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W0530 22:18:22.514000 714703 torch/distributed/run.py:862] *****************************************
W0530 22:18:22.514000 714703 torch/distributed/run.py:862] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0530 22:18:22.514000 714703 torch/distributed/run.py:862] *****************************************
Config: {'project': 'thoughtformer', 'name': 'star-atc-4gpu-bs32-lr1e-5-fixed', 'only_eval': False, 'method': 'thoughtformer', 'data_curriculum': True, 'thought_curriculum': True, 'staging': 'adaptive', 'init_thought_stage': 1, 'init_data_stage': 1, 'patience': 5, 'staging_threshold': 0.9, 'c_thought': 1, 'max_latent_stage': 10, 'uniform_prob': 0.1, 'save_only_improve': False, 'model_id': 'Qwen/Qwen3-0.6B-Base', 'load_model_path': 'None', 'seed': 0, 'resume': 0, 'bf16': False, 'train_path': 'data/star_k10_L10_1000_train.json', 'val_path': 'data/star_k10_L10_100_valid.json', 'reset_optimizer': False, 'lr_schedule': 'none', 'batch_size_training': 4, 'eval_only_trained': False, 'eval_batch_size': 8, 'eval_every': 1, 'debug': False, 'gradient_accumulation_steps': 2, 'num_epochs': 100, 'lr': 1e-05, 'weight_decay': 0.01, 'group': 'star_atc_fixed_lr', 'tags': ['star', 'k10', 'L10', 'atc', 'adaptive', 'lr1e-5', 'best', 'budget_aug'], 'run_type': 'pilot'}
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
[rank0]:[W530 22:18:27.516767397 ProcessGroupNCCL.cpp:5324] Guessing device ID based on global rank. This can cause a hang if rank to GPU mapping is heterogeneous. You can specify device_id in init_process_group()

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Running FSDP on rank = 0, world size = 4
Running FSDP on rank = 3, world size = 4
Running FSDP on rank = 2, world size = 4
Running FSDP on rank = 1, world size = 4
FullyShardedDataParallel(
  (_fsdp_wrapped_module): Coconut(
    (base_causallm): Qwen3ForCausalLM(
      (model): Qwen3Model(
        (embed_tokens): Embedding(151672, 1024)
        (layers): ModuleList(
          (0-27): 28 x FullyShardedDataParallel(
            (_fsdp_wrapped_module): Qwen3DecoderLayer(
              (self_attn): Qwen3Attention(
                (q_proj): Linear(in_features=1024, out_features=2048, bias=False)
                (k_proj): Linear(in_features=1024, out_features=1024, bias=False)
                (v_proj): Linear(in_features=1024, out_features=1024, bias=False)
                (o_proj): Linear(in_features=2048, out_features=1024, bias=False)
                (q_norm): Qwen3RMSNorm((128,), eps=1e-06)
                (k_norm): Qwen3RMSNorm((128,), eps=1e-06)
              )
              (mlp): Qwen3MLP(
                (gate_proj): Linear(in_features=1024, out_features=3072, bias=False)
                (up_proj): Linear(in_features=1024, out_features=3072, bias=False)
                (down_proj): Linear(in_features=3072, out_features=1024, bias=False)
                (act_fn): SiLUActivation()
              )
              (input_layernorm): Qwen3RMSNorm((1024,), eps=1e-06)
              (post_attention_layernorm): Qwen3RMSNorm((1024,), eps=1e-06)
            )
          )
        )
        (norm): Qwen3RMSNorm((1024,), eps=1e-06)
        (rotary_emb): Qwen3RotaryEmbedding()
      )
      (lm_head): Linear(in_features=1024, out_features=151936, bias=False)
    )
    (embedding): Embedding(151672, 1024)
  )
)

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wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/ubuntu/.netrc.
wandb: Currently logged in as: seyedparsa to https://api.wandb.ai. Use `wandb login --relogin` to force relogin

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wandb: Tracking run with wandb version 0.25.1
wandb: Run data is saved locally in /home/ubuntu/thoughtformer/wandb/run-20260530_221857-qeh901q6
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626
wandb: ⭐️ View project at https://wandb.ai/seyedparsa/thoughtformer
wandb: 🚀 View run at https://wandb.ai/seyedparsa/thoughtformer/runs/qeh901q6

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============================================================
EPOCH 0/100 (thought_stage=1, data_stage=1)
============================================================
  thought_stage=1, c_thought=1, max_difficulty=1

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Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 3150.13 examples/s]

Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 3246.28 examples/s]

Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 3147.49 examples/s]

Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 3127.25 examples/s]

Map (num_proc=4):   0%|          | 0/100 [00:00<?, ? examples/s]
Map (num_proc=4):  25%|██▌       | 25/100 [00:00<00:00, 159.09 examples/s]
Map (num_proc=4): 100%|██████████| 100/100 [00:00<00:00, 368.80 examples/s]
Map (num_proc=4): 100%|██████████| 100/100 [00:00<00:00, 233.67 examples/s]

Training Epoch: 1:   0%|          | 0/31 [00:00<?, ?it/s]
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Training Epoch: 1:   0%|          | 0/31 [00:00<?, ?it/s]logging training data
rank 0 | gpu 0 | alloc=3.07 GB | reserved=33.19 GB | max=22.86 GB
rank 2 | gpu 2 | alloc=3.07 GB | reserved=33.20 GB | max=22.87 GBrank 1 | gpu 1 | alloc=3.07 GB | reserved=33.20 GB | max=22.87 GBrank 3 | gpu 3 | alloc=3.07 GB | reserved=33.19 GB | max=22.86 GB



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Training Epoch: 1/100, batch 0/63 completed (loss: 6.9504:   3%|▎         | 1/31 [00:02<01:24,  2.82s/it]
Training Epoch: 1/100, batch 0/63 completed (loss: 7.2336:   3%|▎         | 1/31 [00:02<01:24,  2.82s/it]
Training Epoch: 1/100, batch 0/63 completed (loss: 7.0313:   3%|▎         | 1/31 [00:02<01:24,  2.83s/it]
Training Epoch: 1/100, batch 1/63 completed (loss: 7.0711:   3%|▎         | 1/31 [00:02<01:24,  2.82s/it]
Training Epoch: 1/100, batch 1/63 completed (loss: 7.2277:   3%|▎         | 1/31 [00:02<01:24,  2.82s/it]
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Training Epoch: 1/100, batch 1/63 completed (loss: 6.6952:   3%|▎         | 1/31 [00:02<01:24,  2.83s/it]
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Training Epoch: 1/100, batch 3/63 completed (loss: 3.4333:   6%|▋         | 2/31 [00:04<00:55,  1.90s/it]
Training Epoch: 1/100, batch 3/63 completed (loss: 3.2321:   6%|▋         | 2/31 [00:04<00:55,  1.90s/it]
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Training Epoch: 1/100, batch 4/63 completed (loss: 1.1102:   6%|▋         | 2/31 [00:04<00:55,  1.90s/it]
Training Epoch: 1/100, batch 4/63 completed (loss: 1.0134:   6%|▋         | 2/31 [00:04<00:55,  1.90s/it]
Training Epoch: 1/100, batch 4/63 completed (loss: 1.2236:   6%|▋         | 2/31 [00:04<00:55,  1.90s/it]
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Training Epoch: 1/100, batch 6/63 completed (loss: 0.4805:  10%|▉         | 3/31 [00:06<00:45,  1.63s/it]
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Training Epoch: 1/100, batch 6/63 completed (loss: 0.5207:  10%|▉         | 3/31 [00:06<00:45,  1.63s/it]
Training Epoch: 1/100, batch 6/63 completed (loss: 0.5204:  13%|█▎        | 4/31 [00:06<00:40,  1.50s/it]
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Training Epoch: 1/100, batch 7/63 completed (loss: 0.4498:  13%|█▎        | 4/31 [00:06<00:40,  1.50s/it]
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Training Epoch: 1/100, batch 7/63 completed (loss: 0.5743:  13%|█▎        | 4/31 [00:06<00:40,  1.50s/it]
Training Epoch: 1/100, batch 8/63 completed (loss: 0.2958:  13%|█▎        | 4/31 [00:07<00:40,  1.50s/it]
Training Epoch: 1/100, batch 8/63 completed (loss: 0.5486:  13%|█▎        | 4/31 [00:07<00:40,  1.50s/it]
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Training Epoch: 1/100, batch 8/63 completed (loss: 0.6518:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 8/63 completed (loss: 0.4739:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 8/63 completed (loss: 0.2958:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 8/63 completed (loss: 0.5486:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 9/63 completed (loss: 0.1913:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
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Training Epoch: 1/100, batch 9/63 completed (loss: 0.3264:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 10/63 completed (loss: 0.2327:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 10/63 completed (loss: 0.1175:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
Training Epoch: 1/100, batch 10/63 completed (loss: 0.2422:  16%|█▌        | 5/31 [00:08<00:37,  1.43s/it]
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Training Epoch: 1/100, batch 10/63 completed (loss: 0.2327:  19%|█▉        | 6/31 [00:09<00:34,  1.39s/it]
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Training Epoch: 1/100, batch 10/63 completed (loss: 0.1175:  19%|█▉        | 6/31 [00:09<00:34,  1.39s/it]
Training Epoch: 1/100, batch 11/63 completed (loss: 0.1848:  19%|█▉        | 6/31 [00:09<00:34,  1.39s/it]
Training Epoch: 1/100, batch 11/63 completed (loss: 0.2562:  19%|█▉        | 6/31 [00:09<00:34,  1.39s/it]
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Training Epoch: 1/100, batch 11/63 completed (loss: 0.4129:  19%|█▉        | 6/31 [00:09<00:34,  1.39s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.1971:  19%|█▉        | 6/31 [00:10<00:34,  1.39s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.1856:  19%|█▉        | 6/31 [00:10<00:34,  1.39s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.3605:  19%|█▉        | 6/31 [00:10<00:34,  1.39s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.3254:  19%|█▉        | 6/31 [00:10<00:34,  1.39s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.1971:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.1856:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.3254:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 12/63 completed (loss: 0.3605:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 13/63 completed (loss: 0.4164:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 13/63 completed (loss: 0.2698:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 13/63 completed (loss: 0.1585:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 13/63 completed (loss: 0.2285:  23%|██▎       | 7/31 [00:10<00:32,  1.37s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.3842:  23%|██▎       | 7/31 [00:11<00:32,  1.37s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.3967:  23%|██▎       | 7/31 [00:11<00:32,  1.37s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.3226:  23%|██▎       | 7/31 [00:11<00:32,  1.37s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.1856:  23%|██▎       | 7/31 [00:11<00:32,  1.37s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.3226:  26%|██▌       | 8/31 [00:11<00:31,  1.35s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.3842:  26%|██▌       | 8/31 [00:11<00:31,  1.35s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.3967:  26%|██▌       | 8/31 [00:11<00:31,  1.35s/it]
Training Epoch: 1/100, batch 14/63 completed (loss: 0.1856:  26%|██▌       | 8/31 [00:11<00:31,  1.35s/it]
Training Epoch: 1/100, batch 15/63 completed (loss: 0.3518:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 15/63 completed (loss: 0.3451:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 15/63 completed (loss: 0.4123:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 15/63 completed (loss: 0.3126:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.3288:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.2176:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.4397:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.2143:  26%|██▌       | 8/31 [00:12<00:31,  1.35s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.3288:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.2176:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.4397:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 16/63 completed (loss: 0.2143:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 17/63 completed (loss: 0.229:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it] 
Training Epoch: 1/100, batch 17/63 completed (loss: 0.2521:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 17/63 completed (loss: 0.1882:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 17/63 completed (loss: 0.2468:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
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Training Epoch: 1/100, batch 18/63 completed (loss: 0.3038:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
Training Epoch: 1/100, batch 18/63 completed (loss: 0.4544:  29%|██▉       | 9/31 [00:13<00:29,  1.34s/it]
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Training Epoch: 1/100, batch 18/63 completed (loss: 0.4544:  32%|███▏      | 10/31 [00:14<00:27,  1.33s/it]
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Training Epoch: 1/100, batch 19/63 completed (loss: 0.3893:  32%|███▏      | 10/31 [00:14<00:27,  1.33s/it]
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Training Epoch: 1/100, batch 20/63 completed (loss: 0.3319:  32%|███▏      | 10/31 [00:15<00:27,  1.33s/it]
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Training Epoch: 1/100, batch 20/63 completed (loss: 0.164:  35%|███▌      | 11/31 [00:15<00:26,  1.33s/it]
Training Epoch: 1/100, batch 20/63 completed (loss: 0.4626:  35%|███▌      | 11/31 [00:15<00:26,  1.33s/it]
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Training Epoch: 1/100, batch 21/63 completed (loss: 0.213:  35%|███▌      | 11/31 [00:15<00:26,  1.33s/it]
Training Epoch: 1/100, batch 21/63 completed (loss: 0.1641:  35%|███▌      | 11/31 [00:15<00:26,  1.33s/it]
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Training Epoch: 1/100, batch 22/63 completed (loss: 0.4732:  35%|███▌      | 11/31 [00:16<00:26,  1.33s/it]
Training Epoch: 1/100, batch 22/63 completed (loss: 0.1789:  35%|███▌      | 11/31 [00:16<00:26,  1.33s/it]
Training Epoch: 1/100, batch 22/63 completed (loss: 0.3112:  39%|███▊      | 12/31 [00:17<00:25,  1.32s/it]
Training Epoch: 1/100, batch 22/63 completed (loss: 0.4732:  39%|███▊      | 12/31 [00:17<00:25,  1.32s/it]
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Training Epoch: 1/100, batch 22/63 completed (loss: 0.1789:  39%|███▊      | 12/31 [00:17<00:25,  1.32s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_1 to HF: (Request ID: Root=1-6a1b6290-3b08523c034cdb0a20f37948;16e1f8a0-2799-44bc-8c5b-163759473d24)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_1 to HF: (Request ID: Root=1-6a1b6294-3277df4c10656fba483d141b;bed81b41-4975-4c4b-8f06-212e91b96169)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
[warn] HF upload failed for ckpt_1; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.023307888984813223

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[WRONG] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Fiona'
  Generated: '### Fiona'

Test Accuracy:   3%|▎         | 1/32 [00:08<04:17,  8.31s/it]
Test Accuracy:   3%|▎         | 1/32 [00:08<04:18,  8.32s/it]
Test accuracy: 0.62:   3%|▎         | 1/32 [00:08<04:17,  8.31s/it]
Test accuracy: 0.38:   3%|▎         | 1/32 [00:08<04:18,  8.32s/it]
[WRONG] Example 1 (diff=8)
  Expected : 'Guy'
  Extracted: 'Irma'
  Generated: '### Irma'

Test Accuracy:   3%|▎         | 1/32 [00:08<04:17,  8.32s/it]
Test accuracy: 0.25:   3%|▎         | 1/32 [00:08<04:17,  8.32s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:08<04:13,  8.17s/it]
Test accuracy: 0.38:   3%|▎         | 1/32 [00:08<04:13,  8.17s/it]
Test accuracy: 0.38:   6%|▋         | 2/32 [00:16<04:01,  8.07s/it]
Test accuracy: 0.62:   6%|▋         | 2/32 [00:16<04:01,  8.06s/it]
Test accuracy: 0.5:   6%|▋         | 2/32 [00:16<04:01,  8.07s/it] 
Test accuracy: 0.44:   6%|▋         | 2/32 [00:16<04:01,  8.06s/it]
Test accuracy: 0.25:   6%|▋         | 2/32 [00:16<04:01,  8.06s/it]
Test accuracy: 0.38:   6%|▋         | 2/32 [00:16<04:01,  8.06s/it]
Test accuracy: 0.38:   6%|▋         | 2/32 [00:16<04:00,  8.00s/it]
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Test accuracy: 0.44:   9%|▉         | 3/32 [00:24<04:01,  8.31s/it]
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Test accuracy: 0.38:   9%|▉         | 3/32 [00:24<04:01,  8.31s/it]
Test accuracy: 0.38:   9%|▉         | 3/32 [00:24<04:00,  8.28s/it]
Test accuracy: 0.42:   9%|▉         | 3/32 [00:24<04:01,  8.31s/it]
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Test accuracy: 0.5:   9%|▉         | 3/32 [00:24<04:01,  8.32s/it]
Test accuracy: 0.46:   9%|▉         | 3/32 [00:24<04:01,  8.32s/it]
Test accuracy: 0.46:  12%|█▎        | 4/32 [00:32<03:48,  8.17s/it]
Test accuracy: 0.46:  12%|█▎        | 4/32 [00:32<03:48,  8.16s/it]
Test accuracy: 0.44:  12%|█▎        | 4/32 [00:32<03:48,  8.17s/it]
Test accuracy: 0.5:  12%|█▎        | 4/32 [00:32<03:48,  8.16s/it] 
Test accuracy: 0.42:  12%|█▎        | 4/32 [00:32<03:48,  8.15s/it]
Test accuracy: 0.38:  12%|█▎        | 4/32 [00:32<03:48,  8.15s/it]
Test accuracy: 0.42:  12%|█▎        | 4/32 [00:32<03:48,  8.17s/it]
Test accuracy: 0.44:  12%|█▎        | 4/32 [00:32<03:48,  8.17s/it]
Test accuracy: 0.5:  16%|█▌        | 5/32 [00:40<03:37,  8.06s/it]
Test accuracy: 0.38:  16%|█▌        | 5/32 [00:40<03:37,  8.05s/it]
Test accuracy: 0.47:  16%|█▌        | 5/32 [00:40<03:37,  8.06s/it]
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Test accuracy: 0.47:  16%|█▌        | 5/32 [00:40<03:37,  8.05s/it]
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Test accuracy: 0.38:  16%|█▌        | 5/32 [00:40<03:37,  8.06s/it]
Test accuracy: 0.45:  16%|█▌        | 5/32 [00:40<03:37,  8.06s/it]
Test accuracy: 0.45:  19%|█▉        | 6/32 [00:48<03:27,  7.99s/it]
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Test accuracy: 0.38:  19%|█▉        | 6/32 [00:48<03:27,  7.99s/it]
Test accuracy: 0.48:  19%|█▉        | 6/32 [00:48<03:27,  7.99s/it]
Test accuracy: 0.4:  19%|█▉        | 6/32 [00:48<03:27,  7.99s/it] 
Test accuracy: 0.46:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.48:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.46:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.4:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.45:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.5:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it] 
Test accuracy: 0.39:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.45:  22%|██▏       | 7/32 [00:56<03:18,  7.95s/it]
Test accuracy: 0.5:  25%|██▌       | 8/32 [01:04<03:10,  7.95s/it]
Test accuracy: 0.47:  25%|██▌       | 8/32 [01:04<03:10,  7.95s/it]
Test accuracy: 0.39:  25%|██▌       | 8/32 [01:04<03:10,  7.95s/it]
Test accuracy: 0.41:  25%|██▌       | 8/32 [01:04<03:10,  7.95s/it]
Test accuracy: 0.45:  25%|██▌       | 8/32 [01:04<03:10,  7.94s/it]
Test accuracy: 0.44:  25%|██▌       | 8/32 [01:04<03:10,  7.94s/it]
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Test accuracy: 0.41:  25%|██▌       | 8/32 [01:04<03:10,  7.95s/it]
Test accuracy: 0.41:  28%|██▊       | 9/32 [01:12<03:07,  8.16s/it]
Test accuracy: 0.47:  28%|██▊       | 9/32 [01:12<03:07,  8.16s/it]
Test accuracy: 0.44:  28%|██▊       | 9/32 [01:12<03:07,  8.16s/it]
Test accuracy: 0.46:  28%|██▊       | 9/32 [01:12<03:07,  8.16s/it]
Test accuracy: 0.44:  28%|██▊       | 9/32 [01:12<03:07,  8.15s/it]
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Test accuracy: 0.41:  28%|██▊       | 9/32 [01:12<03:07,  8.16s/it]
Test accuracy: 0.38:  28%|██▊       | 9/32 [01:12<03:07,  8.16s/it]
Test accuracy: 0.38:  31%|███▏      | 10/32 [01:20<02:57,  8.07s/it]
Test accuracy: 0.46:  31%|███▏      | 10/32 [01:20<02:57,  8.08s/it]
Test accuracy: 0.38:  31%|███▏      | 10/32 [01:20<02:57,  8.07s/it]
Test accuracy: 0.46:  31%|███▏      | 10/32 [01:20<02:57,  8.08s/it]
Test accuracy: 0.44:  31%|███▏      | 10/32 [01:20<02:57,  8.07s/it]
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Test accuracy: 0.46:  31%|███▏      | 10/32 [01:20<02:57,  8.08s/it]
Test accuracy: 0.38:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
Test accuracy: 0.36:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
Test accuracy: 0.46:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
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Test accuracy: 0.44:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
Test accuracy: 0.47:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
Test accuracy: 0.48:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
Test accuracy: 0.43:  34%|███▍      | 11/32 [01:28<02:48,  8.01s/it]
Test accuracy: 0.36:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
Test accuracy: 0.41:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
Test accuracy: 0.48:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
Test accuracy: 0.45:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
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Test accuracy: 0.46:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
Test accuracy: 0.43:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
Test accuracy: 0.48:  38%|███▊      | 12/32 [01:36<02:39,  7.97s/it]
Test accuracy: 0.48:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.49:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.46:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.45:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.41:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.48:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.45:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.38:  41%|████      | 13/32 [01:44<02:30,  7.94s/it]
Test accuracy: 0.38:  44%|████▍     | 14/32 [01:52<02:22,  7.93s/it]
Test accuracy: 0.48:  44%|████▍     | 14/32 [01:52<02:22,  7.94s/it]
Test accuracy: 0.37:  44%|████▍     | 14/32 [01:52<02:22,  7.93s/it]
Test accuracy: 0.49:  44%|████▍     | 14/32 [01:52<02:22,  7.94s/it]
Test accuracy: 0.49:  44%|████▍     | 14/32 [01:52<02:22,  7.93s/it]
Test accuracy: 0.5:  44%|████▍     | 14/32 [01:52<02:22,  7.93s/it] 
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Test accuracy: 0.44:  44%|████▍     | 14/32 [01:52<02:22,  7.94s/it]
Test accuracy: 0.49:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.37:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.5:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it] 
Test accuracy: 0.34:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.5:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.44:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.5:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.43:  47%|████▋     | 15/32 [02:00<02:14,  7.91s/it]
Test accuracy: 0.34:  50%|█████     | 16/32 [02:08<02:06,  7.90s/it]
Test accuracy: 0.34:  50%|█████     | 16/32 [02:08<02:06,  7.90s/it]
Test accuracy: 0.5:  50%|█████     | 16/32 [02:08<02:06,  7.90s/it]
Test accuracy: 0.52:  50%|█████     | 16/32 [02:08<02:06,  7.90s/it]
Test accuracy: 0.43:  50%|█████     | 16/32 [02:08<02:06,  7.90s/it]
Test accuracy: 0.42:  50%|█████     | 16/32 [02:08<02:06,  7.90s/it]
Test accuracy: 0.5:  50%|█████     | 16/32 [02:07<02:06,  7.90s/it]
Test accuracy: 0.5:  50%|█████     | 16/32 [02:07<02:06,  7.90s/it]
Test accuracy: 0.42:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.34:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.52:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.44:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.35:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.5:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it] 
Test accuracy: 0.5:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.48:  53%|█████▎    | 17/32 [02:15<01:58,  7.89s/it]
Test accuracy: 0.35:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.44:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.36:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.5:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.43:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.5:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.48:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.48:  56%|█████▋    | 18/32 [02:23<01:50,  7.87s/it]
Test accuracy: 0.36:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.36:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.43:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.43:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.5:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.49:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.48:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.48:  59%|█████▉    | 19/32 [02:31<01:42,  7.89s/it]
Test accuracy: 0.43:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.43:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.36:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.36:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.48:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.49:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.49:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.49:  62%|██████▎   | 20/32 [02:39<01:34,  7.88s/it]
Test accuracy: 0.49:  66%|██████▌   | 21/32 [02:48<01:29,  8.11s/it]
Test accuracy: 0.48:  66%|██████▌   | 21/32 [02:48<01:29,  8.11s/it]
Test accuracy: 0.36:  66%|██████▌   | 21/32 [02:48<01:29,  8.11s/it]
Test accuracy: 0.49:  66%|██████▌   | 21/32 [02:48<01:29,  8.11s/it]
Test accuracy: 0.36:  66%|██████▌   | 21/32 [02:48<01:29,  8.11s/it]
Test accuracy: 0.48:  66%|██████▌   | 21/32 [02:48<01:29,  8.11s/it]
Test accuracy: 0.43:  66%|██████▌   | 21/32 [02:48<01:29,  8.12s/it]
Test accuracy: 0.44:  66%|██████▌   | 21/32 [02:48<01:29,  8.12s/it]
Test accuracy: 0.36:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.48:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.44:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.36:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.48:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.44:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.48:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.49:  69%|██████▉   | 22/32 [02:56<01:20,  8.06s/it]
Test accuracy: 0.49:  72%|███████▏  | 23/32 [03:04<01:12,  8.01s/it]
Test accuracy: 0.44:  72%|███████▏  | 23/32 [03:04<01:12,  8.01s/it]
Test accuracy: 0.48:  72%|███████▏  | 23/32 [03:03<01:12,  8.01s/it]
Test accuracy: 0.44:  72%|███████▏  | 23/32 [03:04<01:12,  8.01s/it]
Test accuracy: 0.48:  72%|███████▏  | 23/32 [03:04<01:12,  8.01s/it]
Test accuracy: 0.36:  72%|███████▏  | 23/32 [03:04<01:12,  8.01s/it]
Test accuracy: 0.48:  72%|███████▏  | 23/32 [03:03<01:12,  8.01s/it]
Test accuracy: 0.36:  72%|███████▏  | 23/32 [03:04<01:12,  8.01s/it]
Test accuracy: 0.44:  75%|███████▌  | 24/32 [03:12<01:05,  8.19s/it]
Test accuracy: 0.43:  75%|███████▌  | 24/32 [03:12<01:05,  8.19s/it]
Test accuracy: 0.48:  75%|███████▌  | 24/32 [03:12<01:05,  8.19s/it]
Test accuracy: 0.36:  75%|███████▌  | 24/32 [03:12<01:05,  8.19s/it]
Test accuracy: 0.48:  75%|███████▌  | 24/32 [03:12<01:05,  8.19s/it]
Test accuracy: 0.37:  75%|███████▌  | 24/32 [03:12<01:05,  8.19s/it]
Test accuracy: 0.48:  75%|███████▌  | 24/32 [03:12<01:05,  8.20s/it]
Test accuracy: 0.48:  75%|███████▌  | 24/32 [03:12<01:05,  8.20s/it]
Test accuracy: 0.48:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.43:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.47:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.43:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.37:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.48:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.38:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.48:  78%|███████▊  | 25/32 [03:20<00:56,  8.12s/it]
Test accuracy: 0.43:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.48:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.44:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.38:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.48:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.47:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.38:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.48:  81%|████████▏ | 26/32 [03:28<00:48,  8.05s/it]
Test accuracy: 0.48:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.44:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.47:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.48:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.43:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.47:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.38:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.38:  84%|████████▍ | 27/32 [03:36<00:40,  8.01s/it]
Test accuracy: 0.47:  88%|████████▊ | 28/32 [03:44<00:32,  8.20s/it]
Test accuracy: 0.46:  88%|████████▊ | 28/32 [03:44<00:32,  8.20s/it]
Test accuracy: 0.47:  88%|████████▊ | 28/32 [03:45<00:32,  8.20s/it]
Test accuracy: 0.38:  88%|████████▊ | 28/32 [03:45<00:32,  8.20s/it]
Test accuracy: 0.47:  88%|████████▊ | 28/32 [03:45<00:32,  8.20s/it]
Test accuracy: 0.4:  88%|████████▊ | 28/32 [03:45<00:32,  8.20s/it] 
Test accuracy: 0.43:  88%|████████▊ | 28/32 [03:45<00:32,  8.21s/it]
Test accuracy: 0.43:  88%|████████▊ | 28/32 [03:45<00:32,  8.21s/it]
Test accuracy: 0.46:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.47:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.43:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.45:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.48:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.44:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.4:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.4:  91%|█████████ | 29/32 [03:52<00:24,  8.11s/it]
Test accuracy: 0.48:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.44:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.47:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.44:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.45:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.45:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.4:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.4:  94%|█████████▍| 30/32 [04:00<00:16,  8.06s/it]
Test accuracy: 0.4:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.4:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.47:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.45:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.48:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.45:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.44:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.44:  97%|█████████▋| 31/32 [04:09<00:08,  8.24s/it]
Test accuracy: 0.45: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.45: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.44: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.44: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.4: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.4: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.48: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.48: 100%|██████████| 32/32 [04:12<00:00,  6.57s/it]
Test accuracy: 0.44: 100%|██████████| 32/32 [04:12<00:00,  7.89s/it]
Device 2: Cor=111, CoT=0, Total=250

Test accuracy: 0.48: 100%|██████████| 32/32 [04:12<00:00,  7.89s/it]
Device 1: Cor=119, CoT=0, Total=250

Test accuracy: 0.4: 100%|██████████| 32/32 [04:12<00:00,  7.89s/it]
Device 3: Cor=101, CoT=0, Total=250

Test accuracy: 0.45: 100%|██████████| 32/32 [04:12<00:00,  7.88s/it]
Device 0: Cor=113, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 0)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=1, data_stage=1, n_latent_tokens=1
Training on difficulties: 0-1

Overall accuracy: 444 / 1000 = 0.4440

Per-difficulty accuracy:
  diff=1: 98/100 = 0.9800 (trained)
  diff=2: 50/100 = 0.5000 (untrained)
  diff=3: 35/100 = 0.3500 (untrained)
  diff=4: 35/100 = 0.3500 (untrained)
  diff=5: 40/100 = 0.4000 (untrained)
  diff=6: 38/100 = 0.3800 (untrained)
  diff=7: 41/100 = 0.4100 (untrained)
  diff=8: 36/100 = 0.3600 (untrained)
  diff=9: 33/100 = 0.3300 (untrained)
  diff=10: 38/100 = 0.3800 (untrained)

Trained (diff 0-1): 98/100 = 0.9800
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9800, acc_diff1=0.9800 >= 0.9 ***
*** Advancing to stage 2 ***

Warning: failed to upload checkpoint_1 to HF: (Request ID: Root=1-6a1b6395-04a825f05758e3e428a2aa03;20d24efa-aa1d-48ea-ace8-e1c4fd155f80)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 1/100 (thought_stage=2, data_stage=2)
============================================================
  thought_stage=2, c_thought=1, max_difficulty=2

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rank 0 | gpu 0 | alloc=4.18 GB | reserved=56.46 GB | max=28.81 GB
rank 1 | gpu 1 | alloc=4.18 GB | reserved=58.31 GB | max=28.81 GBrank 3 | gpu 3 | alloc=4.19 GB | reserved=56.46 GB | max=28.81 GB

rank 2 | gpu 2 | alloc=4.18 GB | reserved=56.45 GB | max=28.81 GB

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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_2 to HF: (Request ID: Root=1-6a1b6422-7f6e6162516f395f64cd2eaf;624ea11f-6f7b-4236-82a7-b8bceea06d06)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_2 to HF: (Request ID: Root=1-6a1b6426-2a46f8f803d93aa160802c78;74a8fad9-bdd4-4fc8-9ffb-f469cb0f8084)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: train_state_1
Deleted old checkpoint: checkpoint_1
[warn] HF upload failed for ckpt_2; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.01061527868016408

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[WRONG] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Morgan'
  Generated: '### Morgan'

Test Accuracy:   3%|▎         | 1/32 [00:08<04:10,  8.09s/it]
[CORRECT] Example 1 (diff=8)
  Expected : 'Guy'
  Extracted: 'Guy'

Test accuracy: 0.38:   3%|▎         | 1/32 [00:08<04:10,  8.09s/it]  Generated: '### Guy'

Test Accuracy:   3%|▎         | 1/32 [00:08<04:10,  8.08s/it]
[WRONG] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Shannon'
  Generated: '### Shannon'

Test accuracy: 0.75:   3%|▎         | 1/32 [00:08<04:10,  8.08s/it]
Test Accuracy:   3%|▎         | 1/32 [00:08<04:10,  8.09s/it]
[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

Test accuracy: 0.62:   3%|▎         | 1/32 [00:08<04:10,  8.09s/it]
[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<04:05,  7.93s/it]
Test accuracy: 0.62:   3%|▎         | 1/32 [00:07<04:05,  7.93s/it]
Test accuracy: 0.62:   6%|▋         | 2/32 [00:15<03:53,  7.79s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:15<03:55,  7.86s/it]
Test accuracy: 0.38:   6%|▋         | 2/32 [00:15<03:55,  7.86s/it]
Test accuracy: 0.62:   6%|▋         | 2/32 [00:15<03:55,  7.86s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:15<03:55,  7.86s/it]
Test accuracy: 0.5:   6%|▋         | 2/32 [00:15<03:55,  7.86s/it] 
Test accuracy: 0.56:   6%|▋         | 2/32 [00:15<03:55,  7.86s/it]
Test accuracy: 0.69:   6%|▋         | 2/32 [00:15<03:53,  7.79s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:24<03:55,  8.14s/it]
Test accuracy: 0.56:   9%|▉         | 3/32 [00:24<03:55,  8.14s/it]
Test accuracy: 0.79:   9%|▉         | 3/32 [00:24<03:55,  8.14s/it]
Test accuracy: 0.58:   9%|▉         | 3/32 [00:24<03:55,  8.14s/it]
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Test accuracy: 0.5:   9%|▉         | 3/32 [00:24<03:56,  8.15s/it]
Test accuracy: 0.5:   9%|▉         | 3/32 [00:24<03:56,  8.15s/it]
Test accuracy: 0.5:  12%|█▎        | 4/32 [00:31<03:43,  7.97s/it]
Test accuracy: 0.67:  12%|█▎        | 4/32 [00:31<03:42,  7.95s/it]
Test accuracy: 0.47:  12%|█▎        | 4/32 [00:31<03:43,  7.97s/it]
Test accuracy: 0.69:  12%|█▎        | 4/32 [00:31<03:42,  7.95s/it]
Test accuracy: 0.58:  12%|█▎        | 4/32 [00:31<03:43,  7.97s/it]
Test accuracy: 0.79:  12%|█▎        | 4/32 [00:31<03:43,  7.97s/it]
Test accuracy: 0.62:  12%|█▎        | 4/32 [00:31<03:43,  7.97s/it]
Test accuracy: 0.75:  12%|█▎        | 4/32 [00:31<03:43,  7.97s/it]
Test accuracy: 0.47:  16%|█▌        | 5/32 [00:39<03:32,  7.87s/it]
Test accuracy: 0.62:  16%|█▌        | 5/32 [00:39<03:32,  7.87s/it]
Test accuracy: 0.47:  16%|█▌        | 5/32 [00:39<03:32,  7.87s/it]
Test accuracy: 0.6:  16%|█▌        | 5/32 [00:39<03:32,  7.87s/it] 
Test accuracy: 0.69:  16%|█▌        | 5/32 [00:39<03:32,  7.86s/it]
Test accuracy: 0.75:  16%|█▌        | 5/32 [00:39<03:32,  7.87s/it]
Test accuracy: 0.7:  16%|█▌        | 5/32 [00:39<03:32,  7.86s/it] 
Test accuracy: 0.73:  16%|█▌        | 5/32 [00:39<03:32,  7.87s/it]
Test accuracy: 0.73:  19%|█▉        | 6/32 [00:47<03:23,  7.81s/it]
Test accuracy: 0.71:  19%|█▉        | 6/32 [00:47<03:23,  7.81s/it]
Test accuracy: 0.6:  19%|█▉        | 6/32 [00:47<03:23,  7.81s/it]
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Test accuracy: 0.47:  19%|█▉        | 6/32 [00:47<03:23,  7.81s/it]
Test accuracy: 0.52:  19%|█▉        | 6/32 [00:47<03:23,  7.81s/it]
Test accuracy: 0.7:  19%|█▉        | 6/32 [00:47<03:22,  7.80s/it]
Test accuracy: 0.73:  19%|█▉        | 6/32 [00:47<03:22,  7.80s/it]
Test accuracy: 0.71:  22%|██▏       | 7/32 [00:55<03:14,  7.77s/it]
Test accuracy: 0.7:  22%|██▏       | 7/32 [00:55<03:14,  7.77s/it] 
Test accuracy: 0.52:  22%|██▏       | 7/32 [00:55<03:14,  7.77s/it]
Test accuracy: 0.6:  22%|██▏       | 7/32 [00:55<03:14,  7.77s/it]
Test accuracy: 0.52:  22%|██▏       | 7/32 [00:55<03:14,  7.77s/it]
Test accuracy: 0.57:  22%|██▏       | 7/32 [00:55<03:14,  7.77s/it]
Test accuracy: 0.73:  22%|██▏       | 7/32 [00:54<03:14,  7.77s/it]
Test accuracy: 0.68:  22%|██▏       | 7/32 [00:54<03:14,  7.77s/it]
Test accuracy: 0.52:  25%|██▌       | 8/32 [01:02<03:05,  7.75s/it]
Test accuracy: 0.53:  25%|██▌       | 8/32 [01:02<03:05,  7.75s/it]
Test accuracy: 0.57:  25%|██▌       | 8/32 [01:02<03:05,  7.75s/it]
Test accuracy: 0.61:  25%|██▌       | 8/32 [01:02<03:05,  7.75s/it]
Test accuracy: 0.7:  25%|██▌       | 8/32 [01:02<03:06,  7.75s/it]
Test accuracy: 0.72:  25%|██▌       | 8/32 [01:02<03:06,  7.75s/it]
Test accuracy: 0.68:  25%|██▌       | 8/32 [01:02<03:05,  7.75s/it]
Test accuracy: 0.66:  25%|██▌       | 8/32 [01:02<03:05,  7.75s/it]
Test accuracy: 0.53:  28%|██▊       | 9/32 [01:11<03:03,  7.98s/it]
Test accuracy: 0.61:  28%|██▊       | 9/32 [01:11<03:03,  7.98s/it]
Test accuracy: 0.66:  28%|██▊       | 9/32 [01:11<03:03,  7.98s/it]
Test accuracy: 0.56:  28%|██▊       | 9/32 [01:11<03:03,  7.98s/it]
Test accuracy: 0.61:  28%|██▊       | 9/32 [01:11<03:03,  7.98s/it]
Test accuracy: 0.65:  28%|██▊       | 9/32 [01:11<03:03,  7.98s/it]
Test accuracy: 0.72:  28%|██▊       | 9/32 [01:11<03:03,  7.99s/it]
Test accuracy: 0.72:  28%|██▊       | 9/32 [01:11<03:03,  7.99s/it]
Test accuracy: 0.65:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.61:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.72:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.66:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.64:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.56:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.74:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.59:  31%|███▏      | 10/32 [01:18<02:53,  7.90s/it]
Test accuracy: 0.59:  34%|███▍      | 11/32 [01:26<02:44,  7.84s/it]
Test accuracy: 0.62:  34%|███▍      | 11/32 [01:26<02:44,  7.84s/it]
Test accuracy: 0.64:  34%|███▍      | 11/32 [01:26<02:44,  7.84s/it]
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Test accuracy: 0.74:  34%|███▍      | 11/32 [01:26<02:44,  7.83s/it]
Test accuracy: 0.73:  34%|███▍      | 11/32 [01:26<02:44,  7.83s/it]
Test accuracy: 0.66:  34%|███▍      | 11/32 [01:26<02:44,  7.83s/it]
Test accuracy: 0.67:  34%|███▍      | 11/32 [01:26<02:44,  7.83s/it]
Test accuracy: 0.73:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.72:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.64:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.62:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.67:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.62:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.61:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.66:  38%|███▊      | 12/32 [01:34<02:35,  7.79s/it]
Test accuracy: 0.61:  41%|████      | 13/32 [01:42<02:27,  7.77s/it]
Test accuracy: 0.72:  41%|████      | 13/32 [01:42<02:27,  7.77s/it]
Test accuracy: 0.61:  41%|████      | 13/32 [01:42<02:27,  7.77s/it]
Test accuracy: 0.62:  41%|████      | 13/32 [01:42<02:27,  7.77s/it]
Test accuracy: 0.69:  41%|████      | 13/32 [01:42<02:27,  7.77s/it]
Test accuracy: 0.64:  41%|████      | 13/32 [01:42<02:27,  7.77s/it]
Test accuracy: 0.66:  41%|████      | 13/32 [01:41<02:27,  7.77s/it]
Test accuracy: 0.67:  41%|████      | 13/32 [01:41<02:27,  7.77s/it]
Test accuracy: 0.69:  44%|████▍     | 14/32 [01:49<02:19,  7.74s/it]
Test accuracy: 0.67:  44%|████▍     | 14/32 [01:49<02:19,  7.74s/it]
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Test accuracy: 0.64:  44%|████▍     | 14/32 [01:49<02:19,  7.75s/it]
Test accuracy: 0.66:  44%|████▍     | 14/32 [01:49<02:19,  7.74s/it]
Test accuracy: 0.67:  44%|████▍     | 14/32 [01:49<02:19,  7.75s/it]
Test accuracy: 0.61:  44%|████▍     | 14/32 [01:49<02:19,  7.75s/it]
Test accuracy: 0.62:  44%|████▍     | 14/32 [01:49<02:19,  7.75s/it]
Test accuracy: 0.67:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
Test accuracy: 0.68:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
Test accuracy: 0.66:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
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Test accuracy: 0.67:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
Test accuracy: 0.62:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
Test accuracy: 0.67:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
Test accuracy: 0.62:  47%|████▋     | 15/32 [01:57<02:11,  7.76s/it]
Test accuracy: 0.68:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it]
Test accuracy: 0.67:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it]
Test accuracy: 0.66:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it]
Test accuracy: 0.62:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it]
Test accuracy: 0.64:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it]
Test accuracy: 0.6:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it] 
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Test accuracy: 0.67:  50%|█████     | 16/32 [02:05<02:04,  7.81s/it]
Test accuracy: 0.6:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.67:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.62:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.67:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.67:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.66:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.64:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.65:  53%|█████▎    | 17/32 [02:13<01:56,  7.78s/it]
Test accuracy: 0.62:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.66:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.61:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.65:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.67:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.67:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.65:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.66:  56%|█████▋    | 18/32 [02:20<01:48,  7.75s/it]
Test accuracy: 0.61:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.65:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.61:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.64:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.67:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.67:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.66:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.66:  59%|█████▉    | 19/32 [02:28<01:40,  7.73s/it]
Test accuracy: 0.61:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.59:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.66:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.64:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.62:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.66:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.67:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.68:  62%|██████▎   | 20/32 [02:36<01:32,  7.72s/it]
Test accuracy: 0.68:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.66:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.62:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.68:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.61:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.64:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.59:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it]
Test accuracy: 0.6:  66%|██████▌   | 21/32 [02:44<01:27,  7.96s/it] 
Test accuracy: 0.68:  69%|██████▉   | 22/32 [02:52<01:18,  7.88s/it]
Test accuracy: 0.64:  69%|██████▉   | 22/32 [02:52<01:18,  7.88s/it]
Test accuracy: 0.67:  69%|██████▉   | 22/32 [02:52<01:18,  7.88s/it]
Test accuracy: 0.64:  69%|██████▉   | 22/32 [02:52<01:18,  7.88s/it]
Test accuracy: 0.61:  69%|██████▉   | 22/32 [02:52<01:18,  7.89s/it]
Test accuracy: 0.6:  69%|██████▉   | 22/32 [02:52<01:18,  7.88s/it]
Test accuracy: 0.6:  69%|██████▉   | 22/32 [02:52<01:18,  7.88s/it]
Test accuracy: 0.61:  69%|██████▉   | 22/32 [02:52<01:18,  7.89s/it]
Test accuracy: 0.6:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.59:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.67:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.67:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.61:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.62:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.64:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.62:  72%|███████▏  | 23/32 [03:00<01:10,  7.83s/it]
Test accuracy: 0.59:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it]
Test accuracy: 0.67:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it]
Test accuracy: 0.6:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it] 
Test accuracy: 0.68:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it]
Test accuracy: 0.62:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it]
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Test accuracy: 0.62:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it]
Test accuracy: 0.63:  75%|███████▌  | 24/32 [03:08<01:04,  8.03s/it]
Test accuracy: 0.68:  78%|███████▊  | 25/32 [03:16<00:55,  7.94s/it]
Test accuracy: 0.69:  78%|███████▊  | 25/32 [03:16<00:55,  7.94s/it]
Test accuracy: 0.62:  78%|███████▊  | 25/32 [03:16<00:55,  7.94s/it]
Test accuracy: 0.62:  78%|███████▊  | 25/32 [03:16<00:55,  7.94s/it]
Test accuracy: 0.6:  78%|███████▊  | 25/32 [03:16<00:55,  7.94s/it]
Test accuracy: 0.6:  78%|███████▊  | 25/32 [03:16<00:55,  7.94s/it]
Test accuracy: 0.63:  78%|███████▊  | 25/32 [03:16<00:55,  7.93s/it]
Test accuracy: 0.63:  78%|███████▊  | 25/32 [03:16<00:55,  7.93s/it]
Test accuracy: 0.63:  81%|████████▏ | 26/32 [03:23<00:47,  7.86s/it]
Test accuracy: 0.62:  81%|████████▏ | 26/32 [03:23<00:47,  7.86s/it]
Test accuracy: 0.69:  81%|████████▏ | 26/32 [03:24<00:47,  7.86s/it]
Test accuracy: 0.62:  81%|████████▏ | 26/32 [03:24<00:47,  7.86s/it]
Test accuracy: 0.6:  81%|████████▏ | 26/32 [03:24<00:47,  7.86s/it]
Test accuracy: 0.68:  81%|████████▏ | 26/32 [03:24<00:47,  7.86s/it]
Test accuracy: 0.61:  81%|████████▏ | 26/32 [03:24<00:47,  7.86s/it]
Test accuracy: 0.6:  81%|████████▏ | 26/32 [03:24<00:47,  7.86s/it]
Test accuracy: 0.6:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.61:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.6:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.6:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it] 
Test accuracy: 0.68:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.68:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.62:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.62:  84%|████████▍ | 27/32 [03:31<00:39,  7.81s/it]
Test accuracy: 0.68:  88%|████████▊ | 28/32 [03:40<00:32,  8.01s/it]
Test accuracy: 0.62:  88%|████████▊ | 28/32 [03:40<00:32,  8.01s/it]
Test accuracy: 0.68:  88%|████████▊ | 28/32 [03:40<00:32,  8.01s/it]
Test accuracy: 0.62:  88%|████████▊ | 28/32 [03:40<00:32,  8.01s/it]
Test accuracy: 0.6:  88%|████████▊ | 28/32 [03:40<00:32,  8.01s/it]
Test accuracy: 0.6:  88%|████████▊ | 28/32 [03:40<00:32,  8.01s/it]
Test accuracy: 0.6:  88%|████████▊ | 28/32 [03:40<00:32,  8.02s/it]
Test accuracy: 0.59:  88%|████████▊ | 28/32 [03:40<00:32,  8.02s/it]
Test accuracy: 0.59:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.62:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.59:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.6:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it] 
Test accuracy: 0.68:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.67:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.6:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.61:  91%|█████████ | 29/32 [03:47<00:23,  7.92s/it]
Test accuracy: 0.6:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.67:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.61:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.66:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.59:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.59:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.61:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.61:  94%|█████████▍| 30/32 [03:55<00:15,  7.85s/it]
Test accuracy: 0.66:  97%|█████████▋| 31/32 [04:04<00:08,  8.05s/it]
Test accuracy: 0.61:  97%|█████████▋| 31/32 [04:04<00:08,  8.05s/it]
Test accuracy: 0.65:  97%|█████████▋| 31/32 [04:04<00:08,  8.05s/it]
Test accuracy: 0.61:  97%|█████████▋| 31/32 [04:04<00:08,  8.05s/it]
Test accuracy: 0.6:  97%|█████████▋| 31/32 [04:04<00:08,  8.05s/it] 
Test accuracy: 0.62:  97%|█████████▋| 31/32 [04:04<00:08,  8.05s/it]
Test accuracy: 0.59:  97%|█████████▋| 31/32 [04:04<00:08,  8.06s/it]
Test accuracy: 0.6:  97%|█████████▋| 31/32 [04:04<00:08,  8.06s/it] 
Test accuracy: 0.62: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.62: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.6: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.65: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.6: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.66: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.6: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.6: 100%|██████████| 32/32 [04:06<00:00,  6.44s/it]
Test accuracy: 0.6: 100%|██████████| 32/32 [04:06<00:00,  7.72s/it]
Device 2: Cor=150, CoT=0, Total=250

Test accuracy: 0.66: 100%|██████████| 32/32 [04:06<00:00,  7.72s/it]
Device 3: Cor=164, CoT=0, Total=250

Test accuracy: 0.6: 100%|██████████| 32/32 [04:06<00:00,  7.72s/it]
Device 1: Cor=151, CoT=0, Total=250

Test accuracy: 0.62: 100%|██████████| 32/32 [04:06<00:00,  7.71s/it]
Device 0: Cor=155, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 1)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=2, data_stage=2, n_latent_tokens=2
Training on difficulties: 0-2

Overall accuracy: 620 / 1000 = 0.6200

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 97/100 = 0.9700 (trained)
  diff=3: 68/100 = 0.6800 (untrained)
  diff=4: 49/100 = 0.4900 (untrained)
  diff=5: 56/100 = 0.5600 (untrained)
  diff=6: 45/100 = 0.4500 (untrained)
  diff=7: 53/100 = 0.5300 (untrained)
  diff=8: 48/100 = 0.4800 (untrained)
  diff=9: 52/100 = 0.5200 (untrained)
  diff=10: 52/100 = 0.5200 (untrained)

Trained (diff 0-2): 197/200 = 0.9850
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9850, acc_diff2=0.9700 >= 0.9 ***
*** Advancing to stage 3 ***

Warning: failed to upload checkpoint_2 to HF: (Request ID: Root=1-6a1b6525-03e546bd213c0e8d2d2d47a2;1c35a3e8-eec8-4dd1-8f3a-28bea12c616d)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 2/100 (thought_stage=3, data_stage=3)
============================================================
  thought_stage=3, c_thought=1, max_difficulty=3

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rank 0 | gpu 0 | alloc=4.19 GB | reserved=54.64 GB | max=30.12 GB
rank 1 | gpu 1 | alloc=4.19 GB | reserved=49.11 GB | max=30.12 GB
rank 3 | gpu 3 | alloc=4.19 GB | reserved=52.80 GB | max=30.12 GB
rank 2 | gpu 2 | alloc=4.19 GB | reserved=54.65 GB | max=30.12 GB

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Training Epoch: 3/100, batch 187/188 completed (loss: 0.0007: 100%|██████████| 94/94 [03:49<00:00,  2.29s/it]
Training Epoch: 3/100, batch 187/188 completed (loss: 0.0016: 100%|██████████| 94/94 [03:49<00:00,  2.29s/it]
Training Epoch: 3/100, batch 187/188 completed (loss: 0.0009: 100%|██████████| 94/94 [03:49<00:00,  2.44s/it]

Training Epoch: 3/100, batch 187/188 completed (loss: 0.0007: 100%|██████████| 94/94 [03:49<00:00,  2.44s/it]

Training Epoch: 3/100, batch 187/188 completed (loss: 0.0039: 100%|██████████| 94/94 [03:49<00:00,  2.44s/it]

Training Epoch: 3/100, batch 187/188 completed (loss: 0.0016: 100%|██████████| 94/94 [03:49<00:00,  2.44s/it]
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_3 to HF: (Request ID: Root=1-6a1b6624-3f43e8ef28da384518a8123d;6bde118e-53ee-4f26-800b-b6c7f20c55b0)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_3 to HF: (Request ID: Root=1-6a1b6627-7357fa426651c24e1f0a8375;c82b506c-e8c0-4fe4-a1b0-3b16b48f882e)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: train_state_2
Deleted old checkpoint: checkpoint_2
[warn] HF upload failed for ckpt_3; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.026827601103210134

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

Test Accuracy:   3%|▎         | 1/32 [00:07<04:03,  7.85s/it]
Test accuracy: 0.38:   3%|▎         | 1/32 [00:07<04:03,  7.85s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<04:03,  7.84s/it]
[CORRECT] Example 1 (diff=8)
  Expected : 'Guy'
  Extracted: 'Guy'
  Generated: '### Guy'
Test accuracy: 0.75:   3%|▎         | 1/32 [00:07<04:03,  7.84s/it]

Test Accuracy:   3%|▎         | 1/32 [00:07<04:03,  7.84s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<04:03,  7.84s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:59,  7.72s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<03:59,  7.72s/it]
Test accuracy: 0.38:   6%|▋         | 2/32 [00:15<03:49,  7.63s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:15<03:48,  7.63s/it]
Test accuracy: 0.5:   6%|▋         | 2/32 [00:15<03:49,  7.63s/it] 
Test accuracy: 0.88:   6%|▋         | 2/32 [00:15<03:48,  7.63s/it]
Test accuracy: 0.62:   6%|▋         | 2/32 [00:15<03:48,  7.63s/it]
Test accuracy: 0.81:   6%|▋         | 2/32 [00:15<03:48,  7.63s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:15<03:47,  7.58s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:15<03:47,  7.58s/it]
Test accuracy: 0.62:   9%|▉         | 3/32 [00:23<03:49,  7.92s/it]
Test accuracy: 0.81:   9%|▉         | 3/32 [00:23<03:49,  7.92s/it]
Test accuracy: 0.58:   9%|▉         | 3/32 [00:23<03:49,  7.92s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:23<03:49,  7.92s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:23<03:48,  7.89s/it]
Test accuracy: 0.79:   9%|▉         | 3/32 [00:23<03:48,  7.89s/it]
Test accuracy: 0.5:   9%|▉         | 3/32 [00:23<03:49,  7.93s/it]
Test accuracy: 0.62:   9%|▉         | 3/32 [00:23<03:49,  7.93s/it]
Test accuracy: 0.62:  12%|█▎        | 4/32 [00:31<03:36,  7.75s/it]
Test accuracy: 0.58:  12%|█▎        | 4/32 [00:31<03:36,  7.75s/it]
Test accuracy: 0.66:  12%|█▎        | 4/32 [00:31<03:36,  7.75s/it]
Test accuracy: 0.62:  12%|█▎        | 4/32 [00:31<03:36,  7.75s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:31<03:36,  7.75s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:31<03:36,  7.75s/it]
Test accuracy: 0.79:  12%|█▎        | 4/32 [00:30<03:36,  7.73s/it]
Test accuracy: 0.75:  12%|█▎        | 4/32 [00:30<03:36,  7.73s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:39<03:31,  7.82s/it]
Test accuracy: 0.82:  16%|█▌        | 5/32 [00:39<03:31,  7.82s/it]
Test accuracy: 0.66:  16%|█▌        | 5/32 [00:39<03:31,  7.82s/it]
Test accuracy: 0.62:  16%|█▌        | 5/32 [00:39<03:31,  7.82s/it]
Test accuracy: 0.65:  16%|█▌        | 5/32 [00:39<03:31,  7.82s/it]
Test accuracy: 0.62:  16%|█▌        | 5/32 [00:39<03:31,  7.82s/it]
Test accuracy: 0.75:  16%|█▌        | 5/32 [00:38<03:30,  7.81s/it]
Test accuracy: 0.77:  16%|█▌        | 5/32 [00:38<03:30,  7.81s/it]
Test accuracy: 0.62:  19%|█▉        | 6/32 [00:46<03:20,  7.70s/it]
Test accuracy: 0.69:  19%|█▉        | 6/32 [00:46<03:20,  7.70s/it]
Test accuracy: 0.65:  19%|█▉        | 6/32 [00:46<03:20,  7.70s/it]
Test accuracy: 0.82:  19%|█▉        | 6/32 [00:46<03:20,  7.70s/it]
Test accuracy: 0.65:  19%|█▉        | 6/32 [00:46<03:20,  7.70s/it]
Test accuracy: 0.81:  19%|█▉        | 6/32 [00:46<03:20,  7.70s/it]
Test accuracy: 0.77:  19%|█▉        | 6/32 [00:46<03:20,  7.69s/it]
Test accuracy: 0.81:  19%|█▉        | 6/32 [00:46<03:20,  7.69s/it]
Test accuracy: 0.69:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.65:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.66:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.62:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.81:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.81:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.77:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it]
Test accuracy: 0.8:  22%|██▏       | 7/32 [00:53<03:10,  7.62s/it] 
Test accuracy: 0.66:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.69:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.62:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.77:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.66:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.8:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.75:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.78:  25%|██▌       | 8/32 [01:01<03:01,  7.57s/it]
Test accuracy: 0.66:  28%|██▊       | 9/32 [01:09<02:59,  7.80s/it]
Test accuracy: 0.69:  28%|██▊       | 9/32 [01:09<02:59,  7.80s/it]
Test accuracy: 0.64:  28%|██▊       | 9/32 [01:09<02:59,  7.80s/it]
Test accuracy: 0.78:  28%|██▊       | 9/32 [01:09<02:59,  7.79s/it]
Test accuracy: 0.72:  28%|██▊       | 9/32 [01:09<02:59,  7.80s/it]
Test accuracy: 0.76:  28%|██▊       | 9/32 [01:09<02:59,  7.79s/it]
Test accuracy: 0.75:  28%|██▊       | 9/32 [01:09<02:59,  7.80s/it]
Test accuracy: 0.75:  28%|██▊       | 9/32 [01:09<02:59,  7.80s/it]
Test accuracy: 0.64:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.62:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.75:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.75:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.76:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.72:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.71:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.75:  31%|███▏      | 10/32 [01:17<02:49,  7.70s/it]
Test accuracy: 0.75:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.72:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.71:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.72:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.62:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.64:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.75:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.74:  34%|███▍      | 11/32 [01:24<02:40,  7.63s/it]
Test accuracy: 0.64:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it]
Test accuracy: 0.64:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it]
Test accuracy: 0.72:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it]
Test accuracy: 0.74:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it]
Test accuracy: 0.7:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it] 
Test accuracy: 0.72:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it]
Test accuracy: 0.73:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it]
Test accuracy: 0.7:  38%|███▊      | 12/32 [01:32<02:31,  7.58s/it] 
Test accuracy: 0.7:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.64:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.71:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.7:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.63:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.73:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.69:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.73:  41%|████      | 13/32 [01:39<02:23,  7.55s/it]
Test accuracy: 0.73:  44%|████▍     | 14/32 [01:46<02:15,  7.53s/it]
Test accuracy: 0.63:  44%|████▍     | 14/32 [01:47<02:15,  7.53s/it]
Test accuracy: 0.69:  44%|████▍     | 14/32 [01:47<02:15,  7.53s/it]
Test accuracy: 0.72:  44%|████▍     | 14/32 [01:46<02:15,  7.53s/it]
Test accuracy: 0.71:  44%|████▍     | 14/32 [01:47<02:15,  7.53s/it]
Test accuracy: 0.64:  44%|████▍     | 14/32 [01:47<02:15,  7.53s/it]
Test accuracy: 0.7:  44%|████▍     | 14/32 [01:47<02:15,  7.53s/it] 
Test accuracy: 0.73:  44%|████▍     | 14/32 [01:47<02:15,  7.53s/it]
Test accuracy: 0.64:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.73:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.65:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.73:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.7:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.72:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.72:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.71:  47%|████▋     | 15/32 [01:54<02:08,  7.57s/it]
Test accuracy: 0.72:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.65:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.72:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.65:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.71:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.7:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it] 
Test accuracy: 0.73:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.73:  50%|█████     | 16/32 [02:02<02:01,  7.57s/it]
Test accuracy: 0.65:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
Test accuracy: 0.67:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
Test accuracy: 0.73:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
Test accuracy: 0.72:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
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Test accuracy: 0.7:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
Test accuracy: 0.71:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
Test accuracy: 0.68:  53%|█████▎    | 17/32 [02:09<01:53,  7.54s/it]
Test accuracy: 0.71:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
Test accuracy: 0.72:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
Test accuracy: 0.68:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
Test accuracy: 0.67:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
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Test accuracy: 0.69:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
Test accuracy: 0.73:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
Test accuracy: 0.74:  56%|█████▋    | 18/32 [02:17<01:45,  7.52s/it]
Test accuracy: 0.67:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it]
Test accuracy: 0.74:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it]
Test accuracy: 0.67:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it]
Test accuracy: 0.71:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it]
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Test accuracy: 0.72:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it]
Test accuracy: 0.69:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it]
Test accuracy: 0.7:  59%|█████▉    | 19/32 [02:24<01:37,  7.51s/it] 
Test accuracy: 0.71:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.69:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.72:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.67:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.7:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.72:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.66:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.69:  62%|██████▎   | 20/32 [02:32<01:29,  7.50s/it]
Test accuracy: 0.72:  66%|██████▌   | 21/32 [02:40<01:25,  7.74s/it]
Test accuracy: 0.69:  66%|██████▌   | 21/32 [02:40<01:25,  7.74s/it]
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Test accuracy: 0.72:  69%|██████▉   | 22/32 [02:48<01:16,  7.67s/it]
Test accuracy: 0.67:  69%|██████▉   | 22/32 [02:48<01:16,  7.67s/it]
Test accuracy: 0.73:  69%|██████▉   | 22/32 [02:48<01:16,  7.67s/it]
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Test accuracy: 0.66:  75%|███████▌  | 24/32 [03:03<01:02,  7.82s/it]
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Test accuracy: 0.73:  78%|███████▊  | 25/32 [03:11<00:54,  7.72s/it]
Test accuracy: 0.66:  78%|███████▊  | 25/32 [03:11<00:54,  7.72s/it]
Test accuracy: 0.68:  78%|███████▊  | 25/32 [03:11<00:54,  7.72s/it]
Test accuracy: 0.73:  78%|███████▊  | 25/32 [03:11<00:54,  7.72s/it]
Test accuracy: 0.67:  78%|███████▊  | 25/32 [03:11<00:54,  7.72s/it]
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Test accuracy: 0.69:  78%|███████▊  | 25/32 [03:11<00:54,  7.72s/it]
Test accuracy: 0.67:  81%|████████▏ | 26/32 [03:19<00:46,  7.71s/it]
Test accuracy: 0.67:  81%|████████▏ | 26/32 [03:18<00:46,  7.71s/it]
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Test accuracy: 0.69:  81%|████████▏ | 26/32 [03:18<00:46,  7.71s/it]
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Test accuracy: 0.65:  84%|████████▍ | 27/32 [03:26<00:38,  7.70s/it]
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Test accuracy: 0.67:  88%|████████▊ | 28/32 [03:34<00:31,  7.89s/it]
Test accuracy: 0.71:  91%|█████████ | 29/32 [03:42<00:23,  7.76s/it]
Test accuracy: 0.67:  91%|█████████ | 29/32 [03:42<00:23,  7.76s/it]
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Test accuracy: 0.67:  91%|█████████ | 29/32 [03:42<00:23,  7.76s/it]
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Test accuracy: 0.7:  94%|█████████▍| 30/32 [03:49<00:15,  7.68s/it]
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Test accuracy: 0.7:  97%|█████████▋| 31/32 [03:58<00:07,  7.87s/it]
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Test accuracy: 0.67:  97%|█████████▋| 31/32 [03:58<00:07,  7.87s/it]
Test accuracy: 0.68:  97%|█████████▋| 31/32 [03:58<00:07,  7.87s/it]
Test accuracy: 0.67:  97%|█████████▋| 31/32 [03:58<00:07,  7.87s/it]
Test accuracy: 0.67: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.66: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.7: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.65: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.7: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.65: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.67: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.67: 100%|██████████| 32/32 [04:00<00:00,  6.29s/it]
Test accuracy: 0.67: 100%|██████████| 32/32 [04:00<00:00,  7.53s/it]

Test accuracy: 0.65: 100%|██████████| 32/32 [04:00<00:00,  7.53s/it]Device 2: Cor=168, CoT=0, Total=250

Device 1: Cor=162, CoT=0, Total=250

Test accuracy: 0.7: 100%|██████████| 32/32 [04:00<00:00,  7.53s/it]
Device 3: Cor=176, CoT=0, Total=250

Test accuracy: 0.66: 100%|██████████| 32/32 [04:00<00:00,  7.53s/it]
Device 0: Cor=166, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 2)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=3, data_stage=3, n_latent_tokens=3
Training on difficulties: 0-3

Overall accuracy: 672 / 1000 = 0.6720

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 99/100 = 0.9900 (trained)
  diff=3: 94/100 = 0.9400 (trained)
  diff=4: 63/100 = 0.6300 (untrained)
  diff=5: 53/100 = 0.5300 (untrained)
  diff=6: 53/100 = 0.5300 (untrained)
  diff=7: 56/100 = 0.5600 (untrained)
  diff=8: 49/100 = 0.4900 (untrained)
  diff=9: 54/100 = 0.5400 (untrained)
  diff=10: 51/100 = 0.5100 (untrained)

Trained (diff 0-3): 293/300 = 0.9767
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9767, acc_diff3=0.9400 >= 0.9 ***
*** Advancing to stage 4 ***

Warning: failed to upload checkpoint_3 to HF: (Request ID: Root=1-6a1b6722-31eeacfc16182155080bf1ee;335cdbbe-fd02-4632-b033-2cb065d3800e)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 3/100 (thought_stage=4, data_stage=4)
============================================================
  thought_stage=4, c_thought=1, max_difficulty=4

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Map (num_proc=4): 100%|██████████| 4000/4000 [00:03<00:00, 1357.88 examples/s]
Map (num_proc=4): 100%|██████████| 4000/4000 [00:03<00:00, 1055.82 examples/s]

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Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 2658.21 examples/s]

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Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 1583.29 examples/s]

Map (num_proc=4):   0%|          | 0/400 [00:00<?, ? examples/s]
Map (num_proc=4):  25%|██▌       | 100/400 [00:00<00:01, 277.48 examples/s]
Map (num_proc=4):  75%|███████▌  | 300/400 [00:00<00:00, 668.94 examples/s]
Map (num_proc=4): 100%|██████████| 400/400 [00:00<00:00, 460.28 examples/s]

Training Epoch: 4:   0%|          | 0/125 [00:00<?, ?it/s]
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Training Epoch: 4:   0%|          | 0/125 [00:00<?, ?it/s]logging training data
rank 0 | gpu 0 | alloc=4.19 GB | reserved=56.54 GB | max=31.55 GB
rank 3 | gpu 3 | alloc=4.19 GB | reserved=56.55 GB | max=31.55 GB
rank 1 | gpu 1 | alloc=4.19 GB | reserved=56.57 GB | max=31.55 GB
rank 2 | gpu 2 | alloc=4.19 GB | reserved=52.84 GB | max=31.55 GB

Training Epoch: 4/100, batch 0/250 completed (loss: 0.0268:   0%|          | 0/125 [00:01<?, ?it/s]
Training Epoch: 4/100, batch 0/250 completed (loss: 0.0094:   0%|          | 0/125 [00:01<?, ?it/s]
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Training Epoch: 4/100, batch 0/250 completed (loss: 0.0018:   1%|          | 1/125 [00:03<06:26,  3.12s/it]
Training Epoch: 4/100, batch 0/250 completed (loss: 0.0268:   1%|          | 1/125 [00:03<06:24,  3.10s/it]
Training Epoch: 4/100, batch 0/250 completed (loss: 0.0094:   1%|          | 1/125 [00:03<06:26,  3.12s/it]
Training Epoch: 4/100, batch 0/250 completed (loss: 0.0114:   1%|          | 1/125 [00:03<06:29,  3.14s/it]
Training Epoch: 4/100, batch 1/250 completed (loss: 0.0009:   1%|          | 1/125 [00:03<06:24,  3.10s/it]
Training Epoch: 4/100, batch 1/250 completed (loss: 0.2074:   1%|          | 1/125 [00:03<06:29,  3.14s/it]
Training Epoch: 4/100, batch 1/250 completed (loss: 0.0066:   1%|          | 1/125 [00:03<06:26,  3.12s/it]
Training Epoch: 4/100, batch 1/250 completed (loss: 0.1535:   1%|          | 1/125 [00:03<06:26,  3.12s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.0105:   1%|          | 1/125 [00:04<06:24,  3.10s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.3618:   1%|          | 1/125 [00:04<06:29,  3.14s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.1828:   1%|          | 1/125 [00:04<06:26,  3.12s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.0013:   1%|          | 1/125 [00:04<06:26,  3.12s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.0105:   2%|▏         | 2/125 [00:05<06:03,  2.96s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.0013:   2%|▏         | 2/125 [00:05<06:04,  2.97s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.1828:   2%|▏         | 2/125 [00:05<06:04,  2.96s/it]
Training Epoch: 4/100, batch 2/250 completed (loss: 0.3618:   2%|▏         | 2/125 [00:05<06:05,  2.97s/it]
Training Epoch: 4/100, batch 3/250 completed (loss: 0.0008:   2%|▏         | 2/125 [00:06<06:03,  2.96s/it]
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Training Epoch: 4/100, batch 4/250 completed (loss: 0.0006:   2%|▏         | 2/125 [00:07<06:05,  2.97s/it]
Training Epoch: 4/100, batch 4/250 completed (loss: 0.0012:   2%|▏         | 2/125 [00:07<06:04,  2.96s/it]
Training Epoch: 4/100, batch 4/250 completed (loss: 0.0288:   2%|▏         | 2/125 [00:07<06:04,  2.97s/it]
Training Epoch: 4/100, batch 4/250 completed (loss: 0.0012:   2%|▏         | 3/125 [00:08<05:54,  2.91s/it]
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Training Epoch: 4/100, batch 5/250 completed (loss: 0.0018:   2%|▏         | 3/125 [00:08<05:55,  2.92s/it]
Training Epoch: 4/100, batch 5/250 completed (loss: 0.0894:   2%|▏         | 3/125 [00:08<05:55,  2.92s/it]
Training Epoch: 4/100, batch 5/250 completed (loss: 0.0009:   2%|▏         | 3/125 [00:08<05:54,  2.91s/it]
Training Epoch: 4/100, batch 5/250 completed (loss: 0.0647:   2%|▏         | 3/125 [00:08<05:56,  2.92s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.0824:   2%|▏         | 3/125 [00:10<05:55,  2.92s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.423:   2%|▏         | 3/125 [00:10<05:54,  2.91s/it] 
Training Epoch: 4/100, batch 6/250 completed (loss: 0.0007:   2%|▏         | 3/125 [00:10<05:55,  2.92s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.0007:   2%|▏         | 3/125 [00:10<05:56,  2.92s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.0824:   3%|▎         | 4/125 [00:11<06:02,  3.00s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.0007:   3%|▎         | 4/125 [00:11<06:03,  3.00s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.423:   3%|▎         | 4/125 [00:11<06:03,  3.01s/it]
Training Epoch: 4/100, batch 6/250 completed (loss: 0.0007:   3%|▎         | 4/125 [00:11<06:03,  3.01s/it]
Training Epoch: 4/100, batch 7/250 completed (loss: 0.0542:   3%|▎         | 4/125 [00:12<06:03,  3.01s/it]
Training Epoch: 4/100, batch 7/250 completed (loss: 0.1342:   3%|▎         | 4/125 [00:12<06:03,  3.01s/it]
Training Epoch: 4/100, batch 7/250 completed (loss: 0.1367:   3%|▎         | 4/125 [00:12<06:02,  3.00s/it]
Training Epoch: 4/100, batch 7/250 completed (loss: 0.0171:   3%|▎         | 4/125 [00:12<06:03,  3.00s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0011:   3%|▎         | 4/125 [00:13<06:02,  3.00s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0041:   3%|▎         | 4/125 [00:13<06:03,  3.01s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0006:   3%|▎         | 4/125 [00:13<06:03,  3.01s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0035:   3%|▎         | 4/125 [00:13<06:03,  3.00s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0011:   4%|▍         | 5/125 [00:15<06:04,  3.04s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0035:   4%|▍         | 5/125 [00:15<06:04,  3.04s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0041:   4%|▍         | 5/125 [00:15<06:05,  3.05s/it]
Training Epoch: 4/100, batch 8/250 completed (loss: 0.0006:   4%|▍         | 5/125 [00:15<06:05,  3.04s/it]
Training Epoch: 4/100, batch 9/250 completed (loss: 0.009:   4%|▍         | 5/125 [00:15<06:04,  3.04s/it] 
Training Epoch: 4/100, batch 9/250 completed (loss: 0.2388:   4%|▍         | 5/125 [00:15<06:05,  3.05s/it]
Training Epoch: 4/100, batch 9/250 completed (loss: 0.5707:   4%|▍         | 5/125 [00:15<06:05,  3.04s/it]
Training Epoch: 4/100, batch 9/250 completed (loss: 0.1445:   4%|▍         | 5/125 [00:15<06:04,  3.04s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0132:   4%|▍         | 5/125 [00:16<06:05,  3.04s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0462:   4%|▍         | 5/125 [00:16<06:05,  3.05s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0518:   4%|▍         | 5/125 [00:16<06:04,  3.04s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0042:   4%|▍         | 5/125 [00:16<06:04,  3.04s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0462:   5%|▍         | 6/125 [00:17<05:55,  2.99s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0042:   5%|▍         | 6/125 [00:17<05:55,  2.99s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0518:   5%|▍         | 6/125 [00:17<05:55,  2.99s/it]
Training Epoch: 4/100, batch 10/250 completed (loss: 0.0132:   5%|▍         | 6/125 [00:17<05:55,  2.99s/it]
Training Epoch: 4/100, batch 11/250 completed (loss: 0.0587:   5%|▍         | 6/125 [00:18<05:55,  2.99s/it]
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Training Epoch: 4/100, batch 11/250 completed (loss: 0.0239:   5%|▍         | 6/125 [00:18<05:55,  2.99s/it]
Training Epoch: 4/100, batch 11/250 completed (loss: 0.1237:   5%|▍         | 6/125 [00:18<05:55,  2.99s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0228:   5%|▍         | 6/125 [00:19<05:55,  2.99s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0008:   5%|▍         | 6/125 [00:19<05:55,  2.99s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0064:   5%|▍         | 6/125 [00:19<05:55,  2.99s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0092:   5%|▍         | 6/125 [00:19<05:55,  2.99s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0064:   6%|▌         | 7/125 [00:20<05:48,  2.96s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0008:   6%|▌         | 7/125 [00:20<05:48,  2.96s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0092:   6%|▌         | 7/125 [00:20<05:49,  2.96s/it]
Training Epoch: 4/100, batch 12/250 completed (loss: 0.0228:   6%|▌         | 7/125 [00:20<05:49,  2.96s/it]
Training Epoch: 4/100, batch 13/250 completed (loss: 0.0013:   6%|▌         | 7/125 [00:20<05:49,  2.96s/it]
Training Epoch: 4/100, batch 13/250 completed (loss: 0.2568:   6%|▌         | 7/125 [00:20<05:48,  2.96s/it]
Training Epoch: 4/100, batch 13/250 completed (loss: 0.0188:   6%|▌         | 7/125 [00:20<05:48,  2.96s/it]
Training Epoch: 4/100, batch 13/250 completed (loss: 0.3154:   6%|▌         | 7/125 [00:20<05:49,  2.96s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0152:   6%|▌         | 7/125 [00:22<05:49,  2.96s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0221:   6%|▌         | 7/125 [00:22<05:48,  2.96s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0041:   6%|▌         | 7/125 [00:22<05:48,  2.96s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0188:   6%|▌         | 7/125 [00:22<05:49,  2.96s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0188:   6%|▋         | 8/125 [00:23<05:43,  2.93s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0041:   6%|▋         | 8/125 [00:23<05:43,  2.93s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0221:   6%|▋         | 8/125 [00:23<05:43,  2.94s/it]
Training Epoch: 4/100, batch 14/250 completed (loss: 0.0152:   6%|▋         | 8/125 [00:23<05:43,  2.94s/it]
Training Epoch: 4/100, batch 15/250 completed (loss: 0.0352:   6%|▋         | 8/125 [00:23<05:43,  2.94s/it]
Training Epoch: 4/100, batch 15/250 completed (loss: 0.0095:   6%|▋         | 8/125 [00:23<05:43,  2.93s/it]
Training Epoch: 4/100, batch 15/250 completed (loss: 0.0062:   6%|▋         | 8/125 [00:23<05:43,  2.94s/it]
Training Epoch: 4/100, batch 15/250 completed (loss: 0.0043:   6%|▋         | 8/125 [00:23<05:43,  2.93s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0049:   6%|▋         | 8/125 [00:25<05:43,  2.94s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0325:   6%|▋         | 8/125 [00:25<05:43,  2.93s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0027:   6%|▋         | 8/125 [00:25<05:43,  2.94s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0234:   6%|▋         | 8/125 [00:25<05:43,  2.93s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0325:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0234:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0027:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it]
Training Epoch: 4/100, batch 16/250 completed (loss: 0.0049:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it]
Training Epoch: 4/100, batch 17/250 completed (loss: 0.0782:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it]
Training Epoch: 4/100, batch 17/250 completed (loss: 0.0171:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it]
Training Epoch: 4/100, batch 17/250 completed (loss: 0.004:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it] 
Training Epoch: 4/100, batch 17/250 completed (loss: 0.101:   7%|▋         | 9/125 [00:26<05:38,  2.92s/it] 
Training Epoch: 4/100, batch 18/250 completed (loss: 0.004:   7%|▋         | 9/125 [00:28<05:38,  2.92s/it]
Training Epoch: 4/100, batch 18/250 completed (loss: 0.3869:   7%|▋         | 9/125 [00:28<05:38,  2.92s/it]
Training Epoch: 4/100, batch 18/250 completed (loss: 0.0258:   7%|▋         | 9/125 [00:28<05:38,  2.92s/it]
Training Epoch: 4/100, batch 18/250 completed (loss: 0.1651:   7%|▋         | 9/125 [00:28<05:38,  2.92s/it]
Training Epoch: 4/100, batch 18/250 completed (loss: 0.004:   8%|▊         | 10/125 [00:29<05:34,  2.91s/it]
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Training Epoch: 4/100, batch 18/250 completed (loss: 0.3869:   8%|▊         | 10/125 [00:29<05:34,  2.91s/it]
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Training Epoch: 4/100, batch 19/250 completed (loss: 0.0999:   8%|▊         | 10/125 [00:29<05:34,  2.91s/it]
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Training Epoch: 4/100, batch 20/250 completed (loss: 0.0007:   8%|▊         | 10/125 [00:31<05:34,  2.91s/it]
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Training Epoch: 4/100, batch 20/250 completed (loss: 0.0007:   9%|▉         | 11/125 [00:32<05:43,  3.01s/it]
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Training Epoch: 4/100, batch 21/250 completed (loss: 0.0017:   9%|▉         | 11/125 [00:32<05:42,  3.01s/it]
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Training Epoch: 4/100, batch 22/250 completed (loss: 0.0177:  10%|▉         | 12/125 [00:35<05:41,  3.02s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_4 to HF: (Request ID: Root=1-6a1b68b5-23991c6827dc80d07ae0d2c9;fc28b740-e3ae-4550-b84b-c33860f2cd67)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_4 to HF: (Request ID: Root=1-6a1b68b9-4a5d13001310caa24f5082db;5915316c-eaeb-46af-b569-ee11e77f7901)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: checkpoint_3
Deleted old checkpoint: train_state_3
[warn] HF upload failed for ckpt_4; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.02820814771577716

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

Test Accuracy:   3%|▎         | 1/32 [00:08<04:10,  8.08s/it]
Test Accuracy:   3%|▎         | 1/32 [00:08<04:10,  8.09s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:08<04:10,  8.08s/it]
[WRONG] Example 1 (diff=8)
  Expected : 'Guy'
Test accuracy: 0.5:   3%|▎         | 1/32 [00:08<04:10,  8.09s/it]
  Extracted: 'Irma'
Test Accuracy:   3%|▎         | 1/32 [00:08<04:10,  8.08s/it]
  Generated: '### Irma'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:08<04:10,  8.08s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<04:06,  7.95s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<04:06,  7.95s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:15<03:48,  7.61s/it]
Test accuracy: 0.5:   6%|▋         | 2/32 [00:15<03:48,  7.61s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:15<03:48,  7.61s/it]
Test accuracy: 0.56:   6%|▋         | 2/32 [00:15<03:48,  7.61s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:15<03:48,  7.61s/it]
Test accuracy: 0.94:   6%|▋         | 2/32 [00:15<03:48,  7.61s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:15<03:46,  7.55s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:15<03:46,  7.55s/it]
Test accuracy: 0.94:   9%|▉         | 3/32 [00:23<03:46,  7.81s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:23<03:46,  7.81s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:23<03:46,  7.81s/it]
Test accuracy: 0.67:   9%|▉         | 3/32 [00:23<03:46,  7.81s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:23<03:45,  7.78s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:23<03:45,  7.78s/it]
Test accuracy: 0.56:   9%|▉         | 3/32 [00:23<03:46,  7.82s/it]
Test accuracy: 0.62:   9%|▉         | 3/32 [00:23<03:46,  7.82s/it]
Test accuracy: 0.75:  12%|█▎        | 4/32 [00:30<03:32,  7.60s/it]
Test accuracy: 0.62:  12%|█▎        | 4/32 [00:30<03:32,  7.60s/it]
Test accuracy: 0.72:  12%|█▎        | 4/32 [00:30<03:32,  7.60s/it]
Test accuracy: 0.66:  12%|█▎        | 4/32 [00:30<03:32,  7.60s/it]
Test accuracy: 0.67:  12%|█▎        | 4/32 [00:30<03:32,  7.60s/it]
Test accuracy: 0.69:  12%|█▎        | 4/32 [00:30<03:32,  7.60s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:30<03:32,  7.58s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:30<03:32,  7.58s/it]
Test accuracy: 0.72:  16%|█▌        | 5/32 [00:37<03:21,  7.48s/it]
Test accuracy: 0.66:  16%|█▌        | 5/32 [00:37<03:21,  7.48s/it]
Test accuracy: 0.69:  16%|█▌        | 5/32 [00:37<03:21,  7.48s/it]
Test accuracy: 0.75:  16%|█▌        | 5/32 [00:37<03:21,  7.48s/it]
Test accuracy: 0.62:  16%|█▌        | 5/32 [00:37<03:21,  7.48s/it]
Test accuracy: 0.65:  16%|█▌        | 5/32 [00:37<03:21,  7.48s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:37<03:21,  7.47s/it]
Test accuracy: 0.85:  16%|█▌        | 5/32 [00:37<03:21,  7.47s/it]
Test accuracy: 0.65:  19%|█▉        | 6/32 [00:45<03:12,  7.41s/it]
Test accuracy: 0.75:  19%|█▉        | 6/32 [00:45<03:12,  7.41s/it]
Test accuracy: 0.67:  19%|█▉        | 6/32 [00:45<03:12,  7.41s/it]
Test accuracy: 0.77:  19%|█▉        | 6/32 [00:45<03:12,  7.41s/it]
Test accuracy: 0.62:  19%|█▉        | 6/32 [00:45<03:12,  7.41s/it]
Test accuracy: 0.67:  19%|█▉        | 6/32 [00:45<03:12,  7.41s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:45<03:12,  7.40s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:45<03:12,  7.40s/it]
Test accuracy: 0.67:  22%|██▏       | 7/32 [00:52<03:04,  7.36s/it]
Test accuracy: 0.77:  22%|██▏       | 7/32 [00:52<03:04,  7.36s/it]
Test accuracy: 0.67:  22%|██▏       | 7/32 [00:52<03:04,  7.36s/it]
Test accuracy: 0.68:  22%|██▏       | 7/32 [00:52<03:04,  7.36s/it]
Test accuracy: 0.77:  22%|██▏       | 7/32 [00:52<03:04,  7.36s/it]
Test accuracy: 0.7:  22%|██▏       | 7/32 [00:52<03:04,  7.36s/it] 
Test accuracy: 0.88:  22%|██▏       | 7/32 [00:52<03:03,  7.36s/it]
Test accuracy: 0.89:  22%|██▏       | 7/32 [00:52<03:03,  7.36s/it]
Test accuracy: 0.68:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it]
Test accuracy: 0.7:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it]
Test accuracy: 0.7:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it] 
Test accuracy: 0.7:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it]
Test accuracy: 0.77:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it]
Test accuracy: 0.8:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it] 
Test accuracy: 0.89:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it]
Test accuracy: 0.89:  25%|██▌       | 8/32 [00:59<02:57,  7.40s/it]
Test accuracy: 0.8:  28%|██▊       | 9/32 [01:08<02:56,  7.67s/it]
Test accuracy: 0.81:  28%|██▊       | 9/32 [01:08<02:56,  7.67s/it]
Test accuracy: 0.7:  28%|██▊       | 9/32 [01:08<02:56,  7.67s/it]
Test accuracy: 0.71:  28%|██▊       | 9/32 [01:08<02:56,  7.67s/it]
Test accuracy: 0.89:  28%|██▊       | 9/32 [01:08<02:56,  7.67s/it]
Test accuracy: 0.89:  28%|██▊       | 9/32 [01:08<02:56,  7.67s/it]
Test accuracy: 0.7:  28%|██▊       | 9/32 [01:08<02:56,  7.68s/it]
Test accuracy: 0.74:  28%|██▊       | 9/32 [01:08<02:56,  7.68s/it]
Test accuracy: 0.81:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it]
Test accuracy: 0.71:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it]
Test accuracy: 0.74:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it]
Test accuracy: 0.8:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it] 
Test accuracy: 0.7:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it] 
Test accuracy: 0.74:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it]
Test accuracy: 0.89:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it]
Test accuracy: 0.88:  31%|███▏      | 10/32 [01:15<02:46,  7.55s/it]
Test accuracy: 0.8:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.81:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.7:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.74:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.88:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.7:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.74:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.85:  34%|███▍      | 11/32 [01:22<02:36,  7.46s/it]
Test accuracy: 0.7:  38%|███▊      | 12/32 [01:30<02:28,  7.40s/it]
Test accuracy: 0.81:  38%|███▊      | 12/32 [01:30<02:28,  7.40s/it]
Test accuracy: 0.68:  38%|███▊      | 12/32 [01:30<02:28,  7.40s/it]
Test accuracy: 0.85:  38%|███▊      | 12/32 [01:29<02:28,  7.40s/it]
Test accuracy: 0.79:  38%|███▊      | 12/32 [01:30<02:28,  7.40s/it]
Test accuracy: 0.74:  38%|███▊      | 12/32 [01:30<02:28,  7.40s/it]
Test accuracy: 0.83:  38%|███▊      | 12/32 [01:29<02:28,  7.40s/it]
Test accuracy: 0.73:  38%|███▊      | 12/32 [01:30<02:28,  7.40s/it]
Test accuracy: 0.79:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.81:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.68:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.83:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.69:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.73:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.73:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.82:  41%|████      | 13/32 [01:37<02:19,  7.36s/it]
Test accuracy: 0.73:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it]
Test accuracy: 0.69:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it]
Test accuracy: 0.81:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it]
Test accuracy: 0.82:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it]
Test accuracy: 0.72:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it]
Test accuracy: 0.7:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it] 
Test accuracy: 0.81:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it]
Test accuracy: 0.8:  44%|████▍     | 14/32 [01:44<02:11,  7.33s/it] 
Test accuracy: 0.81:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.8:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it] 
Test accuracy: 0.7:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.72:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.71:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.73:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.8:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.79:  47%|████▋     | 15/32 [01:51<02:04,  7.31s/it]
Test accuracy: 0.73:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.8:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.71:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.73:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.8:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.71:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.79:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it]
Test accuracy: 0.8:  50%|█████     | 16/32 [01:59<01:57,  7.34s/it] 
Test accuracy: 0.8:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.78:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.71:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.8:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.72:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.73:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.79:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.72:  53%|█████▎    | 17/32 [02:06<01:50,  7.38s/it]
Test accuracy: 0.72:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.72:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.72:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.78:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.71:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.78:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.79:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.79:  56%|█████▋    | 18/32 [02:13<01:42,  7.35s/it]
Test accuracy: 0.72:  59%|█████▉    | 19/32 [02:21<01:35,  7.32s/it]
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Test accuracy: 0.77: 100%|██████████| 32/32 [03:54<00:00,  6.12s/it]
Test accuracy: 0.78: 100%|██████████| 32/32 [03:54<00:00,  6.12s/it]
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Test accuracy: 0.75: 100%|██████████| 32/32 [03:54<00:00,  6.12s/it]
Test accuracy: 0.71: 100%|██████████| 32/32 [03:54<00:00,  6.12s/it]
Test accuracy: 0.74: 100%|██████████| 32/32 [03:54<00:00,  6.12s/it]
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Test accuracy: 0.71: 100%|██████████| 32/32 [03:54<00:00,  7.34s/it]
Device 2: Cor=177, CoT=0, Total=250

Test accuracy: 0.75: 100%|██████████| 32/32 [03:54<00:00,  7.34s/it]
Device 1: Cor=188, CoT=0, Total=250

Test accuracy: 0.74: 100%|██████████| 32/32 [03:54<00:00,  7.34s/it]
Device 3: Cor=184, CoT=0, Total=250

Test accuracy: 0.78: 100%|██████████| 32/32 [03:54<00:00,  7.34s/it]
Device 0: Cor=194, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 3)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=4, data_stage=4, n_latent_tokens=4
Training on difficulties: 0-4

Overall accuracy: 743 / 1000 = 0.7430

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 99/100 = 0.9900 (trained)
  diff=3: 96/100 = 0.9600 (trained)
  diff=4: 92/100 = 0.9200 (trained)
  diff=5: 76/100 = 0.7600 (untrained)
  diff=6: 53/100 = 0.5300 (untrained)
  diff=7: 50/100 = 0.5000 (untrained)
  diff=8: 54/100 = 0.5400 (untrained)
  diff=9: 66/100 = 0.6600 (untrained)
  diff=10: 57/100 = 0.5700 (untrained)

Trained (diff 0-4): 387/400 = 0.9675
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9675, acc_diff4=0.9200 >= 0.9 ***
*** Advancing to stage 5 ***

Warning: failed to upload checkpoint_4 to HF: (Request ID: Root=1-6a1b69b1-2ab9d377552f7ed766a59c7b;e55c7a49-1f86-4d9b-aa0b-6693908daa8b)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 4/100 (thought_stage=5, data_stage=5)
============================================================
  thought_stage=5, c_thought=1, max_difficulty=5

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Map (num_proc=4):  25%|██▌       | 125/500 [00:00<00:01, 333.61 examples/s]
Map (num_proc=4):  75%|███████▌  | 375/500 [00:00<00:00, 838.25 examples/s]
Map (num_proc=4): 100%|██████████| 500/500 [00:00<00:00, 559.06 examples/s]

Training Epoch: 5:   0%|          | 0/156 [00:00<?, ?it/s]
Training Epoch: 5:   0%|          | 0/156 [00:00<?, ?it/s]
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Training Epoch: 5:   0%|          | 0/156 [00:00<?, ?it/s]logging training data
rank 0 | gpu 0 | alloc=4.19 GB | reserved=47.37 GB | max=32.99 GB
rank 3 | gpu 3 | alloc=4.19 GB | reserved=52.92 GB | max=32.99 GBrank 1 | gpu 1 | alloc=4.19 GB | reserved=49.22 GB | max=32.99 GB

rank 2 | gpu 2 | alloc=4.19 GB | reserved=49.23 GB | max=32.99 GB

Training Epoch: 5/100, batch 0/313 completed (loss: 0.002:   0%|          | 0/156 [00:02<?, ?it/s]
Training Epoch: 5/100, batch 0/313 completed (loss: 0.0488:   0%|          | 0/156 [00:02<?, ?it/s]
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Training Epoch: 5/100, batch 0/313 completed (loss: 0.0149:   0%|          | 0/156 [00:02<?, ?it/s]
Training Epoch: 5/100, batch 0/313 completed (loss: 0.002:   1%|          | 1/156 [00:03<09:41,  3.75s/it]
Training Epoch: 5/100, batch 0/313 completed (loss: 0.0821:   1%|          | 1/156 [00:03<09:42,  3.76s/it]
Training Epoch: 5/100, batch 0/313 completed (loss: 0.0149:   1%|          | 1/156 [00:03<09:47,  3.79s/it]
Training Epoch: 5/100, batch 0/313 completed (loss: 0.0488:   1%|          | 1/156 [00:03<09:50,  3.81s/it]
Training Epoch: 5/100, batch 1/313 completed (loss: 0.1902:   1%|          | 1/156 [00:03<09:41,  3.75s/it]
Training Epoch: 5/100, batch 1/313 completed (loss: 0.0015:   1%|          | 1/156 [00:03<09:42,  3.76s/it]
Training Epoch: 5/100, batch 1/313 completed (loss: 0.0123:   1%|          | 1/156 [00:03<09:50,  3.81s/it]
Training Epoch: 5/100, batch 1/313 completed (loss: 0.0074:   1%|          | 1/156 [00:03<09:47,  3.79s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.0053:   1%|          | 1/156 [00:05<09:50,  3.81s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.0059:   1%|          | 1/156 [00:05<09:42,  3.76s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.0072:   1%|          | 1/156 [00:05<09:41,  3.75s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.5244:   1%|          | 1/156 [00:05<09:47,  3.79s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.0059:   1%|▏         | 2/156 [00:07<09:03,  3.53s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.0072:   1%|▏         | 2/156 [00:07<09:03,  3.53s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.5244:   1%|▏         | 2/156 [00:07<09:04,  3.54s/it]
Training Epoch: 5/100, batch 2/313 completed (loss: 0.0053:   1%|▏         | 2/156 [00:07<09:07,  3.55s/it]
Training Epoch: 5/100, batch 3/313 completed (loss: 0.0095:   1%|▏         | 2/156 [00:07<09:07,  3.55s/it]
Training Epoch: 5/100, batch 3/313 completed (loss: 0.0016:   1%|▏         | 2/156 [00:07<09:03,  3.53s/it]
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Training Epoch: 5/100, batch 3/313 completed (loss: 0.1627:   1%|▏         | 2/156 [00:07<09:04,  3.54s/it]
Training Epoch: 5/100, batch 4/313 completed (loss: 0.0309:   1%|▏         | 2/156 [00:08<09:03,  3.53s/it]
Training Epoch: 5/100, batch 4/313 completed (loss: 0.0603:   1%|▏         | 2/156 [00:08<09:07,  3.55s/it]
Training Epoch: 5/100, batch 4/313 completed (loss: 0.1012:   1%|▏         | 2/156 [00:08<09:03,  3.53s/it]
Training Epoch: 5/100, batch 4/313 completed (loss: 0.1537:   1%|▏         | 2/156 [00:08<09:04,  3.54s/it]
Training Epoch: 5/100, batch 4/313 completed (loss: 0.0309:   2%|▏         | 3/156 [00:10<08:48,  3.45s/it]
Training Epoch: 5/100, batch 4/313 completed (loss: 0.1537:   2%|▏         | 3/156 [00:10<08:49,  3.46s/it]
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Training Epoch: 5/100, batch 4/313 completed (loss: 0.0603:   2%|▏         | 3/156 [00:10<08:50,  3.47s/it]
Training Epoch: 5/100, batch 5/313 completed (loss: 0.0433:   2%|▏         | 3/156 [00:10<08:50,  3.47s/it]
Training Epoch: 5/100, batch 5/313 completed (loss: 0.0066:   2%|▏         | 3/156 [00:10<08:48,  3.46s/it]
Training Epoch: 5/100, batch 5/313 completed (loss: 0.02:   2%|▏         | 3/156 [00:10<08:48,  3.45s/it]  
Training Epoch: 5/100, batch 5/313 completed (loss: 0.0036:   2%|▏         | 3/156 [00:10<08:49,  3.46s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.3585:   2%|▏         | 3/156 [00:12<08:50,  3.47s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.0805:   2%|▏         | 3/156 [00:12<08:48,  3.45s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.1691:   2%|▏         | 3/156 [00:12<08:48,  3.46s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.0193:   2%|▏         | 3/156 [00:12<08:49,  3.46s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.1691:   3%|▎         | 4/156 [00:13<08:40,  3.42s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.0805:   3%|▎         | 4/156 [00:13<08:40,  3.42s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.0193:   3%|▎         | 4/156 [00:13<08:40,  3.43s/it]
Training Epoch: 5/100, batch 6/313 completed (loss: 0.3585:   3%|▎         | 4/156 [00:13<08:41,  3.43s/it]
Training Epoch: 5/100, batch 7/313 completed (loss: 0.4544:   3%|▎         | 4/156 [00:13<08:41,  3.43s/it]
Training Epoch: 5/100, batch 7/313 completed (loss: 0.0267:   3%|▎         | 4/156 [00:13<08:40,  3.42s/it]
Training Epoch: 5/100, batch 7/313 completed (loss: 0.0553:   3%|▎         | 4/156 [00:13<08:40,  3.42s/it]
Training Epoch: 5/100, batch 7/313 completed (loss: 0.2483:   3%|▎         | 4/156 [00:14<08:40,  3.43s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.0588:   3%|▎         | 4/156 [00:15<08:40,  3.42s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.1065:   3%|▎         | 4/156 [00:15<08:40,  3.42s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.0527:   3%|▎         | 4/156 [00:15<08:41,  3.43s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.1374:   3%|▎         | 4/156 [00:15<08:40,  3.43s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.1374:   3%|▎         | 5/156 [00:17<08:48,  3.50s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.1065:   3%|▎         | 5/156 [00:17<08:48,  3.50s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.0588:   3%|▎         | 5/156 [00:17<08:48,  3.50s/it]
Training Epoch: 5/100, batch 8/313 completed (loss: 0.0527:   3%|▎         | 5/156 [00:17<08:49,  3.50s/it]
Training Epoch: 5/100, batch 9/313 completed (loss: 0.0604:   3%|▎         | 5/156 [00:17<08:48,  3.50s/it]
Training Epoch: 5/100, batch 9/313 completed (loss: 0.0116:   3%|▎         | 5/156 [00:17<08:48,  3.50s/it]
Training Epoch: 5/100, batch 9/313 completed (loss: 0.5999:   3%|▎         | 5/156 [00:17<08:49,  3.50s/it]
Training Epoch: 5/100, batch 9/313 completed (loss: 0.0325:   3%|▎         | 5/156 [00:17<08:48,  3.50s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0185:   3%|▎         | 5/156 [00:19<08:48,  3.50s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0579:   3%|▎         | 5/156 [00:19<08:49,  3.50s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0021:   3%|▎         | 5/156 [00:19<08:48,  3.50s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0102:   3%|▎         | 5/156 [00:19<08:48,  3.50s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0102:   4%|▍         | 6/156 [00:20<08:39,  3.46s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0185:   4%|▍         | 6/156 [00:20<08:39,  3.46s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0021:   4%|▍         | 6/156 [00:20<08:40,  3.47s/it]
Training Epoch: 5/100, batch 10/313 completed (loss: 0.0579:   4%|▍         | 6/156 [00:20<08:39,  3.47s/it]
Training Epoch: 5/100, batch 11/313 completed (loss: 0.0906:   4%|▍         | 6/156 [00:21<08:39,  3.47s/it]
Training Epoch: 5/100, batch 11/313 completed (loss: 0.0634:   4%|▍         | 6/156 [00:21<08:39,  3.46s/it]
Training Epoch: 5/100, batch 11/313 completed (loss: 0.0419:   4%|▍         | 6/156 [00:21<08:40,  3.47s/it]
Training Epoch: 5/100, batch 11/313 completed (loss: 0.0201:   4%|▍         | 6/156 [00:21<08:39,  3.46s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.0948:   4%|▍         | 6/156 [00:22<08:39,  3.47s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.0027:   4%|▍         | 6/156 [00:22<08:39,  3.46s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.031:   4%|▍         | 6/156 [00:22<08:40,  3.47s/it] 
Training Epoch: 5/100, batch 12/313 completed (loss: 0.1161:   4%|▍         | 6/156 [00:22<08:39,  3.46s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.0027:   4%|▍         | 7/156 [00:24<08:40,  3.49s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.031:   4%|▍         | 7/156 [00:24<08:40,  3.49s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.1161:   4%|▍         | 7/156 [00:24<08:42,  3.51s/it]
Training Epoch: 5/100, batch 12/313 completed (loss: 0.0948:   4%|▍         | 7/156 [00:24<08:41,  3.50s/it]
Training Epoch: 5/100, batch 13/313 completed (loss: 0.2936:   4%|▍         | 7/156 [00:24<08:40,  3.49s/it]
Training Epoch: 5/100, batch 13/313 completed (loss: 0.01:   4%|▍         | 7/156 [00:24<08:40,  3.49s/it]  
Training Epoch: 5/100, batch 13/313 completed (loss: 0.0004:   4%|▍         | 7/156 [00:24<08:41,  3.50s/it]
Training Epoch: 5/100, batch 13/313 completed (loss: 0.0393:   4%|▍         | 7/156 [00:24<08:42,  3.51s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0894:   4%|▍         | 7/156 [00:26<08:40,  3.49s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0182:   4%|▍         | 7/156 [00:26<08:40,  3.49s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0148:   4%|▍         | 7/156 [00:26<08:41,  3.50s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0343:   4%|▍         | 7/156 [00:26<08:42,  3.51s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0182:   5%|▌         | 8/156 [00:27<08:32,  3.46s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0894:   5%|▌         | 8/156 [00:27<08:33,  3.47s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0343:   5%|▌         | 8/156 [00:27<08:32,  3.47s/it]
Training Epoch: 5/100, batch 14/313 completed (loss: 0.0148:   5%|▌         | 8/156 [00:27<08:33,  3.47s/it]
Training Epoch: 5/100, batch 15/313 completed (loss: 0.0189:   5%|▌         | 8/156 [00:27<08:32,  3.46s/it]
Training Epoch: 5/100, batch 15/313 completed (loss: 0.0059:   5%|▌         | 8/156 [00:27<08:33,  3.47s/it]
Training Epoch: 5/100, batch 15/313 completed (loss: 0.0896:   5%|▌         | 8/156 [00:27<08:33,  3.47s/it]
Training Epoch: 5/100, batch 15/313 completed (loss: 0.3069:   5%|▌         | 8/156 [00:28<08:32,  3.47s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.12:   5%|▌         | 8/156 [00:29<08:33,  3.47s/it]  
Training Epoch: 5/100, batch 16/313 completed (loss: 0.3359:   5%|▌         | 8/156 [00:29<08:33,  3.47s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.0397:   5%|▌         | 8/156 [00:29<08:32,  3.46s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.0631:   5%|▌         | 8/156 [00:29<08:32,  3.47s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.0631:   6%|▌         | 9/156 [00:31<08:26,  3.45s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.0397:   6%|▌         | 9/156 [00:31<08:27,  3.45s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.3359:   6%|▌         | 9/156 [00:31<08:27,  3.45s/it]
Training Epoch: 5/100, batch 16/313 completed (loss: 0.12:   6%|▌         | 9/156 [00:31<08:27,  3.45s/it]
Training Epoch: 5/100, batch 17/313 completed (loss: 0.0083:   6%|▌         | 9/156 [00:31<08:27,  3.45s/it]
Training Epoch: 5/100, batch 17/313 completed (loss: 0.0223:   6%|▌         | 9/156 [00:31<08:27,  3.45s/it]
Training Epoch: 5/100, batch 17/313 completed (loss: 0.1257:   6%|▌         | 9/156 [00:31<08:27,  3.45s/it]
Training Epoch: 5/100, batch 17/313 completed (loss: 0.0194:   6%|▌         | 9/156 [00:31<08:26,  3.45s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.1232:   6%|▌         | 9/156 [00:33<08:27,  3.45s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.0142:   6%|▌         | 9/156 [00:33<08:27,  3.45s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.015:   6%|▌         | 9/156 [00:33<08:27,  3.45s/it] 
Training Epoch: 5/100, batch 18/313 completed (loss: 0.0419:   6%|▌         | 9/156 [00:33<08:26,  3.45s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.0142:   6%|▋         | 10/156 [00:34<08:31,  3.51s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.0419:   6%|▋         | 10/156 [00:34<08:32,  3.51s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.1232:   6%|▋         | 10/156 [00:34<08:32,  3.51s/it]
Training Epoch: 5/100, batch 18/313 completed (loss: 0.015:   6%|▋         | 10/156 [00:34<08:32,  3.51s/it]
Training Epoch: 5/100, batch 19/313 completed (loss: 0.0147:   6%|▋         | 10/156 [00:35<08:31,  3.51s/it]
Training Epoch: 5/100, batch 19/313 completed (loss: 0.0223:   6%|▋         | 10/156 [00:35<08:32,  3.51s/it]
Training Epoch: 5/100, batch 19/313 completed (loss: 0.0363:   6%|▋         | 10/156 [00:35<08:32,  3.51s/it]
Training Epoch: 5/100, batch 19/313 completed (loss: 0.0199:   6%|▋         | 10/156 [00:35<08:32,  3.51s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.0185:   6%|▋         | 10/156 [00:36<08:31,  3.51s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.0277:   6%|▋         | 10/156 [00:36<08:32,  3.51s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.2011:   6%|▋         | 10/156 [00:36<08:32,  3.51s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.0082:   6%|▋         | 10/156 [00:36<08:32,  3.51s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.0082:   7%|▋         | 11/156 [00:38<08:24,  3.48s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.2011:   7%|▋         | 11/156 [00:38<08:24,  3.48s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.0185:   7%|▋         | 11/156 [00:38<08:25,  3.48s/it]
Training Epoch: 5/100, batch 20/313 completed (loss: 0.0277:   7%|▋         | 11/156 [00:38<08:24,  3.48s/it]
Training Epoch: 5/100, batch 21/313 completed (loss: 0.0732:   7%|▋         | 11/156 [00:38<08:25,  3.48s/it]
Training Epoch: 5/100, batch 21/313 completed (loss: 0.0447:   7%|▋         | 11/156 [00:38<08:24,  3.48s/it]
Training Epoch: 5/100, batch 21/313 completed (loss: 0.0596:   7%|▋         | 11/156 [00:38<08:24,  3.48s/it]
Training Epoch: 5/100, batch 21/313 completed (loss: 0.0902:   7%|▋         | 11/156 [00:38<08:24,  3.48s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.0099:   7%|▋         | 11/156 [00:40<08:25,  3.48s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.0022:   7%|▋         | 11/156 [00:40<08:24,  3.48s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.0042:   7%|▋         | 11/156 [00:40<08:24,  3.48s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.1628:   7%|▋         | 11/156 [00:40<08:24,  3.48s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.0042:   8%|▊         | 12/156 [00:41<08:25,  3.51s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.0099:   8%|▊         | 12/156 [00:41<08:25,  3.51s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.1628:   8%|▊         | 12/156 [00:41<08:26,  3.52s/it]
Training Epoch: 5/100, batch 22/313 completed (loss: 0.0022:   8%|▊         | 12/156 [00:41<08:25,  3.51s/it]
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Training Epoch: 5/100, batch 23/313 completed (loss: 0.6288:   8%|▊         | 12/156 [00:42<08:25,  3.51s/it]
Training Epoch: 5/100, batch 23/313 completed (loss: 0.0597:   8%|▊         | 12/156 [00:42<08:25,  3.51s/it]
Training Epoch: 5/100, batch 23/313 completed (loss: 0.0178:   8%|▊         | 12/156 [00:42<08:26,  3.52s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0961:   8%|▊         | 12/156 [00:43<08:25,  3.51s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0043:   8%|▊         | 12/156 [00:43<08:25,  3.51s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0141:   8%|▊         | 12/156 [00:43<08:25,  3.51s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0009:   8%|▊         | 12/156 [00:43<08:26,  3.52s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0961:   8%|▊         | 13/156 [00:45<08:17,  3.48s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0141:   8%|▊         | 13/156 [00:45<08:17,  3.48s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0009:   8%|▊         | 13/156 [00:45<08:17,  3.48s/it]
Training Epoch: 5/100, batch 24/313 completed (loss: 0.0043:   8%|▊         | 13/156 [00:45<08:18,  3.48s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_5 to HF: (Request ID: Root=1-6a1b6bf9-2a7be9137ff327f45c2a28f1;8c44734d-ef4c-48b2-ab45-535f78ffbdca)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_5 to HF: (Request ID: Root=1-6a1b6bfd-2f8f46d71da336de7b83a9ea;fbb06ace-391e-4c6e-b5eb-ad767c02dc11)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: train_state_4
Deleted old checkpoint: checkpoint_4
[warn] HF upload failed for ckpt_5; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.012088531409972347

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

[WRONG] Example 1 (diff=8)

Test Accuracy:   3%|▎         | 1/32 [00:07<03:52,  7.48s/it]  Expected : 'Guy'
  Extracted: 'Irma'
  Generated: '### Irma'

Test accuracy: 0.75:   3%|▎         | 1/32 [00:07<03:52,  7.48s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<03:52,  7.49s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:52,  7.49s/it]
  Generated: '### Elvis'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:52,  7.49s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:52,  7.49s/it]
[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:47,  7.35s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<03:47,  7.35s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:14<03:37,  7.25s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:37,  7.25s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:14<03:37,  7.25s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:14<03:37,  7.25s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:37,  7.25s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:14<03:37,  7.25s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:14<03:35,  7.19s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:14<03:35,  7.19s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:22<03:44,  7.74s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:22<03:44,  7.74s/it]
Test accuracy: 0.79:   9%|▉         | 3/32 [00:22<03:44,  7.74s/it]
Test accuracy: 0.71:   9%|▉         | 3/32 [00:22<03:44,  7.74s/it]
Test accuracy: 0.75:   9%|▉         | 3/32 [00:22<03:43,  7.71s/it]
Test accuracy: 0.83:   9%|▉         | 3/32 [00:22<03:43,  7.71s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:22<03:44,  7.75s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:22<03:44,  7.75s/it]
Test accuracy: 0.79:  12%|█▎        | 4/32 [00:29<03:29,  7.48s/it]
Test accuracy: 0.71:  12%|█▎        | 4/32 [00:29<03:29,  7.48s/it]
Test accuracy: 0.75:  12%|█▎        | 4/32 [00:29<03:29,  7.48s/it]
Test accuracy: 0.72:  12%|█▎        | 4/32 [00:29<03:29,  7.48s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:29<03:29,  7.48s/it]
Test accuracy: 0.83:  12%|█▎        | 4/32 [00:29<03:28,  7.46s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:29<03:29,  7.48s/it]
Test accuracy: 0.84:  12%|█▎        | 4/32 [00:29<03:28,  7.46s/it]
Test accuracy: 0.75:  16%|█▌        | 5/32 [00:37<03:18,  7.34s/it]
Test accuracy: 0.75:  16%|█▌        | 5/32 [00:37<03:18,  7.34s/it]
Test accuracy: 0.72:  16%|█▌        | 5/32 [00:37<03:18,  7.34s/it]
Test accuracy: 0.84:  16%|█▌        | 5/32 [00:36<03:17,  7.32s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:37<03:18,  7.34s/it]
Test accuracy: 0.77:  16%|█▌        | 5/32 [00:37<03:18,  7.34s/it]
Test accuracy: 0.85:  16%|█▌        | 5/32 [00:37<03:18,  7.34s/it]
Test accuracy: 0.85:  16%|█▌        | 5/32 [00:36<03:17,  7.32s/it]
Test accuracy: 0.75:  19%|█▉        | 6/32 [00:44<03:08,  7.25s/it]
Test accuracy: 0.77:  19%|█▉        | 6/32 [00:44<03:08,  7.25s/it]
Test accuracy: 0.75:  19%|█▉        | 6/32 [00:44<03:08,  7.25s/it]
Test accuracy: 0.79:  19%|█▉        | 6/32 [00:44<03:08,  7.25s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:44<03:08,  7.25s/it]
Test accuracy: 0.83:  19%|█▉        | 6/32 [00:44<03:08,  7.25s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:43<03:08,  7.24s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:43<03:08,  7.24s/it]
Test accuracy: 0.79:  22%|██▏       | 7/32 [00:51<02:59,  7.19s/it]
Test accuracy: 0.88:  22%|██▏       | 7/32 [00:51<02:59,  7.18s/it]
Test accuracy: 0.75:  22%|██▏       | 7/32 [00:51<02:59,  7.19s/it]
Test accuracy: 0.77:  22%|██▏       | 7/32 [00:51<02:59,  7.19s/it]
Test accuracy: 0.77:  22%|██▏       | 7/32 [00:51<02:59,  7.19s/it]
Test accuracy: 0.83:  22%|██▏       | 7/32 [00:51<02:59,  7.19s/it]
Test accuracy: 0.88:  22%|██▏       | 7/32 [00:51<02:59,  7.18s/it]
Test accuracy: 0.84:  22%|██▏       | 7/32 [00:51<02:59,  7.19s/it]
Test accuracy: 0.77:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.84:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.77:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.86:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.78:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.8:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it] 
Test accuracy: 0.88:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.89:  25%|██▌       | 8/32 [00:58<02:51,  7.15s/it]
Test accuracy: 0.86:  28%|██▊       | 9/32 [01:06<02:52,  7.49s/it]
Test accuracy: 0.78:  28%|██▊       | 9/32 [01:06<02:52,  7.49s/it]
Test accuracy: 0.89:  28%|██▊       | 9/32 [01:06<02:52,  7.49s/it]
Test accuracy: 0.83:  28%|██▊       | 9/32 [01:06<02:52,  7.49s/it]
Test accuracy: 0.81:  28%|██▊       | 9/32 [01:06<02:52,  7.49s/it]
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Test accuracy: 0.8:  34%|███▍      | 11/32 [01:20<02:32,  7.28s/it]
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Test accuracy: 0.82:  38%|███▊      | 12/32 [01:27<02:24,  7.22s/it]
Test accuracy: 0.78:  38%|███▊      | 12/32 [01:27<02:24,  7.22s/it]
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Test accuracy: 0.79:  38%|███▊      | 12/32 [01:27<02:24,  7.22s/it]
Test accuracy: 0.84:  38%|███▊      | 12/32 [01:27<02:24,  7.22s/it]
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Test accuracy: 0.81:  41%|████      | 13/32 [01:34<02:16,  7.17s/it]
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Test accuracy: 0.79:  44%|████▍     | 14/32 [01:41<02:09,  7.21s/it]
Test accuracy: 0.79:  47%|████▋     | 15/32 [01:49<02:01,  7.17s/it]
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Test accuracy: 0.8:  47%|████▋     | 15/32 [01:49<02:01,  7.17s/it] 
Test accuracy: 0.79:  50%|█████     | 16/32 [01:56<01:54,  7.19s/it]
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Test accuracy: 0.78:  50%|█████     | 16/32 [01:56<01:54,  7.19s/it]
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Test accuracy: 0.82:  53%|█████▎    | 17/32 [02:03<01:47,  7.15s/it]
Test accuracy: 0.78:  53%|█████▎    | 17/32 [02:03<01:47,  7.15s/it]
Test accuracy: 0.82:  53%|█████▎    | 17/32 [02:03<01:47,  7.15s/it]
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Test accuracy: 0.79:  56%|█████▋    | 18/32 [02:10<01:39,  7.13s/it]
Test accuracy: 0.83:  56%|█████▋    | 18/32 [02:10<01:39,  7.13s/it]
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Test accuracy: 0.8:  59%|█████▉    | 19/32 [02:17<01:32,  7.12s/it] 
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Test accuracy: 0.8:  59%|█████▉    | 19/32 [02:17<01:32,  7.12s/it] 
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Test accuracy: 0.8:  62%|██████▎   | 20/32 [02:24<01:25,  7.17s/it]
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Test accuracy: 0.79:  62%|██████▎   | 20/32 [02:24<01:25,  7.17s/it]
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Test accuracy: 0.8:  62%|██████▎   | 20/32 [02:24<01:25,  7.17s/it]
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Test accuracy: 0.82:  66%|██████▌   | 21/32 [02:33<01:22,  7.45s/it]
Test accuracy: 0.8:  66%|██████▌   | 21/32 [02:32<01:22,  7.45s/it]
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Test accuracy: 0.8:  66%|██████▌   | 21/32 [02:33<01:22,  7.46s/it]
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Test accuracy: 0.82:  69%|██████▉   | 22/32 [02:40<01:13,  7.35s/it]
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Test accuracy: 0.81:  84%|████████▍ | 27/32 [03:16<00:36,  7.32s/it]
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Test accuracy: 0.8:  88%|████████▊ | 28/32 [03:24<00:29,  7.50s/it]
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Test accuracy: 0.82:  88%|████████▊ | 28/32 [03:24<00:29,  7.50s/it]
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Test accuracy: 0.79:  88%|████████▊ | 28/32 [03:24<00:30,  7.50s/it]
Test accuracy: 0.8:  91%|█████████ | 29/32 [03:31<00:22,  7.37s/it]
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Test accuracy: 0.82:  91%|█████████ | 29/32 [03:31<00:22,  7.37s/it]
Test accuracy: 0.79:  91%|█████████ | 29/32 [03:31<00:22,  7.37s/it]
Test accuracy: 0.82:  91%|█████████ | 29/32 [03:31<00:22,  7.37s/it]
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Test accuracy: 0.8:  91%|█████████ | 29/32 [03:31<00:22,  7.37s/it] 
Test accuracy: 0.82:  91%|█████████ | 29/32 [03:31<00:22,  7.37s/it]
Test accuracy: 0.8:  94%|█████████▍| 30/32 [03:38<00:14,  7.28s/it]
Test accuracy: 0.8:  94%|█████████▍| 30/32 [03:38<00:14,  7.29s/it]
Test accuracy: 0.82:  94%|█████████▍| 30/32 [03:38<00:14,  7.29s/it]
Test accuracy: 0.8:  94%|█████████▍| 30/32 [03:38<00:14,  7.28s/it]
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Test accuracy: 0.82:  94%|█████████▍| 30/32 [03:38<00:14,  7.29s/it]
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Test accuracy: 0.8:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.82:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.8:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.83:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.82:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.82:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.8:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.79:  97%|█████████▋| 31/32 [03:46<00:07,  7.48s/it]
Test accuracy: 0.82: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.82: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.83: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.8: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.82: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.79: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.8: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.79: 100%|██████████| 32/32 [03:49<00:00,  5.96s/it]
Test accuracy: 0.79: 100%|██████████| 32/32 [03:49<00:00,  7.17s/it]
Device 2: Cor=198, CoT=0, Total=250

Test accuracy: 0.82: 100%|██████████| 32/32 [03:49<00:00,  7.17s/it]
Device 1: Cor=206, CoT=0, Total=250

Test accuracy: 0.8: 100%|██████████| 32/32 [03:49<00:00,  7.17s/it]
Device 3: Cor=201, CoT=0, Total=250

Test accuracy: 0.82: 100%|██████████| 32/32 [03:49<00:00,  7.17s/it]
Device 0: Cor=204, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 4)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=5, data_stage=5, n_latent_tokens=5
Training on difficulties: 0-5

Overall accuracy: 809 / 1000 = 0.8090

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 98/100 = 0.9800 (trained)
  diff=4: 98/100 = 0.9800 (trained)
  diff=5: 97/100 = 0.9700 (trained)
  diff=6: 75/100 = 0.7500 (untrained)
  diff=7: 66/100 = 0.6600 (untrained)
  diff=8: 65/100 = 0.6500 (untrained)
  diff=9: 55/100 = 0.5500 (untrained)
  diff=10: 55/100 = 0.5500 (untrained)

Trained (diff 0-5): 493/500 = 0.9860
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9860, acc_diff5=0.9700 >= 0.9 ***
*** Advancing to stage 6 ***

Warning: failed to upload checkpoint_5 to HF: (Request ID: Root=1-6a1b6cf4-7078d5db257f545f3ae07a8e;c8a4a3b7-4ca4-4522-ab79-f066430700a4)

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============================================================
EPOCH 5/100 (thought_stage=6, data_stage=6)
============================================================
  thought_stage=6, c_thought=1, max_difficulty=6

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Training Epoch: 6:   0%|          | 0/187 [00:00<?, ?it/s]
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Training Epoch: 6:   0%|          | 0/187 [00:00<?, ?it/s]logging training data
rank 1 | gpu 1 | alloc=4.19 GB | reserved=52.97 GB | max=34.42 GB
rank 3 | gpu 3 | alloc=4.19 GB | reserved=58.53 GB | max=34.42 GB
rank 0 | gpu 0 | alloc=4.19 GB | reserved=49.30 GB | max=34.42 GB
rank 2 | gpu 2 | alloc=4.19 GB | reserved=56.68 GB | max=34.42 GB

Training Epoch: 6/100, batch 0/375 completed (loss: 0.1068:   0%|          | 0/187 [00:02<?, ?it/s]
Training Epoch: 6/100, batch 0/375 completed (loss: 0.0383:   0%|          | 0/187 [00:02<?, ?it/s]
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Training Epoch: 6/100, batch 0/375 completed (loss: 0.0068:   1%|          | 1/187 [00:04<13:39,  4.41s/it]
Training Epoch: 6/100, batch 0/375 completed (loss: 0.1068:   1%|          | 1/187 [00:04<13:33,  4.37s/it]
Training Epoch: 6/100, batch 0/375 completed (loss: 0.0383:   1%|          | 1/187 [00:04<13:33,  4.38s/it]
Training Epoch: 6/100, batch 0/375 completed (loss: 0.007:   1%|          | 1/187 [00:04<13:49,  4.46s/it]
Training Epoch: 6/100, batch 1/375 completed (loss: 0.0036:   1%|          | 1/187 [00:04<13:33,  4.37s/it]
Training Epoch: 6/100, batch 1/375 completed (loss: 0.4228:   1%|          | 1/187 [00:04<13:33,  4.38s/it]
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Training Epoch: 6/100, batch 1/375 completed (loss: 0.0228:   1%|          | 1/187 [00:04<13:39,  4.41s/it]
Training Epoch: 6/100, batch 2/375 completed (loss: 0.0452:   1%|          | 1/187 [00:06<13:33,  4.38s/it]
Training Epoch: 6/100, batch 2/375 completed (loss: 0.0306:   1%|          | 1/187 [00:06<13:33,  4.37s/it]
Training Epoch: 6/100, batch 2/375 completed (loss: 0.0007:   1%|          | 1/187 [00:06<13:49,  4.46s/it]
Training Epoch: 6/100, batch 2/375 completed (loss: 0.0972:   1%|          | 1/187 [00:06<13:39,  4.41s/it]
Training Epoch: 6/100, batch 2/375 completed (loss: 0.0306:   1%|          | 2/187 [00:08<12:58,  4.21s/it]
Training Epoch: 6/100, batch 2/375 completed (loss: 0.0452:   1%|          | 2/187 [00:08<12:58,  4.21s/it]
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Training Epoch: 6/100, batch 3/375 completed (loss: 0.0059:   1%|          | 2/187 [00:08<13:05,  4.25s/it]
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Training Epoch: 6/100, batch 4/375 completed (loss: 0.0313:   1%|          | 2/187 [00:10<13:05,  4.25s/it]
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Training Epoch: 6/100, batch 4/375 completed (loss: 0.0313:   2%|▏         | 3/187 [00:12<12:29,  4.07s/it]
Training Epoch: 6/100, batch 5/375 completed (loss: 0.0455:   2%|▏         | 3/187 [00:12<12:25,  4.05s/it]
Training Epoch: 6/100, batch 5/375 completed (loss: 0.0045:   2%|▏         | 3/187 [00:12<12:25,  4.05s/it]
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Training Epoch: 6/100, batch 5/375 completed (loss: 0.0358:   2%|▏         | 3/187 [00:12<12:27,  4.06s/it]
Training Epoch: 6/100, batch 6/375 completed (loss: 0.0044:   2%|▏         | 3/187 [00:14<12:25,  4.05s/it]
Training Epoch: 6/100, batch 6/375 completed (loss: 0.0017:   2%|▏         | 3/187 [00:14<12:29,  4.07s/it]
Training Epoch: 6/100, batch 6/375 completed (loss: 0.074:   2%|▏         | 3/187 [00:14<12:25,  4.05s/it] 
Training Epoch: 6/100, batch 6/375 completed (loss: 0.001:   2%|▏         | 3/187 [00:14<12:27,  4.06s/it] 
Training Epoch: 6/100, batch 6/375 completed (loss: 0.074:   2%|▏         | 4/187 [00:16<12:08,  3.98s/it]
Training Epoch: 6/100, batch 6/375 completed (loss: 0.0044:   2%|▏         | 4/187 [00:16<12:09,  3.98s/it]
Training Epoch: 6/100, batch 6/375 completed (loss: 0.001:   2%|▏         | 4/187 [00:16<12:11,  4.00s/it]
Training Epoch: 6/100, batch 6/375 completed (loss: 0.0017:   2%|▏         | 4/187 [00:16<12:11,  4.00s/it]
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Training Epoch: 6/100, batch 7/375 completed (loss: 0.0035:   2%|▏         | 4/187 [00:16<12:09,  3.98s/it]
Training Epoch: 6/100, batch 7/375 completed (loss: 0.0005:   2%|▏         | 4/187 [00:16<12:08,  3.98s/it]
Training Epoch: 6/100, batch 7/375 completed (loss: 0.0065:   2%|▏         | 4/187 [00:16<12:11,  4.00s/it]
Training Epoch: 6/100, batch 8/375 completed (loss: 0.0135:   2%|▏         | 4/187 [00:18<12:11,  4.00s/it]
Training Epoch: 6/100, batch 8/375 completed (loss: 0.0019:   2%|▏         | 4/187 [00:18<12:09,  3.98s/it]
Training Epoch: 6/100, batch 8/375 completed (loss: 0.1038:   2%|▏         | 4/187 [00:18<12:08,  3.98s/it]
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Training Epoch: 6/100, batch 8/375 completed (loss: 0.0019:   3%|▎         | 5/187 [00:20<12:10,  4.02s/it]
Training Epoch: 6/100, batch 8/375 completed (loss: 0.0092:   3%|▎         | 5/187 [00:20<12:11,  4.02s/it]
Training Epoch: 6/100, batch 8/375 completed (loss: 0.1038:   3%|▎         | 5/187 [00:20<12:11,  4.02s/it]
Training Epoch: 6/100, batch 8/375 completed (loss: 0.0135:   3%|▎         | 5/187 [00:20<12:12,  4.03s/it]
Training Epoch: 6/100, batch 9/375 completed (loss: 0.0156:   3%|▎         | 5/187 [00:20<12:12,  4.03s/it]
Training Epoch: 6/100, batch 9/375 completed (loss: 0.0077:   3%|▎         | 5/187 [00:20<12:10,  4.02s/it]
Training Epoch: 6/100, batch 9/375 completed (loss: 0.1641:   3%|▎         | 5/187 [00:20<12:11,  4.02s/it]
Training Epoch: 6/100, batch 9/375 completed (loss: 0.0101:   3%|▎         | 5/187 [00:20<12:11,  4.02s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.2308:   3%|▎         | 5/187 [00:22<12:10,  4.02s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.0328:   3%|▎         | 5/187 [00:22<12:12,  4.03s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.118:   3%|▎         | 5/187 [00:22<12:11,  4.02s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.0633:   3%|▎         | 5/187 [00:22<12:11,  4.02s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.2308:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.118:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.0633:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 10/375 completed (loss: 0.0328:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 11/375 completed (loss: 0.0145:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 11/375 completed (loss: 0.0024:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 11/375 completed (loss: 0.0017:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 11/375 completed (loss: 0.0033:   3%|▎         | 6/187 [00:24<12:20,  4.09s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.0089:   3%|▎         | 6/187 [00:26<12:20,  4.09s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.0542:   3%|▎         | 6/187 [00:26<12:20,  4.09s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.6037:   3%|▎         | 6/187 [00:26<12:20,  4.09s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.0094:   3%|▎         | 6/187 [00:26<12:20,  4.09s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.0089:   4%|▎         | 7/187 [00:28<12:04,  4.03s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.0542:   4%|▎         | 7/187 [00:28<12:04,  4.03s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.0094:   4%|▎         | 7/187 [00:28<12:04,  4.03s/it]
Training Epoch: 6/100, batch 12/375 completed (loss: 0.6037:   4%|▎         | 7/187 [00:28<12:05,  4.03s/it]
Training Epoch: 6/100, batch 13/375 completed (loss: 0.2991:   4%|▎         | 7/187 [00:28<12:05,  4.03s/it]
Training Epoch: 6/100, batch 13/375 completed (loss: 0.0035:   4%|▎         | 7/187 [00:28<12:04,  4.03s/it]
Training Epoch: 6/100, batch 13/375 completed (loss: 0.4163:   4%|▎         | 7/187 [00:28<12:04,  4.03s/it]
Training Epoch: 6/100, batch 13/375 completed (loss: 0.0395:   4%|▎         | 7/187 [00:28<12:04,  4.03s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0013:   4%|▎         | 7/187 [00:30<12:04,  4.03s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0078:   4%|▎         | 7/187 [00:30<12:05,  4.03s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0338:   4%|▎         | 7/187 [00:30<12:04,  4.03s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0041:   4%|▎         | 7/187 [00:30<12:04,  4.03s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0013:   4%|▍         | 8/187 [00:32<11:53,  3.99s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0041:   4%|▍         | 8/187 [00:32<11:53,  3.99s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0338:   4%|▍         | 8/187 [00:32<11:54,  3.99s/it]
Training Epoch: 6/100, batch 14/375 completed (loss: 0.0078:   4%|▍         | 8/187 [00:32<11:53,  3.99s/it]
Training Epoch: 6/100, batch 15/375 completed (loss: 0.0794:   4%|▍         | 8/187 [00:32<11:53,  3.99s/it]
Training Epoch: 6/100, batch 15/375 completed (loss: 0.033:   4%|▍         | 8/187 [00:32<11:54,  3.99s/it] 
Training Epoch: 6/100, batch 15/375 completed (loss: 0.5213:   4%|▍         | 8/187 [00:32<11:53,  3.99s/it]
Training Epoch: 6/100, batch 15/375 completed (loss: 0.0084:   4%|▍         | 8/187 [00:32<11:53,  3.99s/it]
Training Epoch: 6/100, batch 16/375 completed (loss: 0.027:   4%|▍         | 8/187 [00:34<11:53,  3.99s/it] 
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Training Epoch: 6/100, batch 26/375 completed (loss: 0.0082:   7%|▋         | 14/187 [00:56<11:51,  4.12s/it]
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Training Epoch: 6/100, batch 27/375 completed (loss: 0.0016:   7%|▋         | 14/187 [00:56<11:51,  4.12s/it]
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Training Epoch: 6/100, batch 28/375 completed (loss: 0.0005:   7%|▋         | 14/187 [00:58<11:51,  4.11s/it]
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Training Epoch: 6/100, batch 28/375 completed (loss: 0.0256:   7%|▋         | 14/187 [00:58<11:51,  4.12s/it]
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Training Epoch: 6/100, batch 28/375 completed (loss: 0.0256:   8%|▊         | 15/187 [01:00<11:39,  4.06s/it]
Training Epoch: 6/100, batch 28/375 completed (loss: 0.0905:   8%|▊         | 15/187 [01:00<11:40,  4.07s/it]
Training Epoch: 6/100, batch 28/375 completed (loss: 0.2422:   8%|▊         | 15/187 [01:00<11:39,  4.07s/it]
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Training Epoch: 6/100, batch 29/375 completed (loss: 0.0024:   8%|▊         | 15/187 [01:00<11:39,  4.06s/it]
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Training Epoch: 6/100, batch 30/375 completed (loss: 0.0034:   8%|▊         | 15/187 [01:03<11:39,  4.06s/it]
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Training Epoch: 6/100, batch 32/375 completed (loss: 0.0029:   9%|▊         | 16/187 [01:07<11:37,  4.08s/it]
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Training Epoch: 6/100, batch 372/375 completed (loss: 0.0024: 100%|██████████| 187/187 [12:48<00:00,  4.14s/it]
Training Epoch: 6/100, batch 372/375 completed (loss: 0.0098: 100%|██████████| 187/187 [12:48<00:00,  4.14s/it]
Training Epoch: 6/100, batch 372/375 completed (loss: 0.0024: 100%|██████████| 187/187 [12:48<00:00,  4.15s/it]
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Training Epoch: 6/100, batch 373/375 completed (loss: 0.0108: 100%|██████████| 187/187 [12:48<00:00,  4.14s/it]
Training Epoch: 6/100, batch 373/375 completed (loss: 0.0014: 100%|██████████| 187/187 [12:48<00:00,  4.14s/it]
Training Epoch: 6/100, batch 373/375 completed (loss: 0.3091: 100%|██████████| 187/187 [12:48<00:00,  4.14s/it]
Training Epoch: 6/100, batch 373/375 completed (loss: 0.0014: : 188it [12:50,  3.50s/it]                       
Training Epoch: 6/100, batch 373/375 completed (loss: 0.0108: : 188it [12:50,  3.50s/it]                       
Training Epoch: 6/100, batch 373/375 completed (loss: 0.3091: : 188it [12:50,  3.50s/it]                       
Training Epoch: 6/100, batch 373/375 completed (loss: 0.0005: : 188it [12:50,  3.50s/it]                       
Training Epoch: 6/100, batch 374/375 completed (loss: 0.001: : 188it [12:50,  3.50s/it] 
Training Epoch: 6/100, batch 374/375 completed (loss: 0.0062: : 188it [12:50,  3.50s/it]
Training Epoch: 6/100, batch 374/375 completed (loss: 0.0019: : 188it [12:50,  3.50s/it]
Training Epoch: 6/100, batch 374/375 completed (loss: 0.0003: : 188it [12:50,  3.50s/it]
Training Epoch: 6/100, batch 374/375 completed (loss: 0.0062: : 188it [12:50,  4.10s/it]

Training Epoch: 6/100, batch 374/375 completed (loss: 0.001: : 188it [12:50,  4.10s/it]

Training Epoch: 6/100, batch 374/375 completed (loss: 0.0019: : 188it [12:50,  4.10s/it]

Training Epoch: 6/100, batch 374/375 completed (loss: 0.0003: : 188it [12:50,  4.10s/it]
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_6 to HF: (Request ID: Root=1-6a1b7013-36cd88311ce64334771dc95f;de3d736e-7cee-4fc6-95f0-0e1d9f620918)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_6 to HF: (Request ID: Root=1-6a1b7017-1f0103353892dbb260e20b9f;49306127-a9eb-45a5-8e9b-01eafbd701cb)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: train_state_5
Deleted old checkpoint: checkpoint_5
[warn] HF upload failed for ckpt_6; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.01331024678840645

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:46,  7.31s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<03:46,  7.30s/it]
Test accuracy: 0.75:   3%|▎         | 1/32 [00:07<03:46,  7.31s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:46,  7.30s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<03:46,  7.30s/it]
[CORRECT] Example 1 (diff=8)

Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<03:46,  7.30s/it]  Expected : 'Guy'
  Extracted: 'Guy'
  Generated: '### Guy'

[WRONG] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Shannon'
  Generated: '### Shannon'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:42,  7.17s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<03:42,  7.17s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:31,  7.06s/it]
Test accuracy: 0.75:   6%|▋         | 2/32 [00:14<03:32,  7.07s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:14<03:31,  7.06s/it]
Test accuracy: 0.81:   6%|▋         | 2/32 [00:14<03:32,  7.07s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:14<03:31,  7.06s/it]
Test accuracy: 0.81:   6%|▋         | 2/32 [00:14<03:31,  7.06s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:14<03:30,  7.00s/it]
Test accuracy: 0.81:   6%|▋         | 2/32 [00:14<03:30,  7.00s/it]
Test accuracy: 0.81:   9%|▉         | 3/32 [00:21<03:32,  7.33s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:21<03:32,  7.33s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:21<03:32,  7.33s/it]
Test accuracy: 0.92:   9%|▉         | 3/32 [00:21<03:32,  7.33s/it]
Test accuracy: 0.81:   9%|▉         | 3/32 [00:21<03:31,  7.29s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:21<03:31,  7.29s/it]
Test accuracy: 0.81:   9%|▉         | 3/32 [00:21<03:32,  7.33s/it]
Test accuracy: 0.83:   9%|▉         | 3/32 [00:21<03:32,  7.33s/it]
Test accuracy: 0.83:  12%|█▎        | 4/32 [00:28<03:23,  7.26s/it]
Test accuracy: 0.92:  12%|█▎        | 4/32 [00:28<03:23,  7.26s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:28<03:23,  7.26s/it]
Test accuracy: 0.84:  12%|█▎        | 4/32 [00:28<03:23,  7.26s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:28<03:23,  7.26s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:28<03:22,  7.24s/it]
Test accuracy: 0.81:  12%|█▎        | 4/32 [00:28<03:23,  7.26s/it]
Test accuracy: 0.91:  12%|█▎        | 4/32 [00:28<03:22,  7.24s/it]
Test accuracy: 0.84:  16%|█▌        | 5/32 [00:36<03:14,  7.19s/it]
Test accuracy: 0.85:  16%|█▌        | 5/32 [00:36<03:14,  7.19s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:36<03:14,  7.19s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:36<03:14,  7.19s/it]
Test accuracy: 0.81:  16%|█▌        | 5/32 [00:36<03:14,  7.19s/it]
Test accuracy: 0.82:  16%|█▌        | 5/32 [00:36<03:14,  7.19s/it]
Test accuracy: 0.91:  16%|█▌        | 5/32 [00:35<03:13,  7.18s/it]
Test accuracy: 0.93:  16%|█▌        | 5/32 [00:35<03:13,  7.18s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:42<03:04,  7.09s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:42<03:04,  7.09s/it]
Test accuracy: 0.82:  19%|█▉        | 6/32 [00:42<03:04,  7.09s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:42<03:04,  7.09s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:42<03:04,  7.09s/it]
Test accuracy: 0.83:  19%|█▉        | 6/32 [00:42<03:04,  7.09s/it]
Test accuracy: 0.93:  19%|█▉        | 6/32 [00:42<03:04,  7.08s/it]
Test accuracy: 0.94:  19%|█▉        | 6/32 [00:42<03:04,  7.08s/it]
Test accuracy: 0.85:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.83:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.88:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.8:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it] 
Test accuracy: 0.84:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.88:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.94:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.95:  22%|██▏       | 7/32 [00:49<02:55,  7.02s/it]
Test accuracy: 0.88:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.8:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.88:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.84:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.83:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.83:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.95:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.95:  25%|██▌       | 8/32 [00:56<02:47,  6.98s/it]
Test accuracy: 0.83:  28%|██▊       | 9/32 [01:04<02:48,  7.32s/it]
Test accuracy: 0.88:  28%|██▊       | 9/32 [01:04<02:48,  7.32s/it]
Test accuracy: 0.89:  28%|██▊       | 9/32 [01:04<02:48,  7.32s/it]
Test accuracy: 0.83:  28%|██▊       | 9/32 [01:04<02:48,  7.32s/it]
Test accuracy: 0.95:  28%|██▊       | 9/32 [01:04<02:48,  7.32s/it]
Test accuracy: 0.94:  28%|██▊       | 9/32 [01:04<02:48,  7.32s/it]
Test accuracy: 0.83:  28%|██▊       | 9/32 [01:04<02:48,  7.33s/it]
Test accuracy: 0.83:  28%|██▊       | 9/32 [01:04<02:48,  7.33s/it]
Test accuracy: 0.83:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.89:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.85:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.9:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it] 
Test accuracy: 0.94:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.83:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.91:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.84:  31%|███▏      | 10/32 [01:11<02:38,  7.19s/it]
Test accuracy: 0.84:  34%|███▍      | 11/32 [01:18<02:29,  7.10s/it]
Test accuracy: 0.9:  34%|███▍      | 11/32 [01:18<02:29,  7.10s/it]
Test accuracy: 0.84:  34%|███▍      | 11/32 [01:18<02:29,  7.10s/it]
Test accuracy: 0.91:  34%|███▍      | 11/32 [01:18<02:29,  7.10s/it]
Test accuracy: 0.85:  34%|███▍      | 11/32 [01:18<02:29,  7.10s/it]
Test accuracy: 0.91:  34%|███▍      | 11/32 [01:18<02:28,  7.09s/it]
Test accuracy: 0.84:  34%|███▍      | 11/32 [01:18<02:29,  7.10s/it]
Test accuracy: 0.91:  34%|███▍      | 11/32 [01:18<02:28,  7.09s/it]
Test accuracy: 0.91:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.91:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.84:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.91:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.84:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.85:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.85:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.91:  38%|███▊      | 12/32 [01:25<02:20,  7.03s/it]
Test accuracy: 0.91:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.85:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.89:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.87:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.85:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.91:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.84:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.89:  41%|████      | 13/32 [01:32<02:12,  6.99s/it]
Test accuracy: 0.87:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.89:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.88:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.84:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.88:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.83:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.89:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it]
Test accuracy: 0.9:  44%|████▍     | 14/32 [01:39<02:07,  7.06s/it] 
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Test accuracy: 0.82:  56%|█████▋    | 18/32 [02:07<01:37,  6.99s/it]
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Test accuracy: 0.84:  62%|██████▎   | 20/32 [02:21<01:23,  6.97s/it]
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Test accuracy: 0.84:  66%|██████▌   | 21/32 [02:28<01:19,  7.19s/it]
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Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.84: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.84: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.87: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  5.82s/it]
Test accuracy: 0.87: 100%|██████████| 32/32 [03:43<00:00,  6.99s/it]
Device 2: Cor=218, CoT=0, Total=250

Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  6.99s/it]
Device 1: Cor=219, CoT=0, Total=250

Test accuracy: 0.84: 100%|██████████| 32/32 [03:43<00:00,  6.99s/it]
Device 3: Cor=211, CoT=0, Total=250

Test accuracy: 0.88: 100%|██████████| 32/32 [03:43<00:00,  6.99s/it]
Device 0: Cor=220, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 5)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=6, data_stage=6, n_latent_tokens=6
Training on difficulties: 0-6

Overall accuracy: 868 / 1000 = 0.8680

Per-difficulty accuracy:
  diff=1: 98/100 = 0.9800 (trained)
  diff=2: 99/100 = 0.9900 (trained)
  diff=3: 100/100 = 1.0000 (trained)
  diff=4: 100/100 = 1.0000 (trained)
  diff=5: 99/100 = 0.9900 (trained)
  diff=6: 97/100 = 0.9700 (trained)
  diff=7: 80/100 = 0.8000 (untrained)
  diff=8: 73/100 = 0.7300 (untrained)
  diff=9: 63/100 = 0.6300 (untrained)
  diff=10: 59/100 = 0.5900 (untrained)

Trained (diff 0-6): 593/600 = 0.9883
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9883, acc_diff6=0.9700 >= 0.9 ***
*** Advancing to stage 7 ***

Warning: failed to upload checkpoint_6 to HF: (Request ID: Root=1-6a1b710d-55cc61d01da02b0234ebc71e;a30dcadf-f3a1-4f0d-8601-0a8ead664deb)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 6/100 (thought_stage=7, data_stage=7)
============================================================
  thought_stage=7, c_thought=1, max_difficulty=7

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Map (num_proc=4):  77%|███████▋  | 5358/7000 [00:02<00:00, 3269.88 examples/s]
Map (num_proc=4):  84%|████████▎ | 5857/7000 [00:02<00:00, 2902.00 examples/s]
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Map (num_proc=4): 100%|██████████| 7000/7000 [00:04<00:00, 1667.69 examples/s]

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Map (num_proc=4):  94%|█████████▍| 661/700 [00:00<00:00, 1368.36 examples/s]
Map (num_proc=4): 100%|██████████| 700/700 [00:01<00:00, 687.29 examples/s] 

Training Epoch: 7:   0%|          | 0/219 [00:00<?, ?it/s]
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rank 3 | gpu 3 | alloc=4.20 GB | reserved=51.20 GB | max=35.86 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=45.67 GB | max=35.86 GB
rank 1 | gpu 1 | alloc=4.19 GB | reserved=47.53 GB | max=35.86 GB
rank 2 | gpu 2 | alloc=4.19 GB | reserved=47.54 GB | max=35.86 GB

Training Epoch: 7/100, batch 0/438 completed (loss: 0.0061:   0%|          | 0/219 [00:02<?, ?it/s]
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Training Epoch: 7/100, batch 0/438 completed (loss: 0.0061:   0%|          | 1/219 [00:04<17:43,  4.88s/it]
Training Epoch: 7/100, batch 0/438 completed (loss: 0.1256:   0%|          | 1/219 [00:04<17:41,  4.87s/it]
Training Epoch: 7/100, batch 0/438 completed (loss: 0.0022:   0%|          | 1/219 [00:04<18:04,  4.97s/it]
Training Epoch: 7/100, batch 1/438 completed (loss: 0.1593:   0%|          | 1/219 [00:05<17:41,  4.87s/it]
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Training Epoch: 7/100, batch 1/438 completed (loss: 0.0186:   0%|          | 1/219 [00:05<17:43,  4.88s/it]
Training Epoch: 7/100, batch 1/438 completed (loss: 0.092:   0%|          | 1/219 [00:05<17:48,  4.90s/it] 
Training Epoch: 7/100, batch 2/438 completed (loss: 0.005:   0%|          | 1/219 [00:07<18:04,  4.97s/it] 
Training Epoch: 7/100, batch 2/438 completed (loss: 0.0103:   0%|          | 1/219 [00:07<17:41,  4.87s/it]
Training Epoch: 7/100, batch 2/438 completed (loss: 0.0004:   0%|          | 1/219 [00:07<17:43,  4.88s/it]
Training Epoch: 7/100, batch 2/438 completed (loss: 0.0004:   0%|          | 1/219 [00:07<17:48,  4.90s/it]
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Training Epoch: 7/100, batch 2/438 completed (loss: 0.0103:   1%|          | 2/219 [00:09<16:57,  4.69s/it]
Training Epoch: 7/100, batch 2/438 completed (loss: 0.0004:   1%|          | 2/219 [00:09<17:01,  4.71s/it]
Training Epoch: 7/100, batch 2/438 completed (loss: 0.005:   1%|          | 2/219 [00:09<17:06,  4.73s/it]
Training Epoch: 7/100, batch 3/438 completed (loss: 0.0012:   1%|          | 2/219 [00:09<16:57,  4.69s/it]
Training Epoch: 7/100, batch 3/438 completed (loss: 0.0004:   1%|          | 2/219 [00:09<17:06,  4.73s/it]
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Training Epoch: 7/100, batch 3/438 completed (loss: 0.0005:   1%|          | 2/219 [00:09<17:01,  4.71s/it]
Training Epoch: 7/100, batch 4/438 completed (loss: 0.0032:   1%|          | 2/219 [00:11<17:06,  4.73s/it]
Training Epoch: 7/100, batch 4/438 completed (loss: 0.0018:   1%|          | 2/219 [00:11<16:57,  4.69s/it]
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Training Epoch: 7/100, batch 4/438 completed (loss: 0.0018:   1%|▏         | 3/219 [00:13<16:37,  4.62s/it]
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Training Epoch: 7/100, batch 4/438 completed (loss: 0.1217:   1%|▏         | 3/219 [00:14<16:40,  4.63s/it]
Training Epoch: 7/100, batch 4/438 completed (loss: 0.0032:   1%|▏         | 3/219 [00:14<16:43,  4.64s/it]
Training Epoch: 7/100, batch 5/438 completed (loss: 0.0016:   1%|▏         | 3/219 [00:14<16:38,  4.62s/it]
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Training Epoch: 7/100, batch 5/438 completed (loss: 0.0021:   1%|▏         | 3/219 [00:14<16:40,  4.63s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.001:   1%|▏         | 3/219 [00:16<16:43,  4.64s/it] 
Training Epoch: 7/100, batch 6/438 completed (loss: 0.0098:   1%|▏         | 3/219 [00:16<16:38,  4.62s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.0249:   1%|▏         | 3/219 [00:16<16:37,  4.62s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.0007:   1%|▏         | 3/219 [00:16<16:40,  4.63s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.0249:   2%|▏         | 4/219 [00:18<16:13,  4.53s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.0007:   2%|▏         | 4/219 [00:18<16:14,  4.53s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.0098:   2%|▏         | 4/219 [00:18<16:14,  4.53s/it]
Training Epoch: 7/100, batch 6/438 completed (loss: 0.001:   2%|▏         | 4/219 [00:18<16:16,  4.54s/it]
Training Epoch: 7/100, batch 7/438 completed (loss: 0.1776:   2%|▏         | 4/219 [00:18<16:14,  4.53s/it]
Training Epoch: 7/100, batch 7/438 completed (loss: 0.001:   2%|▏         | 4/219 [00:18<16:16,  4.54s/it]
Training Epoch: 7/100, batch 7/438 completed (loss: 0.0072:   2%|▏         | 4/219 [00:18<16:13,  4.53s/it]
Training Epoch: 7/100, batch 7/438 completed (loss: 0.0034:   2%|▏         | 4/219 [00:18<16:14,  4.53s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.0105:   2%|▏         | 4/219 [00:20<16:16,  4.54s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.0434:   2%|▏         | 4/219 [00:20<16:14,  4.53s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.0028:   2%|▏         | 4/219 [00:20<16:13,  4.53s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.1339:   2%|▏         | 4/219 [00:20<16:14,  4.53s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.0434:   2%|▏         | 5/219 [00:23<16:22,  4.59s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.1339:   2%|▏         | 5/219 [00:23<16:23,  4.60s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.0028:   2%|▏         | 5/219 [00:23<16:24,  4.60s/it]
Training Epoch: 7/100, batch 8/438 completed (loss: 0.0105:   2%|▏         | 5/219 [00:23<16:25,  4.60s/it]
Training Epoch: 7/100, batch 9/438 completed (loss: 0.1867:   2%|▏         | 5/219 [00:23<16:24,  4.60s/it]
Training Epoch: 7/100, batch 9/438 completed (loss: 0.0011:   2%|▏         | 5/219 [00:23<16:22,  4.59s/it]
Training Epoch: 7/100, batch 9/438 completed (loss: 0.0003:   2%|▏         | 5/219 [00:23<16:25,  4.60s/it]
Training Epoch: 7/100, batch 9/438 completed (loss: 0.0006:   2%|▏         | 5/219 [00:23<16:23,  4.60s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0092:   2%|▏         | 5/219 [00:25<16:24,  4.60s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0531:   2%|▏         | 5/219 [00:25<16:25,  4.60s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0006:   2%|▏         | 5/219 [00:25<16:22,  4.59s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0013:   2%|▏         | 5/219 [00:25<16:23,  4.60s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0006:   3%|▎         | 6/219 [00:27<16:15,  4.58s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0092:   3%|▎         | 6/219 [00:27<16:15,  4.58s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0013:   3%|▎         | 6/219 [00:27<16:17,  4.59s/it]
Training Epoch: 7/100, batch 10/438 completed (loss: 0.0531:   3%|▎         | 6/219 [00:27<16:16,  4.59s/it]
Training Epoch: 7/100, batch 11/438 completed (loss: 0.0977:   3%|▎         | 6/219 [00:27<16:15,  4.58s/it]
Training Epoch: 7/100, batch 11/438 completed (loss: 0.0012:   3%|▎         | 6/219 [00:27<16:16,  4.59s/it]
Training Epoch: 7/100, batch 11/438 completed (loss: 0.0013:   3%|▎         | 6/219 [00:27<16:15,  4.58s/it]
Training Epoch: 7/100, batch 11/438 completed (loss: 0.0005:   3%|▎         | 6/219 [00:27<16:17,  4.59s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.2997:   3%|▎         | 6/219 [00:29<16:15,  4.58s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.0241:   3%|▎         | 6/219 [00:29<16:16,  4.59s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.0065:   3%|▎         | 6/219 [00:29<16:15,  4.58s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.039:   3%|▎         | 6/219 [00:30<16:17,  4.59s/it] 
Training Epoch: 7/100, batch 12/438 completed (loss: 0.2997:   3%|▎         | 7/219 [00:32<15:58,  4.52s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.0065:   3%|▎         | 7/219 [00:32<15:59,  4.52s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.039:   3%|▎         | 7/219 [00:32<15:59,  4.53s/it]
Training Epoch: 7/100, batch 12/438 completed (loss: 0.0241:   3%|▎         | 7/219 [00:32<16:00,  4.53s/it]
Training Epoch: 7/100, batch 13/438 completed (loss: 0.0071:   3%|▎         | 7/219 [00:32<15:58,  4.52s/it]
Training Epoch: 7/100, batch 13/438 completed (loss: 0.0073:   3%|▎         | 7/219 [00:32<16:00,  4.53s/it]
Training Epoch: 7/100, batch 13/438 completed (loss: 0.0044:   3%|▎         | 7/219 [00:32<15:59,  4.52s/it]
Training Epoch: 7/100, batch 13/438 completed (loss: 0.0451:   3%|▎         | 7/219 [00:32<15:59,  4.53s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.055:   3%|▎         | 7/219 [00:34<16:00,  4.53s/it] 
Training Epoch: 7/100, batch 14/438 completed (loss: 0.0003:   3%|▎         | 7/219 [00:34<15:59,  4.52s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.0005:   3%|▎         | 7/219 [00:34<15:58,  4.52s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.0337:   3%|▎         | 7/219 [00:34<15:59,  4.53s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.0005:   4%|▎         | 8/219 [00:36<15:48,  4.49s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.0337:   4%|▎         | 8/219 [00:36<15:47,  4.49s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.0003:   4%|▎         | 8/219 [00:36<15:48,  4.49s/it]
Training Epoch: 7/100, batch 14/438 completed (loss: 0.055:   4%|▎         | 8/219 [00:36<15:48,  4.50s/it]
Training Epoch: 7/100, batch 15/438 completed (loss: 0.0109:   4%|▎         | 8/219 [00:36<15:48,  4.50s/it]
Training Epoch: 7/100, batch 15/438 completed (loss: 0.0206:   4%|▎         | 8/219 [00:36<15:48,  4.49s/it]
Training Epoch: 7/100, batch 15/438 completed (loss: 0.0009:   4%|▎         | 8/219 [00:36<15:48,  4.49s/it]
Training Epoch: 7/100, batch 15/438 completed (loss: 0.0015:   4%|▎         | 8/219 [00:36<15:47,  4.49s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0571:   4%|▎         | 8/219 [00:39<15:48,  4.50s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0242:   4%|▎         | 8/219 [00:39<15:48,  4.49s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0051:   4%|▎         | 8/219 [00:39<15:48,  4.49s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0296:   4%|▎         | 8/219 [00:39<15:47,  4.49s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0051:   4%|▍         | 9/219 [00:41<15:57,  4.56s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0242:   4%|▍         | 9/219 [00:41<15:58,  4.56s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0296:   4%|▍         | 9/219 [00:41<15:58,  4.56s/it]
Training Epoch: 7/100, batch 16/438 completed (loss: 0.0571:   4%|▍         | 9/219 [00:41<15:57,  4.56s/it]
Training Epoch: 7/100, batch 17/438 completed (loss: 0.0005:   4%|▍         | 9/219 [00:41<15:58,  4.56s/it]
Training Epoch: 7/100, batch 17/438 completed (loss: 0.0442:   4%|▍         | 9/219 [00:41<15:57,  4.56s/it]
Training Epoch: 7/100, batch 17/438 completed (loss: 0.0006:   4%|▍         | 9/219 [00:41<15:57,  4.56s/it]
Training Epoch: 7/100, batch 17/438 completed (loss: 0.1019:   4%|▍         | 9/219 [00:41<15:58,  4.56s/it]
Training Epoch: 7/100, batch 18/438 completed (loss: 0.008:   4%|▍         | 9/219 [00:43<15:57,  4.56s/it] 
Training Epoch: 7/100, batch 18/438 completed (loss: 0.1382:   4%|▍         | 9/219 [00:43<15:58,  4.56s/it]
Training Epoch: 7/100, batch 18/438 completed (loss: 0.001:   4%|▍         | 9/219 [00:43<15:57,  4.56s/it] 
Training Epoch: 7/100, batch 18/438 completed (loss: 0.4276:   4%|▍         | 9/219 [00:43<15:58,  4.56s/it]
Training Epoch: 7/100, batch 18/438 completed (loss: 0.1382:   5%|▍         | 10/219 [00:45<15:56,  4.58s/it]
Training Epoch: 7/100, batch 18/438 completed (loss: 0.001:   5%|▍         | 10/219 [00:45<15:56,  4.58s/it]
Training Epoch: 7/100, batch 18/438 completed (loss: 0.4276:   5%|▍         | 10/219 [00:45<15:58,  4.58s/it]
Training Epoch: 7/100, batch 18/438 completed (loss: 0.008:   5%|▍         | 10/219 [00:45<15:57,  4.58s/it]
Training Epoch: 7/100, batch 19/438 completed (loss: 0.0008:   5%|▍         | 10/219 [00:45<15:57,  4.58s/it]
Training Epoch: 7/100, batch 19/438 completed (loss: 0.005:   5%|▍         | 10/219 [00:45<15:56,  4.58s/it]
Training Epoch: 7/100, batch 19/438 completed (loss: 0.1952:   5%|▍         | 10/219 [00:45<15:56,  4.58s/it]
Training Epoch: 7/100, batch 19/438 completed (loss: 0.1631:   5%|▍         | 10/219 [00:45<15:58,  4.58s/it]
Training Epoch: 7/100, batch 20/438 completed (loss: 0.0032:   5%|▍         | 10/219 [00:48<15:56,  4.58s/it]
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Training Epoch: 7/100, batch 20/438 completed (loss: 0.0016:   5%|▍         | 10/219 [00:48<15:57,  4.58s/it]
Training Epoch: 7/100, batch 20/438 completed (loss: 0.0117:   5%|▍         | 10/219 [00:48<15:58,  4.58s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_7 to HF: (Request ID: Root=1-6a1b7525-42a277ff2c01829c33be4365;71837191-d4c2-432e-bdff-7589277205cf)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_7 to HF: (Request ID: Root=1-6a1b7529-4636bfe142c8b0471fa00503;30fc3756-7cd1-475b-bf8c-6f8fa9ed4256)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: checkpoint_6
Deleted old checkpoint: train_state_6
[warn] HF upload failed for ckpt_7; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.022749400430422447

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

[WRONG] Example 1 (diff=8)
  Expected : 'Guy'
  Extracted: 'Irma'
Test Accuracy:   3%|▎         | 1/32 [00:07<03:47,  7.35s/it]
  Generated: '### Irma'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:47,  7.35s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<03:48,  7.36s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<03:47,  7.35s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:48,  7.36s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:47,  7.35s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:43,  7.21s/it]
Test accuracy: 0.88:   3%|▎         | 1/32 [00:07<03:43,  7.21s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:29,  6.98s/it]
Test accuracy: 0.94:   6%|▋         | 2/32 [00:14<03:29,  6.98s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:29,  6.98s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:29,  6.98s/it]
Test accuracy: 0.94:   6%|▋         | 2/32 [00:14<03:29,  6.98s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:14<03:29,  6.98s/it]
Test accuracy: 0.88:   6%|▋         | 2/32 [00:13<03:27,  6.92s/it]
Test accuracy: 0.81:   6%|▋         | 2/32 [00:13<03:27,  6.92s/it]
Test accuracy: 0.94:   9%|▉         | 3/32 [00:21<03:28,  7.19s/it]
Test accuracy: 0.94:   9%|▉         | 3/32 [00:21<03:28,  7.19s/it]
Test accuracy: 0.96:   9%|▉         | 3/32 [00:21<03:28,  7.19s/it]
Test accuracy: 0.92:   9%|▉         | 3/32 [00:21<03:28,  7.19s/it]
Test accuracy: 0.81:   9%|▉         | 3/32 [00:21<03:27,  7.15s/it]
Test accuracy: 0.88:   9%|▉         | 3/32 [00:21<03:27,  7.15s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:21<03:28,  7.19s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:21<03:28,  7.19s/it]
Test accuracy: 0.92:  12%|█▎        | 4/32 [00:28<03:18,  7.10s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:28<03:18,  7.10s/it]
Test accuracy: 0.91:  12%|█▎        | 4/32 [00:28<03:18,  7.10s/it]
Test accuracy: 0.96:  12%|█▎        | 4/32 [00:28<03:18,  7.10s/it]
Test accuracy: 0.97:  12%|█▎        | 4/32 [00:28<03:18,  7.10s/it]
Test accuracy: 0.84:  12%|█▎        | 4/32 [00:28<03:18,  7.10s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:28<03:18,  7.08s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:28<03:18,  7.08s/it]
Test accuracy: 0.97:  16%|█▌        | 5/32 [00:35<03:09,  7.03s/it]
Test accuracy: 0.91:  16%|█▌        | 5/32 [00:35<03:09,  7.03s/it]
Test accuracy: 0.93:  16%|█▌        | 5/32 [00:35<03:09,  7.03s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:35<03:09,  7.03s/it]
Test accuracy: 0.84:  16%|█▌        | 5/32 [00:35<03:09,  7.03s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:35<03:09,  7.02s/it]
Test accuracy: 0.85:  16%|█▌        | 5/32 [00:35<03:09,  7.03s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:35<03:09,  7.02s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:42<02:59,  6.92s/it]
Test accuracy: 0.85:  19%|█▉        | 6/32 [00:42<02:59,  6.92s/it]
Test accuracy: 0.93:  19%|█▉        | 6/32 [00:42<02:59,  6.92s/it]
Test accuracy: 0.92:  19%|█▉        | 6/32 [00:42<02:59,  6.92s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:41<02:59,  6.91s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:42<02:59,  6.92s/it]
Test accuracy: 0.88:  19%|█▉        | 6/32 [00:42<02:59,  6.92s/it]
Test accuracy: 0.9:  19%|█▉        | 6/32 [00:41<02:59,  6.91s/it] 
Test accuracy: 0.85:  22%|██▏       | 7/32 [00:48<02:51,  6.85s/it]
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Test accuracy: 0.89:  22%|██▏       | 7/32 [00:48<02:51,  6.85s/it]
Test accuracy: 0.92:  22%|██▏       | 7/32 [00:48<02:51,  6.85s/it]
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Test accuracy: 0.89:  25%|██▌       | 8/32 [00:55<02:45,  6.91s/it]
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Test accuracy: 0.91:  28%|██▊       | 9/32 [01:03<02:44,  7.15s/it]
Test accuracy: 0.91:  28%|██▊       | 9/32 [01:03<02:44,  7.15s/it]
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Test accuracy: 0.9:  28%|██▊       | 9/32 [01:03<02:44,  7.15s/it] 
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Test accuracy: 0.89:  28%|██▊       | 9/32 [01:03<02:44,  7.15s/it]
Test accuracy: 0.89:  28%|██▊       | 9/32 [01:03<02:44,  7.15s/it]
Test accuracy: 0.92:  31%|███▏      | 10/32 [01:10<02:34,  7.01s/it]
Test accuracy: 0.9:  31%|███▏      | 10/32 [01:10<02:34,  7.01s/it]
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Test accuracy: 0.88:  31%|███▏      | 10/32 [01:10<02:34,  7.01s/it]
Test accuracy: 0.91:  34%|███▍      | 11/32 [01:16<02:25,  6.92s/it]
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Test accuracy: 0.91:  38%|███▊      | 12/32 [01:23<02:18,  6.91s/it]
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Test accuracy: 0.89:  38%|███▊      | 12/32 [01:23<02:18,  6.91s/it]
Test accuracy: 0.92:  41%|████      | 13/32 [01:30<02:11,  6.90s/it]
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Test accuracy: 0.92:  44%|████▍     | 14/32 [01:37<02:03,  6.84s/it]
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Test accuracy: 0.88:  47%|████▋     | 15/32 [01:44<01:55,  6.80s/it]
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Test accuracy: 0.92:  47%|████▋     | 15/32 [01:44<01:55,  6.80s/it]
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Test accuracy: 0.86:  47%|████▋     | 15/32 [01:43<01:55,  6.80s/it]
Test accuracy: 0.92:  50%|█████     | 16/32 [01:50<01:49,  6.83s/it]
Test accuracy: 0.86:  50%|█████     | 16/32 [01:50<01:49,  6.83s/it]
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Test accuracy: 0.92:  53%|█████▎    | 17/32 [01:57<01:41,  6.80s/it]
Test accuracy: 0.86:  53%|█████▎    | 17/32 [01:57<01:41,  6.79s/it]
Test accuracy: 0.86:  53%|█████▎    | 17/32 [01:57<01:41,  6.79s/it]
Test accuracy: 0.92:  53%|█████▎    | 17/32 [01:57<01:41,  6.80s/it]
Test accuracy: 0.85:  53%|█████▎    | 17/32 [01:57<01:41,  6.79s/it]
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Test accuracy: 0.92:  56%|█████▋    | 18/32 [02:04<01:35,  6.81s/it]
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Test accuracy: 0.92:  59%|█████▉    | 19/32 [02:11<01:28,  6.78s/it]
Test accuracy: 0.85:  59%|█████▉    | 19/32 [02:11<01:28,  6.78s/it]
Test accuracy: 0.92:  59%|█████▉    | 19/32 [02:11<01:28,  6.78s/it]
Test accuracy: 0.86:  59%|█████▉    | 19/32 [02:11<01:28,  6.78s/it]
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Test accuracy: 0.92:  62%|██████▎   | 20/32 [02:18<01:21,  6.82s/it]
Test accuracy: 0.86:  62%|██████▎   | 20/32 [02:18<01:21,  6.82s/it]
Test accuracy: 0.86:  62%|██████▎   | 20/32 [02:18<01:21,  6.82s/it]
Test accuracy: 0.92:  62%|██████▎   | 20/32 [02:18<01:21,  6.82s/it]
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Test accuracy: 0.89:  62%|██████▎   | 20/32 [02:18<01:21,  6.82s/it]
Test accuracy: 0.86:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
Test accuracy: 0.92:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
Test accuracy: 0.86:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
Test accuracy: 0.86:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
Test accuracy: 0.92:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
Test accuracy: 0.85:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
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Test accuracy: 0.89:  66%|██████▌   | 21/32 [02:25<01:17,  7.03s/it]
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Test accuracy: 0.93:  69%|██████▉   | 22/32 [02:32<01:09,  6.98s/it]
Test accuracy: 0.87:  69%|██████▉   | 22/32 [02:32<01:09,  6.98s/it]
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Test accuracy: 0.93:  75%|███████▌  | 24/32 [02:46<00:57,  7.14s/it]
Test accuracy: 0.87:  75%|███████▌  | 24/32 [02:46<00:57,  7.13s/it]
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Test accuracy: 0.88:  78%|███████▊  | 25/32 [02:53<00:49,  7.01s/it]
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Test accuracy: 0.87:  78%|███████▊  | 25/32 [02:53<00:49,  7.01s/it]
Test accuracy: 0.88:  81%|████████▏ | 26/32 [03:00<00:41,  6.97s/it]
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Test accuracy: 0.93:  84%|████████▍ | 27/32 [03:07<00:34,  6.89s/it]
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Test accuracy: 0.88:  84%|████████▍ | 27/32 [03:07<00:34,  6.89s/it]
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Test accuracy: 0.88:  88%|████████▊ | 28/32 [03:14<00:28,  7.14s/it]
Test accuracy: 0.88:  91%|█████████ | 29/32 [03:21<00:21,  7.01s/it]
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Test accuracy: 0.89:  94%|█████████▍| 30/32 [03:28<00:13,  6.98s/it]
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Test accuracy: 0.88: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
Test accuracy: 0.93: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
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Test accuracy: 0.89: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
Test accuracy: 0.93: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
Test accuracy: 0.89: 100%|██████████| 32/32 [03:38<00:00,  5.69s/it]
Test accuracy: 0.88: 100%|██████████| 32/32 [03:38<00:00,  6.83s/it]
Device 2: Cor=221, CoT=0, Total=250

Test accuracy: 0.93: 100%|██████████| 32/32 [03:38<00:00,  6.83s/it]
Device 1: Cor=233, CoT=0, Total=250

Test accuracy: 0.89: 100%|██████████| 32/32 [03:38<00:00,  6.83s/it]
Device 3: Cor=222, CoT=0, Total=250

Test accuracy: 0.88: 100%|██████████| 32/32 [03:38<00:00,  6.83s/it]
Device 0: Cor=220, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 6)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=7, data_stage=7, n_latent_tokens=7
Training on difficulties: 0-7

Overall accuracy: 896 / 1000 = 0.8960

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 100/100 = 1.0000 (trained)
  diff=4: 98/100 = 0.9800 (trained)
  diff=5: 98/100 = 0.9800 (trained)
  diff=6: 97/100 = 0.9700 (trained)
  diff=7: 91/100 = 0.9100 (trained)
  diff=8: 80/100 = 0.8000 (untrained)
  diff=9: 70/100 = 0.7000 (untrained)
  diff=10: 62/100 = 0.6200 (untrained)

Trained (diff 0-7): 684/700 = 0.9771
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9771, acc_diff7=0.9100 >= 0.9 ***
*** Advancing to stage 8 ***

Warning: failed to upload checkpoint_7 to HF: (Request ID: Root=1-6a1b7620-29176ede23ed252165556f86;75876f1e-62ac-4312-b53b-7c47b1c12d57)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 7/100 (thought_stage=8, data_stage=8)
============================================================
  thought_stage=8, c_thought=1, max_difficulty=8

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rank 0 | gpu 0 | alloc=4.20 GB | reserved=49.40 GB | max=37.30 GB
rank 1 | gpu 1 | alloc=4.20 GB | reserved=53.17 GB | max=37.69 GB
rank 3 | gpu 3 | alloc=4.20 GB | reserved=53.12 GB | max=37.69 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=49.40 GB | max=37.30 GB

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Training Epoch: 8/100, batch 499/500 completed (loss: 0.0015: 100%|██████████| 250/250 [21:43<00:00,  5.26s/it]
Training Epoch: 8/100, batch 499/500 completed (loss: 0.0011: 100%|██████████| 250/250 [21:43<00:00,  5.26s/it]
Training Epoch: 8/100, batch 499/500 completed (loss: 0.0004: 100%|██████████| 250/250 [21:43<00:00,  5.26s/it]
Training Epoch: 8/100, batch 499/500 completed (loss: 0.0002: 100%|██████████| 250/250 [21:43<00:00,  5.26s/it]
Training Epoch: 8/100, batch 499/500 completed (loss: 0.0011: 100%|██████████| 250/250 [21:44<00:00,  5.22s/it]

Training Epoch: 8/100, batch 499/500 completed (loss: 0.0004: 100%|██████████| 250/250 [21:44<00:00,  5.22s/it]

Training Epoch: 8/100, batch 499/500 completed (loss: 0.0015: 100%|██████████| 250/250 [21:44<00:00,  5.22s/it]

Training Epoch: 8/100, batch 499/500 completed (loss: 0.0002: 100%|██████████| 250/250 [21:44<00:00,  5.22s/it]
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_8 to HF: (Request ID: Root=1-6a1b7b50-2753ede4063ce85f0ad383a3;89465781-4fcb-4bdf-a002-930ffc675555)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_8 to HF: (Request ID: Root=1-6a1b7b54-5d4aebaa5f0be1e859c156f5;7b96c65c-a67a-4cc4-a929-5aa392a0abe2)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: checkpoint_7
Deleted old checkpoint: train_state_7
[warn] HF upload failed for ckpt_8; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.01170986210985575

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:41,  7.13s/it]
[CORRECT] Example 1 (diff=8)

Test Accuracy:   3%|▎         | 1/32 [00:07<03:41,  7.14s/it]  Expected : 'Guy'
  Extracted: 'Guy'
  Generated: '### Guy'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:41,  7.13s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:41,  7.14s/it]
Test Accuracy:   3%|▎         | 1/32 [00:07<03:41,  7.13s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:41,  7.13s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:07<03:37,  7.00s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:07<03:37,  7.00s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:23,  6.78s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:23,  6.77s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:23,  6.78s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:23,  6.77s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:23,  6.78s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:23,  6.78s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:21,  6.72s/it]
Test accuracy: 0.94:   6%|▋         | 2/32 [00:13<03:21,  6.72s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:22,  6.98s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:22,  6.98s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:22,  6.98s/it]
Test accuracy: 0.96:   9%|▉         | 3/32 [00:20<03:22,  6.98s/it]
Test accuracy: 0.94:   9%|▉         | 3/32 [00:20<03:21,  6.95s/it]
Test accuracy: 0.96:   9%|▉         | 3/32 [00:20<03:21,  6.95s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:22,  6.98s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:22,  6.98s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:27<03:14,  6.96s/it]
Test accuracy: 0.96:  12%|█▎        | 4/32 [00:27<03:14,  6.96s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:27<03:14,  6.96s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:27<03:14,  6.96s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:27<03:14,  6.96s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:27<03:14,  6.96s/it]
Test accuracy: 0.96:  12%|█▎        | 4/32 [00:27<03:14,  6.94s/it]
Test accuracy: 0.97:  12%|█▎        | 4/32 [00:27<03:14,  6.94s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:34<03:03,  6.80s/it]
Test accuracy: 0.98:  16%|█▌        | 5/32 [00:34<03:03,  6.80s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:34<03:03,  6.80s/it]
Test accuracy: 0.97:  16%|█▌        | 5/32 [00:34<03:03,  6.79s/it]
Test accuracy: 0.9:  16%|█▌        | 5/32 [00:34<03:03,  6.80s/it] 
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:34<03:03,  6.80s/it]
Test accuracy: 0.98:  16%|█▌        | 5/32 [00:34<03:03,  6.79s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:34<03:03,  6.80s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:40<02:54,  6.70s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:40<02:54,  6.70s/it]
Test accuracy: 0.96:  19%|█▉        | 6/32 [00:40<02:54,  6.70s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:40<02:54,  6.70s/it]
Test accuracy: 0.9:  19%|█▉        | 6/32 [00:40<02:54,  6.70s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:40<02:53,  6.69s/it]
Test accuracy: 0.92:  19%|█▉        | 6/32 [00:40<02:54,  6.70s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:40<02:53,  6.69s/it]
Test accuracy: 0.96:  22%|██▏       | 7/32 [00:47<02:47,  6.71s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:47<02:47,  6.71s/it]
Test accuracy: 0.96:  22%|██▏       | 7/32 [00:47<02:47,  6.71s/it]
Test accuracy: 0.96:  22%|██▏       | 7/32 [00:47<02:47,  6.71s/it]
Test accuracy: 0.92:  22%|██▏       | 7/32 [00:47<02:47,  6.71s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:47<02:47,  6.70s/it]
Test accuracy: 0.93:  22%|██▏       | 7/32 [00:47<02:47,  6.71s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:47<02:47,  6.70s/it]
Test accuracy: 0.96:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.95:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.93:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.96:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.92:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.97:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:54<02:40,  6.70s/it]
Test accuracy: 0.97:  28%|██▊       | 9/32 [01:01<02:38,  6.87s/it]
Test accuracy: 0.95:  28%|██▊       | 9/32 [01:01<02:38,  6.87s/it]
Test accuracy: 0.97:  28%|██▊       | 9/32 [01:01<02:38,  6.87s/it]
Test accuracy: 0.96:  28%|██▊       | 9/32 [01:01<02:38,  6.87s/it]
Test accuracy: 0.98:  28%|██▊       | 9/32 [01:01<02:38,  6.87s/it]
Test accuracy: 0.99:  28%|██▊       | 9/32 [01:01<02:38,  6.87s/it]
Test accuracy: 0.92:  28%|██▊       | 9/32 [01:01<02:38,  6.88s/it]
Test accuracy: 0.92:  28%|██▊       | 9/32 [01:01<02:38,  6.88s/it]
Test accuracy: 0.96:  31%|███▏      | 10/32 [01:08<02:28,  6.76s/it]
Test accuracy: 0.95:  31%|███▏      | 10/32 [01:08<02:28,  6.76s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:07<02:28,  6.76s/it]
Test accuracy: 0.92:  31%|███▏      | 10/32 [01:08<02:28,  6.76s/it]
Test accuracy: 0.93:  31%|███▏      | 10/32 [01:08<02:28,  6.76s/it]
Test accuracy: 0.96:  31%|███▏      | 10/32 [01:07<02:28,  6.76s/it]
Test accuracy: 0.97:  31%|███▏      | 10/32 [01:08<02:28,  6.76s/it]
Test accuracy: 0.98:  31%|███▏      | 10/32 [01:08<02:28,  6.76s/it]
Test accuracy: 0.98:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.95:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.97:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.96:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.95:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.93:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.95:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.93:  34%|███▍      | 11/32 [01:14<02:21,  6.75s/it]
Test accuracy: 0.95:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.93:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.95:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.97:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.94:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.97:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.95:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.96:  38%|███▊      | 12/32 [01:21<02:14,  6.74s/it]
Test accuracy: 0.97:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.95:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.96:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.94:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.94:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.93:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.96:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.96:  41%|████      | 13/32 [01:27<02:06,  6.67s/it]
Test accuracy: 0.96:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.93:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.96:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.94:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.94:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.95:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.96:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.96:  44%|████▍     | 14/32 [01:34<02:00,  6.68s/it]
Test accuracy: 0.96:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.95:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.94:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.97:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.94:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.93:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.96:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.97:  47%|████▋     | 15/32 [01:41<01:53,  6.68s/it]
Test accuracy: 0.97:  50%|█████     | 16/32 [01:47<01:46,  6.63s/it]
Test accuracy: 0.94:  50%|█████     | 16/32 [01:47<01:46,  6.63s/it]
Test accuracy: 0.93:  50%|█████     | 16/32 [01:47<01:46,  6.63s/it]
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Test accuracy: 0.96: 100%|██████████| 32/32 [03:32<00:00,  5.55s/it]
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Test accuracy: 0.95: 100%|██████████| 32/32 [03:32<00:00,  6.64s/it]
Device 2: Cor=238, CoT=0, Total=250

Test accuracy: 0.96: 100%|██████████| 32/32 [03:32<00:00,  6.64s/it]
Device 1: Cor=239, CoT=0, Total=250

Test accuracy: 0.95: 100%|██████████| 32/32 [03:32<00:00,  6.64s/it]
Device 3: Cor=238, CoT=0, Total=250

Test accuracy: 0.96: 100%|██████████| 32/32 [03:32<00:00,  6.64s/it]
Device 0: Cor=240, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 7)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=8, data_stage=8, n_latent_tokens=8
Training on difficulties: 0-8

Overall accuracy: 955 / 1000 = 0.9550

Per-difficulty accuracy:
  diff=1: 99/100 = 0.9900 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 99/100 = 0.9900 (trained)
  diff=4: 99/100 = 0.9900 (trained)
  diff=5: 100/100 = 1.0000 (trained)
  diff=6: 97/100 = 0.9700 (trained)
  diff=7: 99/100 = 0.9900 (trained)
  diff=8: 94/100 = 0.9400 (trained)
  diff=9: 86/100 = 0.8600 (untrained)
  diff=10: 82/100 = 0.8200 (untrained)

Trained (diff 0-8): 787/800 = 0.9838
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9838, acc_diff8=0.9400 >= 0.9 ***
*** Advancing to stage 9 ***

Warning: failed to upload checkpoint_8 to HF: (Request ID: Root=1-6a1b7c4a-027e0cdc1e917c7f118e3dbb;02f263b4-1644-4aea-b7f6-2e3d3b6a8366)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 8/100 (thought_stage=9, data_stage=9)
============================================================
  thought_stage=9, c_thought=1, max_difficulty=9

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Map (num_proc=4):  31%|███       | 2751/9000 [00:01<00:02, 2797.25 examples/s]
Map (num_proc=4):  35%|███▌      | 3182/9000 [00:01<00:01, 3058.92 examples/s]
Map (num_proc=4):  41%|████      | 3671/9000 [00:02<00:01, 3504.63 examples/s]
Map (num_proc=4):  47%|████▋     | 4259/9000 [00:02<00:01, 4112.84 examples/s]
Map (num_proc=4):  57%|█████▋    | 5093/9000 [00:02<00:00, 4633.66 examples/s]
Map (num_proc=4):  62%|██████▏   | 5579/9000 [00:02<00:00, 3459.74 examples/s]
Map (num_proc=4):  68%|██████▊   | 6116/9000 [00:02<00:01, 2611.27 examples/s]
Map (num_proc=4):  82%|████████▏ | 7421/9000 [00:03<00:00, 2842.46 examples/s]
Map (num_proc=4):  97%|█████████▋| 8750/9000 [00:03<00:00, 2672.04 examples/s]
Map (num_proc=4): 100%|██████████| 9000/9000 [00:04<00:00, 1983.92 examples/s]

Filter (num_proc=4):   0%|          | 0/1000 [00:00<?, ? examples/s]
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Filter (num_proc=4):  25%|██▌       | 250/1000 [00:00<00:00, 1396.30 examples/s]
Filter (num_proc=4):  25%|██▌       | 250/1000 [00:00<00:00, 1346.10 examples/s]
Filter (num_proc=4):  25%|██▌       | 250/1000 [00:00<00:00, 1353.67 examples/s]
Filter (num_proc=4):  25%|██▌       | 250/1000 [00:00<00:00, 811.21 examples/s]
Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 2764.24 examples/s]

Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 2590.87 examples/s]

Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 2635.54 examples/s]

Filter (num_proc=4): 100%|██████████| 1000/1000 [00:00<00:00, 1545.09 examples/s]

Map (num_proc=4):   0%|          | 0/900 [00:00<?, ? examples/s]
Map (num_proc=4):  15%|█▌        | 136/900 [00:00<00:01, 394.84 examples/s]
Map (num_proc=4):  40%|███▉      | 358/900 [00:00<00:00, 922.19 examples/s]
Map (num_proc=4):  79%|███████▉  | 713/900 [00:00<00:00, 1534.68 examples/s]
Map (num_proc=4): 100%|██████████| 900/900 [00:01<00:00, 887.30 examples/s] 

Training Epoch: 9:   0%|          | 0/281 [00:00<?, ?it/s]
Training Epoch: 9:   0%|          | 0/281 [00:00<?, ?it/s]
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Training Epoch: 9:   0%|          | 0/281 [00:00<?, ?it/s]logging training data
rank 1 | gpu 1 | alloc=4.20 GB | reserved=53.19 GB | max=38.74 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=49.47 GB | max=38.74 GB
rank 3 | gpu 3 | alloc=4.20 GB | reserved=47.63 GB | max=38.74 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=49.48 GB | max=38.74 GB

Training Epoch: 9/100, batch 0/563 completed (loss: 0.0003:   0%|          | 0/281 [00:03<?, ?it/s]
Training Epoch: 9/100, batch 0/563 completed (loss: 0.1386:   0%|          | 0/281 [00:03<?, ?it/s]
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Training Epoch: 9/100, batch 0/563 completed (loss: 0.1386:   0%|          | 1/281 [00:05<27:22,  5.87s/it]
Training Epoch: 9/100, batch 0/563 completed (loss: 0.0003:   0%|          | 1/281 [00:05<27:21,  5.86s/it]
Training Epoch: 9/100, batch 0/563 completed (loss: 0.0037:   0%|          | 1/281 [00:05<27:46,  5.95s/it]
Training Epoch: 9/100, batch 0/563 completed (loss: 0.0008:   0%|          | 1/281 [00:06<28:02,  6.01s/it]
Training Epoch: 9/100, batch 1/563 completed (loss: 0.0006:   0%|          | 1/281 [00:06<28:02,  6.01s/it]
Training Epoch: 9/100, batch 1/563 completed (loss: 0.0009:   0%|          | 1/281 [00:06<27:21,  5.86s/it]
Training Epoch: 9/100, batch 1/563 completed (loss: 0.0005:   0%|          | 1/281 [00:06<27:22,  5.87s/it]
Training Epoch: 9/100, batch 1/563 completed (loss: 0.0017:   0%|          | 1/281 [00:06<27:46,  5.95s/it]
Training Epoch: 9/100, batch 2/563 completed (loss: 0.4929:   0%|          | 1/281 [00:08<27:22,  5.87s/it]
Training Epoch: 9/100, batch 2/563 completed (loss: 0.0061:   0%|          | 1/281 [00:08<27:21,  5.86s/it]
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Training Epoch: 9/100, batch 2/563 completed (loss: 0.0061:   1%|          | 2/281 [00:11<26:38,  5.73s/it]
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Training Epoch: 9/100, batch 2/563 completed (loss: 0.1292:   1%|          | 2/281 [00:11<26:44,  5.75s/it]
Training Epoch: 9/100, batch 2/563 completed (loss: 0.0005:   1%|          | 2/281 [00:11<26:54,  5.79s/it]
Training Epoch: 9/100, batch 3/563 completed (loss: 0.0083:   1%|          | 2/281 [00:11<26:54,  5.79s/it]
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Training Epoch: 9/100, batch 3/563 completed (loss: 0.0013:   1%|          | 2/281 [00:11<26:38,  5.73s/it]
Training Epoch: 9/100, batch 3/563 completed (loss: 0.0026:   1%|          | 2/281 [00:11<26:44,  5.75s/it]
Training Epoch: 9/100, batch 4/563 completed (loss: 0.0109:   1%|          | 2/281 [00:14<26:38,  5.73s/it]
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Training Epoch: 9/100, batch 4/563 completed (loss: 0.0889:   1%|          | 3/281 [00:17<26:13,  5.66s/it]
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Training Epoch: 9/100, batch 4/563 completed (loss: 0.0109:   1%|          | 3/281 [00:17<26:14,  5.66s/it]
Training Epoch: 9/100, batch 4/563 completed (loss: 0.005:   1%|          | 3/281 [00:17<26:22,  5.69s/it]
Training Epoch: 9/100, batch 5/563 completed (loss: 0.0008:   1%|          | 3/281 [00:17<26:22,  5.69s/it]
Training Epoch: 9/100, batch 5/563 completed (loss: 0.0031:   1%|          | 3/281 [00:17<26:14,  5.66s/it]
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Training Epoch: 9/100, batch 5/563 completed (loss: 0.0001:   1%|          | 3/281 [00:17<26:15,  5.67s/it]
Training Epoch: 9/100, batch 6/563 completed (loss: 0.0026:   1%|          | 3/281 [00:20<26:14,  5.66s/it]
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Training Epoch: 9/100, batch 6/563 completed (loss: 0.0053:   1%|          | 3/281 [00:20<26:13,  5.66s/it]
Training Epoch: 9/100, batch 6/563 completed (loss: 0.0063:   1%|          | 3/281 [00:20<26:15,  5.67s/it]
Training Epoch: 9/100, batch 6/563 completed (loss: 0.0053:   1%|▏         | 4/281 [00:22<26:11,  5.67s/it]
Training Epoch: 9/100, batch 6/563 completed (loss: 0.0026:   1%|▏         | 4/281 [00:22<26:11,  5.67s/it]
Training Epoch: 9/100, batch 6/563 completed (loss: 0.0063:   1%|▏         | 4/281 [00:22<26:14,  5.68s/it]
Training Epoch: 9/100, batch 6/563 completed (loss: 0.0003:   1%|▏         | 4/281 [00:22<26:18,  5.70s/it]
Training Epoch: 9/100, batch 7/563 completed (loss: 0.0011:   1%|▏         | 4/281 [00:22<26:18,  5.70s/it]
Training Epoch: 9/100, batch 7/563 completed (loss: 0.0143:   1%|▏         | 4/281 [00:22<26:11,  5.67s/it]
Training Epoch: 9/100, batch 7/563 completed (loss: 0.0157:   1%|▏         | 4/281 [00:22<26:11,  5.67s/it]
Training Epoch: 9/100, batch 7/563 completed (loss: 0.0003:   1%|▏         | 4/281 [00:23<26:14,  5.68s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.0006:   1%|▏         | 4/281 [00:25<26:18,  5.70s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.0102:   1%|▏         | 4/281 [00:25<26:11,  5.67s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.1198:   1%|▏         | 4/281 [00:25<26:11,  5.67s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.0184:   1%|▏         | 4/281 [00:25<26:14,  5.68s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.0102:   2%|▏         | 5/281 [00:28<26:00,  5.65s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.0184:   2%|▏         | 5/281 [00:28<26:01,  5.66s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.1198:   2%|▏         | 5/281 [00:28<26:01,  5.66s/it]
Training Epoch: 9/100, batch 8/563 completed (loss: 0.0006:   2%|▏         | 5/281 [00:28<26:03,  5.66s/it]
Training Epoch: 9/100, batch 9/563 completed (loss: 0.0005:   2%|▏         | 5/281 [00:28<26:03,  5.66s/it]
Training Epoch: 9/100, batch 9/563 completed (loss: 0.0062:   2%|▏         | 5/281 [00:28<26:01,  5.66s/it]
Training Epoch: 9/100, batch 9/563 completed (loss: 0.0043:   2%|▏         | 5/281 [00:28<26:00,  5.65s/it]
Training Epoch: 9/100, batch 9/563 completed (loss: 0.0018:   2%|▏         | 5/281 [00:28<26:01,  5.66s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0523:   2%|▏         | 5/281 [00:31<26:03,  5.66s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0046:   2%|▏         | 5/281 [00:31<26:01,  5.66s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0005:   2%|▏         | 5/281 [00:31<26:00,  5.65s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0026:   2%|▏         | 5/281 [00:31<26:01,  5.66s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0005:   2%|▏         | 6/281 [00:34<26:20,  5.75s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0046:   2%|▏         | 6/281 [00:34<26:19,  5.74s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0026:   2%|▏         | 6/281 [00:34<26:19,  5.75s/it]
Training Epoch: 9/100, batch 10/563 completed (loss: 0.0523:   2%|▏         | 6/281 [00:34<26:21,  5.75s/it]
Training Epoch: 9/100, batch 11/563 completed (loss: 0.0017:   2%|▏         | 6/281 [00:34<26:20,  5.75s/it]
Training Epoch: 9/100, batch 11/563 completed (loss: 0.0118:   2%|▏         | 6/281 [00:34<26:19,  5.74s/it]
Training Epoch: 9/100, batch 11/563 completed (loss: 0.0206:   2%|▏         | 6/281 [00:34<26:21,  5.75s/it]
Training Epoch: 9/100, batch 11/563 completed (loss: 0.0004:   2%|▏         | 6/281 [00:34<26:19,  5.75s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0654:   2%|▏         | 6/281 [00:37<26:21,  5.75s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0011:   2%|▏         | 6/281 [00:37<26:20,  5.75s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0006:   2%|▏         | 6/281 [00:37<26:19,  5.74s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0024:   2%|▏         | 6/281 [00:37<26:19,  5.75s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0006:   2%|▏         | 7/281 [00:39<25:50,  5.66s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0011:   2%|▏         | 7/281 [00:39<25:51,  5.66s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0024:   2%|▏         | 7/281 [00:39<25:51,  5.66s/it]
Training Epoch: 9/100, batch 12/563 completed (loss: 0.0654:   2%|▏         | 7/281 [00:39<25:52,  5.67s/it]
Training Epoch: 9/100, batch 13/563 completed (loss: 0.0092:   2%|▏         | 7/281 [00:39<25:52,  5.67s/it]
Training Epoch: 9/100, batch 13/563 completed (loss: 0.0966:   2%|▏         | 7/281 [00:39<25:51,  5.66s/it]
Training Epoch: 9/100, batch 13/563 completed (loss: 0.0018:   2%|▏         | 7/281 [00:39<25:50,  5.66s/it]
Training Epoch: 9/100, batch 13/563 completed (loss: 0.012:   2%|▏         | 7/281 [00:40<25:51,  5.66s/it] 
Training Epoch: 9/100, batch 14/563 completed (loss: 0.1384:   2%|▏         | 7/281 [00:42<25:52,  5.67s/it]
Training Epoch: 9/100, batch 14/563 completed (loss: 0.036:   2%|▏         | 7/281 [00:42<25:50,  5.66s/it] 
Training Epoch: 9/100, batch 14/563 completed (loss: 0.0006:   2%|▏         | 7/281 [00:42<25:51,  5.66s/it]
Training Epoch: 9/100, batch 14/563 completed (loss: 0.0129:   2%|▏         | 7/281 [00:42<25:51,  5.66s/it]
Training Epoch: 9/100, batch 14/563 completed (loss: 0.036:   3%|▎         | 8/281 [00:45<25:43,  5.65s/it]
Training Epoch: 9/100, batch 14/563 completed (loss: 0.0006:   3%|▎         | 8/281 [00:45<25:44,  5.66s/it]
Training Epoch: 9/100, batch 14/563 completed (loss: 0.0129:   3%|▎         | 8/281 [00:45<25:45,  5.66s/it]
Training Epoch: 9/100, batch 14/563 completed (loss: 0.1384:   3%|▎         | 8/281 [00:45<25:45,  5.66s/it]
Training Epoch: 9/100, batch 15/563 completed (loss: 0.0568:   3%|▎         | 8/281 [00:45<25:45,  5.66s/it]
Training Epoch: 9/100, batch 15/563 completed (loss: 0.0025:   3%|▎         | 8/281 [00:45<25:44,  5.66s/it]
Training Epoch: 9/100, batch 15/563 completed (loss: 0.0032:   3%|▎         | 8/281 [00:45<25:43,  5.65s/it]
Training Epoch: 9/100, batch 15/563 completed (loss: 0.0012:   3%|▎         | 8/281 [00:45<25:45,  5.66s/it]
Training Epoch: 9/100, batch 16/563 completed (loss: 0.0023:   3%|▎         | 8/281 [00:48<25:45,  5.66s/it]
Training Epoch: 9/100, batch 16/563 completed (loss: 0.4968:   3%|▎         | 8/281 [00:48<25:43,  5.65s/it]
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Training Epoch: 9/100, batch 16/563 completed (loss: 0.0045:   3%|▎         | 8/281 [00:48<25:45,  5.66s/it]
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Training Epoch: 9/100, batch 16/563 completed (loss: 0.4968:   3%|▎         | 9/281 [00:51<25:41,  5.67s/it]
Training Epoch: 9/100, batch 16/563 completed (loss: 0.0045:   3%|▎         | 9/281 [00:51<25:41,  5.67s/it]
Training Epoch: 9/100, batch 16/563 completed (loss: 0.0023:   3%|▎         | 9/281 [00:51<25:43,  5.67s/it]
Training Epoch: 9/100, batch 17/563 completed (loss: 0.0103:   3%|▎         | 9/281 [00:51<25:41,  5.67s/it]
Training Epoch: 9/100, batch 17/563 completed (loss: 0.0761:   3%|▎         | 9/281 [00:51<25:43,  5.67s/it]
Training Epoch: 9/100, batch 17/563 completed (loss: 0.0007:   3%|▎         | 9/281 [00:51<25:41,  5.67s/it]
Training Epoch: 9/100, batch 17/563 completed (loss: 0.0004:   3%|▎         | 9/281 [00:51<25:41,  5.67s/it]
Training Epoch: 9/100, batch 18/563 completed (loss: 0.0098:   3%|▎         | 9/281 [00:54<25:43,  5.67s/it]
Training Epoch: 9/100, batch 18/563 completed (loss: 0.0106:   3%|▎         | 9/281 [00:54<25:41,  5.67s/it]
Training Epoch: 9/100, batch 18/563 completed (loss: 0.0053:   3%|▎         | 9/281 [00:54<25:41,  5.67s/it]
Training Epoch: 9/100, batch 18/563 completed (loss: 0.0003:   3%|▎         | 9/281 [00:54<25:41,  5.67s/it]
Training Epoch: 9/100, batch 18/563 completed (loss: 0.0053:   4%|▎         | 10/281 [00:56<25:20,  5.61s/it]
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Training Epoch: 9/100, batch 18/563 completed (loss: 0.0098:   4%|▎         | 10/281 [00:56<25:20,  5.61s/it]
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Training Epoch: 9/100, batch 19/563 completed (loss: 0.3964:   4%|▎         | 10/281 [00:56<25:20,  5.61s/it]
Training Epoch: 9/100, batch 19/563 completed (loss: 0.0123:   4%|▎         | 10/281 [00:56<25:20,  5.61s/it]
Training Epoch: 9/100, batch 19/563 completed (loss: 0.036:   4%|▎         | 10/281 [00:56<25:20,  5.61s/it] 
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Training Epoch: 9/100, batch 20/563 completed (loss: 0.0005:   4%|▎         | 10/281 [00:59<25:20,  5.61s/it]
Training Epoch: 9/100, batch 20/563 completed (loss: 0.042:   4%|▍         | 11/281 [01:02<25:18,  5.62s/it]
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Training Epoch: 9/100, batch 20/563 completed (loss: 0.0005:   4%|▍         | 11/281 [01:02<25:19,  5.63s/it]
Training Epoch: 9/100, batch 20/563 completed (loss: 0.0033:   4%|▍         | 11/281 [01:02<25:20,  5.63s/it]
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Training Epoch: 9/100, batch 21/563 completed (loss: 0.0593:   4%|▍         | 11/281 [01:02<25:20,  5.63s/it]
Training Epoch: 9/100, batch 21/563 completed (loss: 0.008:   4%|▍         | 11/281 [01:02<25:18,  5.63s/it] 
Training Epoch: 9/100, batch 21/563 completed (loss: 0.0017:   4%|▍         | 11/281 [01:02<25:19,  5.63s/it]
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Training Epoch: 9/100, batch 22/563 completed (loss: 0.0008:   4%|▍         | 11/281 [01:05<25:20,  5.63s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_9 to HF: (Request ID: Root=1-6a1b82c9-3a56c0b570bc63a9703a1180;d992fd23-3a40-4b9b-a85d-7681950e9556)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_9 to HF: (Request ID: Root=1-6a1b82cc-7a8d4a4e5d5272b91a686632;b64fd20d-fbe9-400e-adba-f2140885082d)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: checkpoint_8
Deleted old checkpoint: train_state_8
[warn] HF upload failed for ckpt_9; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.01550573162660983

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

[CORRECT] Example 1 (diff=8)
Test Accuracy:   3%|▎         | 1/32 [00:06<03:28,  6.73s/it]
Test Accuracy:   3%|▎         | 1/32 [00:06<03:28,  6.73s/it]
Test Accuracy:   3%|▎         | 1/32 [00:06<03:28,  6.73s/it]
  Expected : 'Guy'
  Extracted: 'Guy'
  Generated: '### Guy'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:28,  6.73s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:28,  6.73s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:28,  6.73s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:24,  6.59s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:24,  6.59s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:14,  6.49s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:14,  6.49s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:14,  6.49s/it]
Test accuracy: 0.94:   6%|▋         | 2/32 [00:13<03:14,  6.49s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:14,  6.49s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:13<03:14,  6.49s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.43s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:18,  6.84s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:18,  6.84s/it]
Test accuracy: 0.94:   9%|▉         | 3/32 [00:20<03:18,  6.84s/it]
Test accuracy: 0.92:   9%|▉         | 3/32 [00:20<03:18,  6.84s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:17,  6.81s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:17,  6.81s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:18,  6.84s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:20<03:18,  6.84s/it]
Test accuracy: 0.92:  12%|█▎        | 4/32 [00:26<03:08,  6.73s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:08,  6.73s/it]
Test accuracy: 0.88:  12%|█▎        | 4/32 [00:26<03:08,  6.73s/it]
Test accuracy: 0.97:  12%|█▎        | 4/32 [00:26<03:08,  6.73s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:07,  6.71s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:08,  6.73s/it]
Test accuracy: 0.97:  12%|█▎        | 4/32 [00:26<03:08,  6.73s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:07,  6.71s/it]
Test accuracy: 0.88:  16%|█▌        | 5/32 [00:33<02:57,  6.58s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:33<02:57,  6.57s/it]
Test accuracy: 0.97:  16%|█▌        | 5/32 [00:33<02:57,  6.58s/it]
Test accuracy: 0.97:  16%|█▌        | 5/32 [00:33<02:57,  6.58s/it]
Test accuracy: 0.9:  16%|█▌        | 5/32 [00:33<02:57,  6.58s/it] 
Test accuracy: 0.98:  16%|█▌        | 5/32 [00:33<02:57,  6.58s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:33<02:57,  6.57s/it]
Test accuracy: 0.98:  16%|█▌        | 5/32 [00:33<02:57,  6.58s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 0.9:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 0.92:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:39<02:50,  6.55s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 0.96:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 0.92:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 0.93:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:46<02:43,  6.53s/it]
Test accuracy: 0.96:  25%|██▌       | 8/32 [00:52<02:35,  6.47s/it]
Test accuracy: 0.97:  25%|██▌       | 8/32 [00:52<02:35,  6.47s/it]
Test accuracy: 0.93:  25%|██▌       | 8/32 [00:52<02:35,  6.47s/it]
Test accuracy: 0.94:  25%|██▌       | 8/32 [00:52<02:35,  6.47s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:52<02:35,  6.47s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:52<02:35,  6.47s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:52<02:35,  6.46s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:52<02:35,  6.46s/it]
Test accuracy: 0.97:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.98:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.99:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.97:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.98:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.99:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.94:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.94:  28%|██▊       | 9/32 [00:59<02:34,  6.71s/it]
Test accuracy: 0.94:  31%|███▏      | 10/32 [01:06<02:26,  6.64s/it]
Test accuracy: 0.97:  31%|███▏      | 10/32 [01:06<02:26,  6.65s/it]
Test accuracy: 0.95:  31%|███▏      | 10/32 [01:06<02:26,  6.64s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:06<02:26,  6.65s/it]
Test accuracy: 0.98:  31%|███▏      | 10/32 [01:06<02:26,  6.65s/it]
Test accuracy: 0.98:  31%|███▏      | 10/32 [01:06<02:26,  6.65s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:06<02:26,  6.64s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:06<02:26,  6.64s/it]
Test accuracy: 0.98:  34%|███▍      | 11/32 [01:12<02:17,  6.54s/it]
Test accuracy: 0.98:  34%|███▍      | 11/32 [01:12<02:17,  6.54s/it]
Test accuracy: 0.95:  34%|███▍      | 11/32 [01:12<02:17,  6.54s/it]
Test accuracy: 0.98:  34%|███▍      | 11/32 [01:12<02:17,  6.54s/it]
Test accuracy: 0.98:  34%|███▍      | 11/32 [01:12<02:17,  6.54s/it]
Test accuracy: 0.95:  34%|███▍      | 11/32 [01:12<02:17,  6.54s/it]
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Test accuracy: 0.98: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.96: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.96: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.96: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.96: 100%|██████████| 32/32 [03:26<00:00,  5.40s/it]
Test accuracy: 0.96: 100%|██████████| 32/32 [03:26<00:00,  6.46s/it]
Device 2: Cor=240, CoT=0, Total=250

Test accuracy: 0.98: 100%|██████████| 32/32 [03:26<00:00,  6.46s/it]
Device 1: Cor=246, CoT=0, Total=250

Test accuracy: 0.96: 100%|██████████| 32/32 [03:26<00:00,  6.46s/it]
Device 3: Cor=241, CoT=0, Total=250

Test accuracy: 0.98: 100%|██████████| 32/32 [03:26<00:00,  6.46s/it]
Device 0: Cor=244, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 8)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=9, data_stage=9, n_latent_tokens=9
Training on difficulties: 0-9

Overall accuracy: 971 / 1000 = 0.9710

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 100/100 = 1.0000 (trained)
  diff=4: 98/100 = 0.9800 (trained)
  diff=5: 98/100 = 0.9800 (trained)
  diff=6: 98/100 = 0.9800 (trained)
  diff=7: 98/100 = 0.9800 (trained)
  diff=8: 98/100 = 0.9800 (trained)
  diff=9: 95/100 = 0.9500 (trained)
  diff=10: 86/100 = 0.8600 (untrained)

Trained (diff 0-9): 885/900 = 0.9833
============================================================


*** ADAPTIVE STAGING: acc_trained=0.9833, acc_diff9=0.9500 >= 0.9 ***
*** Advancing to stage 10 ***

Warning: failed to upload checkpoint_9 to HF: (Request ID: Root=1-6a1b83c4-17125c1b184cc12d05791a2d;cb320e8e-b4d4-4302-860e-3724fd216c0c)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 9/100 (thought_stage=10, data_stage=10)
============================================================
  thought_stage=10, c_thought=1, max_difficulty=10

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rank 1 | gpu 1 | alloc=4.20 GB | reserved=51.40 GB | max=40.18 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=49.52 GB | max=40.18 GB
rank 3 | gpu 3 | alloc=4.20 GB | reserved=49.59 GB | max=40.18 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=51.43 GB | max=40.18 GB

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Training Epoch: 10/100, batch 0/625 completed (loss: 0.0031:   0%|          | 1/312 [00:06<33:48,  6.52s/it]
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Training Epoch: 10/100, batch 9/625 completed (loss: 0.0017:   2%|▏         | 5/312 [00:31<31:41,  6.19s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
saving model + train state.
Warning: failed to upload checkpoint_10 to HF: (Request ID: Root=1-6a1b8ba9-102d79710a5658845e156107;aeafabc3-d412-4dd2-822f-1471c12ce758)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Warning: failed to upload train_state_10 to HF: (Request ID: Root=1-6a1b8bad-3fb99c7d75fa1c5901c8754a;ef06875d-a6b8-4715-8cbd-e864fa8214e5)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.
Deleted old checkpoint: train_state_9
Deleted old checkpoint: checkpoint_9
[warn] HF upload failed for ckpt_10; kept locally, earlier ckpts deleted.

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.008637622485929612

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

[CORRECT] Example 1 (diff=8)
  Expected : 'Guy'
  Extracted: 'Guy'
  Generated: '### Guy'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
  Extracted: 'Elvis'
  Generated: '### Elvis'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:18,  6.41s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:18,  6.41s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:14,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:14,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.41s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.41s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.68s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.68s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.72s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.72s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.49s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.49s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.49s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.49s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.49s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:25<03:01,  6.47s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.49s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:25<03:01,  6.47s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:52,  6.41s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:52,  6.41s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.36s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:44<02:38,  6.35s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:38,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:38,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:38,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:38,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:38,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:38,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:44<02:38,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:57<02:28,  6.47s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 0.99:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:57<02:28,  6.47s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:29,  6.48s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:29,  6.48s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 0.99:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 0.99:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 0.99:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 0.99:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 0.99:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 0.99:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 0.99:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 0.99:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.32s/it]
Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 0.99:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 0.99:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 0.99:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 0.99:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 0.99:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 0.99:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 0.98:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 0.98:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 1.0:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 1.0:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.99:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.98:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 1.0:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.99:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.99:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.99:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 0.98:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 0.99:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 1.0:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 0.98:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 0.99:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 1.0:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 0.99:  56%|█████▋    | 18/32 [01:54<01:28,  6.30s/it]
Test accuracy: 1.0:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
Test accuracy: 0.99:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
Test accuracy: 0.98:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
Test accuracy: 0.99:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
Test accuracy: 1.0:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
Test accuracy: 0.99:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
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Test accuracy: 0.99:  59%|█████▉    | 19/32 [02:00<01:21,  6.25s/it]
Test accuracy: 1.0:  62%|██████▎   | 20/32 [02:07<01:15,  6.26s/it]
Test accuracy: 0.98:  62%|██████▎   | 20/32 [02:07<01:15,  6.26s/it]
Test accuracy: 0.99:  62%|██████▎   | 20/32 [02:07<01:15,  6.26s/it]
Test accuracy: 1.0:  62%|██████▎   | 20/32 [02:07<01:15,  6.26s/it]
Test accuracy: 0.99:  62%|██████▎   | 20/32 [02:06<01:15,  6.26s/it]
Test accuracy: 0.98:  62%|██████▎   | 20/32 [02:07<01:15,  6.26s/it]
Test accuracy: 0.99:  62%|██████▎   | 20/32 [02:07<01:15,  6.26s/it]
Test accuracy: 0.99:  62%|██████▎   | 20/32 [02:06<01:15,  6.26s/it]
Test accuracy: 1.0:  66%|██████▌   | 21/32 [02:14<01:11,  6.48s/it]
Test accuracy: 0.99:  66%|██████▌   | 21/32 [02:13<01:11,  6.48s/it]
Test accuracy: 0.99:  66%|██████▌   | 21/32 [02:13<01:11,  6.48s/it]
Test accuracy: 1.0:  66%|██████▌   | 21/32 [02:14<01:11,  6.48s/it]
Test accuracy: 0.99:  66%|██████▌   | 21/32 [02:14<01:11,  6.48s/it]
Test accuracy: 0.98:  66%|██████▌   | 21/32 [02:13<01:11,  6.48s/it]
Test accuracy: 0.98:  66%|██████▌   | 21/32 [02:14<01:11,  6.49s/it]
Test accuracy: 0.98:  66%|██████▌   | 21/32 [02:14<01:11,  6.49s/it]
Test accuracy: 0.99:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
Test accuracy: 1.0:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
Test accuracy: 0.98:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
Test accuracy: 1.0:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
Test accuracy: 0.99:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
Test accuracy: 0.98:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
Test accuracy: 0.98:  69%|██████▉   | 22/32 [02:19<01:03,  6.38s/it]
Test accuracy: 0.98:  69%|██████▉   | 22/32 [02:19<01:03,  6.38s/it]
Test accuracy: 1.0:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 0.99:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 0.98:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 1.0:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 0.98:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 0.99:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 0.98:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 0.98:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
Test accuracy: 1.0:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.99:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.98:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 1.0:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.99:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.98:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.98:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.98:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
Test accuracy: 0.98:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 1.0:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 0.99:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 1.0:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 0.99:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 1.0:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it] 
Test accuracy: 0.98:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 0.99:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:46<00:38,  6.43s/it]
Test accuracy: 0.99:  81%|████████▏ | 26/32 [02:46<00:38,  6.43s/it]
Test accuracy: 0.99:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 0.99:  81%|████████▏ | 26/32 [02:46<00:38,  6.43s/it]
Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:46<00:38,  6.43s/it]
Test accuracy: 0.99:  81%|████████▏ | 26/32 [02:46<00:38,  6.43s/it]
Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:46<00:38,  6.43s/it]
Test accuracy: 0.99:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 1.0:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 1.0:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.56s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.56s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 2: Cor=247, CoT=0, Total=250

Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 1: Cor=248, CoT=0, Total=250

Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 3: Cor=249, CoT=0, Total=250

Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 0: Cor=247, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 9)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=10, data_stage=10, n_latent_tokens=10
Training on difficulties: 0-10

Overall accuracy: 991 / 1000 = 0.9910

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 99/100 = 0.9900 (trained)
  diff=3: 100/100 = 1.0000 (trained)
  diff=4: 100/100 = 1.0000 (trained)
  diff=5: 100/100 = 1.0000 (trained)
  diff=6: 99/100 = 0.9900 (trained)
  diff=7: 99/100 = 0.9900 (trained)
  diff=8: 100/100 = 1.0000 (trained)
  diff=9: 99/100 = 0.9900 (trained)
  diff=10: 95/100 = 0.9500 (trained)

Trained (diff 0-10): 991/1000 = 0.9910
============================================================

Warning: failed to upload checkpoint_10 to HF: (Request ID: Root=1-6a1b8ca6-2ff1258758810ae61700b616;e470b32e-a63f-4b20-9042-f167a0f5cefe)

403 Forbidden: You need to setup automatic credit recharge in order to upload more data. You can do so at /settings/billing..
Cannot access content at: https://huggingface.co/seyedparsa/thoughtformer-checkpoints.git/info/lfs/objects/batch.
Make sure your token has the correct permissions.

============================================================
EPOCH 10/100 (thought_stage=10, data_stage=10)
============================================================
  thought_stage=10, c_thought=1, max_difficulty=10

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Training Epoch: 11:   0%|          | 0/312 [00:00<?, ?it/s]
Training Epoch: 11:   0%|          | 0/312 [00:00<?, ?it/s]
Training Epoch: 11:   0%|          | 0/312 [00:00<?, ?it/s]
Training Epoch: 11:   0%|          | 0/312 [00:00<?, ?it/s]logging training data
rank 3 | gpu 3 | alloc=4.20 GB | reserved=49.57 GB | max=41.62 GB
rank 1 | gpu 1 | alloc=4.20 GB | reserved=51.49 GB | max=41.62 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=49.58 GB | max=41.63 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=53.36 GB | max=41.63 GB

Training Epoch: 11/100, batch 0/625 completed (loss: 0.0003:   0%|          | 0/312 [00:03<?, ?it/s]
Training Epoch: 11/100, batch 0/625 completed (loss: 0.5785:   0%|          | 0/312 [00:03<?, ?it/s]
Training Epoch: 11/100, batch 0/625 completed (loss: 0.0001:   0%|          | 0/312 [00:03<?, ?it/s]
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Training Epoch: 11/100, batch 0/625 completed (loss: 0.0003:   0%|          | 1/312 [00:06<33:24,  6.45s/it]
Training Epoch: 11/100, batch 0/625 completed (loss: 0.0001:   0%|          | 1/312 [00:06<33:27,  6.45s/it]
Training Epoch: 11/100, batch 0/625 completed (loss: 0.0092:   0%|          | 1/312 [00:06<33:45,  6.51s/it]
Training Epoch: 11/100, batch 0/625 completed (loss: 0.5785:   0%|          | 1/312 [00:06<34:14,  6.60s/it]
Training Epoch: 11/100, batch 1/625 completed (loss: 0.0004:   0%|          | 1/312 [00:06<34:14,  6.60s/it]
Training Epoch: 11/100, batch 1/625 completed (loss: 0.1027:   0%|          | 1/312 [00:06<33:24,  6.45s/it]
Training Epoch: 11/100, batch 1/625 completed (loss: 0.0005:   0%|          | 1/312 [00:06<33:27,  6.45s/it]
Training Epoch: 11/100, batch 1/625 completed (loss: 0.0118:   0%|          | 1/312 [00:06<33:45,  6.51s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0691:   0%|          | 1/312 [00:09<33:24,  6.45s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0004:   0%|          | 1/312 [00:09<34:14,  6.60s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0001:   0%|          | 1/312 [00:09<33:27,  6.45s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0011:   0%|          | 1/312 [00:09<33:45,  6.51s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0691:   1%|          | 2/312 [00:12<31:38,  6.13s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0001:   1%|          | 2/312 [00:12<31:41,  6.13s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0011:   1%|          | 2/312 [00:12<31:48,  6.16s/it]
Training Epoch: 11/100, batch 2/625 completed (loss: 0.0004:   1%|          | 2/312 [00:12<31:59,  6.19s/it]
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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
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Processing Files (0 / 1)      :   0%|          | 54.6kB / 4.77GB, 5.34kB/s  


  ...e-c3ff1626/train_state_11:   1%|          | 28.6MB / 4.77GB            
Processing Files (0 / 1)      :   1%|          | 28.6MB / 4.77GB, 2.78MB/s  

New Data Upload               :  14%|█▍        | 28.6MB /  201MB, 2.77MB/s  


  ...e-c3ff1626/train_state_11:   2%|▏         |  107MB / 4.77GB            
Processing Files (0 / 1)      :   2%|▏         |  107MB / 4.77GB, 10.2MB/s  

New Data Upload               :  53%|█████▎    |  107MB /  201MB, 10.2MB/s  


  ...e-c3ff1626/train_state_11:   3%|▎         |  129MB / 4.77GB            
Processing Files (0 / 1)      :   3%|▎         |  129MB / 4.77GB, 12.1MB/s  

New Data Upload               :  64%|██████▍   |  128MB /  201MB, 12.1MB/s  


  ...e-c3ff1626/train_state_11:   3%|▎         |  133MB / 4.77GB            
Processing Files (0 / 1)      :   3%|▎         |  133MB / 4.77GB, 12.2MB/s  

New Data Upload               :  66%|██████▌   |  133MB /  201MB, 12.2MB/s  


  ...e-c3ff1626/train_state_11:   3%|▎         |  134MB / 4.77GB            
Processing Files (0 / 1)      :   3%|▎         |  134MB / 4.77GB, 12.0MB/s  

New Data Upload               :  66%|██████▋   |  134MB /  201MB, 12.0MB/s  


  ...e-c3ff1626/train_state_11:   4%|▍         |  181MB / 4.77GB            
Processing Files (0 / 1)      :   4%|▍         |  181MB / 4.77GB, 16.1MB/s  

New Data Upload               :  54%|█████▍    |  181MB /  335MB, 16.1MB/s  


  ...e-c3ff1626/train_state_11:   6%|▌         |  266MB / 4.77GB            
Processing Files (0 / 1)      :   6%|▌         |  266MB / 4.77GB, 23.7MB/s  

New Data Upload               :  79%|███████▉  |  266MB /  335MB, 23.7MB/s  


  ...e-c3ff1626/train_state_11:   6%|▌         |  287MB / 4.77GB            
Processing Files (0 / 1)      :   6%|▌         |  287MB / 4.77GB, 25.1MB/s  

New Data Upload               :  61%|██████    |  287MB /  470MB, 25.1MB/s  


  ...e-c3ff1626/train_state_11:   8%|▊         |  364MB / 4.77GB            
Processing Files (0 / 1)      :   8%|▊         |  364MB / 4.77GB, 31.6MB/s  

New Data Upload               :  77%|███████▋  |  364MB /  470MB, 31.6MB/s  


  ...e-c3ff1626/train_state_11:   9%|▊         |  414MB / 4.77GB            
Processing Files (0 / 1)      :   9%|▊         |  414MB / 4.77GB, 35.5MB/s  

New Data Upload               :  77%|███████▋  |  414MB /  537MB, 35.5MB/s  


  ...e-c3ff1626/train_state_11:  10%|▉         |  474MB / 4.77GB            
Processing Files (0 / 1)      :  10%|▉         |  474MB / 4.77GB, 40.1MB/s  

New Data Upload               :  79%|███████▊  |  474MB /  604MB, 40.1MB/s  


  ...e-c3ff1626/train_state_11:  10%|█         |  482MB / 4.77GB            
Processing Files (0 / 1)      :  10%|█         |  482MB / 4.77GB, 40.0MB/s  

New Data Upload               :  72%|███████▏  |  482MB /  671MB, 40.0MB/s  


  ...e-c3ff1626/train_state_11:  12%|█▏        |  552MB / 4.77GB            
Processing Files (0 / 1)      :  12%|█▏        |  552MB / 4.77GB, 45.3MB/s  

New Data Upload               :  75%|███████▍  |  552MB /  738MB, 45.3MB/s  


  ...e-c3ff1626/train_state_11:  13%|█▎        |  633MB / 4.77GB            
Processing Files (0 / 1)      :  13%|█▎        |  633MB / 4.77GB, 51.6MB/s  

New Data Upload               :  79%|███████▊  |  633MB /  805MB, 51.6MB/s  


  ...e-c3ff1626/train_state_11:  15%|█▍        |  710MB / 4.77GB            
Processing Files (0 / 1)      :  15%|█▍        |  710MB / 4.77GB, 57.3MB/s  

New Data Upload               :  81%|████████▏ |  710MB /  872MB, 57.3MB/s  


  ...e-c3ff1626/train_state_11:  16%|█▌        |  754MB / 4.77GB            
Processing Files (0 / 1)      :  16%|█▌        |  754MB / 4.77GB, 60.0MB/s  

New Data Upload               :  80%|████████  |  754MB /  939MB, 60.0MB/s  


  ...e-c3ff1626/train_state_11:  18%|█▊        |  859MB / 4.77GB            
Processing Files (0 / 1)      :  18%|█▊        |  859MB / 4.77GB, 67.9MB/s  

New Data Upload               :  85%|████████▌ |  859MB / 1.01GB, 67.8MB/s  


  ...e-c3ff1626/train_state_11:  21%|██        |  979MB / 4.77GB            
Processing Files (0 / 1)      :  21%|██        |  979MB / 4.77GB, 76.9MB/s  

New Data Upload               :  86%|████████▌ |  979MB / 1.14GB, 76.9MB/s  


  ...e-c3ff1626/train_state_11:  23%|██▎       | 1.08GB / 4.77GB            
Processing Files (0 / 1)      :  23%|██▎       | 1.08GB / 4.77GB, 84.3MB/s  

New Data Upload               :  90%|████████▉ | 1.08GB / 1.21GB, 84.3MB/s  


  ...e-c3ff1626/train_state_11:  24%|██▍       | 1.16GB / 4.77GB            
Processing Files (0 / 1)      :  24%|██▍       | 1.16GB / 4.77GB, 89.1MB/s  

New Data Upload               :  91%|█████████ | 1.16GB / 1.27GB, 89.1MB/s  


  ...e-c3ff1626/train_state_11:  25%|██▌       | 1.21GB / 4.77GB            
Processing Files (0 / 1)      :  25%|██▌       | 1.21GB / 4.77GB, 91.6MB/s  

New Data Upload               :  90%|█████████ | 1.21GB / 1.34GB, 91.6MB/s  


  ...e-c3ff1626/train_state_11:  27%|██▋       | 1.27GB / 4.77GB            
Processing Files (0 / 1)      :  27%|██▋       | 1.27GB / 4.77GB, 95.1MB/s  

New Data Upload               :  90%|█████████ | 1.27GB / 1.41GB, 95.1MB/s  


  ...e-c3ff1626/train_state_11:  28%|██▊       | 1.35GB / 4.77GB            
Processing Files (0 / 1)      :  28%|██▊       | 1.35GB / 4.77GB, 99.7MB/s  

New Data Upload               :  88%|████████▊ | 1.35GB / 1.54GB, 99.7MB/s  


  ...e-c3ff1626/train_state_11:  30%|███       | 1.45GB / 4.77GB            
Processing Files (0 / 1)      :  30%|███       | 1.45GB / 4.77GB,  106MB/s  

New Data Upload               :  90%|████████▉ | 1.45GB / 1.61GB,  106MB/s  


  ...e-c3ff1626/train_state_11:  32%|███▏      | 1.51GB / 4.77GB            
Processing Files (0 / 1)      :  32%|███▏      | 1.51GB / 4.77GB,  109MB/s  

New Data Upload               :  90%|█████████ | 1.51GB / 1.68GB,  109MB/s  


  ...e-c3ff1626/train_state_11:  34%|███▍      | 1.62GB / 4.77GB            
Processing Files (0 / 1)      :  34%|███▍      | 1.62GB / 4.77GB,  115MB/s  

New Data Upload               :  89%|████████▉ | 1.61GB / 1.81GB,  115MB/s  


  ...e-c3ff1626/train_state_11:  36%|███▋      | 1.74GB / 4.77GB            
Processing Files (0 / 1)      :  36%|███▋      | 1.74GB / 4.77GB,  123MB/s  

New Data Upload               :  92%|█████████▏| 1.74GB / 1.88GB,  123MB/s  


  ...e-c3ff1626/train_state_11:  39%|███▊      | 1.84GB / 4.77GB            
Processing Files (0 / 1)      :  39%|███▊      | 1.84GB / 4.77GB,  129MB/s  

New Data Upload               :  91%|█████████ | 1.83GB / 2.01GB,  129MB/s  


  ...e-c3ff1626/train_state_11:  41%|████▏     | 1.98GB / 4.77GB            
Processing Files (0 / 1)      :  41%|████▏     | 1.98GB / 4.77GB,  138MB/s  

New Data Upload               :  95%|█████████▌| 1.98GB / 2.08GB,  138MB/s  


  ...e-c3ff1626/train_state_11:  44%|████▎     | 2.08GB / 4.77GB            
Processing Files (0 / 1)      :  44%|████▎     | 2.08GB / 4.77GB,  144MB/s  

New Data Upload               :  94%|█████████▍| 2.08GB / 2.21GB,  144MB/s  


  ...e-c3ff1626/train_state_11:  45%|████▌     | 2.16GB / 4.77GB            
Processing Files (0 / 1)      :  45%|████▌     | 2.16GB / 4.77GB,  147MB/s  

New Data Upload               :  95%|█████████▍| 2.16GB / 2.28GB,  147MB/s  


  ...e-c3ff1626/train_state_11:  47%|████▋     | 2.22GB / 4.77GB            
Processing Files (0 / 1)      :  47%|████▋     | 2.22GB / 4.77GB,  149MB/s  

New Data Upload               :  95%|█████████▍| 2.22GB / 2.35GB,  149MB/s  


  ...e-c3ff1626/train_state_11:  49%|████▉     | 2.34GB / 4.77GB            
Processing Files (0 / 1)      :  49%|████▉     | 2.34GB / 4.77GB,  157MB/s  

New Data Upload               :  94%|█████████▍| 2.34GB / 2.48GB,  157MB/s  


  ...e-c3ff1626/train_state_11:  51%|█████▏    | 2.45GB / 4.77GB            
Processing Files (0 / 1)      :  51%|█████▏    | 2.45GB / 4.77GB,  162MB/s  

New Data Upload               :  96%|█████████▌| 2.45GB / 2.55GB,  162MB/s  


  ...e-c3ff1626/train_state_11:  53%|█████▎    | 2.51GB / 4.77GB            
Processing Files (0 / 1)      :  53%|█████▎    | 2.51GB / 4.77GB,  164MB/s  

New Data Upload               :  94%|█████████▎| 2.51GB / 2.68GB,  164MB/s  


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Processing Files (0 / 1)      :  55%|█████▌    | 2.63GB / 4.77GB,  170MB/s  

New Data Upload               :  96%|█████████▌| 2.63GB / 2.75GB,  170MB/s  


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Processing Files (0 / 1)      :  57%|█████▋    | 2.70GB / 4.77GB,  172MB/s  

New Data Upload               :  94%|█████████▎| 2.70GB / 2.88GB,  172MB/s  


  ...e-c3ff1626/train_state_11:  59%|█████▊    | 2.79GB / 4.77GB            
Processing Files (0 / 1)      :  59%|█████▊    | 2.79GB / 4.77GB,  176MB/s  

New Data Upload               :  95%|█████████▍| 2.79GB / 2.95GB,  176MB/s  


  ...e-c3ff1626/train_state_11:  61%|██████    | 2.91GB / 4.77GB            
Processing Files (0 / 1)      :  61%|██████    | 2.91GB / 4.77GB,  182MB/s  

New Data Upload               :  94%|█████████▍| 2.91GB / 3.09GB,  182MB/s  


  ...e-c3ff1626/train_state_11:  63%|██████▎   | 2.98GB / 4.77GB            
Processing Files (0 / 1)      :  63%|██████▎   | 2.98GB / 4.77GB,  185MB/s  

New Data Upload               :  95%|█████████▍| 2.98GB / 3.15GB,  185MB/s  


  ...e-c3ff1626/train_state_11:  64%|██████▍   | 3.07GB / 4.77GB            
Processing Files (0 / 1)      :  64%|██████▍   | 3.07GB / 4.77GB,  188MB/s  

New Data Upload               :  95%|█████████▌| 3.07GB / 3.22GB,  188MB/s  


  ...e-c3ff1626/train_state_11:  66%|██████▋   | 3.16GB / 4.77GB            
Processing Files (0 / 1)      :  66%|██████▋   | 3.16GB / 4.77GB,  191MB/s  

New Data Upload               :  94%|█████████▍| 3.16GB / 3.35GB,  191MB/s  


  ...e-c3ff1626/train_state_11:  69%|██████▊   | 3.27GB / 4.77GB            
Processing Files (0 / 1)      :  69%|██████▊   | 3.27GB / 4.77GB,  197MB/s  

New Data Upload               :  96%|█████████▌| 3.27GB / 3.42GB,  196MB/s  


  ...e-c3ff1626/train_state_11:  70%|███████   | 3.34GB / 4.77GB            
Processing Files (0 / 1)      :  70%|███████   | 3.34GB / 4.77GB,  198MB/s  

New Data Upload               :  96%|█████████▌| 3.34GB / 3.49GB,  198MB/s  


  ...e-c3ff1626/train_state_11:  73%|███████▎  | 3.48GB / 4.77GB            
Processing Files (0 / 1)      :  73%|███████▎  | 3.48GB / 4.77GB,  205MB/s  

New Data Upload               :  96%|█████████▌| 3.48GB / 3.62GB,  205MB/s  


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Processing Files (0 / 1)      :  75%|███████▍  | 3.56GB / 4.77GB,  208MB/s  

New Data Upload               :  97%|█████████▋| 3.56GB / 3.69GB,  208MB/s  


  ...e-c3ff1626/train_state_11:  76%|███████▋  | 3.64GB / 4.77GB            
Processing Files (0 / 1)      :  76%|███████▋  | 3.64GB / 4.77GB,  210MB/s  

New Data Upload               :  95%|█████████▌| 3.64GB / 3.82GB,  210MB/s  


  ...e-c3ff1626/train_state_11:  79%|███████▊  | 3.75GB / 4.77GB            
Processing Files (0 / 1)      :  79%|███████▊  | 3.75GB / 4.77GB,  214MB/s  

New Data Upload               :  96%|█████████▋| 3.75GB / 3.89GB,  214MB/s  


  ...e-c3ff1626/train_state_11:  80%|████████  | 3.81GB / 4.77GB            
Processing Files (0 / 1)      :  80%|████████  | 3.81GB / 4.77GB,  215MB/s  

New Data Upload               :  96%|█████████▋| 3.81GB / 3.96GB,  215MB/s  


  ...e-c3ff1626/train_state_11:  81%|████████  | 3.87GB / 4.77GB            
Processing Files (0 / 1)      :  81%|████████  | 3.87GB / 4.77GB,  215MB/s  

New Data Upload               :  95%|█████████▍| 3.87GB / 4.09GB,  215MB/s  


  ...e-c3ff1626/train_state_11:  83%|████████▎ | 3.96GB / 4.77GB            
Processing Files (0 / 1)      :  83%|████████▎ | 3.96GB / 4.77GB,  218MB/s  

New Data Upload               :  95%|█████████▌| 3.96GB / 4.16GB,  218MB/s  


  ...e-c3ff1626/train_state_11:  85%|████████▌ | 4.06GB / 4.77GB            
Processing Files (0 / 1)      :  85%|████████▌ | 4.06GB / 4.77GB,  221MB/s  

New Data Upload               :  94%|█████████▍| 4.06GB / 4.29GB,  221MB/s  


  ...e-c3ff1626/train_state_11:  87%|████████▋ | 4.16GB / 4.77GB            
Processing Files (0 / 1)      :  87%|████████▋ | 4.16GB / 4.77GB,  225MB/s  

New Data Upload               :  95%|█████████▌| 4.16GB / 4.36GB,  225MB/s  


  ...e-c3ff1626/train_state_11:  90%|████████▉ | 4.28GB / 4.77GB            
Processing Files (0 / 1)      :  90%|████████▉ | 4.28GB / 4.77GB,  230MB/s  

New Data Upload               :  95%|█████████▌| 4.28GB / 4.49GB,  230MB/s  


  ...e-c3ff1626/train_state_11:  92%|█████████▏| 4.37GB / 4.77GB            
Processing Files (0 / 1)      :  92%|█████████▏| 4.37GB / 4.77GB,  233MB/s  

New Data Upload               :  96%|█████████▌| 4.37GB / 4.56GB,  233MB/s  


  ...e-c3ff1626/train_state_11:  94%|█████████▍| 4.48GB / 4.77GB            
Processing Files (0 / 1)      :  94%|█████████▍| 4.48GB / 4.77GB,  236MB/s  

New Data Upload               :  97%|█████████▋| 4.48GB / 4.63GB,  236MB/s  


  ...e-c3ff1626/train_state_11:  95%|█████████▌| 4.55GB / 4.77GB            
Processing Files (0 / 1)      :  95%|█████████▌| 4.55GB / 4.77GB,  237MB/s  

New Data Upload               :  96%|█████████▌| 4.55GB / 4.76GB,  237MB/s  


  ...e-c3ff1626/train_state_11:  98%|█████████▊| 4.66GB / 4.77GB            
Processing Files (0 / 1)      :  98%|█████████▊| 4.66GB / 4.77GB,  241MB/s  

New Data Upload               :  98%|█████████▊| 4.66GB / 4.77GB,  241MB/s  


  ...e-c3ff1626/train_state_11:  99%|█████████▉| 4.72GB / 4.77GB            
Processing Files (0 / 1)      :  99%|█████████▉| 4.72GB / 4.77GB,  242MB/s  

New Data Upload               :  99%|█████████▉| 4.72GB / 4.77GB,  242MB/s  


  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.74GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.74GB / 4.77GB,  238MB/s  

New Data Upload               : 100%|█████████▉| 4.74GB / 4.77GB,  238MB/s  


  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.76GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.76GB / 4.77GB,  235MB/s  

New Data Upload               : 100%|█████████▉| 4.76GB / 4.77GB,  235MB/s  


  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.76GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.76GB / 4.77GB,  231MB/s  

New Data Upload               : 100%|█████████▉| 4.76GB / 4.77GB,  231MB/s  


  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  227MB/s  

New Data Upload               : 100%|█████████▉| 4.77GB / 4.77GB,  227MB/s  


  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  222MB/s  

New Data Upload               : 100%|█████████▉| 4.77GB / 4.77GB,  222MB/s  


  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  206MB/s  

New Data Upload               : 100%|██████████| 4.77GB / 4.77GB,  206MB/s  
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  206MB/s  

New Data Upload               : 100%|██████████| 4.77GB / 4.77GB,  206MB/s  

  ...e-c3ff1626/train_state_11: 100%|█████████▉| 4.77GB / 4.77GB            
Uploaded train_state_11 to seyedparsa/thoughtformer-checkpoints/checkpoints/star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626/train_state_11
Deleted old checkpoint: train_state_10
Deleted old checkpoint: checkpoint_10

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.0027601271326097455

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
[CORRECT] Example 1 (diff=8)
Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
  Expected : 'Guy'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]  Extracted: 'Guy'
  Generated: '### Guy'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.55s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:18,  6.40s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:18,  6.40s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.46s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.46s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.46s/it]
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Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
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Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 1.0: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 2: Cor=250, CoT=0, Total=250

Test accuracy: 1.0: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 3: Cor=249, CoT=0, Total=250

Test accuracy: 1.0: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 1: Cor=250, CoT=0, Total=250

Test accuracy: 1.0: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 0: Cor=249, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 10)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=10, data_stage=10, n_latent_tokens=10
Training on difficulties: 0-10

Overall accuracy: 998 / 1000 = 0.9980

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 100/100 = 1.0000 (trained)
  diff=4: 100/100 = 1.0000 (trained)
  diff=5: 100/100 = 1.0000 (trained)
  diff=6: 100/100 = 1.0000 (trained)
  diff=7: 99/100 = 0.9900 (trained)
  diff=8: 100/100 = 1.0000 (trained)
  diff=9: 99/100 = 0.9900 (trained)
  diff=10: 100/100 = 1.0000 (trained)

Trained (diff 0-10): 998/1000 = 0.9980
============================================================


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No files have been modified since last commit. Skipping to prevent empty commit.
Uploaded checkpoint_11 to seyedparsa/thoughtformer-checkpoints/checkpoints/star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626/checkpoint_11

============================================================
EPOCH 11/100 (thought_stage=10, data_stage=10)
============================================================
  thought_stage=10, c_thought=1, max_difficulty=10

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rank 1 | gpu 1 | alloc=4.20 GB | reserved=47.73 GB | max=41.62 GB
rank 3 | gpu 3 | alloc=4.20 GB | reserved=47.73 GB | max=41.62 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=53.36 GB | max=41.63 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=47.74 GB | max=41.63 GB

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/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
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  ...e-c3ff1626/train_state_12:   3%|▎         |  130MB / 4.77GB            
Processing Files (0 / 1)      :   3%|▎         |  130MB / 4.77GB, 12.6MB/s  

New Data Upload               :  65%|██████▍   |  130MB /  201MB, 12.6MB/s  


  ...e-c3ff1626/train_state_12:   3%|▎         |  134MB / 4.77GB            
Processing Files (0 / 1)      :   3%|▎         |  134MB / 4.77GB, 12.6MB/s  

New Data Upload               :  66%|██████▋   |  134MB /  201MB, 12.6MB/s  


  ...e-c3ff1626/train_state_12:   3%|▎         |  134MB / 4.77GB            
Processing Files (0 / 1)      :   3%|▎         |  134MB / 4.77GB, 12.0MB/s  

New Data Upload               :  66%|██████▋   |  134MB /  201MB, 12.0MB/s  


  ...e-c3ff1626/train_state_12:   4%|▍         |  209MB / 4.77GB            
Processing Files (0 / 1)      :   4%|▍         |  209MB / 4.77GB, 18.9MB/s  

New Data Upload               :  62%|██████▏   |  209MB /  335MB, 18.9MB/s  


  ...e-c3ff1626/train_state_12:   6%|▋         |  305MB / 4.77GB            
Processing Files (0 / 1)      :   6%|▋         |  305MB / 4.77GB, 27.4MB/s  

New Data Upload               :  76%|███████▌  |  304MB /  402MB, 27.4MB/s  


  ...e-c3ff1626/train_state_12:   8%|▊         |  364MB / 4.77GB            
Processing Files (0 / 1)      :   8%|▊         |  364MB / 4.77GB, 32.3MB/s  

New Data Upload               :  77%|███████▋  |  364MB /  469MB, 32.3MB/s  


  ...e-c3ff1626/train_state_12:   8%|▊         |  402MB / 4.77GB            
Processing Files (0 / 1)      :   8%|▊         |  402MB / 4.77GB, 35.0MB/s  

New Data Upload               :  86%|████████▌ |  402MB /  469MB, 35.0MB/s  


  ...e-c3ff1626/train_state_12:   9%|▉         |  432MB / 4.77GB            
Processing Files (0 / 1)      :   9%|▉         |  432MB / 4.77GB, 37.1MB/s  

New Data Upload               :  81%|████████  |  432MB /  536MB, 37.1MB/s  


  ...e-c3ff1626/train_state_12:  12%|█▏        |  558MB / 4.77GB            
Processing Files (0 / 1)      :  12%|█▏        |  558MB / 4.77GB, 47.7MB/s  

New Data Upload               :  83%|████████▎ |  558MB /  671MB, 47.7MB/s  


  ...e-c3ff1626/train_state_12:  14%|█▎        |  652MB / 4.77GB            
Processing Files (0 / 1)      :  14%|█▎        |  652MB / 4.77GB, 55.2MB/s  

New Data Upload               :  88%|████████▊ |  652MB /  738MB, 55.1MB/s  


  ...e-c3ff1626/train_state_12:  15%|█▌        |  721MB / 4.77GB            
Processing Files (0 / 1)      :  15%|█▌        |  721MB / 4.77GB, 60.2MB/s  

New Data Upload               :  83%|████████▎ |  721MB /  872MB, 60.2MB/s  


  ...e-c3ff1626/train_state_12:  17%|█▋        |  801MB / 4.77GB            
Processing Files (0 / 1)      :  17%|█▋        |  801MB / 4.77GB, 66.0MB/s  

New Data Upload               :  85%|████████▌ |  801MB /  939MB, 66.0MB/s  


  ...e-c3ff1626/train_state_12:  19%|█▉        |  897MB / 4.77GB            
Processing Files (0 / 1)      :  19%|█▉        |  897MB / 4.77GB, 73.2MB/s  

New Data Upload               :  89%|████████▉ |  897MB / 1.01GB, 73.2MB/s  


  ...e-c3ff1626/train_state_12:  21%|██        |  989MB / 4.77GB            
Processing Files (0 / 1)      :  21%|██        |  989MB / 4.77GB, 79.8MB/s  

New Data Upload               :  87%|████████▋ |  989MB / 1.14GB, 79.8MB/s  


  ...e-c3ff1626/train_state_12:  23%|██▎       | 1.09GB / 4.77GB            
Processing Files (0 / 1)      :  23%|██▎       | 1.09GB / 4.77GB, 87.3MB/s  

New Data Upload               :  91%|█████████ | 1.09GB / 1.21GB, 87.3MB/s  


  ...e-c3ff1626/train_state_12:  25%|██▍       | 1.18GB / 4.77GB            
Processing Files (0 / 1)      :  25%|██▍       | 1.18GB / 4.77GB, 92.9MB/s  

New Data Upload               :  88%|████████▊ | 1.18GB / 1.34GB, 92.9MB/s  


  ...e-c3ff1626/train_state_12:  27%|██▋       | 1.28GB / 4.77GB            
Processing Files (0 / 1)      :  27%|██▋       | 1.28GB / 4.77GB, 99.7MB/s  

New Data Upload               :  91%|█████████ | 1.28GB / 1.41GB, 99.7MB/s  


  ...e-c3ff1626/train_state_12:  29%|██▉       | 1.38GB / 4.77GB            
Processing Files (0 / 1)      :  29%|██▉       | 1.38GB / 4.77GB,  106MB/s  

New Data Upload               :  89%|████████▉ | 1.38GB / 1.54GB,  106MB/s  


  ...e-c3ff1626/train_state_12:  31%|███▏      | 1.49GB / 4.77GB            
Processing Files (0 / 1)      :  31%|███▏      | 1.49GB / 4.77GB,  113MB/s  

New Data Upload               :  93%|█████████▎| 1.49GB / 1.61GB,  113MB/s  


  ...e-c3ff1626/train_state_12:  34%|███▍      | 1.61GB / 4.77GB            
Processing Files (0 / 1)      :  34%|███▍      | 1.61GB / 4.77GB,  122MB/s  

New Data Upload               :  92%|█████████▏| 1.61GB / 1.74GB,  122MB/s  


  ...e-c3ff1626/train_state_12:  36%|███▌      | 1.71GB / 4.77GB            
Processing Files (0 / 1)      :  36%|███▌      | 1.71GB / 4.77GB,  127MB/s  

New Data Upload               :  94%|█████████▍| 1.71GB / 1.81GB,  127MB/s  


  ...e-c3ff1626/train_state_12:  38%|███▊      | 1.79GB / 4.77GB            
Processing Files (0 / 1)      :  38%|███▊      | 1.79GB / 4.77GB,  132MB/s  

New Data Upload               :  92%|█████████▏| 1.79GB / 1.95GB,  132MB/s  


  ...e-c3ff1626/train_state_12:  40%|████      | 1.91GB / 4.77GB            
Processing Files (0 / 1)      :  40%|████      | 1.91GB / 4.77GB,  139MB/s  

New Data Upload               :  95%|█████████▌| 1.91GB / 2.01GB,  139MB/s  


  ...e-c3ff1626/train_state_12:  42%|████▏     | 2.01GB / 4.77GB            
Processing Files (0 / 1)      :  42%|████▏     | 2.01GB / 4.77GB,  145MB/s  

New Data Upload               :  94%|█████████▍| 2.01GB / 2.15GB,  145MB/s  


  ...e-c3ff1626/train_state_12:  44%|████▍     | 2.12GB / 4.77GB            
Processing Files (0 / 1)      :  44%|████▍     | 2.12GB / 4.77GB,  150MB/s  

New Data Upload               :  96%|█████████▌| 2.12GB / 2.21GB,  150MB/s  


  ...e-c3ff1626/train_state_12:  47%|████▋     | 2.22GB / 4.77GB            
Processing Files (0 / 1)      :  47%|████▋     | 2.22GB / 4.77GB,  156MB/s  

New Data Upload               :  95%|█████████▍| 2.22GB / 2.35GB,  156MB/s  


  ...e-c3ff1626/train_state_12:  49%|████▊     | 2.31GB / 4.77GB            
Processing Files (0 / 1)      :  49%|████▊     | 2.31GB / 4.77GB,  161MB/s  

New Data Upload               :  96%|█████████▌| 2.31GB / 2.41GB,  161MB/s  


  ...e-c3ff1626/train_state_12:  50%|█████     | 2.41GB / 4.77GB            
Processing Files (0 / 1)      :  50%|█████     | 2.41GB / 4.77GB,  165MB/s  

New Data Upload               :  94%|█████████▍| 2.41GB / 2.55GB,  165MB/s  


  ...e-c3ff1626/train_state_12:  53%|█████▎    | 2.53GB / 4.77GB            
Processing Files (0 / 1)      :  53%|█████▎    | 2.53GB / 4.77GB,  172MB/s  

New Data Upload               :  94%|█████████▍| 2.53GB / 2.68GB,  172MB/s  


  ...e-c3ff1626/train_state_12:  55%|█████▌    | 2.63GB / 4.77GB            
Processing Files (0 / 1)      :  55%|█████▌    | 2.63GB / 4.77GB,  176MB/s  

New Data Upload               :  96%|█████████▌| 2.63GB / 2.75GB,  176MB/s  


  ...e-c3ff1626/train_state_12:  57%|█████▋    | 2.72GB / 4.77GB            
Processing Files (0 / 1)      :  57%|█████▋    | 2.72GB / 4.77GB,  181MB/s  

New Data Upload               :  94%|█████████▍| 2.72GB / 2.88GB,  181MB/s  


  ...e-c3ff1626/train_state_12:  59%|█████▉    | 2.83GB / 4.77GB            
Processing Files (0 / 1)      :  59%|█████▉    | 2.83GB / 4.77GB,  186MB/s  

New Data Upload               :  96%|█████████▌| 2.83GB / 2.95GB,  186MB/s  


  ...e-c3ff1626/train_state_12:  61%|██████▏   | 2.93GB / 4.77GB            
Processing Files (0 / 1)      :  61%|██████▏   | 2.93GB / 4.77GB,  190MB/s  

New Data Upload               :  97%|█████████▋| 2.93GB / 3.02GB,  190MB/s  


  ...e-c3ff1626/train_state_12:  64%|██████▎   | 3.03GB / 4.77GB            
Processing Files (0 / 1)      :  64%|██████▎   | 3.03GB / 4.77GB,  195MB/s  

New Data Upload               :  96%|█████████▌| 3.03GB / 3.15GB,  195MB/s  


  ...e-c3ff1626/train_state_12:  65%|██████▌   | 3.12GB / 4.77GB            
Processing Files (0 / 1)      :  65%|██████▌   | 3.12GB / 4.77GB,  198MB/s  

New Data Upload               :  95%|█████████▍| 3.12GB / 3.29GB,  198MB/s  


  ...e-c3ff1626/train_state_12:  68%|██████▊   | 3.23GB / 4.77GB            
Processing Files (0 / 1)      :  68%|██████▊   | 3.23GB / 4.77GB,  203MB/s  

New Data Upload               :  96%|█████████▋| 3.23GB / 3.35GB,  203MB/s  


  ...e-c3ff1626/train_state_12:  69%|██████▉   | 3.31GB / 4.77GB            
Processing Files (0 / 1)      :  69%|██████▉   | 3.31GB / 4.77GB,  205MB/s  

New Data Upload               :  97%|█████████▋| 3.31GB / 3.42GB,  205MB/s  


  ...e-c3ff1626/train_state_12:  72%|███████▏  | 3.42GB / 4.77GB            
Processing Files (0 / 1)      :  72%|███████▏  | 3.42GB / 4.77GB,  210MB/s  

New Data Upload               :  96%|█████████▌| 3.42GB / 3.55GB,  210MB/s  


  ...e-c3ff1626/train_state_12:  74%|███████▎  | 3.51GB / 4.77GB            
Processing Files (0 / 1)      :  74%|███████▎  | 3.51GB / 4.77GB,  213MB/s  

New Data Upload               :  97%|█████████▋| 3.51GB / 3.62GB,  213MB/s  


  ...e-c3ff1626/train_state_12:  75%|███████▌  | 3.60GB / 4.77GB            
Processing Files (0 / 1)      :  75%|███████▌  | 3.60GB / 4.77GB,  216MB/s  

New Data Upload               :  96%|█████████▌| 3.60GB / 3.76GB,  216MB/s  


  ...e-c3ff1626/train_state_12:  77%|███████▋  | 3.69GB / 4.77GB            
Processing Files (0 / 1)      :  77%|███████▋  | 3.69GB / 4.77GB,  219MB/s  

New Data Upload               :  97%|█████████▋| 3.69GB / 3.82GB,  219MB/s  


  ...e-c3ff1626/train_state_12:  80%|███████▉  | 3.80GB / 4.77GB            
Processing Files (0 / 1)      :  80%|███████▉  | 3.80GB / 4.77GB,  223MB/s  

New Data Upload               :  96%|█████████▌| 3.80GB / 3.96GB,  223MB/s  


  ...e-c3ff1626/train_state_12:  82%|████████▏ | 3.90GB / 4.77GB            
Processing Files (0 / 1)      :  82%|████████▏ | 3.90GB / 4.77GB,  227MB/s  

New Data Upload               :  97%|█████████▋| 3.90GB / 4.02GB,  227MB/s  


  ...e-c3ff1626/train_state_12:  84%|████████▎ | 3.99GB / 4.77GB            
Processing Files (0 / 1)      :  84%|████████▎ | 3.99GB / 4.77GB,  229MB/s  

New Data Upload               :  97%|█████████▋| 3.99GB / 4.09GB,  229MB/s  


  ...e-c3ff1626/train_state_12:  86%|████████▌ | 4.08GB / 4.77GB            
Processing Files (0 / 1)      :  86%|████████▌ | 4.08GB / 4.77GB,  232MB/s  

New Data Upload               :  97%|█████████▋| 4.08GB / 4.23GB,  232MB/s  


  ...e-c3ff1626/train_state_12:  87%|████████▋ | 4.16GB / 4.77GB            
Processing Files (0 / 1)      :  87%|████████▋ | 4.16GB / 4.77GB,  234MB/s  

New Data Upload               :  97%|█████████▋| 4.16GB / 4.29GB,  234MB/s  


  ...e-c3ff1626/train_state_12:  89%|████████▉ | 4.26GB / 4.77GB            
Processing Files (0 / 1)      :  89%|████████▉ | 4.26GB / 4.77GB,  237MB/s  

New Data Upload               :  98%|█████████▊| 4.26GB / 4.36GB,  237MB/s  


  ...e-c3ff1626/train_state_12:  92%|█████████▏| 4.36GB / 4.77GB            
Processing Files (0 / 1)      :  92%|█████████▏| 4.36GB / 4.77GB,  240MB/s  

New Data Upload               :  97%|█████████▋| 4.36GB / 4.49GB,  240MB/s  


  ...e-c3ff1626/train_state_12:  94%|█████████▎| 4.47GB / 4.77GB            
Processing Files (0 / 1)      :  94%|█████████▎| 4.47GB / 4.77GB,  244MB/s  

New Data Upload               :  98%|█████████▊| 4.47GB / 4.56GB,  244MB/s  


  ...e-c3ff1626/train_state_12:  95%|█████████▌| 4.55GB / 4.77GB            
Processing Files (0 / 1)      :  95%|█████████▌| 4.55GB / 4.77GB,  246MB/s  

New Data Upload               :  97%|█████████▋| 4.55GB / 4.69GB,  246MB/s  


  ...e-c3ff1626/train_state_12:  98%|█████████▊| 4.66GB / 4.77GB            
Processing Files (0 / 1)      :  98%|█████████▊| 4.66GB / 4.77GB,  249MB/s  

New Data Upload               :  98%|█████████▊| 4.66GB / 4.76GB,  249MB/s  


  ...e-c3ff1626/train_state_12: 100%|█████████▉| 4.74GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.74GB / 4.77GB,  251MB/s  

New Data Upload               : 100%|█████████▉| 4.74GB / 4.77GB,  251MB/s  


  ...e-c3ff1626/train_state_12: 100%|█████████▉| 4.76GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.76GB / 4.77GB,  248MB/s  

New Data Upload               : 100%|█████████▉| 4.76GB / 4.77GB,  248MB/s  


  ...e-c3ff1626/train_state_12: 100%|█████████▉| 4.76GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.76GB / 4.77GB,  243MB/s  

New Data Upload               : 100%|█████████▉| 4.76GB / 4.77GB,  243MB/s  


  ...e-c3ff1626/train_state_12: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  238MB/s  

New Data Upload               : 100%|█████████▉| 4.77GB / 4.77GB,  238MB/s  


  ...e-c3ff1626/train_state_12: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  234MB/s  

New Data Upload               : 100%|██████████| 4.77GB / 4.77GB,  234MB/s  
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  234MB/s  

New Data Upload               : 100%|██████████| 4.77GB / 4.77GB,  234MB/s  

  ...e-c3ff1626/train_state_12: 100%|█████████▉| 4.77GB / 4.77GB            
Uploaded train_state_12 to seyedparsa/thoughtformer-checkpoints/checkpoints/star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626/train_state_12
Deleted old checkpoint: train_state_11
Deleted old checkpoint: checkpoint_11

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.014764151562029303

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

[CORRECT] Example 1 (diff=8)
  Expected : 'Guy'
  Extracted: 'Guy'
  Generated: '### Guy'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:21,  6.49s/it]
Test Accuracy:   3%|▎         | 1/32 [00:06<03:21,  6.49s/it]
Test Accuracy:   3%|▎         | 1/32 [00:06<03:21,  6.49s/it]
[CORRECT] Example 2 (diff=10)
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:21,  6.49s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:21,  6.49s/it]

Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:21,  6.49s/it]  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:17,  6.38s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:17,  6.38s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.43s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:11,  6.39s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:11,  6.39s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.69s/it]
Test accuracy: 0.96:   9%|▉         | 3/32 [00:19<03:13,  6.69s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.69s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.69s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.66s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.66s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.69s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.69s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.47s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.47s/it]
Test accuracy: 0.96:  12%|█▎        | 4/32 [00:26<03:01,  6.47s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:25<03:00,  6.45s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.47s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.47s/it]
Test accuracy: 0.97:  12%|█▎        | 4/32 [00:26<03:01,  6.47s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:25<03:00,  6.45s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.41s/it]
Test accuracy: 0.97:  16%|█▌        | 5/32 [00:32<02:53,  6.41s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.41s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.41s/it]
Test accuracy: 0.98:  16%|█▌        | 5/32 [00:32<02:53,  6.41s/it]
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Test accuracy: 1.0:  22%|██▏       | 7/32 [00:44<02:38,  6.35s/it]
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Test accuracy: 1.0:  22%|██▏       | 7/32 [00:44<02:38,  6.35s/it]
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Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
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Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
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Test accuracy: 1.0:  28%|██▊       | 9/32 [00:57<02:28,  6.47s/it]
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Test accuracy: 0.99:  28%|██▊       | 9/32 [00:57<02:28,  6.47s/it]
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Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.43s/it]
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Test accuracy: 0.99:  34%|███▍      | 11/32 [01:10<02:14,  6.39s/it]
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Test accuracy: 0.99:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
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Test accuracy: 1.0:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
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Test accuracy: 0.99:  38%|███▊      | 12/32 [01:16<02:06,  6.31s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 0.99:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
Test accuracy: 1.0:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
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Test accuracy: 0.98:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
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Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.31s/it]
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Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.31s/it]
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Test accuracy: 0.98:  44%|████▍     | 14/32 [01:29<01:53,  6.31s/it]
Test accuracy: 1.0:  44%|████▍     | 14/32 [01:29<01:53,  6.31s/it]
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Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
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Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
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Test accuracy: 1.0:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
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Test accuracy: 1.0:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.99:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.99:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
Test accuracy: 1.0:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
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Test accuracy: 1.0:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
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Test accuracy: 0.99:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
Test accuracy: 0.99:  59%|█████▉    | 19/32 [02:00<01:21,  6.24s/it]
Test accuracy: 1.0:  59%|█████▉    | 19/32 [02:00<01:21,  6.24s/it]
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Test accuracy: 1.0:  59%|█████▉    | 19/32 [02:00<01:21,  6.24s/it]
Test accuracy: 0.98:  59%|█████▉    | 19/32 [02:00<01:21,  6.24s/it]
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Test accuracy: 0.99:  62%|██████▎   | 20/32 [02:06<01:15,  6.26s/it]
Test accuracy: 1.0:  62%|██████▎   | 20/32 [02:06<01:15,  6.26s/it]
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Test accuracy: 1.0:  62%|██████▎   | 20/32 [02:06<01:15,  6.26s/it]
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Test accuracy: 0.99:  62%|██████▎   | 20/32 [02:06<01:15,  6.26s/it]
Test accuracy: 0.98:  66%|██████▌   | 21/32 [02:13<01:11,  6.48s/it]
Test accuracy: 1.0:  66%|██████▌   | 21/32 [02:13<01:11,  6.48s/it]
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Test accuracy: 0.98:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
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Test accuracy: 1.0:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it] 
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Test accuracy: 0.98:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 0.98:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
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Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 0.98:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
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Test accuracy: 0.98:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
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Test accuracy: 1.0:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.98:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
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Test accuracy: 1.0:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:51<00:31,  6.34s/it]
Test accuracy: 0.99:  84%|████████▍ | 27/32 [02:51<00:31,  6.34s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.98:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:58<00:26,  6.53s/it]
Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.98:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
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Test accuracy: 0.98:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
Test accuracy: 0.98:  91%|█████████ | 29/32 [03:05<00:19,  6.47s/it]
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Test accuracy: 0.98:  91%|█████████ | 29/32 [03:05<00:19,  6.47s/it]
Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.47s/it]
Test accuracy: 0.98:  91%|█████████ | 29/32 [03:05<00:19,  6.47s/it]
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Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.47s/it]
Test accuracy: 0.99:  91%|█████████ | 29/32 [03:05<00:19,  6.47s/it]
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Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
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Test accuracy: 0.98:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.98:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.98:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.98:  97%|█████████▋| 31/32 [03:18<00:06,  6.55s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.98: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 2: Cor=245, CoT=0, Total=250

Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 1: Cor=248, CoT=0, Total=250

Test accuracy: 0.98: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 3: Cor=245, CoT=0, Total=250

Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  6.28s/it]
Device 0: Cor=248, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 11)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=10, data_stage=10, n_latent_tokens=10
Training on difficulties: 0-10

Overall accuracy: 986 / 1000 = 0.9860

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 100/100 = 1.0000 (trained)
  diff=4: 99/100 = 0.9900 (trained)
  diff=5: 99/100 = 0.9900 (trained)
  diff=6: 99/100 = 0.9900 (trained)
  diff=7: 98/100 = 0.9800 (trained)
  diff=8: 99/100 = 0.9900 (trained)
  diff=9: 96/100 = 0.9600 (trained)
  diff=10: 96/100 = 0.9600 (trained)

Trained (diff 0-10): 986/1000 = 0.9860
============================================================


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No files have been modified since last commit. Skipping to prevent empty commit.
Uploaded checkpoint_12 to seyedparsa/thoughtformer-checkpoints/checkpoints/star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626/checkpoint_12

============================================================
EPOCH 12/100 (thought_stage=10, data_stage=10)
============================================================
  thought_stage=10, c_thought=1, max_difficulty=10

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rank 1 | gpu 1 | alloc=4.20 GB | reserved=47.73 GB | max=41.62 GB
rank 3 | gpu 3 | alloc=4.20 GB | reserved=47.73 GB | max=41.62 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=49.61 GB | max=41.63 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=47.74 GB | max=41.63 GB

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Training Epoch: 13/100, batch 624/625 completed (loss: 0.0015: : 313it [33:06,  6.35s/it]

Training Epoch: 13/100, batch 624/625 completed (loss: 0.0003: : 313it [33:06,  6.35s/it]
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
  return func(*args, **kwargs)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:821: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/_optim_utils.py:1172: UserWarning: `_get_pg_default_device` will be deprecated, it only stays for backward-compatibility reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. 
  device = _get_pg_default_device(group)
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:828: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  FullyShardedDataParallel.set_state_dict_type(
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  ...e-c3ff1626/train_state_13:  20%|█▉        |  941MB / 4.77GB            
Processing Files (0 / 1)      :  20%|█▉        |  941MB / 4.77GB, 75.5MB/s  

New Data Upload               :  88%|████████▊ |  941MB / 1.07GB, 75.5MB/s  


  ...e-c3ff1626/train_state_13:  22%|██▏       | 1.03GB / 4.77GB            
Processing Files (0 / 1)      :  22%|██▏       | 1.03GB / 4.77GB, 81.4MB/s  

New Data Upload               :  85%|████████▌ | 1.03GB / 1.21GB, 81.4MB/s  


  ...e-c3ff1626/train_state_13:  24%|██▎       | 1.13GB / 4.77GB            
Processing Files (0 / 1)      :  24%|██▎       | 1.13GB / 4.77GB, 88.8MB/s  

New Data Upload               :  89%|████████▊ | 1.13GB / 1.27GB, 88.8MB/s  


  ...e-c3ff1626/train_state_13:  26%|██▌       | 1.24GB / 4.77GB            
Processing Files (0 / 1)      :  26%|██▌       | 1.24GB / 4.77GB, 96.7MB/s  

New Data Upload               :  93%|█████████▎| 1.24GB / 1.34GB, 96.7MB/s  


  ...e-c3ff1626/train_state_13:  28%|██▊       | 1.32GB / 4.77GB            
Processing Files (0 / 1)      :  28%|██▊       | 1.32GB / 4.77GB,  101MB/s  

New Data Upload               :  89%|████████▉ | 1.32GB / 1.48GB,  101MB/s  


  ...e-c3ff1626/train_state_13:  30%|███       | 1.43GB / 4.77GB            
Processing Files (0 / 1)      :  30%|███       | 1.43GB / 4.77GB,  109MB/s  

New Data Upload               :  89%|████████▉ | 1.43GB / 1.61GB,  109MB/s  


  ...e-c3ff1626/train_state_13:  33%|███▎      | 1.55GB / 4.77GB            
Processing Files (0 / 1)      :  33%|███▎      | 1.55GB / 4.77GB,  117MB/s  

New Data Upload               :  93%|█████████▎| 1.55GB / 1.68GB,  117MB/s  


  ...e-c3ff1626/train_state_13:  35%|███▍      | 1.65GB / 4.77GB            
Processing Files (0 / 1)      :  35%|███▍      | 1.65GB / 4.77GB,  123MB/s  

New Data Upload               :  91%|█████████ | 1.65GB / 1.81GB,  123MB/s  


  ...e-c3ff1626/train_state_13:  37%|███▋      | 1.78GB / 4.77GB            
Processing Files (0 / 1)      :  37%|███▋      | 1.78GB / 4.77GB,  132MB/s  

New Data Upload               :  95%|█████████▌| 1.78GB / 1.88GB,  132MB/s  


  ...e-c3ff1626/train_state_13:  39%|███▉      | 1.86GB / 4.77GB            
Processing Files (0 / 1)      :  39%|███▉      | 1.86GB / 4.77GB,  136MB/s  

New Data Upload               :  93%|█████████▎| 1.86GB / 2.01GB,  136MB/s  


  ...e-c3ff1626/train_state_13:  42%|████▏     | 1.98GB / 4.77GB            
Processing Files (0 / 1)      :  42%|████▏     | 1.98GB / 4.77GB,  143MB/s  

New Data Upload               :  92%|█████████▏| 1.98GB / 2.15GB,  143MB/s  


  ...e-c3ff1626/train_state_13:  44%|████▍     | 2.10GB / 4.77GB            
Processing Files (0 / 1)      :  44%|████▍     | 2.10GB / 4.77GB,  150MB/s  

New Data Upload               :  92%|█████████▏| 2.10GB / 2.28GB,  150MB/s  


  ...e-c3ff1626/train_state_13:  47%|████▋     | 2.25GB / 4.77GB            
Processing Files (0 / 1)      :  47%|████▋     | 2.25GB / 4.77GB,  159MB/s  

New Data Upload               :  96%|█████████▌| 2.25GB / 2.35GB,  159MB/s  


  ...e-c3ff1626/train_state_13:  49%|████▊     | 2.32GB / 4.77GB            
Processing Files (0 / 1)      :  49%|████▊     | 2.32GB / 4.77GB,  162MB/s  

New Data Upload               :  96%|█████████▌| 2.32GB / 2.41GB,  162MB/s  


  ...e-c3ff1626/train_state_13:  51%|█████     | 2.41GB / 4.77GB            
Processing Files (0 / 1)      :  51%|█████     | 2.41GB / 4.77GB,  166MB/s  

New Data Upload               :  95%|█████████▍| 2.41GB / 2.55GB,  166MB/s  


  ...e-c3ff1626/train_state_13:  53%|█████▎    | 2.53GB / 4.77GB            
Processing Files (0 / 1)      :  53%|█████▎    | 2.53GB / 4.77GB,  172MB/s  

New Data Upload               :  94%|█████████▍| 2.53GB / 2.68GB,  172MB/s  


  ...e-c3ff1626/train_state_13:  55%|█████▌    | 2.62GB / 4.77GB            
Processing Files (0 / 1)      :  55%|█████▌    | 2.62GB / 4.77GB,  177MB/s  

New Data Upload               :  95%|█████████▌| 2.62GB / 2.75GB,  177MB/s  


  ...e-c3ff1626/train_state_13:  57%|█████▋    | 2.71GB / 4.77GB            
Processing Files (0 / 1)      :  57%|█████▋    | 2.71GB / 4.77GB,  181MB/s  

New Data Upload               :  94%|█████████▍| 2.71GB / 2.88GB,  181MB/s  


  ...e-c3ff1626/train_state_13:  59%|█████▉    | 2.83GB / 4.77GB            
Processing Files (0 / 1)      :  59%|█████▉    | 2.83GB / 4.77GB,  187MB/s  

New Data Upload               :  96%|█████████▌| 2.83GB / 2.95GB,  187MB/s  


  ...e-c3ff1626/train_state_13:  61%|██████▏   | 2.92GB / 4.77GB            
Processing Files (0 / 1)      :  61%|██████▏   | 2.92GB / 4.77GB,  190MB/s  

New Data Upload               :  95%|█████████▍| 2.92GB / 3.08GB,  190MB/s  


  ...e-c3ff1626/train_state_13:  63%|██████▎   | 3.00GB / 4.77GB            
Processing Files (0 / 1)      :  63%|██████▎   | 3.00GB / 4.77GB,  192MB/s  

New Data Upload               :  95%|█████████▌| 3.00GB / 3.15GB,  192MB/s  


  ...e-c3ff1626/train_state_13:  65%|██████▌   | 3.12GB / 4.77GB            
Processing Files (0 / 1)      :  65%|██████▌   | 3.12GB / 4.77GB,  198MB/s  

New Data Upload               :  95%|█████████▍| 3.12GB / 3.29GB,  198MB/s  


  ...e-c3ff1626/train_state_13:  68%|██████▊   | 3.22GB / 4.77GB            
Processing Files (0 / 1)      :  68%|██████▊   | 3.22GB / 4.77GB,  202MB/s  

New Data Upload               :  96%|█████████▌| 3.22GB / 3.35GB,  202MB/s  


  ...e-c3ff1626/train_state_13:  70%|██████▉   | 3.31GB / 4.77GB            
Processing Files (0 / 1)      :  70%|██████▉   | 3.31GB / 4.77GB,  206MB/s  

New Data Upload               :  97%|█████████▋| 3.31GB / 3.42GB,  206MB/s  


  ...e-c3ff1626/train_state_13:  72%|███████▏  | 3.41GB / 4.77GB            
Processing Files (0 / 1)      :  72%|███████▏  | 3.41GB / 4.77GB,  210MB/s  

New Data Upload               :  96%|█████████▌| 3.41GB / 3.55GB,  210MB/s  


  ...e-c3ff1626/train_state_13:  74%|███████▎  | 3.51GB / 4.77GB            
Processing Files (0 / 1)      :  74%|███████▎  | 3.51GB / 4.77GB,  214MB/s  

New Data Upload               :  97%|█████████▋| 3.51GB / 3.62GB,  214MB/s  


  ...e-c3ff1626/train_state_13:  76%|███████▌  | 3.62GB / 4.77GB            
Processing Files (0 / 1)      :  76%|███████▌  | 3.62GB / 4.77GB,  218MB/s  

New Data Upload               :  96%|█████████▋| 3.62GB / 3.76GB,  218MB/s  


  ...e-c3ff1626/train_state_13:  77%|███████▋  | 3.69GB / 4.77GB            
Processing Files (0 / 1)      :  77%|███████▋  | 3.69GB / 4.77GB,  220MB/s  

New Data Upload               :  97%|█████████▋| 3.69GB / 3.82GB,  220MB/s  


  ...e-c3ff1626/train_state_13:  79%|███████▉  | 3.78GB / 4.77GB            
Processing Files (0 / 1)      :  79%|███████▉  | 3.78GB / 4.77GB,  222MB/s  

New Data Upload               :  97%|█████████▋| 3.78GB / 3.89GB,  222MB/s  


  ...e-c3ff1626/train_state_13:  81%|████████▏ | 3.88GB / 4.77GB            
Processing Files (0 / 1)      :  81%|████████▏ | 3.88GB / 4.77GB,  226MB/s  

New Data Upload               :  96%|█████████▋| 3.88GB / 4.02GB,  226MB/s  


  ...e-c3ff1626/train_state_13:  83%|████████▎ | 3.96GB / 4.77GB            
Processing Files (0 / 1)      :  83%|████████▎ | 3.96GB / 4.77GB,  228MB/s  

New Data Upload               :  97%|█████████▋| 3.96GB / 4.09GB,  228MB/s  


  ...e-c3ff1626/train_state_13:  85%|████████▍ | 4.04GB / 4.77GB            
Processing Files (0 / 1)      :  85%|████████▍ | 4.04GB / 4.77GB,  230MB/s  

New Data Upload               :  97%|█████████▋| 4.04GB / 4.16GB,  230MB/s  


  ...e-c3ff1626/train_state_13:  86%|████████▋ | 4.12GB / 4.77GB            
Processing Files (0 / 1)      :  86%|████████▋ | 4.12GB / 4.77GB,  231MB/s  

New Data Upload               :  96%|█████████▌| 4.12GB / 4.29GB,  231MB/s  


  ...e-c3ff1626/train_state_13:  89%|████████▉ | 4.24GB / 4.77GB            
Processing Files (0 / 1)      :  89%|████████▉ | 4.24GB / 4.77GB,  236MB/s  

New Data Upload               :  97%|█████████▋| 4.24GB / 4.36GB,  236MB/s  


  ...e-c3ff1626/train_state_13:  91%|█████████▏| 4.35GB / 4.77GB            
Processing Files (0 / 1)      :  91%|█████████▏| 4.35GB / 4.77GB,  240MB/s  

New Data Upload               :  97%|█████████▋| 4.35GB / 4.49GB,  240MB/s  


  ...e-c3ff1626/train_state_13:  93%|█████████▎| 4.45GB / 4.77GB            
Processing Files (0 / 1)      :  93%|█████████▎| 4.45GB / 4.77GB,  243MB/s  

New Data Upload               :  97%|█████████▋| 4.45GB / 4.56GB,  243MB/s  


  ...e-c3ff1626/train_state_13:  95%|█████████▌| 4.54GB / 4.77GB            
Processing Files (0 / 1)      :  95%|█████████▌| 4.54GB / 4.77GB,  246MB/s  

New Data Upload               :  97%|█████████▋| 4.54GB / 4.69GB,  246MB/s  


  ...e-c3ff1626/train_state_13:  97%|█████████▋| 4.60GB / 4.77GB            
Processing Files (0 / 1)      :  97%|█████████▋| 4.60GB / 4.77GB,  245MB/s  

New Data Upload               :  97%|█████████▋| 4.60GB / 4.76GB,  245MB/s  


  ...e-c3ff1626/train_state_13:  99%|█████████▊| 4.71GB / 4.77GB            
Processing Files (0 / 1)      :  99%|█████████▊| 4.71GB / 4.77GB,  249MB/s  

New Data Upload               :  99%|█████████▊| 4.71GB / 4.77GB,  249MB/s  


  ...e-c3ff1626/train_state_13: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  249MB/s  

New Data Upload               : 100%|█████████▉| 4.77GB / 4.77GB,  249MB/s  


  ...e-c3ff1626/train_state_13: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  239MB/s  

New Data Upload               : 100%|█████████▉| 4.77GB / 4.77GB,  239MB/s  


  ...e-c3ff1626/train_state_13: 100%|█████████▉| 4.77GB / 4.77GB            
Processing Files (0 / 1)      : 100%|█████████▉| 4.77GB / 4.77GB,  235MB/s  

New Data Upload               : 100%|█████████▉| 4.77GB / 4.77GB,  235MB/s  


  ...e-c3ff1626/train_state_13: 100%|██████████| 4.77GB / 4.77GB            
Processing Files (1 / 1)      : 100%|██████████| 4.77GB / 4.77GB,  230MB/s  

New Data Upload               : 100%|██████████| 4.77GB / 4.77GB,  230MB/s  
Processing Files (1 / 1)      : 100%|██████████| 4.77GB / 4.77GB,  230MB/s  

New Data Upload               : 100%|██████████| 4.77GB / 4.77GB,  230MB/s  

  ...e-c3ff1626/train_state_13: 100%|██████████| 4.77GB / 4.77GB            
Uploaded train_state_13 to seyedparsa/thoughtformer-checkpoints/checkpoints/star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626/train_state_13
Deleted old checkpoint: train_state_12
Deleted old checkpoint: checkpoint_12

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]eval loss 0.014295161018491768

Test Accuracy:   0%|          | 0/32 [00:00<?, ?it/s]
[CORRECT] Example 0 (diff=3)
  Expected : 'Claude'
  Extracted: 'Claude'
  Generated: '### Claude'

[CORRECT] Example 1 (diff=8)

Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]  Expected : 'Guy'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]  Extracted: 'Guy'
Test Accuracy:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
  Generated: '### Guy'

Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:23,  6.56s/it]
[CORRECT] Example 2 (diff=10)
  Expected : 'Elvis'
  Extracted: 'Elvis'
  Generated: '### Elvis'

[CORRECT] Example 3 (diff=8)
  Expected : 'Coral'
  Extracted: 'Coral'
  Generated: '### Coral'

[CORRECT] Example 4 (diff=1)
  Expected : 'Patricia'
  Extracted: 'Patricia'
  Generated: '### Patricia'

Test Accuracy:   3%|▎         | 1/32 [00:06<03:18,  6.41s/it]
Test accuracy: 1.0:   3%|▎         | 1/32 [00:06<03:18,  6.41s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:14,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:14,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:13,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:14,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:14,  6.47s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.40s/it]
Test accuracy: 1.0:   6%|▋         | 2/32 [00:12<03:12,  6.40s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 0.96:   9%|▉         | 3/32 [00:19<03:14,  6.71s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.67s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:13,  6.67s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.72s/it]
Test accuracy: 1.0:   9%|▉         | 3/32 [00:19<03:14,  6.72s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.48s/it]
Test accuracy: 0.96:  12%|█▎        | 4/32 [00:26<03:01,  6.48s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.48s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.48s/it]
Test accuracy: 0.97:  12%|█▎        | 4/32 [00:26<03:01,  6.48s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:26<03:01,  6.48s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:25<03:00,  6.46s/it]
Test accuracy: 1.0:  12%|█▎        | 4/32 [00:25<03:00,  6.46s/it]
Test accuracy: 0.97:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 0.98:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:53,  6.42s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:52,  6.41s/it]
Test accuracy: 1.0:  16%|█▌        | 5/32 [00:32<02:52,  6.41s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 0.98:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.37s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.36s/it]
Test accuracy: 1.0:  19%|█▉        | 6/32 [00:38<02:45,  6.36s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:45<02:39,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:39,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:39,  6.36s/it]
Test accuracy: 0.96:  22%|██▏       | 7/32 [00:45<02:39,  6.36s/it]
Test accuracy: 0.98:  22%|██▏       | 7/32 [00:45<02:39,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:45<02:39,  6.36s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:44<02:38,  6.35s/it]
Test accuracy: 1.0:  22%|██▏       | 7/32 [00:44<02:38,  6.35s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 0.96:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 0.95:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 0.98:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 1.0:  25%|██▌       | 8/32 [00:51<02:32,  6.34s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 0.98:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 0.99:  28%|██▊       | 9/32 [00:58<02:28,  6.48s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:57<02:28,  6.47s/it]
Test accuracy: 1.0:  28%|██▊       | 9/32 [00:57<02:28,  6.47s/it]
Test accuracy: 0.95:  28%|██▊       | 9/32 [00:58<02:29,  6.48s/it]
Test accuracy: 0.94:  28%|██▊       | 9/32 [00:58<02:29,  6.48s/it]
Test accuracy: 0.94:  31%|███▏      | 10/32 [01:04<02:21,  6.44s/it]
Test accuracy: 0.99:  31%|███▏      | 10/32 [01:04<02:21,  6.44s/it]
Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.44s/it]
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Test accuracy: 1.0:  31%|███▏      | 10/32 [01:04<02:21,  6.44s/it]
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Test accuracy: 0.95:  34%|███▍      | 11/32 [01:10<02:14,  6.40s/it]
Test accuracy: 1.0:  34%|███▍      | 11/32 [01:10<02:14,  6.40s/it]
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Test accuracy: 0.96:  38%|███▊      | 12/32 [01:16<02:06,  6.32s/it]
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Test accuracy: 0.96:  41%|████      | 13/32 [01:23<02:00,  6.32s/it]
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Test accuracy: 1.0:  47%|████▋     | 15/32 [01:35<01:46,  6.26s/it]
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Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 1.0:  50%|█████     | 16/32 [01:41<01:40,  6.27s/it]
Test accuracy: 0.96:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
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Test accuracy: 0.99:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
Test accuracy: 0.94:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
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Test accuracy: 1.0:  53%|█████▎    | 17/32 [01:48<01:34,  6.28s/it]
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Test accuracy: 0.94:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
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Test accuracy: 1.0:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
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Test accuracy: 1.0:  56%|█████▋    | 18/32 [01:54<01:28,  6.29s/it]
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Test accuracy: 0.95:  59%|█████▉    | 19/32 [02:00<01:21,  6.24s/it]
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Test accuracy: 1.0:  59%|█████▉    | 19/32 [02:00<01:21,  6.24s/it]
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Test accuracy: 0.99:  66%|██████▌   | 21/32 [02:14<01:11,  6.48s/it]
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Test accuracy: 1.0:  66%|██████▌   | 21/32 [02:13<01:11,  6.48s/it]
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Test accuracy: 0.99:  66%|██████▌   | 21/32 [02:14<01:11,  6.49s/it]
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Test accuracy: 0.95:  69%|██████▉   | 22/32 [02:20<01:03,  6.38s/it]
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Test accuracy: 1.0:  69%|██████▉   | 22/32 [02:19<01:03,  6.38s/it]
Test accuracy: 1.0:  69%|██████▉   | 22/32 [02:19<01:03,  6.38s/it]
Test accuracy: 0.95:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
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Test accuracy: 1.0:  72%|███████▏  | 23/32 [02:26<00:57,  6.36s/it]
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Test accuracy: 0.96:  75%|███████▌  | 24/32 [02:33<00:52,  6.55s/it]
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Test accuracy: 0.99:  78%|███████▊  | 25/32 [02:39<00:45,  6.48s/it]
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Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 1.0:  81%|████████▏ | 26/32 [02:45<00:38,  6.43s/it]
Test accuracy: 0.96:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
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Test accuracy: 1.0:  84%|████████▍ | 27/32 [02:52<00:31,  6.34s/it]
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Test accuracy: 1.0:  88%|████████▊ | 28/32 [02:59<00:26,  6.53s/it]
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Test accuracy: 0.99:  88%|████████▊ | 28/32 [02:59<00:26,  6.54s/it]
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Test accuracy: 0.96:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
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Test accuracy: 1.0:  91%|█████████ | 29/32 [03:05<00:19,  6.48s/it]
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Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
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Test accuracy: 1.0:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  94%|█████████▍| 30/32 [03:11<00:12,  6.37s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.56s/it]
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Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.56s/it]
Test accuracy: 0.99:  97%|█████████▋| 31/32 [03:18<00:06,  6.56s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.97: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.97: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:20<00:00,  5.26s/it]
Test accuracy: 0.99: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 2: Cor=247, CoT=0, Total=250

Test accuracy: 0.99: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 1: Cor=247, CoT=0, Total=250

Test accuracy: 0.97: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 3: Cor=242, CoT=0, Total=250

Test accuracy: 0.99: 100%|██████████| 32/32 [03:21<00:00,  6.28s/it]
Device 0: Cor=248, CoT=0, Total=250

============================================================
EVALUATION RESULTS (Epoch 12)
============================================================
Config: data_curriculum=ON, thought_curriculum=ON
thought_stage=10, data_stage=10, n_latent_tokens=10
Training on difficulties: 0-10

Overall accuracy: 984 / 1000 = 0.9840

Per-difficulty accuracy:
  diff=1: 100/100 = 1.0000 (trained)
  diff=2: 100/100 = 1.0000 (trained)
  diff=3: 99/100 = 0.9900 (trained)
  diff=4: 99/100 = 0.9900 (trained)
  diff=5: 100/100 = 1.0000 (trained)
  diff=6: 98/100 = 0.9800 (trained)
  diff=7: 100/100 = 1.0000 (trained)
  diff=8: 97/100 = 0.9700 (trained)
  diff=9: 95/100 = 0.9500 (trained)
  diff=10: 96/100 = 0.9600 (trained)

Trained (diff 0-10): 984/1000 = 0.9840
============================================================


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No files have been modified since last commit. Skipping to prevent empty commit.
Uploaded checkpoint_13 to seyedparsa/thoughtformer-checkpoints/checkpoints/star-atc-4gpu-bs32-lr1e-5-fixed_n1000_qwen3-0.6b-base_thoughtformer_lr1e-5_no-reset_dc_tc_adaptive-c3ff1626/checkpoint_13

============================================================
EPOCH 13/100 (thought_stage=10, data_stage=10)
============================================================
  thought_stage=10, c_thought=1, max_difficulty=10

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rank 1 | gpu 1 | alloc=4.20 GB | reserved=53.36 GB | max=41.62 GB
rank 0 | gpu 0 | alloc=4.20 GB | reserved=45.87 GB | max=41.63 GB
rank 3 | gpu 3 | alloc=4.20 GB | reserved=52.94 GB | max=41.63 GB
rank 2 | gpu 2 | alloc=4.20 GB | reserved=47.74 GB | max=41.63 GB

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Training Epoch: 14/100, batch 256/625 completed (loss: 0.0016:  41%|████      | 128/312 [13:36<19:47,  6.45s/it]
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Training Epoch: 14/100, batch 268/625 completed (loss: 0.0029:  43%|████▎     | 134/312 [14:14<18:53,  6.37s/it]
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Training Epoch: 14/100, batch 270/625 completed (loss: 0.0025:  43%|████▎     | 135/312 [14:20<18:46,  6.36s/it]
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Training Epoch: 14/100, batch 271/625 completed (loss: 0.3204:  44%|████▎     | 136/312 [14:24<18:54,  6.45s/it]
Training Epoch: 14/100, batch 272/625 completed (loss: 0.0001:  44%|████▎     | 136/312 [14:27<18:53,  6.44s/it]
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Training Epoch: 14/100, batch 272/625 completed (loss: 0.0001:  44%|████▎     | 136/312 [14:27<18:54,  6.45s/it]
Training Epoch: 14/100, batch 272/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:30<18:31,  6.35s/it]
Training Epoch: 14/100, batch 272/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:30<18:31,  6.35s/it]
Training Epoch: 14/100, batch 272/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:30<18:32,  6.36s/it]
Training Epoch: 14/100, batch 272/625 completed (loss: 0.0:  44%|████▍     | 137/312 [14:30<18:31,  6.35s/it]
Training Epoch: 14/100, batch 273/625 completed (loss: 0.0003:  44%|████▍     | 137/312 [14:30<18:31,  6.35s/it]
Training Epoch: 14/100, batch 273/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:30<18:31,  6.35s/it]
Training Epoch: 14/100, batch 273/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:30<18:31,  6.35s/it]
Training Epoch: 14/100, batch 273/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:30<18:32,  6.36s/it]
Training Epoch: 14/100, batch 274/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:33<18:31,  6.35s/it]
Training Epoch: 14/100, batch 274/625 completed (loss: 0.0001:  44%|████▍     | 137/312 [14:33<18:31,  6.35s/it]
Training Epoch: 14/100, batch 274/625 completed (loss: 0.0002:  44%|████▍     | 137/312 [14:33<18:31,  6.35s/it]
Training Epoch: 14/100, batch 274/625 completed (loss: 0.0:  44%|████▍     | 137/312 [14:33<18:32,  6.36s/it]   W0531 03:29:35.666000 714703 torch/distributed/elastic/agent/server/api.py:753] Received 15 death signal, shutting down workers
W0531 03:29:35.667000 714703 torch/distributed/elastic/multiprocessing/api.py:1014] Sending process 714786 closing signal SIGTERM
W0531 03:29:35.668000 714703 torch/distributed/elastic/multiprocessing/api.py:1014] Sending process 714787 closing signal SIGTERM
W0531 03:29:35.669000 714703 torch/distributed/elastic/multiprocessing/api.py:1014] Sending process 714788 closing signal SIGTERM
W0531 03:29:35.670000 714703 torch/distributed/elastic/multiprocessing/api.py:1014] Sending process 714789 closing signal SIGTERM
Traceback (most recent call last):
  File "/home/ubuntu/thoughtformer/.venv/bin/torchrun", line 8, in <module>
    sys.exit(main())
             ^^^^^^
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 367, in wrapper
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/run.py", line 1016, in main
    run(args)
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/run.py", line 1007, in run
    elastic_launch(
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 191, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 362, in launch_agent
    result = agent.run()
             ^^^^^^^^^^^
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
    result = f(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/elastic/agent/server/api.py", line 745, in run
    result = self._invoke_run(role)
             ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/elastic/agent/server/api.py", line 923, in _invoke_run
    time.sleep(monitor_interval)
  File "/home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
torch.distributed.elastic.multiprocessing.api.SignalException: Process 714703 got signal: 15