patience=0, pjm_train_texts='', pjm_train_texts_p=1.0, pjm_train_csv='', pjm_texts='../CrocoSign/data/texts_pl_fixed.h5', pjm_lang='Polish', pjm_split='ms', lora=False, lora_rank=16, lora_alpha=32, lora_dropout=0.05, lora_target='q,v', lora_ckpt='', freeze_visual=False, task='SLT', label_smoothing=0.2, online_video='', rank=0, gpu=0, distributed=True, dist_backend='nccl')
+Creating dataset:
+  PJM split family: ms (suffix='_ms')
+<datasets.S2T_Dataset_PJM object at 0x732d41d86650>
+<datasets.S2T_Dataset_PJM object at 0x732c78d0f520>
+<datasets.S2T_Dataset_PJM object at 0x732c78d0f850>
+Creating model:
+/home/croco/Uni-Sign/.venv/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+***********************************
+Load Checkpoint...
+***********************************
+Missing keys: 
+ 
+Unexpected keys: 
+ 
+number of params: 587.747368M
+Using deepspeed to train...
+Initializing deepspeed...
+[2026-10-31 22:29:22,436] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed info: version=0.16.3, git-hash=unknown, git-branch=unknown
+[2026-10-31 22:29:22,436] [INFO] [comm.py:677:init_distributed] Distributed backend already initialized
+[2026-10-31 22:29:22,436] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 1
+[2026-10-31 22:29:22,468] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
+[2026-10-31 22:29:22,468] [INFO] [logging.py:128:log_dist] [Rank 0] Using client Optimizer as basic optimizer
+[2026-10-31 22:29:22,468] [INFO] [logging.py:128:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
+[2026-10-31 22:29:22,476] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Basic Optimizer = AdamW
+[2026-10-31 22:29:22,477] [INFO] [utils.py:59:is_zero_supported_optimizer] Checking ZeRO support for optimizer=AdamW type=<class 'torch.optim.adamw.AdamW'>
+[2026-10-31 22:29:22,477] [INFO] [logging.py:128:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
+[2026-10-31 22:29:22,477] [INFO] [stage_1_and_2.py:149:__init__] Reduce bucket size 500000000
+[2026-10-31 22:29:22,477] [INFO] [stage_1_and_2.py:150:__init__] Allgather bucket size 500000000
+[2026-10-31 22:29:22,477] [INFO] [stage_1_and_2.py:151:__init__] CPU Offload: False
+[2026-10-31 22:29:22,477] [INFO] [stage_1_and_2.py:152:__init__] Round robin gradient partitioning: False
+[2026-10-31 22:29:22,912] [INFO] [utils.py:781:see_memory_usage] Before initializing optimizer states
+[2026-10-31 22:29:22,912] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 6.57 GB         CA 7.31 GB         Max_CA 7 GB 
+[2026-10-31 22:29:22,913] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 6.57 GB, percent = 10.9%
+[2026-10-31 22:29:22,975] [INFO] [utils.py:781:see_memory_usage] After initializing optimizer states
+[2026-10-31 22:29:22,975] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 7.66 GB         CA 9.5 GB         Max_CA 10 GB 
+[2026-10-31 22:29:22,975] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 6.57 GB, percent = 10.9%
+[2026-10-31 22:29:22,975] [INFO] [stage_1_and_2.py:545:__init__] optimizer state initialized
+[2026-10-31 22:29:23,036] [INFO] [utils.py:781:see_memory_usage] After initializing ZeRO optimizer
+[2026-10-31 22:29:23,036] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 5.48 GB         CA 9.5 GB         Max_CA 10 GB 
+[2026-10-31 22:29:23,036] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 6.57 GB, percent = 10.9%
+[2026-10-31 22:29:23,038] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedZeroOptimizer
+[2026-10-31 22:29:23,038] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed using client LR scheduler
+[2026-10-31 22:29:23,039] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed LR Scheduler = <torch.optim.lr_scheduler.LambdaLR object at 0x732c5592f8b0>
+[2026-10-31 22:29:23,039] [INFO] [logging.py:128:log_dist] [Rank 0] step=0, skipped=0, lr=[0.001, 0.001], mom=[(0.9, 0.999), (0.9, 0.999)]
+[2026-10-31 22:29:23,039] [INFO] [config.py:999:print] DeepSpeedEngine configuration:
+[2026-10-31 22:29:23,039] [INFO] [config.py:1003:print]   activation_checkpointing_config  {
+    "partition_activations": false, 
+    "contiguous_memory_optimization": false, 
+    "cpu_checkpointing": false, 
+    "number_checkpoints": null, 
+    "synchronize_checkpoint_boundary": false, 
+    "profile": false
+}
+[2026-10-31 22:29:23,039] [INFO] [config.py:1003:print]   aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True, 'use_gds': False}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   amp_enabled .................. False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   amp_params ................... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   autotuning_config ............ {
+    "enabled": false, 
+    "start_step": null, 
+    "end_step": null, 
+    "metric_path": null, 
+    "arg_mappings": null, 
+    "metric": "throughput", 
+    "model_info": null, 
+    "results_dir": "autotuning_results", 
+    "exps_dir": "autotuning_exps", 
+    "overwrite": true, 
+    "fast": true, 
+    "start_profile_step": 3, 
+    "end_profile_step": 5, 
+    "tuner_type": "gridsearch", 
+    "tuner_early_stopping": 5, 
+    "tuner_num_trials": 50, 
+    "model_info_path": null, 
+    "mp_size": 1, 
+    "max_train_batch_size": null, 
+    "min_train_batch_size": 1, 
+    "max_train_micro_batch_size_per_gpu": 1.024000e+03, 
+    "min_train_micro_batch_size_per_gpu": 1, 
+    "num_tuning_micro_batch_sizes": 3
+}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   bfloat16_enabled ............. True
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   bfloat16_immediate_grad_update  False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   checkpoint_parallel_write_pipeline  False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   checkpoint_tag_validation_enabled  True
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   checkpoint_tag_validation_fail  False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x732c545d0f40>
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   communication_data_type ...... None
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   curriculum_enabled_legacy .... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   curriculum_params_legacy ..... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   data_efficiency_enabled ...... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   dataloader_drop_last ......... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   disable_allgather ............ False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   dump_state ................... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   dynamic_loss_scale_args ...... None
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_enabled ........... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_gas_boundary_resolution  1
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_layer_name ........ bert.encoder.layer
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_layer_num ......... 0
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_max_iter .......... 100
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_stability ......... 1e-06
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_tol ............... 0.01
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   eigenvalue_verbose ........... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   elasticity_enabled ........... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   flops_profiler_config ........ {
+    "enabled": false, 
+    "recompute_fwd_factor": 0.0, 
+    "profile_step": 1, 
+    "module_depth": -1, 
+    "top_modules": 1, 
+    "detailed": true, 
+    "output_file": null
+}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   fp16_auto_cast ............... None
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   fp16_enabled ................. False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   fp16_master_weights_and_gradients  False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   global_rank .................. 0
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   grad_accum_dtype ............. None
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   gradient_accumulation_steps .. 8
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   gradient_clipping ............ 1.0
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   gradient_predivide_factor .... 1.0
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   graph_harvesting ............. False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   initial_dynamic_scale ........ 1
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   load_universal_checkpoint .... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   loss_scale ................... 1.0
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   memory_breakdown ............. False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   mics_hierarchial_params_gather  False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   mics_shard_size .............. -1
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='/ds_tensorboard_logs/', job_name='_tensorboard') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName')
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   nebula_config ................ {
+    "enabled": false, 
+    "persistent_storage_path": null, 
+    "persistent_time_interval": 100, 
+    "num_of_version_in_retention": 2, 
+    "enable_nebula_load": true, 
+    "load_path": null
+}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   optimizer_legacy_fusion ...... False
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   optimizer_name ............... None
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   optimizer_params ............. None
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
+[2026-10-31 22:29:23,040] [INFO] [config.py:1003:print]   pld_enabled .................. False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   pld_params ................... False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   prescale_gradients ........... False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   scheduler_name ............... None
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   scheduler_params ............. None
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   seq_parallel_communication_data_type  torch.float32
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   sparse_attention ............. None
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   sparse_gradients_enabled ..... False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   steps_per_print .............. 10
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   timers_config ................ enabled=True synchronized=True
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   train_batch_size ............. 64
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   train_micro_batch_size_per_gpu  8
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   use_data_before_expert_parallel_  False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   use_node_local_storage ....... False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   wall_clock_breakdown ......... False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   weight_quantization_config ... None
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   world_size ................... 1
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   zero_allow_untested_optimizer  False
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', nvme_path=None, buffer_count=5, buffer_size=100000000, max_in_cpu=1000000000, pin_memory=False) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='none', nvme_path=None, buffer_count=4, pin_memory=False, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=30000000 param_persistence_threshold=10000 model_persistence_threshold=9223372036854775807 max_live_parameters=30000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=False module_granularity_threshold=0 use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False zeropp_loco_param=None mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=False pipeline_loading_checkpoint=False override_module_apply=True
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   zero_enabled ................. True
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   zero_force_ds_cpu_optimizer .. True
+[2026-10-31 22:29:23,041] [INFO] [config.py:1003:print]   zero_optimization_stage ...... 2
+[2026-10-31 22:29:23,041] [INFO] [config.py:989:print_user_config]   json = {
+    "steps_per_print": 10, 
+    "zero_optimization": {
+        "stage": 2, 
+        "offload_param": {
+            "device": "none"
+        }, 
+        "offload_optimizer": {
+            "device": "none"
+        }, 
+        "stage3_param_persistence_threshold": 1.000000e+04, 
+        "stage3_max_live_parameters": 3.000000e+07, 
+        "stage3_prefetch_bucket_size": 3.000000e+07, 
+        "memory_efficient_linear": false
+    }, 
+    "bfloat16": {
+        "enabled": true
+    }, 
+    "gradient_clipping": 1.0, 
+    "prescale_gradients": false, 
+    "wall_clock_breakdown": false, 
+    "hybrid_engine": {
+        "enabled": false, 
+        "max_out_tokens": 512, 
+        "inference_tp_size": 1, 
+        "release_inference_cache": false, 
+        "pin_parameters": true, 
+        "tp_gather_partition_size": 8
+    }, 
+    "tensorboard": {
+        "enabled": false, 
+        "output_path": "/ds_tensorboard_logs/", 
+        "job_name": "_tensorboard"
+    }, 
+    "train_micro_batch_size_per_gpu": 8, 
+    "gradient_accumulation_steps": 8
+}
+<deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer object at 0x732c545d33a0>
+📄 dev result
+Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
+Test:  [  0/375]  eta: 0:07:14  loss: 5.4062 (5.4062)  time: 1.1579  data: 0.1248  max mem: 5848
+Test:  [ 10/375]  eta: 0:04:50  loss: 5.2500 (5.0426)  time: 0.7952  data: 0.0114  max mem: 6164
+Test:  [ 20/375]  eta: 0:04:23  loss: 4.8750 (4.8929)  time: 0.7229  data: 0.0001  max mem: 6164
+Test:  [ 30/375]  eta: 0:04:07  loss: 5.0000 (5.0696)  time: 0.6753  data: 0.0001  max mem: 6469
+Test:  [ 40/375]  eta: 0:04:02  loss: 5.3125 (5.0777)  time: 0.7023  data: 0.0001  max mem: 6469
+Test:  [ 50/375]  eta: 0:03:49  loss: 5.1250 (5.0741)  time: 0.6854  data: 0.0001  max mem: 6469
+Test:  [ 60/375]  eta: 0:03:36  loss: 5.0000 (5.0845)  time: 0.6173  data: 0.0001  max mem: 6469
+Test:  [ 70/375]  eta: 0:03:32  loss: 4.9688 (5.0678)  time: 0.6730  data: 0.0001  max mem: 6469
+Test:  [ 80/375]  eta: 0:03:22  loss: 4.8750 (5.0440)  time: 0.6749  data: 0.0001  max mem: 6469
+Test:  [ 90/375]  eta: 0:03:11  loss: 4.8750 (5.0268)  time: 0.5926  data: 0.0001  max mem: 6469
+Test:  [100/375]  eta: 0:03:04  loss: 4.9062 (5.0121)  time: 0.6085  data: 0.0001  max mem: 6472
+Test:  [110/375]  eta: 0:02:56  loss: 4.7500 (4.9885)  time: 0.6231  data: 0.0001  max mem: 6472
+Test:  [120/375]  eta: 0:02:48  loss: 4.9375 (5.0142)  time: 0.6165  data: 0.0001  max mem: 6472
+Test:  [130/375]  eta: 0:02:42  loss: 5.1562 (5.0095)  time: 0.6590  data: 0.0001  max mem: 6472
+Test:  [140/375]  eta: 0:02:34  loss: 5.0000 (5.0098)  time: 0.6441  data: 0.0001  max mem: 6472
+Test:  [150/375]  eta: 0:02:25  loss: 5.0625 (5.0097)  time: 0.5494  data: 0.0001  max mem: 6472
+Test:  [160/375]  eta: 0:02:17  loss: 5.0625 (5.0056)  time: 0.5174  data: 0.0001  max mem: 6472
+Test:  [170/375]  eta: 0:02:08  loss: 5.1250 (5.0207)  time: 0.4627  data: 0.0001  max mem: 6472
+Test:  [180/375]  eta: 0:02:01  loss: 5.1875 (5.0271)  time: 0.4836  data: 0.0001  max mem: 6472
+Test:  [190/375]  eta: 0:01:55  loss: 5.1250 (5.0301)  time: 0.5908  data: 0.0001  max mem: 6505
+Test:  [200/375]  eta: 0:01:48  loss: 5.1250 (5.0337)  time: 0.5576  data: 0.0001  max mem: 6505
+Test:  [210/375]  eta: 0:01:40  loss: 5.0000 (5.0317)  time: 0.4505  data: 0.0001  max mem: 6505
+Test:  [220/375]  eta: 0:01:33  loss: 4.9688 (5.0294)  time: 0.4882  data: 0.0001  max mem: 6624
+Test:  [230/375]  eta: 0:01:27  loss: 4.8125 (5.0225)  time: 0.5747  data: 0.0001  max mem: 6624
+Test:  [240/375]  eta: 0:01:20  loss: 4.8125 (5.0179)  time: 0.4957  data: 0.0001  max mem: 6624
+Test:  [250/375]  eta: 0:01:14  loss: 4.9375 (5.0209)  time: 0.4796  data: 0.0001  max mem: 6624
+Test:  [260/375]  eta: 0:01:07  loss: 5.0625 (5.0250)  time: 0.4752  data: 0.0001  max mem: 6624
+Test:  [270/375]  eta: 0:01:00  loss: 5.0312 (5.0269)  time: 0.3971  data: 0.0001  max mem: 6624
+Test:  [280/375]  eta: 0:00:54  loss: 5.0625 (5.0289)  time: 0.4485  data: 0.0001  max mem: 6624
+Test:  [290/375]  eta: 0:00:48  loss: 5.0625 (5.0319)  time: 0.4807  data: 0.0001  max mem: 6624
+Test:  [300/375]  eta: 0:00:42  loss: 5.0625 (5.0298)  time: 0.4347  data: 0.0000  max mem: 6624
+Test:  [310/375]  eta: 0:00:36  loss: 5.2812 (5.0434)  time: 0.4209  data: 0.0001  max mem: 6624
+Test:  [320/375]  eta: 0:00:30  loss: 5.3125 (5.0468)  time: 0.3519  data: 0.0001  max mem: 6624
+Test:  [330/375]  eta: 0:00:24  loss: 5.1250 (5.0457)  time: 0.3905  data: 0.0001  max mem: 6624
+Test:  [340/375]  eta: 0:00:19  loss: 4.9375 (5.0390)  time: 0.3773  data: 0.0000  max mem: 6624
+Test:  [350/375]  eta: 0:00:13  loss: 4.9062 (5.0438)  time: 0.2881  data: 0.0000  max mem: 6624
+Test:  [360/375]  eta: 0:00:08  loss: 5.1250 (5.0435)  time: 0.3782  data: 0.0000  max mem: 6624
+Test:  [370/375]  eta: 0:00:02  loss: 5.0000 (5.0435)  time: 0.3957  data: 0.0000  max mem: 6624
+Test:  [374/375]  eta: 0:00:00  loss: 4.9375 (5.0412)  time: 0.4326  data: 0.0000  max mem: 6624
+Test: Total time: 0:03:18 (0.5297 s / it)
+{'bleu1': 29.90381993071833, 'bleu2': 15.852641429872563, 'bleu3': 10.06348870225436, 'bleu4': 6.752617336925911, 'chrf': 24.1800995739229, 'avg_ref_len': 6.857666666666667, 'avg_hyp_len': 6.467333333333333, 'length_ratio': 0.9430807368881544, 'pct_empty': 0.0, 'pct_very_short': 8.666666666666666, 'pct_repetitive': 0.13333333333333333}
+Rouge: 17.18
+--- 5 example translations (dev) ---
+[183_033_R]
+  ref: Bo nie zdążył.
+  hyp: I wtedy się uda.
+[183_126_R]
+  ref: Jak migasz "ławka"?
+  hyp: W ogóle nie mam ławki.
+[183_137_R]
+  ref: Ja migam dwa razy.
+  hyp: Ja jestem wolny.
+[183_006_R]
+  ref: Ostrożnie, bo jest ślisko.
+  hyp: W jakim miejscu, gdzie?
+[183_134_R]
+  ref: Pytam - ja głucha.
+  hyp: Myszka Miki nie wie.
+--- end examples ---
+📄 test result
+Test:  [  0/374]  eta: 0:02:06  loss: 5.5625 (5.5625)  time: 0.3382  data: 0.1282  max mem: 6624
+Test:  [ 10/374]  eta: 0:02:05  loss: 5.0938 (5.1534)  time: 0.3438  data: 0.0117  max mem: 6624
+Test:  [ 20/374]  eta: 0:01:50  loss: 5.0000 (5.0908)  time: 0.3100  data: 0.0000  max mem: 6624
+Test:  [ 30/374]  eta: 0:01:53  loss: 5.0000 (5.1270)  time: 0.3208  data: 0.0000  max mem: 6624
+Test:  [ 40/374]  eta: 0:01:48  loss: 5.1250 (5.1181)  time: 0.3377  data: 0.0000  max mem: 6624
+Test:  [ 50/374]  eta: 0:01:47  loss: 5.0938 (5.1091)  time: 0.3393  data: 0.0000  max mem: 6624
+Test:  [ 60/374]  eta: 0:01:39  loss: 5.0938 (5.1199)  time: 0.2971  data: 0.0000  max mem: 6624
+Test:  [ 70/374]  eta: 0:01:41  loss: 5.1250 (5.1232)  time: 0.3323  data: 0.0000  max mem: 6624
+Test:  [ 80/374]  eta: 0:01:37  loss: 5.1250 (5.1100)  time: 0.3809  data: 0.0000  max mem: 6624
+Test:  [ 90/374]  eta: 0:01:33  loss: 5.0000 (5.0913)  time: 0.3189  data: 0.0000  max mem: 6624
+Test:  [100/374]  eta: 0:01:29  loss: 5.0000 (5.0919)  time: 0.3096  data: 0.0000  max mem: 6624
+Test:  [110/374]  eta: 0:01:27  loss: 5.0938 (5.0977)  time: 0.3351  data: 0.0000  max mem: 6624
+Test:  [120/374]  eta: 0:01:23  loss: 5.2188 (5.1338)  time: 0.3345  data: 0.0000  max mem: 6624
+Test:  [130/374]  eta: 0:01:19  loss: 5.1875 (5.1322)  time: 0.2882  data: 0.0000  max mem: 6624
+Test:  [140/374]  eta: 0:01:14  loss: 4.9688 (5.1206)  time: 0.2659  data: 0.0000  max mem: 6624
+Test:  [150/374]  eta: 0:01:11  loss: 4.7812 (5.1002)  time: 0.2754  data: 0.0000  max mem: 6624
+Test:  [160/374]  eta: 0:01:07  loss: 4.9375 (5.1017)  time: 0.2741  data: 0.0000  max mem: 6624
+Test:  [170/374]  eta: 0:01:03  loss: 5.1875 (5.1210)  time: 0.2431  data: 0.0000  max mem: 6624
+Test:  [180/374]  eta: 0:01:01  loss: 4.9375 (5.1172)  time: 0.3244  data: 0.0000  max mem: 6624
+Test:  [190/374]  eta: 0:00:57  loss: 4.8750 (5.1083)  time: 0.3473  data: 0.0000  max mem: 6624
+Test:  [200/374]  eta: 0:00:54  loss: 4.9375 (5.1161)  time: 0.3230  data: 0.0001  max mem: 6624
+Test:  [210/374]  eta: 0:00:52  loss: 5.2500 (5.1294)  time: 0.3584  data: 0.0000  max mem: 6624
+Test:  [220/374]  eta: 0:00:48  loss: 5.1562 (5.1322)  time: 0.3169  data: 0.0000  max mem: 6624
+Test:  [230/374]  eta: 0:00:45  loss: 5.1250 (5.1353)  time: 0.2744  data: 0.0000  max mem: 6624
+Test:  [240/374]  eta: 0:00:42  loss: 5.1875 (5.1338)  time: 0.3059  data: 0.0000  max mem: 6624
+Test:  [250/374]  eta: 0:00:38  loss: 5.0000 (5.1271)  time: 0.3086  data: 0.0000  max mem: 6624
+Test:  [260/374]  eta: 0:00:35  loss: 4.8750 (5.1189)  time: 0.2682  data: 0.0000  max mem: 6624
+Test:  [270/374]  eta: 0:00:32  loss: 5.0625 (5.1191)  time: 0.2557  data: 0.0000  max mem: 6624
+Test:  [280/374]  eta: 0:00:28  loss: 5.0625 (5.1151)  time: 0.2586  data: 0.0000  max mem: 6624
+Test:  [290/374]  eta: 0:00:25  loss: 4.8438 (5.1040)  time: 0.2907  data: 0.0000  max mem: 6624
+Test:  [300/374]  eta: 0:00:22  loss: 4.8750 (5.1049)  time: 0.2692  data: 0.0000  max mem: 6624
+Test:  [310/374]  eta: 0:00:19  loss: 5.0625 (5.1068)  time: 0.3305  data: 0.0001  max mem: 6624
+Test:  [320/374]  eta: 0:00:16  loss: 5.0625 (5.1077)  time: 0.3477  data: 0.0001  max mem: 6624
+Test:  [330/374]  eta: 0:00:13  loss: 5.0000 (5.1034)  time: 0.2828  data: 0.0000  max mem: 6624
+Test:  [340/374]  eta: 0:00:10  loss: 4.9688 (5.1037)  time: 0.2605  data: 0.0000  max mem: 6624
+Test:  [350/374]  eta: 0:00:07  loss: 5.0625 (5.1039)  time: 0.2508  data: 0.0000  max mem: 6624
+Test:  [360/374]  eta: 0:00:04  loss: 5.0000 (5.0999)  time: 0.3307  data: 0.0001  max mem: 6624
+Test:  [370/374]  eta: 0:00:01  loss: 5.0000 (5.0997)  time: 0.3533  data: 0.0001  max mem: 6624
+Test:  [373/374]  eta: 0:00:00  loss: 5.0312 (5.0981)  time: 0.3636  data: 0.0000  max mem: 6624
+Test: Total time: 0:01:55 (0.3080 s / it)
+{'bleu1': 28.91094895192965, 'bleu2': 14.841459509327095, 'bleu3': 9.212056123525025, 'bleu4': 5.989543995054258, 'chrf': 23.211310693350974, 'avg_ref_len': 6.882490793438232, 'avg_hyp_len': 6.443588885169066, 'length_ratio': 0.9362292051756008, 'pct_empty': 0.0, 'pct_very_short': 9.13960495480415, 'pct_repetitive': 0.1004352192835621}
+Rouge: 15.79
+--- 5 example translations (test) ---
+[183_116_R]
+  ref: Wymyślił to i na tej podstawie to robi, to fajnie.
+  hyp: Dokładnie zamigać nie wolno.
+[472_011_M]
+  ref: Kradnie kosz i stawia ją na rower.
+  hyp: Prowadził kosz na rowerze.
+[247_042_R]
+  ref: Wiesz, płot....
+  hyp: Uciekają i uciekają.
+[183_002_R]
+  ref: Jest ślisko i można się przewrócić.
+  hyp: No tak, tak.
+[001_003_M]
+  ref: Mówiłam, pies patrzy z góry na żabę siedzącą w słoju.
+  hyp: Pies patrzy w górę i patrzy w górę.
+--- end examples ---

results/polish/pjm_pl_ms_how2sign/train.log.gz

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+oid sha256:da965acb9711f87005f3d8d623a8c5e509107b870ffa62d675d891576ff0d9bf
+size 234027

results/polish/pjm_pl_si_how2sign/log.txt

@@ -0,0 +1,504 @@
+[2026-11-01 05:55:30,381] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+| distributed init (rank 0): env://
+[2026-11-01 05:55:30,918] [INFO] [comm.py:652:init_distributed] cdb=None
+[2026-11-01 05:55:30,918] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
+Namespace(batch_size=8, gradient_accumulation_steps=8, gradient_clipping=1.0, epochs=20, world_size=1, dist_url='env://', local_rank=0, hidden_dim=256, finetune='out/polish/pjm_pl_si_how2sign/best_checkpoint.pth', opt='adamw', opt_eps=1e-09, opt_betas=None, clip_grad=None, momentum=0.9, weight_decay=0.0001, sched='cosine', lr=0.001, min_lr=1e-08, warmup_epochs=0, output_dir='out/polish/eval_pjm_pl_si_how2sign', seed=42, eval=True, num_workers=4, pin_mem=True, offload=False, dtype='bf16', zero_stage=2, compute_fp32_loss=False, quick_break=0, rgb_support=False, max_length=256, dataset='PJM', wandb=False, wandb_project='uni-sign', wandb_dir='./wandb_logs', bertscore=False, num_examples=5, patience=0, pjm_train_texts='', pjm_train_texts_p=1.0, pjm_train_csv='', pjm_texts='../CrocoSign/data/texts_pl_fixed.h5', pjm_lang='Polish', pjm_split='filtered', lora=False, lora_rank=16, lora_alpha=32, lora_dropout=0.05, lora_target='q,v', lora_ckpt='', freeze_visual=False, task='SLT', label_smoothing=0.2, online_video='', rank=0, gpu=0, distributed=True, dist_backend='nccl')
+Creating dataset:
+  PJM split family: filtered (suffix='_filtered')
+<datasets.S2T_Dataset_PJM object at 0x7a31e6b86650>
+<datasets.S2T_Dataset_PJM object at 0x7a312190b4f0>
+<datasets.S2T_Dataset_PJM object at 0x7a312190b820>
+Creating model:
+/home/croco/Uni-Sign/.venv/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+***********************************
+Load Checkpoint...
+***********************************
+Missing keys: 
+ 
+Unexpected keys: 
+ 
+number of params: 587.747368M
+Using deepspeed to train...
+Initializing deepspeed...
+[2026-11-01 05:55:33,962] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed info: version=0.16.3, git-hash=unknown, git-branch=unknown
+[2026-11-01 05:55:33,962] [INFO] [comm.py:677:init_distributed] Distributed backend already initialized
+[2026-11-01 05:55:33,962] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 1
+[2026-11-01 05:55:33,994] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
+[2026-11-01 05:55:33,994] [INFO] [logging.py:128:log_dist] [Rank 0] Using client Optimizer as basic optimizer
+[2026-11-01 05:55:33,994] [INFO] [logging.py:128:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
+[2026-11-01 05:55:34,003] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Basic Optimizer = AdamW
+[2026-11-01 05:55:34,003] [INFO] [utils.py:59:is_zero_supported_optimizer] Checking ZeRO support for optimizer=AdamW type=<class 'torch.optim.adamw.AdamW'>
+[2026-11-01 05:55:34,003] [INFO] [logging.py:128:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
+[2026-11-01 05:55:34,003] [INFO] [stage_1_and_2.py:149:__init__] Reduce bucket size 500000000
+[2026-11-01 05:55:34,003] [INFO] [stage_1_and_2.py:150:__init__] Allgather bucket size 500000000
+[2026-11-01 05:55:34,003] [INFO] [stage_1_and_2.py:151:__init__] CPU Offload: False
+[2026-11-01 05:55:34,003] [INFO] [stage_1_and_2.py:152:__init__] Round robin gradient partitioning: False
+[2026-11-01 05:55:34,439] [INFO] [utils.py:781:see_memory_usage] Before initializing optimizer states
+[2026-11-01 05:55:34,439] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 6.57 GB         CA 7.31 GB         Max_CA 7 GB 
+[2026-11-01 05:55:34,439] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 5.95 GB, percent = 9.8%
+[2026-11-01 05:55:34,501] [INFO] [utils.py:781:see_memory_usage] After initializing optimizer states
+[2026-11-01 05:55:34,501] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 7.66 GB         CA 9.5 GB         Max_CA 10 GB 
+[2026-11-01 05:55:34,501] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 5.95 GB, percent = 9.8%
+[2026-11-01 05:55:34,501] [INFO] [stage_1_and_2.py:545:__init__] optimizer state initialized
+[2026-11-01 05:55:34,561] [INFO] [utils.py:781:see_memory_usage] After initializing ZeRO optimizer
+[2026-11-01 05:55:34,561] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 5.48 GB         CA 9.5 GB         Max_CA 10 GB 
+[2026-11-01 05:55:34,561] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 5.95 GB, percent = 9.8%
+[2026-11-01 05:55:34,564] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedZeroOptimizer
+[2026-11-01 05:55:34,564] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed using client LR scheduler
+[2026-11-01 05:55:34,564] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed LR Scheduler = <torch.optim.lr_scheduler.LambdaLR object at 0x7a30ed607880>
+[2026-11-01 05:55:34,564] [INFO] [logging.py:128:log_dist] [Rank 0] step=0, skipped=0, lr=[0.001, 0.001], mom=[(0.9, 0.999), (0.9, 0.999)]
+[2026-11-01 05:55:34,565] [INFO] [config.py:999:print] DeepSpeedEngine configuration:
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   activation_checkpointing_config  {
+    "partition_activations": false, 
+    "contiguous_memory_optimization": false, 
+    "cpu_checkpointing": false, 
+    "number_checkpoints": null, 
+    "synchronize_checkpoint_boundary": false, 
+    "profile": false
+}
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True, 'use_gds': False}
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   amp_enabled .................. False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   amp_params ................... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   autotuning_config ............ {
+    "enabled": false, 
+    "start_step": null, 
+    "end_step": null, 
+    "metric_path": null, 
+    "arg_mappings": null, 
+    "metric": "throughput", 
+    "model_info": null, 
+    "results_dir": "autotuning_results", 
+    "exps_dir": "autotuning_exps", 
+    "overwrite": true, 
+    "fast": true, 
+    "start_profile_step": 3, 
+    "end_profile_step": 5, 
+    "tuner_type": "gridsearch", 
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+    "tuner_num_trials": 50, 
+    "model_info_path": null, 
+    "mp_size": 1, 
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+    "min_train_batch_size": 1, 
+    "max_train_micro_batch_size_per_gpu": 1.024000e+03, 
+    "min_train_micro_batch_size_per_gpu": 1, 
+    "num_tuning_micro_batch_sizes": 3
+}
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   bfloat16_enabled ............. True
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   bfloat16_immediate_grad_update  False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   checkpoint_parallel_write_pipeline  False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   checkpoint_tag_validation_enabled  True
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   checkpoint_tag_validation_fail  False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7a30ec4d8f10>
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   communication_data_type ...... None
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   curriculum_enabled_legacy .... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   curriculum_params_legacy ..... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   data_efficiency_enabled ...... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   dataloader_drop_last ......... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   disable_allgather ............ False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   dump_state ................... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   dynamic_loss_scale_args ...... None
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_enabled ........... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_gas_boundary_resolution  1
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_layer_name ........ bert.encoder.layer
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_layer_num ......... 0
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_max_iter .......... 100
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_stability ......... 1e-06
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_tol ............... 0.01
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   eigenvalue_verbose ........... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   elasticity_enabled ........... False
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   flops_profiler_config ........ {
+    "enabled": false, 
+    "recompute_fwd_factor": 0.0, 
+    "profile_step": 1, 
+    "module_depth": -1, 
+    "top_modules": 1, 
+    "detailed": true, 
+    "output_file": null
+}
+[2026-11-01 05:55:34,565] [INFO] [config.py:1003:print]   fp16_auto_cast ............... None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   fp16_enabled ................. False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   fp16_master_weights_and_gradients  False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   global_rank .................. 0
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   grad_accum_dtype ............. None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   gradient_accumulation_steps .. 8
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   gradient_clipping ............ 1.0
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   gradient_predivide_factor .... 1.0
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   graph_harvesting ............. False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   initial_dynamic_scale ........ 1
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   load_universal_checkpoint .... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   loss_scale ................... 1.0
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   memory_breakdown ............. False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   mics_hierarchial_params_gather  False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   mics_shard_size .............. -1
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='/ds_tensorboard_logs/', job_name='_tensorboard') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName')
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   nebula_config ................ {
+    "enabled": false, 
+    "persistent_storage_path": null, 
+    "persistent_time_interval": 100, 
+    "num_of_version_in_retention": 2, 
+    "enable_nebula_load": true, 
+    "load_path": null
+}
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   optimizer_legacy_fusion ...... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   optimizer_name ............... None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   optimizer_params ............. None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   pld_enabled .................. False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   pld_params ................... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   prescale_gradients ........... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   scheduler_name ............... None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   scheduler_params ............. None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   seq_parallel_communication_data_type  torch.float32
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   sparse_attention ............. None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   sparse_gradients_enabled ..... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   steps_per_print .............. 10
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   timers_config ................ enabled=True synchronized=True
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   train_batch_size ............. 64
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   train_micro_batch_size_per_gpu  8
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   use_data_before_expert_parallel_  False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   use_node_local_storage ....... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   wall_clock_breakdown ......... False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   weight_quantization_config ... None
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   world_size ................... 1
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   zero_allow_untested_optimizer  False
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', nvme_path=None, buffer_count=5, buffer_size=100000000, max_in_cpu=1000000000, pin_memory=False) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='none', nvme_path=None, buffer_count=4, pin_memory=False, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=30000000 param_persistence_threshold=10000 model_persistence_threshold=9223372036854775807 max_live_parameters=30000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=False module_granularity_threshold=0 use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False zeropp_loco_param=None mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=False pipeline_loading_checkpoint=False override_module_apply=True
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   zero_enabled ................. True
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   zero_force_ds_cpu_optimizer .. True
+[2026-11-01 05:55:34,566] [INFO] [config.py:1003:print]   zero_optimization_stage ...... 2
+[2026-11-01 05:55:34,566] [INFO] [config.py:989:print_user_config]   json = {
+    "steps_per_print": 10, 
+    "zero_optimization": {
+        "stage": 2, 
+        "offload_param": {
+            "device": "none"
+        }, 
+        "offload_optimizer": {
+            "device": "none"
+        }, 
+        "stage3_param_persistence_threshold": 1.000000e+04, 
+        "stage3_max_live_parameters": 3.000000e+07, 
+        "stage3_prefetch_bucket_size": 3.000000e+07, 
+        "memory_efficient_linear": false
+    }, 
+    "bfloat16": {
+        "enabled": true
+    }, 
+    "gradient_clipping": 1.0, 
+    "prescale_gradients": false, 
+    "wall_clock_breakdown": false, 
+    "hybrid_engine": {
+        "enabled": false, 
+        "max_out_tokens": 512, 
+        "inference_tp_size": 1, 
+        "release_inference_cache": false, 
+        "pin_parameters": true, 
+        "tp_gather_partition_size": 8
+    }, 
+    "tensorboard": {
+        "enabled": false, 
+        "output_path": "/ds_tensorboard_logs/", 
+        "job_name": "_tensorboard"
+    }, 
+    "train_micro_batch_size_per_gpu": 8, 
+    "gradient_accumulation_steps": 8
+}
+<deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer object at 0x7a30ec4db3d0>
+📄 dev result
+Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
+Test:  [   0/1245]  eta: 0:25:46  loss: 7.3125 (7.3125)  time: 1.2418  data: 0.1366  max mem: 6083
+Test:  [  10/1245]  eta: 0:17:28  loss: 7.0625 (6.9545)  time: 0.8489  data: 0.0125  max mem: 6123
+Test:  [  20/1245]  eta: 0:17:19  loss: 6.5000 (6.6071)  time: 0.8287  data: 0.0001  max mem: 6457
+Test:  [  30/1245]  eta: 0:17:17  loss: 5.6250 (6.1734)  time: 0.8569  data: 0.0001  max mem: 6457
+Test:  [  40/1245]  eta: 0:16:14  loss: 5.6250 (6.1288)  time: 0.7679  data: 0.0001  max mem: 6463
+Test:  [  50/1245]  eta: 0:15:20  loss: 6.1250 (6.1373)  time: 0.6414  data: 0.0001  max mem: 6463
+Test:  [  60/1245]  eta: 0:14:44  loss: 5.8125 (6.0384)  time: 0.6170  data: 0.0001  max mem: 6463
+Test:  [  70/1245]  eta: 0:14:51  loss: 5.6562 (6.0057)  time: 0.7295  data: 0.0001  max mem: 6463
+Test:  [  80/1245]  eta: 0:14:14  loss: 5.6250 (5.9286)  time: 0.6962  data: 0.0001  max mem: 6463
+Test:  [  90/1245]  eta: 0:13:44  loss: 5.8125 (5.9677)  time: 0.5537  data: 0.0001  max mem: 6463
+Test:  [ 100/1245]  eta: 0:13:37  loss: 5.8125 (5.9134)  time: 0.6345  data: 0.0001  max mem: 6463
+Test:  [ 110/1245]  eta: 0:12:48  loss: 5.5625 (5.9226)  time: 0.5098  data: 0.0001  max mem: 6463
+Test:  [ 120/1245]  eta: 0:12:16  loss: 6.1562 (5.9587)  time: 0.3546  data: 0.0000  max mem: 6463
+Test:  [ 130/1245]  eta: 0:11:57  loss: 6.0938 (5.9656)  time: 0.4590  data: 0.0000  max mem: 6463
+Test:  [ 140/1245]  eta: 0:11:28  loss: 6.0000 (5.9681)  time: 0.4336  data: 0.0001  max mem: 6463
+Test:  [ 150/1245]  eta: 0:11:20  loss: 6.0000 (5.9888)  time: 0.4716  data: 0.0001  max mem: 6463
+Test:  [ 160/1245]  eta: 0:11:05  loss: 6.4375 (6.0140)  time: 0.5457  data: 0.0001  max mem: 6463
+Test:  [ 170/1245]  eta: 0:10:47  loss: 6.0625 (5.9892)  time: 0.4588  data: 0.0001  max mem: 6463
+Test:  [ 180/1245]  eta: 0:10:31  loss: 5.8125 (5.9833)  time: 0.4229  data: 0.0001  max mem: 6463
+Test:  [ 190/1245]  eta: 0:10:12  loss: 5.9375 (5.9959)  time: 0.3996  data: 0.0001  max mem: 6463
+Test:  [ 200/1245]  eta: 0:09:58  loss: 6.1875 (6.0118)  time: 0.3931  data: 0.0001  max mem: 6463
+Test:  [ 210/1245]  eta: 0:09:43  loss: 6.1875 (6.0188)  time: 0.3992  data: 0.0001  max mem: 6465
+Test:  [ 220/1245]  eta: 0:09:32  loss: 6.0625 (6.0233)  time: 0.4188  data: 0.0001  max mem: 6465
+Test:  [ 230/1245]  eta: 0:09:25  loss: 5.7500 (6.0093)  time: 0.4886  data: 0.0001  max mem: 6465
+Test:  [ 240/1245]  eta: 0:09:08  loss: 5.6250 (6.0027)  time: 0.3980  data: 0.0001  max mem: 6465
+Test:  [ 250/1245]  eta: 0:08:59  loss: 5.8750 (5.9912)  time: 0.3702  data: 0.0001  max mem: 6465
+Test:  [ 260/1245]  eta: 0:08:53  loss: 6.0312 (5.9981)  time: 0.4902  data: 0.0001  max mem: 6465
+Test:  [ 270/1245]  eta: 0:08:36  loss: 5.9375 (5.9903)  time: 0.3785  data: 0.0001  max mem: 6465
+Test:  [ 280/1245]  eta: 0:08:22  loss: 5.6562 (5.9722)  time: 0.2521  data: 0.0000  max mem: 6465
+Test:  [ 290/1245]  eta: 0:08:12  loss: 5.7500 (5.9726)  time: 0.3124  data: 0.0001  max mem: 6465
+Test:  [ 300/1245]  eta: 0:08:05  loss: 5.7500 (5.9595)  time: 0.4173  data: 0.0001  max mem: 6465
+Test:  [ 310/1245]  eta: 0:07:54  loss: 5.4375 (5.9506)  time: 0.3916  data: 0.0001  max mem: 6504
+Test:  [ 320/1245]  eta: 0:07:44  loss: 6.2500 (5.9835)  time: 0.3289  data: 0.0001  max mem: 6504
+Test:  [ 330/1245]  eta: 0:07:41  loss: 6.2500 (5.9721)  time: 0.4585  data: 0.0001  max mem: 6504
+Test:  [ 340/1245]  eta: 0:07:34  loss: 5.7812 (5.9748)  time: 0.4960  data: 0.0001  max mem: 6504
+Test:  [ 350/1245]  eta: 0:07:27  loss: 6.0625 (5.9808)  time: 0.4198  data: 0.0001  max mem: 6504
+Test:  [ 360/1245]  eta: 0:07:16  loss: 5.9375 (5.9757)  time: 0.3547  data: 0.0001  max mem: 6504
+Test:  [ 370/1245]  eta: 0:07:06  loss: 5.6875 (5.9639)  time: 0.2751  data: 0.0001  max mem: 6504
+Test:  [ 380/1245]  eta: 0:06:55  loss: 5.6875 (5.9619)  time: 0.2427  data: 0.0000  max mem: 6504
+Test:  [ 390/1245]  eta: 0:06:44  loss: 5.8125 (5.9602)  time: 0.2132  data: 0.0000  max mem: 6504
+Test:  [ 400/1245]  eta: 0:06:40  loss: 5.9375 (5.9637)  time: 0.3592  data: 0.0001  max mem: 6504
+Test:  [ 410/1245]  eta: 0:06:35  loss: 6.1250 (5.9635)  time: 0.4749  data: 0.0001  max mem: 6504
+Test:  [ 420/1245]  eta: 0:06:28  loss: 5.6875 (5.9630)  time: 0.3910  data: 0.0001  max mem: 6504
+Test:  [ 430/1245]  eta: 0:06:22  loss: 5.6250 (5.9598)  time: 0.3772  data: 0.0001  max mem: 6504
+Test:  [ 440/1245]  eta: 0:06:13  loss: 5.5625 (5.9599)  time: 0.3334  data: 0.0000  max mem: 6504
+Test:  [ 450/1245]  eta: 0:06:08  loss: 5.5000 (5.9525)  time: 0.3560  data: 0.0001  max mem: 6504
+Test:  [ 460/1245]  eta: 0:06:00  loss: 5.7500 (5.9496)  time: 0.3601  data: 0.0001  max mem: 6504
+Test:  [ 470/1245]  eta: 0:05:53  loss: 5.7500 (5.9455)  time: 0.2833  data: 0.0000  max mem: 6504
+Test:  [ 480/1245]  eta: 0:05:48  loss: 5.8125 (5.9490)  time: 0.3463  data: 0.0001  max mem: 6504
+Test:  [ 490/1245]  eta: 0:05:41  loss: 6.0000 (5.9515)  time: 0.3669  data: 0.0001  max mem: 6504
+Test:  [ 500/1245]  eta: 0:05:34  loss: 5.9688 (5.9489)  time: 0.3016  data: 0.0000  max mem: 6504
+Test:  [ 510/1245]  eta: 0:05:26  loss: 5.9062 (5.9535)  time: 0.2372  data: 0.0000  max mem: 6504
+Test:  [ 520/1245]  eta: 0:05:21  loss: 6.1250 (5.9535)  time: 0.2998  data: 0.0000  max mem: 6504
+Test:  [ 530/1245]  eta: 0:05:15  loss: 5.8750 (5.9499)  time: 0.3475  data: 0.0000  max mem: 6504
+Test:  [ 540/1245]  eta: 0:05:09  loss: 5.7500 (5.9476)  time: 0.3409  data: 0.0001  max mem: 6504
+Test:  [ 550/1245]  eta: 0:05:03  loss: 5.7500 (5.9447)  time: 0.3218  data: 0.0001  max mem: 6504
+Test:  [ 560/1245]  eta: 0:04:55  loss: 6.0938 (5.9547)  time: 0.2287  data: 0.0000  max mem: 6504
+Test:  [ 570/1245]  eta: 0:04:49  loss: 6.3750 (5.9610)  time: 0.2308  data: 0.0000  max mem: 6504
+Test:  [ 580/1245]  eta: 0:04:42  loss: 6.4375 (5.9678)  time: 0.2439  data: 0.0001  max mem: 6504
+Test:  [ 590/1245]  eta: 0:04:38  loss: 6.6250 (5.9810)  time: 0.3267  data: 0.0001  max mem: 6504
+Test:  [ 600/1245]  eta: 0:04:34  loss: 6.3750 (5.9879)  time: 0.4060  data: 0.0001  max mem: 6504
+Test:  [ 610/1245]  eta: 0:04:29  loss: 6.2500 (5.9835)  time: 0.3790  data: 0.0001  max mem: 6504
+Test:  [ 620/1245]  eta: 0:04:22  loss: 5.5625 (5.9776)  time: 0.2982  data: 0.0001  max mem: 6504
+Test:  [ 630/1245]  eta: 0:04:17  loss: 6.0000 (5.9850)  time: 0.2518  data: 0.0001  max mem: 6504
+Test:  [ 640/1245]  eta: 0:04:12  loss: 6.2500 (5.9878)  time: 0.2913  data: 0.0001  max mem: 6504
+Test:  [ 650/1245]  eta: 0:04:06  loss: 6.0000 (5.9868)  time: 0.2589  data: 0.0000  max mem: 6504
+Test:  [ 660/1245]  eta: 0:04:01  loss: 6.0000 (5.9896)  time: 0.3047  data: 0.0001  max mem: 6504
+Test:  [ 670/1245]  eta: 0:03:56  loss: 6.0938 (5.9905)  time: 0.3505  data: 0.0001  max mem: 6504
+Test:  [ 680/1245]  eta: 0:03:51  loss: 5.7812 (5.9806)  time: 0.2937  data: 0.0000  max mem: 6504
+Test:  [ 690/1245]  eta: 0:03:46  loss: 5.6250 (5.9807)  time: 0.2785  data: 0.0001  max mem: 6504
+Test:  [ 700/1245]  eta: 0:03:41  loss: 6.0625 (5.9837)  time: 0.3187  data: 0.0000  max mem: 6504
+Test:  [ 710/1245]  eta: 0:03:37  loss: 5.6875 (5.9783)  time: 0.3345  data: 0.0001  max mem: 6504
+Test:  [ 720/1245]  eta: 0:03:32  loss: 5.6562 (5.9746)  time: 0.2917  data: 0.0001  max mem: 6504
+Test:  [ 730/1245]  eta: 0:03:27  loss: 5.6875 (5.9705)  time: 0.2693  data: 0.0000  max mem: 6504
+Test:  [ 740/1245]  eta: 0:03:23  loss: 5.8125 (5.9716)  time: 0.3354  data: 0.0001  max mem: 6504
+Test:  [ 750/1245]  eta: 0:03:18  loss: 6.0312 (5.9777)  time: 0.3677  data: 0.0001  max mem: 6506
+Test:  [ 760/1245]  eta: 0:03:14  loss: 6.0938 (5.9794)  time: 0.3232  data: 0.0000  max mem: 6510
+Test:  [ 770/1245]  eta: 0:03:08  loss: 6.1562 (5.9818)  time: 0.2716  data: 0.0000  max mem: 6510
+Test:  [ 780/1245]  eta: 0:03:03  loss: 5.9375 (5.9781)  time: 0.2250  data: 0.0000  max mem: 6510
+Test:  [ 790/1245]  eta: 0:02:59  loss: 6.1562 (5.9837)  time: 0.2944  data: 0.0000  max mem: 6510
+Test:  [ 800/1245]  eta: 0:02:55  loss: 6.0000 (5.9792)  time: 0.3374  data: 0.0000  max mem: 6510
+Test:  [ 810/1245]  eta: 0:02:50  loss: 5.9688 (5.9823)  time: 0.2564  data: 0.0000  max mem: 6510
+Test:  [ 820/1245]  eta: 0:02:45  loss: 6.1250 (5.9830)  time: 0.2097  data: 0.0000  max mem: 6510
+Test:  [ 830/1245]  eta: 0:02:40  loss: 5.9688 (5.9851)  time: 0.2270  data: 0.0000  max mem: 6510
+Test:  [ 840/1245]  eta: 0:02:37  loss: 5.9688 (5.9866)  time: 0.3272  data: 0.0001  max mem: 6510
+Test:  [ 850/1245]  eta: 0:02:33  loss: 6.2188 (5.9934)  time: 0.4264  data: 0.0001  max mem: 6510
+Test:  [ 860/1245]  eta: 0:02:29  loss: 6.3125 (5.9964)  time: 0.3483  data: 0.0001  max mem: 6510
+Test:  [ 870/1245]  eta: 0:02:24  loss: 6.3750 (6.0050)  time: 0.2837  data: 0.0000  max mem: 6510
+Test:  [ 880/1245]  eta: 0:02:20  loss: 6.1250 (6.0052)  time: 0.3253  data: 0.0001  max mem: 6510
+Test:  [ 890/1245]  eta: 0:02:17  loss: 6.0000 (6.0047)  time: 0.3923  data: 0.0001  max mem: 6510
+Test:  [ 900/1245]  eta: 0:02:13  loss: 5.8750 (6.0019)  time: 0.4094  data: 0.0001  max mem: 6510
+Test:  [ 910/1245]  eta: 0:02:08  loss: 5.7500 (6.0015)  time: 0.3091  data: 0.0001  max mem: 6510
+Test:  [ 920/1245]  eta: 0:02:04  loss: 6.0000 (6.0011)  time: 0.2355  data: 0.0001  max mem: 6510
+Test:  [ 930/1245]  eta: 0:02:00  loss: 5.9062 (5.9986)  time: 0.2350  data: 0.0000  max mem: 6510
+Test:  [ 940/1245]  eta: 0:01:56  loss: 5.8125 (5.9961)  time: 0.2703  data: 0.0000  max mem: 6510
+Test:  [ 950/1245]  eta: 0:01:51  loss: 5.9375 (5.9991)  time: 0.2670  data: 0.0000  max mem: 6510
+Test:  [ 960/1245]  eta: 0:01:48  loss: 5.9375 (5.9950)  time: 0.3271  data: 0.0000  max mem: 6510
+Test:  [ 970/1245]  eta: 0:01:44  loss: 5.8750 (5.9967)  time: 0.3945  data: 0.0001  max mem: 6510
+Test:  [ 980/1245]  eta: 0:01:40  loss: 5.9375 (5.9965)  time: 0.3620  data: 0.0001  max mem: 6510
+Test:  [ 990/1245]  eta: 0:01:36  loss: 6.1875 (5.9994)  time: 0.2955  data: 0.0001  max mem: 6510
+Test:  [1000/1245]  eta: 0:01:32  loss: 6.0938 (5.9967)  time: 0.2243  data: 0.0000  max mem: 6510
+Test:  [1010/1245]  eta: 0:01:28  loss: 5.9375 (5.9975)  time: 0.2960  data: 0.0001  max mem: 6510
+Test:  [1020/1245]  eta: 0:01:24  loss: 6.1250 (6.0018)  time: 0.3741  data: 0.0001  max mem: 6510
+Test:  [1030/1245]  eta: 0:01:20  loss: 6.2188 (6.0029)  time: 0.3091  data: 0.0000  max mem: 6510
+Test:  [1040/1245]  eta: 0:01:16  loss: 5.9375 (5.9991)  time: 0.3163  data: 0.0001  max mem: 6510
+Test:  [1050/1245]  eta: 0:01:12  loss: 5.3125 (5.9933)  time: 0.3460  data: 0.0001  max mem: 6510
+Test:  [1060/1245]  eta: 0:01:09  loss: 5.4375 (5.9880)  time: 0.3189  data: 0.0001  max mem: 6510
+Test:  [1070/1245]  eta: 0:01:05  loss: 5.6250 (5.9883)  time: 0.2926  data: 0.0001  max mem: 6510
+Test:  [1080/1245]  eta: 0:01:01  loss: 5.7188 (5.9838)  time: 0.2442  data: 0.0000  max mem: 6510
+Test:  [1090/1245]  eta: 0:00:57  loss: 5.6875 (5.9829)  time: 0.2234  data: 0.0000  max mem: 6510
+Test:  [1100/1245]  eta: 0:00:53  loss: 5.7500 (5.9812)  time: 0.2402  data: 0.0000  max mem: 6510
+Test:  [1110/1245]  eta: 0:00:49  loss: 5.6250 (5.9775)  time: 0.2960  data: 0.0000  max mem: 6510
+Test:  [1120/1245]  eta: 0:00:45  loss: 5.8438 (5.9766)  time: 0.3053  data: 0.0000  max mem: 6510
+Test:  [1130/1245]  eta: 0:00:42  loss: 5.8438 (5.9746)  time: 0.2457  data: 0.0000  max mem: 6510
+Test:  [1140/1245]  eta: 0:00:38  loss: 5.6250 (5.9715)  time: 0.2806  data: 0.0001  max mem: 6510
+Test:  [1150/1245]  eta: 0:00:34  loss: 5.6562 (5.9699)  time: 0.3190  data: 0.0001  max mem: 6510
+Test:  [1160/1245]  eta: 0:00:31  loss: 6.0000 (5.9713)  time: 0.3065  data: 0.0000  max mem: 6510
+Test:  [1170/1245]  eta: 0:00:27  loss: 6.0000 (5.9706)  time: 0.3095  data: 0.0001  max mem: 6510
+Test:  [1180/1245]  eta: 0:00:23  loss: 5.7500 (5.9699)  time: 0.3104  data: 0.0001  max mem: 6510
+Test:  [1190/1245]  eta: 0:00:20  loss: 5.6875 (5.9683)  time: 0.3102  data: 0.0001  max mem: 6510
+Test:  [1200/1245]  eta: 0:00:16  loss: 5.9375 (5.9691)  time: 0.3458  data: 0.0001  max mem: 6510
+Test:  [1210/1245]  eta: 0:00:12  loss: 6.0000 (5.9690)  time: 0.3397  data: 0.0001  max mem: 6510
+Test:  [1220/1245]  eta: 0:00:09  loss: 6.1875 (5.9733)  time: 0.3130  data: 0.0001  max mem: 6510
+Test:  [1230/1245]  eta: 0:00:05  loss: 6.2188 (5.9730)  time: 0.2852  data: 0.0001  max mem: 6510
+Test:  [1240/1245]  eta: 0:00:01  loss: 6.1250 (5.9728)  time: 0.2153  data: 0.0000  max mem: 6510
+Test:  [1244/1245]  eta: 0:00:00  loss: 5.9375 (5.9730)  time: 0.2898  data: 0.0000  max mem: 6510
+Test: Total time: 0:07:30 (0.3617 s / it)
+{'bleu1': 26.02170573595225, 'bleu2': 11.683104437935318, 'bleu3': 6.650106518366639, 'bleu4': 4.080432761834349, 'chrf': 20.339850290025495, 'avg_ref_len': 7.021193250301326, 'avg_hyp_len': 6.862093210124548, 'length_ratio': 0.9773400283249646, 'pct_empty': 0.0, 'pct_very_short': 7.382482924869425, 'pct_repetitive': 0.020088388911209322}
+Rouge: 12.01
+--- 5 example translations (dev) ---
+[008_002_L]
+  ref: W poniedziałek nie pracowałem.
+  hyp: Od poniedziałku do piątku pracuję.
+[008_007_L]
+  ref: Ja w poniedziałek zawsze odprowadzam kogoś.
+  hyp: Ja nie udawałem, że ten człowiek przychodzi do lekarza i kręci głową.
+[008_008_L]
+  ref: Odprowadzam go za rękę, dlatego nie mogę w tym terminie.
+  hyp: On bierze zamach i dotyka ręką policjanta.
+[008_009_L]
+  ref: Odprowadzam małego wnuka do przedszkola.
+  hyp: Dobrze, nie mogę, ale pies szybko biegnie i wpada do wody.
+[008_010_L]
+  ref: Wnuk zawsze gra z dziećmi w piłkę nożną.
+  hyp: Są rolki oraz deskorolka.
+--- end examples ---
+📄 test result
+Test:  [   0/1239]  eta: 0:06:21  loss: 6.6250 (6.6250)  time: 0.3081  data: 0.1291  max mem: 6510
+Test:  [  10/1239]  eta: 0:04:58  loss: 6.5000 (6.4545)  time: 0.2429  data: 0.0118  max mem: 6510
+Test:  [  20/1239]  eta: 0:04:46  loss: 6.5000 (6.6592)  time: 0.2318  data: 0.0001  max mem: 6510
+Test:  [  30/1239]  eta: 0:04:47  loss: 6.2812 (6.4607)  time: 0.2352  data: 0.0001  max mem: 6510
+Test:  [  40/1239]  eta: 0:04:50  loss: 5.9375 (6.2370)  time: 0.2491  data: 0.0001  max mem: 6510
+Test:  [  50/1239]  eta: 0:04:50  loss: 5.7500 (6.2181)  time: 0.2548  data: 0.0001  max mem: 6510
+Test:  [  60/1239]  eta: 0:04:57  loss: 6.1562 (6.2177)  time: 0.2724  data: 0.0001  max mem: 6510
+Test:  [  70/1239]  eta: 0:04:52  loss: 5.9062 (6.1673)  time: 0.2653  data: 0.0001  max mem: 6510
+Test:  [  80/1239]  eta: 0:04:45  loss: 5.6875 (6.1215)  time: 0.2291  data: 0.0001  max mem: 6510
+Test:  [  90/1239]  eta: 0:04:36  loss: 5.1875 (6.0062)  time: 0.2065  data: 0.0001  max mem: 6510
+Test:  [ 100/1239]  eta: 0:04:29  loss: 5.3438 (6.0294)  time: 0.1987  data: 0.0001  max mem: 6510
+Test:  [ 110/1239]  eta: 0:04:23  loss: 6.3125 (6.0828)  time: 0.2021  data: 0.0000  max mem: 6510
+Test:  [ 120/1239]  eta: 0:04:17  loss: 6.8125 (6.1606)  time: 0.1952  data: 0.0000  max mem: 6510
+Test:  [ 130/1239]  eta: 0:04:12  loss: 6.8125 (6.1951)  time: 0.1954  data: 0.0000  max mem: 6510
+Test:  [ 140/1239]  eta: 0:04:07  loss: 6.7500 (6.2371)  time: 0.1980  data: 0.0001  max mem: 6510
+Test:  [ 150/1239]  eta: 0:04:04  loss: 6.3438 (6.2169)  time: 0.2031  data: 0.0001  max mem: 6510
+Test:  [ 160/1239]  eta: 0:04:04  loss: 5.8125 (6.1904)  time: 0.2378  data: 0.0000  max mem: 6510
+Test:  [ 170/1239]  eta: 0:03:58  loss: 5.9375 (6.1895)  time: 0.2149  data: 0.0000  max mem: 6510
+Test:  [ 180/1239]  eta: 0:03:55  loss: 6.0625 (6.1768)  time: 0.1832  data: 0.0000  max mem: 6510
+Test:  [ 190/1239]  eta: 0:03:53  loss: 6.0000 (6.1718)  time: 0.2118  data: 0.0001  max mem: 6510
+Test:  [ 200/1239]  eta: 0:03:53  loss: 6.0000 (6.1608)  time: 0.2460  data: 0.0001  max mem: 6510
+Test:  [ 210/1239]  eta: 0:03:51  loss: 5.8125 (6.1475)  time: 0.2494  data: 0.0001  max mem: 6510
+Test:  [ 220/1239]  eta: 0:03:52  loss: 6.0000 (6.1455)  time: 0.2689  data: 0.0001  max mem: 6510
+Test:  [ 230/1239]  eta: 0:03:50  loss: 6.0625 (6.1403)  time: 0.2691  data: 0.0001  max mem: 6510
+Test:  [ 240/1239]  eta: 0:03:48  loss: 5.9688 (6.1249)  time: 0.2303  data: 0.0001  max mem: 6510
+Test:  [ 250/1239]  eta: 0:03:48  loss: 5.8750 (6.1128)  time: 0.2525  data: 0.0001  max mem: 6510
+Test:  [ 260/1239]  eta: 0:03:47  loss: 5.7188 (6.0965)  time: 0.2783  data: 0.0001  max mem: 6510
+Test:  [ 270/1239]  eta: 0:03:45  loss: 5.6250 (6.0814)  time: 0.2656  data: 0.0001  max mem: 6510
+Test:  [ 280/1239]  eta: 0:03:43  loss: 5.5000 (6.0656)  time: 0.2404  data: 0.0001  max mem: 6510
+Test:  [ 290/1239]  eta: 0:03:39  loss: 5.5000 (6.0563)  time: 0.2118  data: 0.0001  max mem: 6510
+Test:  [ 300/1239]  eta: 0:03:37  loss: 5.6875 (6.0472)  time: 0.2170  data: 0.0000  max mem: 6510
+Test:  [ 310/1239]  eta: 0:03:35  loss: 5.8125 (6.0438)  time: 0.2326  data: 0.0001  max mem: 6510
+Test:  [ 320/1239]  eta: 0:03:33  loss: 5.9062 (6.0404)  time: 0.2424  data: 0.0001  max mem: 6510
+Test:  [ 330/1239]  eta: 0:03:32  loss: 5.7500 (6.0329)  time: 0.2707  data: 0.0001  max mem: 6517
+Test:  [ 340/1239]  eta: 0:03:29  loss: 5.6875 (6.0251)  time: 0.2468  data: 0.0000  max mem: 6517
+Test:  [ 350/1239]  eta: 0:03:26  loss: 5.6875 (6.0143)  time: 0.2110  data: 0.0000  max mem: 6517
+Test:  [ 360/1239]  eta: 0:03:25  loss: 5.7812 (6.0182)  time: 0.2373  data: 0.0001  max mem: 6517
+Test:  [ 370/1239]  eta: 0:03:25  loss: 6.1250 (6.0156)  time: 0.2943  data: 0.0001  max mem: 6517
+Test:  [ 380/1239]  eta: 0:03:23  loss: 5.9062 (6.0160)  time: 0.2934  data: 0.0001  max mem: 6517
+Test:  [ 390/1239]  eta: 0:03:20  loss: 5.9375 (6.0165)  time: 0.2425  data: 0.0001  max mem: 6517
+Test:  [ 400/1239]  eta: 0:03:18  loss: 5.8750 (6.0166)  time: 0.2366  data: 0.0000  max mem: 6517
+Test:  [ 410/1239]  eta: 0:03:15  loss: 5.9375 (6.0186)  time: 0.2230  data: 0.0000  max mem: 6517
+Test:  [ 420/1239]  eta: 0:03:13  loss: 5.9375 (6.0139)  time: 0.2376  data: 0.0000  max mem: 6517
+Test:  [ 430/1239]  eta: 0:03:10  loss: 5.8125 (6.0143)  time: 0.2394  data: 0.0000  max mem: 6517
+Test:  [ 440/1239]  eta: 0:03:08  loss: 6.4375 (6.0303)  time: 0.2143  data: 0.0001  max mem: 6517
+Test:  [ 450/1239]  eta: 0:03:07  loss: 6.5000 (6.0365)  time: 0.2722  data: 0.0001  max mem: 6517
+Test:  [ 460/1239]  eta: 0:03:04  loss: 6.0000 (6.0299)  time: 0.2732  data: 0.0000  max mem: 6517
+Test:  [ 470/1239]  eta: 0:03:03  loss: 5.9062 (6.0304)  time: 0.2706  data: 0.0000  max mem: 6517
+Test:  [ 480/1239]  eta: 0:03:02  loss: 6.0000 (6.0328)  time: 0.3227  data: 0.0001  max mem: 6517
+Test:  [ 490/1239]  eta: 0:03:00  loss: 5.8125 (6.0258)  time: 0.3044  data: 0.0001  max mem: 6517
+Test:  [ 500/1239]  eta: 0:02:59  loss: 6.0625 (6.0293)  time: 0.2785  data: 0.0001  max mem: 6517
+Test:  [ 510/1239]  eta: 0:02:56  loss: 6.1250 (6.0322)  time: 0.2668  data: 0.0001  max mem: 6517
+Test:  [ 520/1239]  eta: 0:02:53  loss: 5.8125 (6.0277)  time: 0.2252  data: 0.0001  max mem: 6517
+Test:  [ 530/1239]  eta: 0:02:51  loss: 5.5625 (6.0182)  time: 0.2250  data: 0.0000  max mem: 6517
+Test:  [ 540/1239]  eta: 0:02:48  loss: 5.4062 (6.0087)  time: 0.2251  data: 0.0000  max mem: 6517
+Test:  [ 550/1239]  eta: 0:02:46  loss: 5.6250 (6.0096)  time: 0.2215  data: 0.0000  max mem: 6517
+Test:  [ 560/1239]  eta: 0:02:43  loss: 5.8750 (6.0078)  time: 0.2295  data: 0.0000  max mem: 6517
+Test:  [ 570/1239]  eta: 0:02:41  loss: 5.8125 (6.0044)  time: 0.2359  data: 0.0000  max mem: 6517
+Test:  [ 580/1239]  eta: 0:02:38  loss: 5.8438 (6.0069)  time: 0.2498  data: 0.0000  max mem: 6517
+Test:  [ 590/1239]  eta: 0:02:37  loss: 5.9375 (6.0060)  time: 0.2850  data: 0.0001  max mem: 6517
+Test:  [ 600/1239]  eta: 0:02:35  loss: 5.9688 (6.0067)  time: 0.3126  data: 0.0001  max mem: 6517
+Test:  [ 610/1239]  eta: 0:02:34  loss: 5.5000 (5.9993)  time: 0.3410  data: 0.0001  max mem: 6517
+Test:  [ 620/1239]  eta: 0:02:33  loss: 5.9688 (6.0068)  time: 0.3636  data: 0.0001  max mem: 6517
+Test:  [ 630/1239]  eta: 0:02:30  loss: 5.8750 (5.9967)  time: 0.2902  data: 0.0000  max mem: 6517
+Test:  [ 640/1239]  eta: 0:02:29  loss: 5.3438 (5.9926)  time: 0.2925  data: 0.0000  max mem: 6517
+Test:  [ 650/1239]  eta: 0:02:26  loss: 5.4375 (5.9876)  time: 0.2982  data: 0.0000  max mem: 6517
+Test:  [ 660/1239]  eta: 0:02:23  loss: 5.5000 (5.9858)  time: 0.2453  data: 0.0000  max mem: 6517
+Test:  [ 670/1239]  eta: 0:02:21  loss: 5.8438 (5.9843)  time: 0.2537  data: 0.0000  max mem: 6517
+Test:  [ 680/1239]  eta: 0:02:19  loss: 5.6250 (5.9825)  time: 0.2612  data: 0.0000  max mem: 6517
+Test:  [ 690/1239]  eta: 0:02:16  loss: 5.7500 (5.9797)  time: 0.2725  data: 0.0001  max mem: 6517
+Test:  [ 700/1239]  eta: 0:02:14  loss: 5.7812 (5.9790)  time: 0.2452  data: 0.0001  max mem: 6517
+Test:  [ 710/1239]  eta: 0:02:11  loss: 5.5625 (5.9716)  time: 0.2151  data: 0.0000  max mem: 6517
+Test:  [ 720/1239]  eta: 0:02:09  loss: 5.8125 (5.9768)  time: 0.2552  data: 0.0000  max mem: 6517
+Test:  [ 730/1239]  eta: 0:02:07  loss: 6.1875 (5.9804)  time: 0.3010  data: 0.0001  max mem: 6517
+Test:  [ 740/1239]  eta: 0:02:04  loss: 6.1875 (5.9815)  time: 0.2858  data: 0.0000  max mem: 6517
+Test:  [ 750/1239]  eta: 0:02:01  loss: 6.2500 (5.9892)  time: 0.2253  data: 0.0000  max mem: 6517
+Test:  [ 760/1239]  eta: 0:01:59  loss: 6.3750 (5.9903)  time: 0.1924  data: 0.0000  max mem: 6517
+Test:  [ 770/1239]  eta: 0:01:57  loss: 6.0625 (5.9897)  time: 0.2769  data: 0.0001  max mem: 6517
+Test:  [ 780/1239]  eta: 0:01:54  loss: 5.9375 (5.9897)  time: 0.3175  data: 0.0001  max mem: 6517
+Test:  [ 790/1239]  eta: 0:01:51  loss: 5.7812 (5.9852)  time: 0.2243  data: 0.0000  max mem: 6517
+Test:  [ 800/1239]  eta: 0:01:49  loss: 5.7500 (5.9824)  time: 0.2065  data: 0.0000  max mem: 6517
+Test:  [ 810/1239]  eta: 0:01:47  loss: 5.6250 (5.9768)  time: 0.2784  data: 0.0001  max mem: 6627
+Test:  [ 820/1239]  eta: 0:01:44  loss: 5.7188 (5.9776)  time: 0.2727  data: 0.0000  max mem: 6627
+Test:  [ 830/1239]  eta: 0:01:41  loss: 6.0000 (5.9817)  time: 0.1882  data: 0.0000  max mem: 6627
+Test:  [ 840/1239]  eta: 0:01:38  loss: 6.4062 (5.9853)  time: 0.1743  data: 0.0000  max mem: 6627
+Test:  [ 850/1239]  eta: 0:01:36  loss: 6.3125 (5.9864)  time: 0.2169  data: 0.0000  max mem: 6627
+Test:  [ 860/1239]  eta: 0:01:33  loss: 6.0938 (5.9868)  time: 0.2362  data: 0.0000  max mem: 6627
+Test:  [ 870/1239]  eta: 0:01:31  loss: 6.0312 (5.9860)  time: 0.2680  data: 0.0001  max mem: 6627
+Test:  [ 880/1239]  eta: 0:01:29  loss: 5.6875 (5.9807)  time: 0.3245  data: 0.0001  max mem: 6627
+Test:  [ 890/1239]  eta: 0:01:27  loss: 5.7500 (5.9821)  time: 0.3848  data: 0.0001  max mem: 6627
+Test:  [ 900/1239]  eta: 0:01:25  loss: 5.9375 (5.9822)  time: 0.3426  data: 0.0001  max mem: 6627
+Test:  [ 910/1239]  eta: 0:01:22  loss: 5.9062 (5.9809)  time: 0.2480  data: 0.0000  max mem: 6627
+Test:  [ 920/1239]  eta: 0:01:20  loss: 6.1250 (5.9851)  time: 0.2272  data: 0.0000  max mem: 6627
+Test:  [ 930/1239]  eta: 0:01:17  loss: 6.1250 (5.9828)  time: 0.2451  data: 0.0001  max mem: 6627
+Test:  [ 940/1239]  eta: 0:01:15  loss: 5.7812 (5.9806)  time: 0.2516  data: 0.0000  max mem: 6627
+Test:  [ 950/1239]  eta: 0:01:12  loss: 5.7812 (5.9794)  time: 0.2881  data: 0.0000  max mem: 6627
+Test:  [ 960/1239]  eta: 0:01:10  loss: 5.9375 (5.9804)  time: 0.2961  data: 0.0000  max mem: 6627
+Test:  [ 970/1239]  eta: 0:01:07  loss: 5.7188 (5.9768)  time: 0.2464  data: 0.0000  max mem: 6627
+Test:  [ 980/1239]  eta: 0:01:05  loss: 5.5625 (5.9742)  time: 0.2181  data: 0.0001  max mem: 6627
+Test:  [ 990/1239]  eta: 0:01:02  loss: 5.9062 (5.9756)  time: 0.2683  data: 0.0000  max mem: 6627
+Test:  [1000/1239]  eta: 0:01:00  loss: 5.9062 (5.9713)  time: 0.3222  data: 0.0001  max mem: 6627
+Test:  [1010/1239]  eta: 0:00:57  loss: 5.4688 (5.9672)  time: 0.3022  data: 0.0001  max mem: 6627
+Test:  [1020/1239]  eta: 0:00:55  loss: 5.6250 (5.9652)  time: 0.2421  data: 0.0000  max mem: 6627
+Test:  [1030/1239]  eta: 0:00:52  loss: 6.2500 (5.9703)  time: 0.1744  data: 0.0000  max mem: 6627
+Test:  [1040/1239]  eta: 0:00:49  loss: 6.5000 (5.9745)  time: 0.1817  data: 0.0000  max mem: 6627
+Test:  [1050/1239]  eta: 0:00:47  loss: 6.5000 (5.9792)  time: 0.2191  data: 0.0001  max mem: 6627
+Test:  [1060/1239]  eta: 0:00:44  loss: 5.8125 (5.9727)  time: 0.2269  data: 0.0000  max mem: 6627
+Test:  [1070/1239]  eta: 0:00:42  loss: 5.6875 (5.9753)  time: 0.2472  data: 0.0000  max mem: 6627
+Test:  [1080/1239]  eta: 0:00:39  loss: 5.6875 (5.9707)  time: 0.2742  data: 0.0000  max mem: 6627
+Test:  [1090/1239]  eta: 0:00:37  loss: 5.5312 (5.9681)  time: 0.2710  data: 0.0001  max mem: 6627
+Test:  [1100/1239]  eta: 0:00:34  loss: 5.6562 (5.9666)  time: 0.2765  data: 0.0001  max mem: 6627
+Test:  [1110/1239]  eta: 0:00:32  loss: 5.5625 (5.9654)  time: 0.2637  data: 0.0001  max mem: 6627
+Test:  [1120/1239]  eta: 0:00:29  loss: 5.5625 (5.9625)  time: 0.2394  data: 0.0001  max mem: 6627
+Test:  [1130/1239]  eta: 0:00:27  loss: 5.6562 (5.9619)  time: 0.2296  data: 0.0001  max mem: 6627
+Test:  [1140/1239]  eta: 0:00:24  loss: 6.0000 (5.9632)  time: 0.2646  data: 0.0001  max mem: 6627
+Test:  [1150/1239]  eta: 0:00:22  loss: 6.0000 (5.9651)  time: 0.3053  data: 0.0000  max mem: 6627
+Test:  [1160/1239]  eta: 0:00:19  loss: 6.3125 (5.9696)  time: 0.3009  data: 0.0001  max mem: 6627
+Test:  [1170/1239]  eta: 0:00:17  loss: 6.1875 (5.9689)  time: 0.2620  data: 0.0001  max mem: 6627
+Test:  [1180/1239]  eta: 0:00:14  loss: 6.0000 (5.9720)  time: 0.2148  data: 0.0000  max mem: 6627
+Test:  [1190/1239]  eta: 0:00:12  loss: 6.0000 (5.9727)  time: 0.2442  data: 0.0001  max mem: 6627
+Test:  [1200/1239]  eta: 0:00:09  loss: 5.8750 (5.9735)  time: 0.3186  data: 0.0001  max mem: 6627
+Test:  [1210/1239]  eta: 0:00:07  loss: 6.1250 (5.9748)  time: 0.3227  data: 0.0001  max mem: 6627
+Test:  [1220/1239]  eta: 0:00:04  loss: 6.1250 (5.9750)  time: 0.2511  data: 0.0000  max mem: 6627
+Test:  [1230/1239]  eta: 0:00:02  loss: 5.8438 (5.9726)  time: 0.2251  data: 0.0000  max mem: 6627
+Test:  [1238/1239]  eta: 0:00:00  loss: 5.8125 (5.9726)  time: 0.3087  data: 0.0000  max mem: 6627
+Test: Total time: 0:05:14 (0.2540 s / it)
+{'bleu1': 25.725140898508933, 'bleu2': 11.199775545814571, 'bleu3': 6.319816191210624, 'bleu4': 3.845575199557246, 'chrf': 19.639151846839816, 'avg_ref_len': 6.686074672048436, 'avg_hyp_len': 6.530373360242179, 'length_ratio': 0.9767125975339198, 'pct_empty': 0.0, 'pct_very_short': 7.911200807265389, 'pct_repetitive': 0.04036326942482341}
+Rouge: 11.41
+--- 5 example translations (test) ---
+[008_000_R]
+  ref: Tak samo przez cały miesiąc.
+  hyp: Dobrze, zgadzam się z tobą.
+[008_003_R]
+  ref: Co chcesz robić? Pomyśl co zrobisz w domu.
+  hyp: W pracy można, myślę, że w domu.
+[008_004_R]
+  ref: Nie chcesz?
+  hyp: Dobra, powiedziałaś mi.
+[008_005_R]
+  ref: Luty dwa tysiące jedenastego roku?
+  hyp: Wtorek, dwudziestego trzeciego.
+[008_006_R]
+  ref: Dwa tysiące jedenasty rok.
+  hyp: Policjant biegnie za nimi.
+--- end examples ---

results/polish/pjm_pl_si_how2sign/train.log.gz

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+oid sha256:632bb8ff11b6c561354fbed1f3853edcfb652fd313e8bfe6a01a9d9c94056d30
+size 269214

results/spamo/2026-05-04T09-09-26_test_v27_quad_xattn/bleurt_Elron_bleurt-large-512.json

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results/spamo/2026-05-04T09-09-26_test_v27_quad_xattn/bleurt_lucadiliello_BLEURT-20.json

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+}

results/spamo/2026-05-04T09-09-26_test_v27_quad_xattn/config.yaml

@@ -0,0 +1,187 @@
+_wandb:
+    value:
+        cli_version: 0.23.1
+        e:
+            v9bz95rceb2ik2jjh7p1x585c0emnxfi:
+                args:
+                    - -c
+                    - configs/finetune_v27_quad_xattn_from_upstream.yaml
+                    - --ckpt
+                    - logs/2026-05-03T23-47-30_finetune_v27_quad_xattn/checkpoints/epoch=00010-step=0032461-bleu4=5.40.ckpt
+                    - -t
+                    - "False"
+                    - --test
+                    - "True"
+                    - -n
+                    - test_v27_quad_xattn
+                    - --tags
+                    - test
+                    - pjm-ms
+                    - quad
+                    - upstream-init
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 12
+                cpu_count_logical: 24
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "1967317549056"
+                        used: "1598675161088"
+                email: s28855@pjwstk.edu.pl
+                executable: /home/croco/SpaMo-PJM/.venv/bin/python
+                git:
+                    commit: 4f11b72511d6fd9692336b6b89e43f98e516e01c
+                    remote: https://github.com/croco-corp/SpaMo-PJM.git
+                gpu: NVIDIA GeForce RTX 3090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-5c32b58e-e363-6bec-ff14-3d6044d0ae84
+                host: lowca-desktop
+                memory:
+                    total: "64894398464"
+                os: Linux-6.8.0-110-generic-x86_64-with-glibc2.39
+                program: /home/croco/SpaMo-PJM/main.py
+                python: CPython 3.10.19
+                root: logs/2026-05-04T09-09-26_test_v27_quad_xattn
+                startedAt: "2026-05-04T07:09:31.256917Z"
+                writerId: v9bz95rceb2ik2jjh7p1x585c0emnxfi
+        m:
+            - "1": trainer/global_step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "2":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "3":
+                - 7
+                - 13
+                - 14
+                - 15
+                - 16
+                - 66
+            "4": 3.10.19
+            "5": 0.23.1
+            "6": 4.57.1
+            "12": 0.23.1
+            "13": linux-x86_64
+alpha:
+    value: 1
+aux_input_size:
+    value: 2048
+beam_size:
+    value: 5
+cache_dir:
+    value: /home/croco/.cache/huggingface/hub
+combined_loss:
+    value: true
+config_files:
+    value:
+        - configs/finetune_v27_quad_xattn_from_upstream.yaml
+cross_modal_align:
+    value: true
+data:
+    value:
+        aux_features_path: features/hand_vit_feat_pjm.h5
+        batch_size: 2
+        device: cuda
+        keypoint_features_path: features/mediapipe_feat_pjm.h5
+        max_frame_length: 512
+        motion_features_path: features/mae_feat_pjm.h5
+        multilang_path: features/texts_multilang_pjm.h5
+        num_workers: 4
+        set_to_test: test
+        target_lang: English
+        test_split_path: /home/croco/CrocoSign/data/split_test_ms.csv
+        texts_path: features/texts_eng.h5
+        train_split_path: /home/croco/CrocoSign/data/split_train_ms.csv
+        val_split_path: /home/croco/CrocoSign/data/split_val_ms.csv
+        visual_features_path: features/vit_feat_pjm.h5
+evaluation:
+    value: mse
+frame_sample_rate:
+    value: 1
+fusion_dropout:
+    value: 0.3
+fusion_lr:
+    value: 1e-05
+fusion_mode:
+    value: joint
+input_size:
+    value: 2048
+inter_hidden:
+    value: 768
+keypoint_dim:
+    value: 258
+lora_alpha:
+    value: 32
+lora_dropout:
+    value: 0.1
+lora_r:
+    value: 16
+lr:
+    value: 0.0001
+lr_patience:
+    value: 5
+lr_scheduler_mode:
+    value: max
+max_frame_len:
+    value: 512
+max_txt_len:
+    value: 64
+model_name:
+    value: google/flan-t5-xl
+monitor:
+    value: val/bleu4
+motion_input_size:
+    value: 1024
+new_stream_lr:
+    value: 0.0001
+num_in_context:
+    value: 3
+prompt:
+    value: Translate the given sentence into {}.
+queue_size:
+    value: 0
+scheduler_config:
+    value:
+        params:
+            lr_max: 1
+            lr_min: 0.1
+            lr_start: 0
+            max_decay_steps: 30000
+            warm_up_steps: 1000
+        target: spamo.lr_scheduler.LambdaWarmUpCosineScheduler
+seed:
+    value: 0
+tuning_type:
+    value: lora
+use_in_context:
+    value: true
+warm_up_steps:
+    value: 0

results/spamo/2026-05-04T09-09-26_test_v27_quad_xattn/output.log

@@ -0,0 +1,47 @@
+Restoring states from the checkpoint path at logs/2026-05-03T23-47-30_finetune_v27_quad_xattn/checkpoints/epoch=00010-step=0032461-bleu4=5.40.ckpt
+/home/croco/SpaMo-PJM/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:283: Be aware that when using `ckpt_path`, callbacks used to create the checkpoint need to be provided during `Trainer` instantiation. Please add the following callbacks: ["ModelCheckpoint{'monitor': 'val/bleu4', 'mode': 'max', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None}", "EarlyStopping{'monitor': 'val/bleu4', 'mode': 'max'}"].
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loaded model weights from the checkpoint at logs/2026-05-03T23-47-30_finetune_v27_quad_xattn/checkpoints/epoch=00010-step=0032461-bleu4=5.40.ckpt
+/home/croco/.local/share/uv/python/cpython-3.10.19-linux-x86_64-gnu/lib/python3.10/contextlib.py:103: FutureWarning: `torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature.
+  self.gen = func(*args, **kwds)
+/home/croco/SpaMo-PJM/spamo/t5_slt.py:585: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1857.)
+  log_dict[f"{split}/visual_feat_std"] = visual_outputs.mean(1).std(0).mean().item()
+
+===== Validation Examples =====
+Reference: i said, the dog looks down at the frog sitting in the jar.
+Generated: the dog looks into the jar.
+--------------------------------------------------
+Reference: he cried out and fell back.
+Generated: the cat is in the middle of the gutter.
+--------------------------------------------------
+Reference: the child hugs the dog close.
+Generated: he fell asleep, covered himself with a quilt.
+--------------------------------------------------
+Reference: the frog has disappeared and is not coming back.
+Generated: there is no such thing at all.
+--------------------------------------------------
+Reference: the boy searched and put his hand in a burrow underground.
+Generated: the boy looked into the burrow.
+--------------------------------------------------
+Dumped 2935 pairs → logs/2026-05-04T09-09-26_test_v27_quad_xattn/test_outputs.txt
+Logged 2935 translations to W&B test/translations
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     test/bertscore_f1     │    0.8767930865287781     │
+│        test/bleu1         │    27.903766632080078     │
+│        test/bleu2         │    13.140559196472168     │
+│        test/bleu3         │     7.757347106933594     │
+│        test/bleu4         │     5.059134006500244     │
+│         test/chrf         │    20.937597274780273     │
+│     test/contra_loss      │    0.4464213252067566     │
+│     test/logit_scale      │    116.04837799072266     │
+│      test/rougeL_f1       │    0.18864305317401886    │
+│   test/rougeL_precision   │    0.2017328441143036     │
+│    test/rougeL_recall     │    0.19402141869068146    │
+│  test/speaker_bleu4_std   │    2.8405442237854004     │
+│   test/visual_feat_std    │            nan            │
+└───────────────────────────┴───────────────────────────┘
+Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1468/1468 0:08:18 • 0:00:00 2.64it/s

results/spamo/2026-05-04T09-09-26_test_v27_quad_xattn/wandb-summary.json

@@ -0,0 +1 @@
+{"test/bleu2":13.140559196472168,"_wandb":{"runtime":512},"_step":2,"test/bertscore_f1":0.8767930865287781,"_runtime":512,"test/translations":{"path":"media/table/test/translations_0_39801b3ce4db33e702c1.table.json","log_mode":"IMMUTABLE","artifact_path":"wandb-client-artifact://1anr600w60w71a5g0oofp930xhc76a60dfgj6ptpqjljkva7ssp0108od2zar38mqq0zrgvamlcd2qe7lpzf24rz091msot6d6sf7haqubwuucmeqnl5wdx9n1xrohpj/test/translations.table.json","size":312304,"ncols":3,"nrows":2935,"_type":"table-file","sha256":"39801b3ce4db33e702c17ae47cdec2540e16611ccefd1840e0db896d605d500f","_latest_artifact_path":"wandb-client-artifact://ry5f31wgcelk3mf9z2rdvqldljmzen1l45gxxguriz66dndqxzcew4vdcuh0k36tz1wxo6gf0hs6m05xds29qcmr7c6pd1eeitx4vwrsxjkwwght3lgbttwtx5l2ilrs:latest/test/translations.table.json"},"test/bleu4":5.059134006500244,"test/rougeL_recall":0.19402141869068146,"epoch":0,"test/chrf":20.937597274780273,"test/bleu3":7.757347106933594,"test/bleu1":27.903766632080078,"test/rougeL_f1":0.18864305317401886,"trainer/global_step":0,"test/per_speaker_metrics":{"path":"media/table/test/per_speaker_metrics_1_b5c8267dd6d964f16493.table.json","nrows":151,"_type":"table-file","log_mode":"IMMUTABLE","_latest_artifact_path":"wandb-client-artifact://7a3w409m04y23jkoa63e3lvrqv6oa483fp1557xep8e7mv3der68xg2njvps8b5p450rsvoz4m8ukf0k38mnkj935a8337ocuth09z213fvv5pjtrabgrxsu9dnt25wx:latest/test/per_speaker_metrics.table.json","artifact_path":"wandb-client-artifact://a03auae5b6y1y106mnxtynpob0u50o1cw76lkk5molj5ptomo6kzh6qkp043e8r8g7g5j4f4xe7pg4bb0d0d7euxndrm6hivpkf4d67e7j2lqzt1ocm527vlrri5cl9u/test/per_speaker_metrics.table.json","sha256":"b5c8267dd6d964f16493cffc0cdc21539541616a90c1b546b841b1fc45cb536f","ncols":4,"size":5411},"test/speaker_bleu4_std":2.8405442237854004,"test/rougeL_precision":0.2017328441143036,"test/contra_loss":0.4464213252067566,"test/visual_feat_std":NaN,"test/logit_scale":116.04837799072266,"_timestamp":1.7778790833759375e+09}

results/spamo/2026-05-05T21-58-47_test_v30_dual_ms_scratch/config.yaml

@@ -0,0 +1,183 @@
+_wandb:
+    value:
+        cli_version: 0.23.1
+        e:
+            9tedcvvfjtp60jcqh861xwbs9exycbcy:
+                args:
+                    - -c
+                    - configs/finetune_v30_dual_ms_scratch.yaml
+                    - --ckpt
+                    - logs/2026-05-04T10-59-07_finetune_v30_dual_ms_scratch/checkpoints/epoch=00028-step=0085579-bleu4=1.97.ckpt
+                    - -t
+                    - "False"
+                    - --test
+                    - "True"
+                    - -n
+                    - test_v30_dual_ms_scratch
+                    - --tags
+                    - test
+                    - pjm-ms
+                    - dual
+                    - scratch
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 12
+                cpu_count_logical: 24
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "1967317549056"
+                        used: "1649338347520"
+                email: s28855@pjwstk.edu.pl
+                executable: /home/croco/SpaMo-PJM/.venv/bin/python
+                git:
+                    commit: 4f11b72511d6fd9692336b6b89e43f98e516e01c
+                    remote: https://github.com/croco-corp/SpaMo-PJM.git
+                gpu: NVIDIA GeForce RTX 3090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-5c32b58e-e363-6bec-ff14-3d6044d0ae84
+                host: lowca-desktop
+                memory:
+                    total: "64894398464"
+                os: Linux-6.8.0-110-generic-x86_64-with-glibc2.39
+                program: /home/croco/SpaMo-PJM/main.py
+                python: CPython 3.10.19
+                root: logs/2026-05-05T21-58-47_test_v30_dual_ms_scratch
+                startedAt: "2026-05-05T19:58:52.724376Z"
+                writerId: 9tedcvvfjtp60jcqh861xwbs9exycbcy
+        m:
+            - "1": trainer/global_step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "2":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "3":
+                - 7
+                - 13
+                - 14
+                - 15
+                - 16
+                - 66
+            "4": 3.10.19
+            "5": 0.23.1
+            "6": 4.57.1
+            "12": 0.23.1
+            "13": linux-x86_64
+alpha:
+    value: 1
+aux_input_size:
+    value: 0
+beam_size:
+    value: 5
+cache_dir:
+    value: /home/croco/.cache/huggingface/hub
+combined_loss:
+    value: true
+config_files:
+    value:
+        - configs/finetune_v30_dual_ms_scratch.yaml
+cross_modal_align:
+    value: true
+data:
+    value:
+        batch_size: 2
+        device: cuda
+        max_frame_length: 512
+        motion_features_path: features/mae_feat_pjm.h5
+        multilang_path: features/texts_multilang_pjm.h5
+        num_workers: 4
+        set_to_test: test
+        target_lang: English
+        test_split_path: /home/croco/CrocoSign/data/split_test_ms.csv
+        texts_path: features/texts_eng.h5
+        train_split_path: /home/croco/CrocoSign/data/split_train_ms.csv
+        val_split_path: /home/croco/CrocoSign/data/split_val_ms.csv
+        visual_features_path: features/vit_feat_pjm.h5
+evaluation:
+    value: mse
+frame_sample_rate:
+    value: 1
+fusion_dropout:
+    value: 0.3
+fusion_lr:
+    value: 1e-05
+fusion_mode:
+    value: joint
+input_size:
+    value: 2048
+inter_hidden:
+    value: 768
+keypoint_dim:
+    value: 0
+lora_alpha:
+    value: 32
+lora_dropout:
+    value: 0.1
+lora_r:
+    value: 16
+lr:
+    value: 0.0001
+lr_patience:
+    value: 5
+lr_scheduler_mode:
+    value: max
+max_frame_len:
+    value: 512
+max_txt_len:
+    value: 64
+model_name:
+    value: google/flan-t5-xl
+monitor:
+    value: val/bleu4
+motion_input_size:
+    value: 1024
+num_in_context:
+    value: 3
+prompt:
+    value: Translate the given sentence into {}.
+queue_size:
+    value: 0
+scheduler_config:
+    value:
+        params:
+            lr_max: 1
+            lr_min: 0.1
+            lr_start: 0
+            max_decay_steps: 50000
+            warm_up_steps: 1000
+        target: spamo.lr_scheduler.LambdaWarmUpCosineScheduler
+seed:
+    value: 0
+tuning_type:
+    value: lora
+use_in_context:
+    value: true
+warm_up_steps:
+    value: 0

results/spamo/2026-05-05T21-58-47_test_v30_dual_ms_scratch/output.log

@@ -0,0 +1,46 @@
+Restoring states from the checkpoint path at logs/2026-05-04T10-59-07_finetune_v30_dual_ms_scratch/checkpoints/epoch=00028-step=0085579-bleu4=1.97.ckpt
+/home/croco/SpaMo-PJM/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:283: Be aware that when using `ckpt_path`, callbacks used to create the checkpoint need to be provided during `Trainer` instantiation. Please add the following callbacks: ["EarlyStopping{'monitor': 'val/bleu4', 'mode': 'max'}", "ModelCheckpoint{'monitor': 'val/bleu4', 'mode': 'max', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None}"].
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loaded model weights from the checkpoint at logs/2026-05-04T10-59-07_finetune_v30_dual_ms_scratch/checkpoints/epoch=00028-step=0085579-bleu4=1.97.ckpt
+/home/croco/SpaMo-PJM/spamo/t5_slt.py:585: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1857.)
+  log_dict[f"{split}/visual_feat_std"] = visual_outputs.mean(1).std(0).mean().item()
+
+===== Validation Examples =====
+Reference: i said, the dog looks down at the frog sitting in the jar.
+Generated: the dog looks through binoculars.
+--------------------------------------------------
+Reference: he cried out and fell back.
+Generated: i think it's a duck.
+--------------------------------------------------
+Reference: the child hugs the dog close.
+Generated: he sleeps soundly and does not want to get up.
+--------------------------------------------------
+Reference: the frog has disappeared and is not coming back.
+Generated: aha, aha, aha.
+--------------------------------------------------
+Reference: the boy searched and put his hand in a burrow underground.
+Generated: he climbs the tree.
+--------------------------------------------------
+Dumped 2935 pairs → logs/2026-05-05T21-58-47_test_v30_dual_ms_scratch/test_outputs.txt
+Logged 2935 translations to W&B test/translations
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     test/bertscore_f1     │    0.8612825274467468     │
+│        test/bleu1         │    18.552621841430664     │
+│        test/bleu2         │    6.5959014892578125     │
+│        test/bleu3         │     3.398465394973755     │
+│        test/bleu4         │    2.0304346084594727     │
+│       test/bleurt20       │    0.30252590775489807    │
+│         test/chrf         │    15.321980476379395     │
+│     test/contra_loss      │    0.6871479153633118     │
+│     test/logit_scale      │     62.30245590209961     │
+│      test/rougeL_f1       │    0.12370917201042175    │
+│   test/rougeL_precision   │    0.13015751540660858    │
+│    test/rougeL_recall     │    0.1327442079782486     │
+│  test/speaker_bleu4_std   │    1.5873953104019165     │
+│   test/visual_feat_std    │            nan            │
+└───────────────────────────┴───────────────────────────┘
+Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1468/1468 0:11:01 • 0:00:00 2.18it/s

results/spamo/2026-05-05T21-58-47_test_v30_dual_ms_scratch/wandb-summary.json

@@ -0,0 +1 @@
+{"_timestamp":1.7780118178223538e+09,"test/contra_loss":0.6871479153633118,"test/visual_feat_std":NaN,"trainer/global_step":0,"test/bleu2":6.5959014892578125,"_wandb":{"runtime":685},"test/chrf":15.321980476379395,"test/logit_scale":62.30245590209961,"test/bertscore_f1":0.8612825274467468,"test/translations":{"log_mode":"IMMUTABLE","size":336140,"sha256":"a71f5d724fada50c99bb7077501db71fdfdd728c4f6d4118ff57b91cedd412cf","_type":"table-file","artifact_path":"wandb-client-artifact://1anr600w60w71a5g0oofp930xhc76a60dfgj6ptpqjljkva7ssp0108od2zar38mqq0zrgvamlcd2qe7lpzf24rz091msot6d6sf7haqubwuucmeqnl5wdx9n1xrohpj/test/translations.table.json","ncols":3,"path":"media/table/test/translations_0_a71f5d724fada50c99bb.table.json","nrows":2935,"_latest_artifact_path":"wandb-client-artifact://ry5f31wgcelk3mf9z2rdvqldljmzen1l45gxxguriz66dndqxzcew4vdcuh0k36tz1wxo6gf0hs6m05xds29qcmr7c6pd1eeitx4vwrsxjkwwght3lgbttwtx5l2ilrs:latest/test/translations.table.json"},"test/speaker_bleu4_std":1.5873953104019165,"_runtime":685,"test/bleurt20":0.30252590775489807,"test/rougeL_recall":0.1327442079782486,"epoch":0,"test/per_speaker_metrics":{"nrows":151,"ncols":4,"_type":"table-file","size":5400,"path":"media/table/test/per_speaker_metrics_1_6e04f24d7537b9929b36.table.json","_latest_artifact_path":"wandb-client-artifact://7a3w409m04y23jkoa63e3lvrqv6oa483fp1557xep8e7mv3der68xg2njvps8b5p450rsvoz4m8ukf0k38mnkj935a8337ocuth09z213fvv5pjtrabgrxsu9dnt25wx:latest/test/per_speaker_metrics.table.json","sha256":"6e04f24d7537b9929b361374d686ea4f705fc9ccbfa950a9ddf32d685aea1136","log_mode":"IMMUTABLE","artifact_path":"wandb-client-artifact://a03auae5b6y1y106mnxtynpob0u50o1cw76lkk5molj5ptomo6kzh6qkp043e8r8g7g5j4f4xe7pg4bb0d0d7euxndrm6hivpkf4d67e7j2lqzt1ocm527vlrri5cl9u/test/per_speaker_metrics.table.json"},"test/bleu1":18.552621841430664,"test/bleu3":3.398465394973755,"_step":2,"test/rougeL_precision":0.13015751540660858,"test/bleu4":2.0304346084594727,"test/rougeL_f1":0.12370917201042175}

results/spamo/2026-05-05T22-10-23_test_v31_quad_ms_scratch/config.yaml

@@ -0,0 +1,187 @@
+_wandb:
+    value:
+        cli_version: 0.23.1
+        e:
+            r8q1fvf03dx2dazx2tb1cxamexs5pfxx:
+                args:
+                    - -c
+                    - configs/finetune_v31_quad_xattn_ms_scratch.yaml
+                    - --ckpt
+                    - logs/2026-05-05T09-06-44_finetune_v31_quad_xattn_ms_scratch/checkpoints/epoch=00009-step=0029510-bleu4=1.84.ckpt
+                    - -t
+                    - "False"
+                    - --test
+                    - "True"
+                    - -n
+                    - test_v31_quad_ms_scratch
+                    - --tags
+                    - test
+                    - pjm-ms
+                    - quad
+                    - scratch
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 12
+                cpu_count_logical: 24
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "1967317549056"
+                        used: "1649339273216"
+                email: s28855@pjwstk.edu.pl
+                executable: /home/croco/SpaMo-PJM/.venv/bin/python
+                git:
+                    commit: 4f11b72511d6fd9692336b6b89e43f98e516e01c
+                    remote: https://github.com/croco-corp/SpaMo-PJM.git
+                gpu: NVIDIA GeForce RTX 3090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-5c32b58e-e363-6bec-ff14-3d6044d0ae84
+                host: lowca-desktop
+                memory:
+                    total: "64894398464"
+                os: Linux-6.8.0-110-generic-x86_64-with-glibc2.39
+                program: /home/croco/SpaMo-PJM/main.py
+                python: CPython 3.10.19
+                root: logs/2026-05-05T22-10-23_test_v31_quad_ms_scratch
+                startedAt: "2026-05-05T20:10:27.832202Z"
+                writerId: r8q1fvf03dx2dazx2tb1cxamexs5pfxx
+        m:
+            - "1": trainer/global_step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "2":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "3":
+                - 7
+                - 13
+                - 14
+                - 15
+                - 16
+                - 66
+            "4": 3.10.19
+            "5": 0.23.1
+            "6": 4.57.1
+            "12": 0.23.1
+            "13": linux-x86_64
+alpha:
+    value: 1
+aux_input_size:
+    value: 2048
+beam_size:
+    value: 5
+cache_dir:
+    value: /home/croco/.cache/huggingface/hub
+combined_loss:
+    value: true
+config_files:
+    value:
+        - configs/finetune_v31_quad_xattn_ms_scratch.yaml
+cross_modal_align:
+    value: true
+data:
+    value:
+        aux_features_path: features/hand_vit_feat_pjm.h5
+        batch_size: 2
+        device: cuda
+        keypoint_features_path: features/mediapipe_feat_pjm.h5
+        max_frame_length: 512
+        motion_features_path: features/mae_feat_pjm.h5
+        multilang_path: features/texts_multilang_pjm.h5
+        num_workers: 4
+        set_to_test: test
+        target_lang: English
+        test_split_path: /home/croco/CrocoSign/data/split_test_ms.csv
+        texts_path: features/texts_eng.h5
+        train_split_path: /home/croco/CrocoSign/data/split_train_ms.csv
+        val_split_path: /home/croco/CrocoSign/data/split_val_ms.csv
+        visual_features_path: features/vit_feat_pjm.h5
+evaluation:
+    value: mse
+frame_sample_rate:
+    value: 1
+fusion_dropout:
+    value: 0.3
+fusion_lr:
+    value: 1e-05
+fusion_mode:
+    value: joint
+input_size:
+    value: 2048
+inter_hidden:
+    value: 768
+keypoint_dim:
+    value: 258
+lora_alpha:
+    value: 32
+lora_dropout:
+    value: 0.1
+lora_r:
+    value: 16
+lr:
+    value: 0.0001
+lr_patience:
+    value: 5
+lr_scheduler_mode:
+    value: max
+max_frame_len:
+    value: 512
+max_txt_len:
+    value: 64
+model_name:
+    value: google/flan-t5-xl
+monitor:
+    value: val/bleu4
+motion_input_size:
+    value: 1024
+new_stream_lr:
+    value: 0.0001
+num_in_context:
+    value: 3
+prompt:
+    value: Translate the given sentence into {}.
+queue_size:
+    value: 0
+scheduler_config:
+    value:
+        params:
+            lr_max: 1
+            lr_min: 0.1
+            lr_start: 0
+            max_decay_steps: 50000
+            warm_up_steps: 1000
+        target: spamo.lr_scheduler.LambdaWarmUpCosineScheduler
+seed:
+    value: 0
+tuning_type:
+    value: lora
+use_in_context:
+    value: true
+warm_up_steps:
+    value: 0

results/spamo/2026-05-05T22-10-23_test_v31_quad_ms_scratch/output.log

@@ -0,0 +1,48 @@
+Restoring states from the checkpoint path at logs/2026-05-05T09-06-44_finetune_v31_quad_xattn_ms_scratch/checkpoints/epoch=00009-step=0029510-bleu4=1.84.ckpt
+/home/croco/SpaMo-PJM/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:283: Be aware that when using `ckpt_path`, callbacks used to create the checkpoint need to be provided during `Trainer` instantiation. Please add the following callbacks: ["ModelCheckpoint{'monitor': 'val/bleu4', 'mode': 'max', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None}", "EarlyStopping{'monitor': 'val/bleu4', 'mode': 'max'}"].
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loaded model weights from the checkpoint at logs/2026-05-05T09-06-44_finetune_v31_quad_xattn_ms_scratch/checkpoints/epoch=00009-step=0029510-bleu4=1.84.ckpt
+/home/croco/.local/share/uv/python/cpython-3.10.19-linux-x86_64-gnu/lib/python3.10/contextlib.py:103: FutureWarning: `torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature.
+  self.gen = func(*args, **kwds)
+/home/croco/SpaMo-PJM/spamo/t5_slt.py:585: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1857.)
+  log_dict[f"{split}/visual_feat_std"] = visual_outputs.mean(1).std(0).mean().item()
+
+===== Validation Examples =====
+Reference: i said, the dog looks down at the frog sitting in the jar.
+Generated: he looks through binoculars and sees that there is a frog.
+--------------------------------------------------
+Reference: he cried out and fell back.
+Generated: he puts a watermelon on his head.
+--------------------------------------------------
+Reference: the child hugs the dog close.
+Generated: i can't, i can't, i can't, i can't, i can't, i can't, i can't, i can't, i can't, i can't, i can
+--------------------------------------------------
+Reference: the frog has disappeared and is not coming back.
+Generated: i don't know.
+--------------------------------------------------
+Reference: the boy searched and put his hand in a burrow underground.
+Generated: he looks through binoculars and sees that there is a lot of frogs.
+--------------------------------------------------
+Dumped 2935 pairs → logs/2026-05-05T22-10-23_test_v31_quad_ms_scratch/test_outputs.txt
+Logged 2935 translations to W&B test/translations
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     test/bertscore_f1     │    0.8602100014686584     │
+│        test/bleu1         │    18.453319549560547     │
+│        test/bleu2         │     6.044188976287842     │
+│        test/bleu3         │    2.8546669483184814     │
+│        test/bleu4         │    1.5552290678024292     │
+│       test/bleurt20       │    0.30417200922966003    │
+│         test/chrf         │     14.50250244140625     │
+│     test/contra_loss      │    0.4879496097564697     │
+│     test/logit_scale      │     37.6885986328125      │
+│      test/rougeL_f1       │    0.1191704198718071     │
+│   test/rougeL_precision   │    0.1266443431377411     │
+│    test/rougeL_recall     │    0.1267620176076889     │
+│  test/speaker_bleu4_std   │    1.3776135444641113     │
+│   test/visual_feat_std    │            nan            │
+└───────────────────────────┴───────────────────────────┘
+Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1468/1468 0:10:51 • 0:00:00 2.04it/s

results/spamo/2026-05-05T22-10-23_test_v31_quad_ms_scratch/wandb-summary.json

@@ -0,0 +1 @@
+{"test/rougeL_f1":0.1191704198718071,"trainer/global_step":0,"test/contra_loss":0.4879496097564697,"_step":2,"test/logit_scale":37.6885986328125,"_runtime":675,"_wandb":{"runtime":675},"epoch":0,"test/bleurt20":0.30417200922966003,"test/rougeL_precision":0.1266443431377411,"test/visual_feat_std":NaN,"test/translations":{"ncols":3,"sha256":"a4f89027a807d65a92d7ae72f6618b0a13837b9fc9a8fb500450a7937525bc44","_latest_artifact_path":"wandb-client-artifact://ry5f31wgcelk3mf9z2rdvqldljmzen1l45gxxguriz66dndqxzcew4vdcuh0k36tz1wxo6gf0hs6m05xds29qcmr7c6pd1eeitx4vwrsxjkwwght3lgbttwtx5l2ilrs:latest/test/translations.table.json","artifact_path":"wandb-client-artifact://1anr600w60w71a5g0oofp930xhc76a60dfgj6ptpqjljkva7ssp0108od2zar38mqq0zrgvamlcd2qe7lpzf24rz091msot6d6sf7haqubwuucmeqnl5wdx9n1xrohpj/test/translations.table.json","size":332802,"path":"media/table/test/translations_0_a4f89027a807d65a92d7.table.json","log_mode":"IMMUTABLE","_type":"table-file","nrows":2935},"test/chrf":14.50250244140625,"_timestamp":1.7780125027608464e+09,"test/bleu1":18.453319549560547,"test/speaker_bleu4_std":1.3776135444641113,"test/bleu2":6.044188976287842,"test/per_speaker_metrics":{"nrows":151,"artifact_path":"wandb-client-artifact://a03auae5b6y1y106mnxtynpob0u50o1cw76lkk5molj5ptomo6kzh6qkp043e8r8g7g5j4f4xe7pg4bb0d0d7euxndrm6hivpkf4d67e7j2lqzt1ocm527vlrri5cl9u/test/per_speaker_metrics.table.json","log_mode":"IMMUTABLE","_latest_artifact_path":"wandb-client-artifact://7a3w409m04y23jkoa63e3lvrqv6oa483fp1557xep8e7mv3der68xg2njvps8b5p450rsvoz4m8ukf0k38mnkj935a8337ocuth09z213fvv5pjtrabgrxsu9dnt25wx:latest/test/per_speaker_metrics.table.json","path":"media/table/test/per_speaker_metrics_1_e3aba32d33a1f9480a46.table.json","ncols":4,"sha256":"e3aba32d33a1f9480a460fad3cd9e0ce49d65962cb79c061a362505503c02938","_type":"table-file","size":5398},"test/bertscore_f1":0.8602100014686584,"test/bleu4":1.5552290678024292,"test/rougeL_recall":0.1267620176076889,"test/bleu3":2.8546669483184814}

results/spamo/2026-05-05T23-00-55_test_upstream_pjm_ms_dual/config.yaml

@@ -0,0 +1,183 @@
+_wandb:
+    value:
+        cli_version: 0.23.1
+        e:
+            izsgqz5lr1uu2yjooktvnvbtuffi3fbf:
+                args:
+                    - -c
+                    - configs/finetune_v26_dual_paper_faithful_from_upstream.yaml
+                    - --ckpt
+                    - /home/croco/SpaMo/logs/2026-05-02T08-39-04_finetune_pjm_ms_upstream_v2/checkpoints/epoch=00043-step=0129844-bleu4=5.36.ckpt
+                    - -t
+                    - "False"
+                    - --test
+                    - "True"
+                    - -n
+                    - test_upstream_pjm_ms_dual
+                    - --tags
+                    - test
+                    - pjm-ms
+                    - dual
+                    - phoenix-upstream-pjm
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 12
+                cpu_count_logical: 24
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "1967317549056"
+                        used: "1649346523136"
+                email: s28855@pjwstk.edu.pl
+                executable: /home/croco/SpaMo-PJM/.venv/bin/python
+                git:
+                    commit: 4f11b72511d6fd9692336b6b89e43f98e516e01c
+                    remote: https://github.com/croco-corp/SpaMo-PJM.git
+                gpu: NVIDIA GeForce RTX 3090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-5c32b58e-e363-6bec-ff14-3d6044d0ae84
+                host: lowca-desktop
+                memory:
+                    total: "64894398464"
+                os: Linux-6.8.0-110-generic-x86_64-with-glibc2.39
+                program: /home/croco/SpaMo-PJM/main.py
+                python: CPython 3.10.19
+                root: logs/2026-05-05T23-00-55_test_upstream_pjm_ms_dual
+                startedAt: "2026-05-05T21:00:59.918207Z"
+                writerId: izsgqz5lr1uu2yjooktvnvbtuffi3fbf
+        m:
+            - "1": trainer/global_step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "2":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "3":
+                - 7
+                - 13
+                - 14
+                - 15
+                - 16
+                - 66
+            "4": 3.10.19
+            "5": 0.23.1
+            "6": 4.57.1
+            "12": 0.23.1
+            "13": linux-x86_64
+alpha:
+    value: 1
+aux_input_size:
+    value: 0
+beam_size:
+    value: 5
+cache_dir:
+    value: /home/croco/.cache/huggingface/hub
+combined_loss:
+    value: true
+config_files:
+    value:
+        - configs/finetune_v26_dual_paper_faithful_from_upstream.yaml
+cross_modal_align:
+    value: true
+data:
+    value:
+        batch_size: 2
+        device: cuda
+        max_frame_length: 512
+        motion_features_path: features/mae_feat_pjm.h5
+        multilang_path: features/texts_multilang_pjm.h5
+        num_workers: 4
+        set_to_test: test
+        target_lang: English
+        test_split_path: /home/croco/CrocoSign/data/split_test_ms.csv
+        texts_path: features/texts_eng.h5
+        train_split_path: /home/croco/CrocoSign/data/split_train_ms.csv
+        val_split_path: /home/croco/CrocoSign/data/split_val_ms.csv
+        visual_features_path: features/vit_feat_pjm.h5
+evaluation:
+    value: mse
+frame_sample_rate:
+    value: 1
+fusion_dropout:
+    value: 0.3
+fusion_lr:
+    value: 1e-05
+fusion_mode:
+    value: joint
+input_size:
+    value: 2048
+inter_hidden:
+    value: 768
+keypoint_dim:
+    value: 0
+lora_alpha:
+    value: 32
+lora_dropout:
+    value: 0.1
+lora_r:
+    value: 16
+lr:
+    value: 0.0001
+lr_patience:
+    value: 5
+lr_scheduler_mode:
+    value: max
+max_frame_len:
+    value: 512
+max_txt_len:
+    value: 64
+model_name:
+    value: google/flan-t5-xl
+monitor:
+    value: val/bleu4
+motion_input_size:
+    value: 1024
+num_in_context:
+    value: 3
+prompt:
+    value: Translate the given sentence into {}.
+queue_size:
+    value: 0
+scheduler_config:
+    value:
+        params:
+            lr_max: 1
+            lr_min: 0.1
+            lr_start: 0
+            max_decay_steps: 30000
+            warm_up_steps: 1000
+        target: spamo.lr_scheduler.LambdaWarmUpCosineScheduler
+seed:
+    value: 0
+tuning_type:
+    value: lora
+use_in_context:
+    value: true
+warm_up_steps:
+    value: 0

results/spamo/2026-05-05T23-00-55_test_upstream_pjm_ms_dual/output.log

@@ -0,0 +1,47 @@
+Restoring states from the checkpoint path at /home/croco/SpaMo/logs/2026-05-02T08-39-04_finetune_pjm_ms_upstream_v2/checkpoints/epoch=00043-step=0129844-bleu4=5.36.ckpt
+Lightning automatically upgraded your loaded checkpoint from v1.9.5 to v2.6.1. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../SpaMo/logs/2026-05-02T08-39-04_finetune_pjm_ms_upstream_v2/checkpoints/epoch=00043-step=0129844-bleu4=5.36.ckpt`
+/home/croco/SpaMo-PJM/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:283: Be aware that when using `ckpt_path`, callbacks used to create the checkpoint need to be provided during `Trainer` instantiation. Please add the following callbacks: ["EarlyStopping{'monitor': 'val/bleu4', 'mode': 'max'}", "ModelCheckpoint{'monitor': 'val/bleu4', 'mode': 'max', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None}"].
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loaded model weights from the checkpoint at /home/croco/SpaMo/logs/2026-05-02T08-39-04_finetune_pjm_ms_upstream_v2/checkpoints/epoch=00043-step=0129844-bleu4=5.36.ckpt
+/home/croco/SpaMo-PJM/spamo/t5_slt.py:585: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1857.)
+  log_dict[f"{split}/visual_feat_std"] = visual_outputs.mean(1).std(0).mean().item()
+
+===== Validation Examples =====
+Reference: i said, the dog looks down at the frog sitting in the jar.
+Generated: the dog looks into the jar.
+--------------------------------------------------
+Reference: he cried out and fell back.
+Generated: the cat jumped out of the window.
+--------------------------------------------------
+Reference: the child hugs the dog close.
+Generated: the baby fell asleep, the dog also fell asleep.
+--------------------------------------------------
+Reference: the frog has disappeared and is not coming back.
+Generated: there is no such sign.
+--------------------------------------------------
+Reference: the boy searched and put his hand in a burrow underground.
+Generated: the boy looks inside.
+--------------------------------------------------
+Dumped 2935 pairs → logs/2026-05-05T23-00-55_test_upstream_pjm_ms_dual/test_outputs.txt
+Logged 2935 translations to W&B test/translations
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     test/bertscore_f1     │    0.8778611421585083     │
+│        test/bleu1         │     28.86847686767578     │
+│        test/bleu2         │    13.760455131530762     │
+│        test/bleu3         │     8.273240089416504     │
+│        test/bleu4         │     5.543374061584473     │
+│       test/bleurt20       │    0.35117584466934204    │
+│         test/chrf         │    21.055646896362305     │
+│     test/contra_loss      │    0.47302475571632385    │
+│     test/logit_scale      │    125.73445129394531     │
+│      test/rougeL_f1       │    0.1966341733932495     │
+│   test/rougeL_precision   │    0.21004807949066162    │
+│    test/rougeL_recall     │    0.2010912448167801     │
+│  test/speaker_bleu4_std   │     3.436262845993042     │
+│   test/visual_feat_std    │            nan            │
+└───────────────────────────┴───────────────────────────┘
+Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1468/1468 0:08:14 • 0:00:00 2.79it/s

results/spamo/2026-05-05T23-00-55_test_upstream_pjm_ms_dual/wandb-summary.json

@@ -0,0 +1 @@
+{"test/bleu3":8.273240089416504,"test/bleurt20":0.35117584466934204,"test/rougeL_precision":0.21004807949066162,"test/visual_feat_std":NaN,"test/chrf":21.055646896362305,"test/translations":{"size":310677,"_type":"table-file","log_mode":"IMMUTABLE","_latest_artifact_path":"wandb-client-artifact://ry5f31wgcelk3mf9z2rdvqldljmzen1l45gxxguriz66dndqxzcew4vdcuh0k36tz1wxo6gf0hs6m05xds29qcmr7c6pd1eeitx4vwrsxjkwwght3lgbttwtx5l2ilrs:latest/test/translations.table.json","nrows":2935,"artifact_path":"wandb-client-artifact://1anr600w60w71a5g0oofp930xhc76a60dfgj6ptpqjljkva7ssp0108od2zar38mqq0zrgvamlcd2qe7lpzf24rz091msot6d6sf7haqubwuucmeqnl5wdx9n1xrohpj/test/translations.table.json","path":"media/table/test/translations_0_58b8b82c36d3ad5022b6.table.json","ncols":3,"sha256":"58b8b82c36d3ad5022b68454a4743e387562833f6b35fa394c9ee81767cc4476"},"test/bleu4":5.543374061584473,"test/bleu1":28.86847686767578,"test/bleu2":13.760455131530762,"test/rougeL_recall":0.2010912448167801,"test/logit_scale":125.73445129394531,"_timestamp":1.7780153766640835e+09,"test/rougeL_f1":0.1966341733932495,"test/contra_loss":0.47302475571632385,"epoch":0,"test/speaker_bleu4_std":3.436262845993042,"test/bertscore_f1":0.8778611421585083,"_runtime":517,"_step":2,"trainer/global_step":0,"test/per_speaker_metrics":{"size":5410,"path":"media/table/test/per_speaker_metrics_1_f6d1cbac8a1e0e6ef0cd.table.json","ncols":4,"_type":"table-file","log_mode":"IMMUTABLE","sha256":"f6d1cbac8a1e0e6ef0cda011bfbfe34d4f142879631b3f2a46b9e641c170dcf0","nrows":151,"_latest_artifact_path":"wandb-client-artifact://7a3w409m04y23jkoa63e3lvrqv6oa483fp1557xep8e7mv3der68xg2njvps8b5p450rsvoz4m8ukf0k38mnkj935a8337ocuth09z213fvv5pjtrabgrxsu9dnt25wx:latest/test/per_speaker_metrics.table.json","artifact_path":"wandb-client-artifact://a03auae5b6y1y106mnxtynpob0u50o1cw76lkk5molj5ptomo6kzh6qkp043e8r8g7g5j4f4xe7pg4bb0d0d7euxndrm6hivpkf4d67e7j2lqzt1ocm527vlrri5cl9u/test/per_speaker_metrics.table.json"},"_wandb":{"runtime":517}}

results/spamo/2026-05-06T15-27-35_test_v32_dual_si_scratch/config.yaml

@@ -0,0 +1,183 @@
+_wandb:
+    value:
+        cli_version: 0.23.1
+        e:
+            9u9cyar8iugo2xf3zoukwcgyqehainwo:
+                args:
+                    - -c
+                    - configs/finetune_v32_dual_si_scratch.yaml
+                    - --ckpt
+                    - logs/2026-05-06T03-29-53_finetune_v32_dual_si_scratch/checkpoints/epoch=00008-step=0012510-bleu4=0.99.ckpt
+                    - -t
+                    - "False"
+                    - --test
+                    - "True"
+                    - -n
+                    - test_v32_dual_si_scratch
+                    - --tags
+                    - test
+                    - pjm-si
+                    - dual
+                    - scratch
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 12
+                cpu_count_logical: 24
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "1967317549056"
+                        used: "1674701602816"
+                email: s28855@pjwstk.edu.pl
+                executable: /home/croco/SpaMo-PJM/.venv/bin/python
+                git:
+                    commit: 4f11b72511d6fd9692336b6b89e43f98e516e01c
+                    remote: https://github.com/croco-corp/SpaMo-PJM.git
+                gpu: NVIDIA GeForce RTX 3090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-5c32b58e-e363-6bec-ff14-3d6044d0ae84
+                host: lowca-desktop
+                memory:
+                    total: "64894398464"
+                os: Linux-6.8.0-110-generic-x86_64-with-glibc2.39
+                program: /home/croco/SpaMo-PJM/main.py
+                python: CPython 3.10.19
+                root: logs/2026-05-06T15-27-35_test_v32_dual_si_scratch
+                startedAt: "2026-05-06T13:27:41.258680Z"
+                writerId: 9u9cyar8iugo2xf3zoukwcgyqehainwo
+        m:
+            - "1": trainer/global_step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "2":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "3":
+                - 7
+                - 13
+                - 14
+                - 15
+                - 16
+                - 66
+            "4": 3.10.19
+            "5": 0.23.1
+            "6": 4.57.1
+            "12": 0.23.1
+            "13": linux-x86_64
+alpha:
+    value: 1
+aux_input_size:
+    value: 0
+beam_size:
+    value: 5
+cache_dir:
+    value: /home/croco/.cache/huggingface/hub
+combined_loss:
+    value: true
+config_files:
+    value:
+        - configs/finetune_v32_dual_si_scratch.yaml
+cross_modal_align:
+    value: true
+data:
+    value:
+        batch_size: 2
+        device: cuda
+        max_frame_length: 512
+        motion_features_path: features/mae_feat_pjm.h5
+        multilang_path: features/texts_multilang_pjm.h5
+        num_workers: 4
+        set_to_test: test
+        target_lang: English
+        test_split_path: /home/croco/CrocoSign/data/split_test.csv
+        texts_path: features/texts_eng.h5
+        train_split_path: /home/croco/CrocoSign/data/split_train.csv
+        val_split_path: /home/croco/CrocoSign/data/split_val.csv
+        visual_features_path: features/vit_feat_pjm.h5
+evaluation:
+    value: mse
+frame_sample_rate:
+    value: 1
+fusion_dropout:
+    value: 0.3
+fusion_lr:
+    value: 1e-05
+fusion_mode:
+    value: joint
+input_size:
+    value: 2048
+inter_hidden:
+    value: 768
+keypoint_dim:
+    value: 0
+lora_alpha:
+    value: 32
+lora_dropout:
+    value: 0.1
+lora_r:
+    value: 16
+lr:
+    value: 0.0001
+lr_patience:
+    value: 5
+lr_scheduler_mode:
+    value: max
+max_frame_len:
+    value: 512
+max_txt_len:
+    value: 64
+model_name:
+    value: google/flan-t5-xl
+monitor:
+    value: val/bleu4
+motion_input_size:
+    value: 1024
+num_in_context:
+    value: 3
+prompt:
+    value: Translate the given sentence into {}.
+queue_size:
+    value: 0
+scheduler_config:
+    value:
+        params:
+            lr_max: 1
+            lr_min: 0.1
+            lr_start: 0
+            max_decay_steps: 40000
+            warm_up_steps: 1000
+        target: spamo.lr_scheduler.LambdaWarmUpCosineScheduler
+seed:
+    value: 0
+tuning_type:
+    value: lora
+use_in_context:
+    value: true
+warm_up_steps:
+    value: 0

results/spamo/2026-05-06T15-27-35_test_v32_dual_si_scratch/output.log

@@ -0,0 +1,44 @@
+Restoring states from the checkpoint path at logs/2026-05-06T03-29-53_finetune_v32_dual_si_scratch/checkpoints/epoch=00008-step=0012510-bleu4=0.99.ckpt
+/home/croco/SpaMo-PJM/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:283: Be aware that when using `ckpt_path`, callbacks used to create the checkpoint need to be provided during `Trainer` instantiation. Please add the following callbacks: ["EarlyStopping{'monitor': 'val/bleu4', 'mode': 'max'}", "ModelCheckpoint{'monitor': 'val/bleu4', 'mode': 'max', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None}"].
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loaded model weights from the checkpoint at logs/2026-05-06T03-29-53_finetune_v32_dual_si_scratch/checkpoints/epoch=00008-step=0012510-bleu4=0.99.ckpt
+
+===== Validation Examples =====
+Reference: the same for the entire month.
+Generated: charlie chaplin fixes a broken window.
+--------------------------------------------------
+Reference: either way.
+Generated: i don't know, i don't know.
+--------------------------------------------------
+Reference: what do you want to do? think about what you will do at home.
+Generated: mickey mouse with an apple on his head.
+--------------------------------------------------
+Reference: don't you want to?
+Generated: i don't know, i don't know.
+--------------------------------------------------
+Reference: february two thousand and eleven?
+Generated: i can't, i'm busy.
+--------------------------------------------------
+Dumped 10850 pairs → logs/2026-05-06T15-27-35_test_v32_dual_si_scratch/test_outputs.txt
+Logged 10850 translations to W&B test/translations
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     test/bertscore_f1     │    0.8575076460838318     │
+│        test/bleu1         │    17.649003982543945     │
+│        test/bleu2         │     4.52895975112915      │
+│        test/bleu3         │    1.8570435047149658     │
+│        test/bleu4         │     0.931571900844574     │
+│       test/bleurt20       │    0.27966323494911194    │
+│         test/chrf         │    13.139008522033691     │
+│     test/contra_loss      │    0.6187865138053894     │
+│     test/logit_scale      │     27.26032829284668     │
+│      test/rougeL_f1       │    0.09283461421728134    │
+│   test/rougeL_precision   │    0.0970124751329422     │
+│    test/rougeL_recall     │    0.10088594257831573    │
+│  test/speaker_bleu4_std   │    0.5458764433860779     │
+│   test/visual_feat_std    │    0.07659861445426941    │
+└───────────────────────────┴───────────────────────────┘
+Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5425/5425 0:35:07 • 0:00:00 2.14it/s

results/spamo/2026-05-06T15-27-35_test_v32_dual_si_scratch/wandb-summary.json

@@ -0,0 +1 @@
+{"test/translations":{"path":"media/table/test/translations_0_387811b41725bc8e1d5f.table.json","log_mode":"IMMUTABLE","_type":"table-file","size":1025827,"artifact_path":"wandb-client-artifact://58xg70zfxnbeuv86gksioh7q6gn8yun5vdx24k9ekedsugy5yhw8o7zqhqd5su3fs1l712yz0xfxbg91bsogo7e8xjy86746r5zghk5629acno4dnh8nircnnpsfdynl/test/translations.table.json","nrows":10850,"sha256":"387811b41725bc8e1d5f23f4e03bb30caa5580702c30c55e95a2db8f54ae2c29","_latest_artifact_path":"wandb-client-artifact://msij8n3mech9p94vgf1irouh080i4flntmlfoy09wvb1qi9ar7iiezxxzzb7zts3q5zoz20wig3ddgtsp1zfk0wjp83v5ytsl1qt183qvdw38whhiywuqn1vo9ng6od4:latest/test/translations.table.json","ncols":3},"test/contra_loss":0.6187865138053894,"test/bleu2":4.52895975112915,"test/per_speaker_metrics":{"_type":"table-file","size":1880,"log_mode":"IMMUTABLE","_latest_artifact_path":"wandb-client-artifact://83knle219posx65ilznuzp0yq4xi901n98jxgyuue40tq0f3z1sune6eroyb09gpsufojfhqri0hbbbuy56sy3kido2uq3fc006yatcny4kgqd8etr73m6ch1unnljoo:latest/test/per_speaker_metrics.table.json","artifact_path":"wandb-client-artifact://x1zhrlmwfjtg779by5lwye3oc7wk5k4wngqudftfoay43mxld7jmf5vf31wyofz4mdw49f1csaec29vgkgmswmmdak557sjqdqkemd4m9fub3ph9j3hq1lobiqixl19j/test/per_speaker_metrics.table.json","sha256":"9a72b278e43c128a719f32c776bbf3e81fb954e4d7b9e8a541f32a9764eda9c4","path":"media/table/test/per_speaker_metrics_1_9a72b278e43c128a719f.table.json","ncols":4,"nrows":50},"_timestamp":1.7780762212087872e+09,"_wandb":{"runtime":2159},"test/chrf":13.139008522033691,"test/rougeL_recall":0.10088594257831573,"test/bleu1":17.649003982543945,"test/bleu3":1.8570435047149658,"_runtime":2159,"test/speaker_bleu4_std":0.5458764433860779,"test/rougeL_f1":0.09283461421728134,"test/bertscore_f1":0.8575076460838318,"_step":2,"test/bleurt20":0.27966323494911194,"test/bleu4":0.931571900844574,"test/visual_feat_std":0.07659861445426941,"test/logit_scale":27.26032829284668,"trainer/global_step":0,"test/rougeL_precision":0.0970124751329422,"epoch":0}

results/spamo/2026-05-07T04-18-55_test_v33_quad_si_scratch/config.yaml

@@ -0,0 +1,187 @@
+_wandb:
+    value:
+        cli_version: 0.23.1
+        e:
+            uu1tyz4dykwndmkdjsbnabn2r6s5akvc:
+                args:
+                    - -c
+                    - configs/finetune_v33_quad_xattn_si_scratch.yaml
+                    - --ckpt
+                    - logs/2026-05-06T16-03-46_finetune_v33_quad_xattn_si_scratch/checkpoints/epoch=00026-step=0037530-bleu4=1.06.ckpt
+                    - -t
+                    - "False"
+                    - --test
+                    - "True"
+                    - -n
+                    - test_v33_quad_si_scratch
+                    - --tags
+                    - test
+                    - pjm-si
+                    - quad
+                    - scratch
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 12
+                cpu_count_logical: 24
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "1967317549056"
+                        used: "1680692973568"
+                email: s28855@pjwstk.edu.pl
+                executable: /home/croco/SpaMo-PJM/.venv/bin/python
+                git:
+                    commit: 4f11b72511d6fd9692336b6b89e43f98e516e01c
+                    remote: https://github.com/croco-corp/SpaMo-PJM.git
+                gpu: NVIDIA GeForce RTX 3090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-5c32b58e-e363-6bec-ff14-3d6044d0ae84
+                host: lowca-desktop
+                memory:
+                    total: "64894398464"
+                os: Linux-6.8.0-110-generic-x86_64-with-glibc2.39
+                program: /home/croco/SpaMo-PJM/main.py
+                python: CPython 3.10.19
+                root: logs/2026-05-07T04-18-55_test_v33_quad_si_scratch
+                startedAt: "2026-05-07T02:19:03.025728Z"
+                writerId: uu1tyz4dykwndmkdjsbnabn2r6s5akvc
+        m:
+            - "1": trainer/global_step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
+              "7": []
+        python_version: 3.10.19
+        t:
+            "1":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "2":
+                - 1
+                - 9
+                - 11
+                - 49
+                - 71
+                - 98
+                - 103
+            "3":
+                - 7
+                - 13
+                - 14
+                - 15
+                - 16
+                - 66
+            "4": 3.10.19
+            "5": 0.23.1
+            "6": 4.57.1
+            "12": 0.23.1
+            "13": linux-x86_64
+alpha:
+    value: 1
+aux_input_size:
+    value: 2048
+beam_size:
+    value: 5
+cache_dir:
+    value: /home/croco/.cache/huggingface/hub
+combined_loss:
+    value: true
+config_files:
+    value:
+        - configs/finetune_v33_quad_xattn_si_scratch.yaml
+cross_modal_align:
+    value: true
+data:
+    value:
+        aux_features_path: features/hand_vit_feat_pjm.h5
+        batch_size: 2
+        device: cuda
+        keypoint_features_path: features/mediapipe_feat_pjm.h5
+        max_frame_length: 512
+        motion_features_path: features/mae_feat_pjm.h5
+        multilang_path: features/texts_multilang_pjm.h5
+        num_workers: 4
+        set_to_test: test
+        target_lang: English
+        test_split_path: /home/croco/CrocoSign/data/split_test.csv
+        texts_path: features/texts_eng.h5
+        train_split_path: /home/croco/CrocoSign/data/split_train.csv
+        val_split_path: /home/croco/CrocoSign/data/split_val.csv
+        visual_features_path: features/vit_feat_pjm.h5
+evaluation:
+    value: mse
+frame_sample_rate:
+    value: 1
+fusion_dropout:
+    value: 0.3
+fusion_lr:
+    value: 1e-05
+fusion_mode:
+    value: joint
+input_size:
+    value: 2048
+inter_hidden:
+    value: 768
+keypoint_dim:
+    value: 258
+lora_alpha:
+    value: 32
+lora_dropout:
+    value: 0.1
+lora_r:
+    value: 16
+lr:
+    value: 0.0001
+lr_patience:
+    value: 5
+lr_scheduler_mode:
+    value: max
+max_frame_len:
+    value: 512
+max_txt_len:
+    value: 64
+model_name:
+    value: google/flan-t5-xl
+monitor:
+    value: val/bleu4
+motion_input_size:
+    value: 1024
+new_stream_lr:
+    value: 0.0001
+num_in_context:
+    value: 3
+prompt:
+    value: Translate the given sentence into {}.
+queue_size:
+    value: 0
+scheduler_config:
+    value:
+        params:
+            lr_max: 1
+            lr_min: 0.1
+            lr_start: 0
+            max_decay_steps: 40000
+            warm_up_steps: 1000
+        target: spamo.lr_scheduler.LambdaWarmUpCosineScheduler
+seed:
+    value: 0
+tuning_type:
+    value: lora
+use_in_context:
+    value: true
+warm_up_steps:
+    value: 0

results/spamo/2026-05-07T04-18-55_test_v33_quad_si_scratch/output.log

@@ -0,0 +1,46 @@
+Restoring states from the checkpoint path at logs/2026-05-06T16-03-46_finetune_v33_quad_xattn_si_scratch/checkpoints/epoch=00026-step=0037530-bleu4=1.06.ckpt
+/home/croco/SpaMo-PJM/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:283: Be aware that when using `ckpt_path`, callbacks used to create the checkpoint need to be provided during `Trainer` instantiation. Please add the following callbacks: ["ModelCheckpoint{'monitor': 'val/bleu4', 'mode': 'max', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None}", "EarlyStopping{'monitor': 'val/bleu4', 'mode': 'max'}"].
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loaded model weights from the checkpoint at logs/2026-05-06T16-03-46_finetune_v33_quad_xattn_si_scratch/checkpoints/epoch=00026-step=0037530-bleu4=1.06.ckpt
+/home/croco/.local/share/uv/python/cpython-3.10.19-linux-x86_64-gnu/lib/python3.10/contextlib.py:103: FutureWarning: `torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature.
+  self.gen = func(*args, **kwds)
+
+===== Validation Examples =====
+Reference: the same for the entire month.
+Generated: he threw a stone, threw a stone, and threw a second time.
+--------------------------------------------------
+Reference: either way.
+Generated: he threw it away.
+--------------------------------------------------
+Reference: what do you want to do? think about what you will do at home.
+Generated: a fish swims in a glass spherical aquarium.
+--------------------------------------------------
+Reference: don't you want to?
+Generated: i know, i know.
+--------------------------------------------------
+Reference: february two thousand and eleven?
+Generated: he has a bicycle and a bag of berries.
+--------------------------------------------------
+Dumped 10850 pairs → logs/2026-05-07T04-18-55_test_v33_quad_si_scratch/test_outputs.txt
+Logged 10850 translations to W&B test/translations
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     test/bertscore_f1     │    0.8603217005729675     │
+│        test/bleu1         │    18.782516479492188     │
+│        test/bleu2         │     5.233978271484375     │
+│        test/bleu3         │    2.2732200622558594     │
+│        test/bleu4         │     1.171427607536316     │
+│       test/bleurt20       │    0.2767305076122284     │
+│         test/chrf         │    14.138314247131348     │
+│     test/contra_loss      │    0.8353700041770935     │
+│     test/logit_scale      │    47.965736389160156     │
+│      test/rougeL_f1       │    0.10122055560350418    │
+│   test/rougeL_precision   │    0.10573126375675201    │
+│    test/rougeL_recall     │    0.11064910143613815    │
+│  test/speaker_bleu4_std   │     0.618619441986084     │
+│   test/visual_feat_std    │    0.07244817912578583    │
+└───────────────────────────┴───────────────────────────┘
+Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5425/5425 0:33:53 • 0:00:00 2.21it/s

results/spamo/2026-05-07T04-18-55_test_v33_quad_si_scratch/wandb-summary.json

@@ -0,0 +1 @@
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results/spamo/finetune_pjm_ms.log.gz

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