[tab] /fast/project/HFMI_SynergyUnit/mihir/logs/nemo/qwen3_600m_finetune_34693.log (log)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:2
#SBATCH --output=/fast/project/HFMI_SynergyUnit/mihir/logs/nemo/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/mihir/logs/nemo/%x_%j.log
#SBATCH --job-name=qwen3_600m_finetune
#SBATCH --mem=400GB

# Log the sbatch script
cat $0

source .uv_venv/bin/activate

python "$1"/fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
─ Entering Experiment qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_… ─
[14:14:35] INFO     Log directory is:                     local_scheduler.py:777
                    /home/mihir.mahajan/.nemo_run/experim                       
                    ents/qwen3_600m_full_finetune_crowd_c                       
                    ode_dataset_32k_tokens_batch_size_1/q                       
                    wen3_600m_full_finetune_crowd_code_da                       
                    taset_32k_tokens_batch_size_1_1764076                       
                    474/qwen3_600m_full_finetune_crowd_co                       
                    de_dataset_32k_tokens_batch_size_1                          
[14:14:35] Launching job                                       experiment.py:744
           qwen3_600m_full_finetune_crowd_code_dataset_32k_tok                  
           ens_batch_size_1 for experiment                                      
           qwen3_600m_full_finetune_crowd_code_dataset_32k_tok                  
           ens_batch_size_1                                                     
           INFO     Log directory is:                     local_scheduler.py:777
                    /home/mihir.mahajan/.nemo_run/experim                       
                    ents/qwen3_600m_full_finetune_crowd_c                       
                    ode_dataset_32k_tokens_batch_size_1/q                       
                    wen3_600m_full_finetune_crowd_code_da                       
                    taset_32k_tokens_batch_size_1_1764076                       
                    474/qwen3_600m_full_finetune_crowd_co                       
                    de_dataset_32k_tokens_batch_size_1                          
           INFO     Launched app:                                launcher.py:111
                    local_persistent://nemo_run/qwen3_600m_full_                
                    finetune_crowd_code_dataset_32k_tokens_batch                
                    _size_1-bgj79n6jqntqkc                                      
─ Waiting for Experiment qwen3_600m_full_finetune_crowd_code_dataset_32k_toke… ─


Experiment Status for 
qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1_1764076474

Task 0: qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1
- Status: RUNNING
- Executor: LocalExecutor
- Job id: qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1-bgj79n6jqntqkc
- Local Directory: /home/mihir.mahajan/.nemo_run/experiments/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1_1764076474/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1

           INFO     Waiting for job                              launcher.py:130
                    qwen3_600m_full_finetune_crowd_code_dataset_                
                    32k_tokens_batch_size_1-bgj79n6jqntqkc to                   
                    finish [log=True]...                                        
tch_size_1/0 /fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
tch_size_1/0   import pynvml  # type: ignore[import]
tch_size_1/0 I1125 14:14:37.578000 748303 torch/distributed/run.py:657] Using nproc_per_node=2.
tch_size_1/0 W1125 14:14:37.579000 748303 torch/distributed/run.py:774] 
tch_size_1/0 W1125 14:14:37.579000 748303 torch/distributed/run.py:774] *****************************************
tch_size_1/0 W1125 14:14:37.579000 748303 torch/distributed/run.py:774] 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. 
tch_size_1/0 W1125 14:14:37.579000 748303 torch/distributed/run.py:774] *****************************************
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199] Starting elastic_operator with launch configs:
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   entrypoint         : nemo_run.core.runners.fdl_runner
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   min_nodes          : 1
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   max_nodes          : 1
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   nproc_per_node     : 2
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   run_id             : 7418
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   rdzv_backend       : c10d
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   rdzv_endpoint      : localhost:0
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   rdzv_configs       : {'timeout': 900}
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   max_restarts       : 0
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   monitor_interval   : 0.1
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   log_dir            : /home/mihir.mahajan/.nemo_run/experiments/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1_1764076474/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1/nemo_run/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1-bgj79n6jqntqkc/torchelastic/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   metrics_cfg        : {}
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199]   event_log_handler  : null
tch_size_1/0 I1125 14:14:37.582000 748303 torch/distributed/launcher/api.py:199] 
tch_size_1/0 I1125 14:14:37.588000 748303 torch/distributed/elastic/agent/server/api.py:869] [default] starting workers for entrypoint: python3
tch_size_1/0 I1125 14:14:37.588000 748303 torch/distributed/elastic/agent/server/api.py:677] [default] Rendezvous'ing worker group
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523] [default] Rendezvous complete for workers. Result:
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   restart_count=0
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   master_addr=hai004.haicore.berlin
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   master_port=38939
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   group_rank=0
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   group_world_size=1
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   local_ranks=[0, 1]
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   role_ranks=[0, 1]
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   global_ranks=[0, 1]
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   role_world_sizes=[2, 2]
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   global_world_sizes=[2, 2]
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523]   event_log_handler=null
tch_size_1/0 I1125 14:14:37.665000 748303 torch/distributed/elastic/agent/server/api.py:523] 
tch_size_1/0 I1125 14:14:37.666000 748303 torch/distributed/elastic/agent/server/api.py:685] [default] Starting worker group
tch_size_1/0 I1125 14:14:37.666000 748303 torch/distributed/elastic/agent/server/local_elastic_agent.py:296] use_agent_store: True
tch_size_1/0 I1125 14:14:37.667000 748303 torch/distributed/elastic/agent/server/local_elastic_agent.py:192] Environment variable 'TORCHELASTIC_ENABLE_FILE_TIMER' not found. Do not start FileTimerServer.
tch_size_1/0 I1125 14:14:37.668000 748303 torch/distributed/elastic/agent/server/local_elastic_agent.py:236] Environment variable 'TORCHELASTIC_HEALTH_CHECK_PORT' not found. Do not start health check.
tch_size_1/0 [default1]:/fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
tch_size_1/0 [default1]:  import pynvml  # type: ignore[import]
tch_size_1/0 [default0]:/fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
tch_size_1/0 [default0]:  import pynvml  # type: ignore[import]
tch_size_1/0 [default1]:trainer.num_devices: 2
tch_size_1/0 [default1]:trainer.num_nodes: 1
tch_size_1/0 [default1]:trainer.strategy.tensor_model_parallel_size: 2
tch_size_1/0 [default1]:trainer.strategy.pipeline_model_parallel_size: 1
tch_size_1/0 [default1]:trainer.strategy.context_parallel_size: 1
tch_size_1/0 [default1]:data.global_batch_size: 4
tch_size_1/0 [default1]:data.micro_batch_size: 1
tch_size_1/0 [default1]:[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
tch_size_1/0 [default1]:[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
tch_size_1/0 [default1]:[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
tch_size_1/0 [default1]:[W1125 14:15:00.655873105 ProcessGroupNCCL.cpp:981] Warning: TORCH_NCCL_AVOID_RECORD_STREAMS is the default now, this environment variable is thus deprecated. (function operator())
tch_size_1/0 [default0]:trainer.num_devices: 2
tch_size_1/0 [default0]:trainer.num_nodes: 1
tch_size_1/0 [default0]:trainer.strategy.tensor_model_parallel_size: 2
tch_size_1/0 [default0]:trainer.strategy.pipeline_model_parallel_size: 1
tch_size_1/0 [default0]:trainer.strategy.context_parallel_size: 1
tch_size_1/0 [default0]:data.global_batch_size: 4
tch_size_1/0 [default0]:data.micro_batch_size: 1
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:04 nemo_logger:162] Experiments will be logged at /fast/project/HFMI_SynergyUnit/tab_model/mihir/logs/qwen3_600m_full_finetune_crowd_code_dataset_32k_tokens_batch_size_1/2025-11-25_14-15-04
tch_size_1/0 [default0]:GPU available: True (cuda), used: True
tch_size_1/0 [default0]:TPU available: False, using: 0 TPU cores
tch_size_1/0 [default0]:HPU available: False, using: 0 HPUs
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:04 nemo_logger:190] "update_logger_directory" is True. Overwriting tensorboard logger "save_dir" to /fast/project/HFMI_SynergyUnit/tab_model/mihir/logs/tb_logs
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:04 nemo_logger:209] The Trainer already contains a ModelCheckpoint callback. This will be overwritten.
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:04 nemo_logger:239] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 1000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:04 megatron_strategy:460] Fixing mis-match between ddp-config & mcore-optimizer config
tch_size_1/0 [default0]:You are using a CUDA device ('NVIDIA H100 80GB HBM3') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:449] Rank 0 has data parallel group : [0]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:455] Rank 0 has combined group of data parallel and context parallel : [0]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:460] All data parallel group ranks with context parallel combined: [[0], [1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:463] Ranks 0 has data parallel rank: 0
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:471] Rank 0 has context parallel group: [0]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:474] All context parallel group ranks: [[0], [1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:475] Ranks 0 has context parallel rank: 0
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:482] Rank 0 has model parallel group: [0, 1]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:483] All model parallel group ranks: [[0, 1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:492] Rank 0 has tensor model parallel group: [0, 1]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:496] All tensor model parallel group ranks: [[0, 1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:497] Rank 0 has tensor model parallel rank: 0
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:518] All expert tensor parallel group ranks: [[0, 1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:519] Rank 0 has expert tensor parallel rank: 0
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:532] Rank 0 has pipeline model parallel group: [0]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:544] Rank 0 has embedding group: [0]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:550] All pipeline model parallel group ranks: [[0], [1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:551] Rank 0 has pipeline model parallel rank 0
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:552] All embedding group ranks: [[0], [1]]
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 megatron_init:553] Rank 0 has embedding rank: 0
tch_size_1/0 [default0]:[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
tch_size_1/0 [default0]:[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
tch_size_1/0 [default0]:[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
tch_size_1/0 [default0]:[W1125 14:15:05.707080091 ProcessGroupNCCL.cpp:981] Warning: TORCH_NCCL_AVOID_RECORD_STREAMS is the default now, this environment variable is thus deprecated. (function operator())
tch_size_1/0 [default0]:----------------------------------------------------------------------------------------------------
tch_size_1/0 [default0]:distributed_backend=nccl
tch_size_1/0 [default0]:All distributed processes registered. Starting with 2 processes
tch_size_1/0 [default0]:----------------------------------------------------------------------------------------------------
tch_size_1/0 [default0]:
tch_size_1/0 [default1]:[Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1
tch_size_1/0 [default0]:[Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:05 base:373] Use preset vocab_size: 151936, original vocab_size: 151669, dummy tokens: 267.
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 megatron_strategy:472] Copying Trainer's 'max_steps' (1000) to LR scheduler's 'max_steps'.
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 num_microbatches_calculator:228] setting number of microbatches to constant 4
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 megatron_parallel:584]  > number of parameters on (tensor, pipeline) model parallel rank (0 ,0): 298057728
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 utils:673] Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=True, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=True, num_distributed_optimizer_instances=1, check_for_nan_in_grad=True, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, reuse_grad_buf_for_mxfp8_param_ag=False, use_megatron_fsdp=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache=False, nccl_ub=False, fsdp_double_buffer=False, outer_dp_sharding_strategy='no_shard', disable_symmetric_registration=False, delay_wgrad_compute=False)
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 utils:694] Number of buckets for gradient all-reduce / reduce-scatter: 1
tch_size_1/0 [default0]:    Params for bucket 1 (298057728 elements, 298057728 padded size):
tch_size_1/0 [default0]:    	module.decoder.layers.23.self_attention.linear_qkv.weight
tch_size_1/0 [default0]:    	module.decoder.layers.20.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.18.self_attention.k_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.15.mlp.linear_fc2.weight
tch_size_1/0 [default0]:    	module.decoder.layers.7.self_attention.linear_proj.weight
tch_size_1/0 [default0]:    	module.decoder.layers.4.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.3.self_attention.linear_qkv.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.0.self_attention.q_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.27.self_attention.q_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.13.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.12.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.11.self_attention.linear_qkv.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.4.self_attention.q_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.20.self_attention.linear_proj.weight
tch_size_1/0 [default0]:    	module.decoder.layers.17.self_attention.k_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.16.self_attention.k_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.10.mlp.linear_fc2.weight
tch_size_1/0 [default0]:    	module.decoder.layers.8.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.8.self_attention.linear_qkv.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.2.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.26.mlp.linear_fc2.weight
tch_size_1/0 [default0]:    	module.decoder.layers.22.self_attention.k_layernorm.weight
tch_size_1/0 [default0]:    	module.decoder.layers.19.self_attention.linear_qkv.layer_norm_weight
tch_size_1/0 [default1]:LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]
tch_size_1/0 [default0]:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:06 serialization:184] DEPRECATED: Passing 'checkpoint_dir' as a Path object in load_common_state_dict will no longer be supported in a future release. Please pass it as a string instead.
tch_size_1/0 [default0]:    	module.decoder.layers.13.mlp.linear_fc2.weight
tch_size_1/0 [default0]:    	module.decoder.layers.13.self_attention.linear_qkv.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.5.mlp.linear_fc1.weight
tch_size_1/0 [default0]:    	module.decoder.layers.2.self_attention.linear_qkv.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.25.self_attention.linear_qkv.weight
tch_size_1/0 [default0]:    	module.decoder.layers.21.mlp.linear_fc1.weight
tch_size_1/0 [default0]:    	module.decoder.layers.19.mlp.linear_fc1.layer_norm_weight
tch_size_1/0 [default0]:    	module.decoder.layers.15.self_attention.k_layernorm.weight
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tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 utils:673] Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=5e-06, min_lr=None, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp8_recipe='delayed', fp16=False, bf16=True, reuse_grad_buf_for_mxfp8_param_ag=False, params_dtype=torch.bfloat16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.98, adam_eps=1e-05, sgd_momentum=0.9, use_distributed_optimizer=True, overlap_param_gather=False, overlap_param_gather_with_optimizer_step=False, use_megatron_fsdp=False, optimizer_cpu_offload=False, optimizer_offload_fraction=0.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=False, timers=None, config_logger_dir='')
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 megatron_strategy:1045] Doing selective restore from RestoreConfig(path='/fast/project/HFMI_SynergyUnit/tab_model/data/checkpoints/nemo_converted_weights_qwen3-600m', load_model_state=True, load_optim_state=False, load_artifacts=True)
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 pl:411] Loaded sharded_state_dict_metadata from checkpoint: {'distrib_optim_sharding_type': 'fully_sharded_model_space'}
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:06 pl:275] Using <megatron.core.dist_checkpointing.strategies.fully_parallel.FullyParallelLoadStrategyWrapper object at 0x7ff03c3d42e0> dist-ckpt load strategy.
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:07 pl:297] Global Checkpoint Load : Rank : 0 : Start time : 1764076506.160s : Time spent in load_checkpoint: 0.920s
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:07 megatron_strategy:1050] Restoring model weights from RestoreConfig(path='/fast/project/HFMI_SynergyUnit/tab_model/data/checkpoints/nemo_converted_weights_qwen3-600m', load_model_state=True, load_optim_state=False, load_artifacts=True)
tch_size_1/0 [default0]:
tch_size_1/0 [default0]:  | Name   | Type | Params | Mode 
tch_size_1/0 [default0]:----------------------------------------
tch_size_1/0 [default0]:0 | module | DDP  | 298 M  | train
tch_size_1/0 [default0]:----------------------------------------
tch_size_1/0 [default0]:298 M     Trainable params
tch_size_1/0 [default0]:0         Non-trainable params
tch_size_1/0 [default0]:298 M     Total params
tch_size_1/0 [default0]:1,192.231 Total estimated model params size (MB)
tch_size_1/0 [default0]:571       Modules in train mode
tch_size_1/0 [default0]:0         Modules in eval mode
tch_size_1/0 [default0]:[NeMo I 2025-11-25 14:15:07 megatron_strategy:1058] Finished restoring from RestoreConfig(path='/fast/project/HFMI_SynergyUnit/tab_model/data/checkpoints/nemo_converted_weights_qwen3-600m', load_model_state=True, load_optim_state=False, load_artifacts=True), cleaning up.
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:07 nemo_logging:361] /fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=4` in the `DataLoader` to improve performance.
tch_size_1/0 [default0]:    
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:43 nemo_logging:361] /fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:431: It is recommended to use `self.log('global_batch_size', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
tch_size_1/0 [default0]:    
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:43 nemo_logging:361] /fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:431: It is recommended to use `self.log('val_loss', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
tch_size_1/0 [default0]:    
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:15:43 nemo_logging:361] /fast/home/mihir.mahajan/Projects/crowd-pilot/.uv_venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=4` in the `DataLoader` to improve performance.
tch_size_1/0 [default0]:    
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:16:18 rerun_state_machine:1300] Implicit initialization of Rerun State Machine!
tch_size_1/0 [default0]:[NeMo W 2025-11-25 14:16:18 rerun_state_machine:238] RerunStateMachine initialized in mode disabled
tch_size_1/0 [default0]:Training epoch 0, iteration 0/999 | lr: 9.804e-08 | global_batch_size: 4 | global_step: 0 | reduced_train_loss: 0.597 | train_step_timing in s: 35.36