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	3: W0902 18:31:19.695000 3352467 torch/distributed/run.py:792]
	3: W0902 18:31:19.695000 3352467 torch/distributed/run.py:792] *****************************************
	3: W0902 18:31:19.695000 3352467 torch/distributed/run.py:792] 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.
	3: W0902 18:31:19.695000 3352467 torch/distributed/run.py:792] *****************************************
	2: W0902 18:31:19.695000 2766555 torch/distributed/run.py:792]
	2: W0902 18:31:19.695000 2766555 torch/distributed/run.py:792] *****************************************
	2: W0902 18:31:19.695000 2766555 torch/distributed/run.py:792] 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.
	2: W0902 18:31:19.695000 2766555 torch/distributed/run.py:792] *****************************************
	0: W0902 18:31:19.696000 801759 torch/distributed/run.py:792]
	0: W0902 18:31:19.696000 801759 torch/distributed/run.py:792] *****************************************
	0: W0902 18:31:19.696000 801759 torch/distributed/run.py:792] 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.
	0: W0902 18:31:19.696000 801759 torch/distributed/run.py:792] *****************************************
	1: W0902 18:31:19.696000 3191159 torch/distributed/run.py:792]
	1: W0902 18:31:19.696000 3191159 torch/distributed/run.py:792] *****************************************
	1: W0902 18:31:19.696000 3191159 torch/distributed/run.py:792] 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.
	1: W0902 18:31:19.696000 3191159 torch/distributed/run.py:792] *****************************************
	0: [2025-09-02 18:31:52,971] [INFO] [axolotl.utils.schemas.validation.check_eval_packing:119] [PID:801836] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`[39m
	2: [2025-09-02 18:31:52,971] [INFO] [axolotl.utils.schemas.validation.check_eval_packing:119] [PID:2766631] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`[39m
	1: [2025-09-02 18:31:52,971] [INFO] [axolotl.utils.schemas.validation.check_eval_packing:119] [PID:3191235] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`[39m
	0: [2025-09-02 18:31:52,972] [INFO] [axolotl.utils.schemas.validation.hint_sample_packing_padding:218] [PID:801836] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing[39m
	2: [2025-09-02 18:31:52,972] [INFO] [axolotl.utils.schemas.validation.hint_sample_packing_padding:218] [PID:2766631] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing[39m
	3: [2025-09-02 18:31:52,972] [INFO] [axolotl.utils.schemas.validation.check_eval_packing:119] [PID:3352543] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`[39m
	1: [2025-09-02 18:31:52,972] [INFO] [axolotl.utils.schemas.validation.hint_sample_packing_padding:218] [PID:3191235] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing[39m
	3: [2025-09-02 18:31:52,972] [INFO] [axolotl.utils.schemas.validation.hint_sample_packing_padding:218] [PID:3352543] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing[39m
	0: [2025-09-02 18:31:57,072] [INFO] [axolotl.cli.config.load_cfg:245] [PID:801836] [RANK:0] config:
	0: {
	0: "activation_offloading": false,
	0: "auto_resume_from_checkpoints": true,
	0: "axolotl_config_path": "/lustre/fswork/projects/rech/dgo/udv55np/train/tmp/1756826505612058881.yaml",
	0: "base_model": "/lustre/fswork/projects/rech/qwv/udv55np/Qwen/Qwen2.5-1.5B_ift",
	0: "base_model_config": "/lustre/fswork/projects/rech/qwv/udv55np/Qwen/Qwen2.5-1.5B_ift",
	0: "batch_size": 16,
	0: "bf16": true,
	0: "capabilities": {
	0: "bf16": true,
	0: "compute_capability": "sm_90",
	0: "fp8": false,
	0: "n_gpu": 16,
	0: "n_node": 1
	0: },
	0: "chat_template": "qwen_25",
	0: "context_parallel_size": 1,
	0: "dataloader_num_workers": 16,
	0: "dataloader_pin_memory": true,
	0: "dataloader_prefetch_factor": 256,
	0: "dataset_prepared_path": "/lustre/fsn1/projects/rech/dgo/udv55np/dataset_math/Qwen3-235B-A22B/0",
	0: "dataset_processes": 192,
	0: "datasets": [
	0: {
	0: "chat_template": "tokenizer_default",
	0: "field_messages": "conversations",
	0: "message_property_mappings": {
	0: "content": "content",
	0: "role": "role"
	0: },
	0: "path": "/lustre/fswork/projects/rech/qwv/udv55np/dataset/math/hf/no_thinking_text/generator/default-d32b2cae8ea7e541/0.0.0",
	0: "trust_remote_code": false,
	0: "type": "chat_template"
	0: }
	0: ],
	0: "ddp": true,
	0: "deepspeed": {
	0: "bf16": {
	0: "enabled": true
	0: },
	0: "gradient_accumulation_steps": "auto",
	0: "gradient_clipping": "auto",
	0: "train_batch_size": "auto",
	0: "train_micro_batch_size_per_gpu": "auto",
	0: "wall_clock_breakdown": false,
	0: "zero_optimization": {
	0: "contiguous_gradients": true,
	0: "overlap_comm": true,
	0: "reduce_bucket_size": "auto",
	0: "stage": 3,
	0: "stage3_gather_16bit_weights_on_model_save": true,
	0: "stage3_param_persistence_threshold": "auto",
	0: "stage3_prefetch_bucket_size": "auto",
	0: "sub_group_size": 0
	0: }
	0: },
	0: "device": "cuda:0",
	0: "device_map": {
	0: "": 0
	0: },
	0: "dion_rank_fraction": 1.0,
	0: "dion_rank_multiple_of": 1,
	0: "env_capabilities": {
	0: "torch_version": "2.6.0"
	0: },
	0: "eval_batch_size": 1,
	0: "eval_causal_lm_metrics": [
	0: "sacrebleu",
	0: "comet",
	0: "ter",
	0: "chrf"
	0: ],
	0: "eval_max_new_tokens": 128,
	0: "eval_sample_packing": true,
	0: "eval_table_size": 0,
	0: "evals_per_epoch": 0,
	0: "flash_attention": true,
	0: "fp16": false,
	0: "gradient_accumulation_steps": 1,
	0: "gradient_checkpointing": true,
	0: "gradient_checkpointing_kwargs": {
	0: "use_reentrant": true
	0: },
	0: "learning_rate": 7e-06,
	0: "lisa_layers_attribute": "model.layers",
	0: "load_best_model_at_end": false,
	0: "load_in_4bit": false,
	0: "load_in_8bit": false,
	0: "local_rank": 0,
	0: "logging_steps": 10,
	0: "lora_dropout": 0.0,
	0: "loraplus_lr_embedding": 1e-06,
	0: "lr_scheduler": "warmup_stable_decay",
	0: "lr_scheduler_kwargs": {
	0: "min_lr_ratio": 0.1,
	0: "num_decay_steps": 300
	0: },
	0: "max_prompt_len": 512,
	0: "mean_resizing_embeddings": false,
	0: "micro_batch_size": 1,
	0: "model_config_type": "qwen2",
	0: "num_epochs": 1.0,
	0: "optimizer": "adamw_torch_fused",
	0: "output_dir": "/lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0",
	0: "pad_to_sequence_len": true,
	0: "pretrain_multipack_attn": true,
	0: "pretrain_multipack_buffer_size": 10000,
	0: "profiler_steps_start": 0,
	0: "qlora_sharded_model_loading": false,
	0: "ray_num_workers": 1,
	0: "resources_per_worker": {
	0: "GPU": 1
	0: },
	0: "sample_packing": true,
	0: "sample_packing_bin_size": 200,
	0: "sample_packing_group_size": 100000,
	0: "save_only_model": false,
	0: "save_safetensors": true,
	0: "save_steps": 0.2,
	0: "save_total_limit": 20,
	0: "sequence_len": 16384,
	0: "shuffle_before_merging_datasets": false,
	0: "shuffle_merged_datasets": true,
	0: "skip_prepare_dataset": false,
	0: "special_tokens": {
	0: "bos_token": "<\|im_start\|>",
	0: "eos_token": "<\|im_end\|>",
	0: "pad_token": "<\|endoftext\|>"
	0: },
	0: "strict": false,
	0: "tensor_parallel_size": 1,
	0: "tf32": false,
	0: "tiled_mlp_use_original_mlp": true,
	0: "tokenizer_config": "/lustre/fswork/projects/rech/qwv/udv55np/Qwen/Qwen2.5-1.5B_ift",
	0: "torch_dtype": "torch.bfloat16",
	0: "train_on_inputs": false,
	0: "trl": {
	0: "log_completions": false,
	0: "mask_truncated_completions": false,
	0: "ref_model_mixup_alpha": 0.9,
	0: "ref_model_sync_steps": 64,
	0: "scale_rewards": true,
	0: "sync_ref_model": false,
	0: "use_vllm": false,
	0: "vllm_server_host": "0.0.0.0",
	0: "vllm_server_port": 8000
	0: },
	0: "use_ray": false,
	0: "use_tensorboard": true,
	0: "val_set_size": 0.0,
	0: "vllm": {
	0: "device": "auto",
	0: "dtype": "auto",
	0: "gpu_memory_utilization": 0.9,
	0: "host": "0.0.0.0",
	0: "port": 8000
	0: },
	0: "warmup_steps": 150,
	0: "weight_decay": 0.0,
	0: "world_size": 16
	0: }[39m
	0: [2025-09-02 18:31:57,074] [INFO] [axolotl.cli.checks.check_user_token:35] [PID:801836] [RANK:0] Skipping HuggingFace token verification because HF_HUB_OFFLINE is set to True. Only local files will be used.[39m
	3: [2025-09-02 18:31:57,658] [INFO] [axolotl.utils.data.sft._load_raw_datasets:314] [PID:3352543] [RANK:0] Loading raw datasets...[39m
	3: [2025-09-02 18:31:57,926] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:88] [PID:3352543] [RANK:0] Loading dataset: /lustre/fswork/projects/rech/qwv/udv55np/dataset/math/hf/no_thinking_text/generator/default-d32b2cae8ea7e541/0.0.0 with base_type: chat_template and prompt_style: None[39m
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	3: ▋ \| 214248/321773 [00:26<00:07, 14194.53 examples/s] Tokenizing Prompts (num_proc=192): 67%\|██████▋ \| 216276/321773 [00:26<00:07, 15061.91 examples/s] Tokenizing Prompts (num_proc=192): 68%\|██████▊ \| 218628/321773 [00:27<00:08, 12262.17 examples/s] Tokenizing Prompts (num_proc=192): 68%\|██████▊ \| 220304/321773 [00:27<00:09, 10696.70 examples/s] Tokenizing Prompts (num_proc=192): 69%\|██████▉ \| 222304/321773 [00:27<00:08, 11855.66 examples/s] Tokenizing Prompts (num_proc=192): 71%\|███████ \| 227008/321773 [00:27<00:05, 18143.36 examples/s] Tokenizing Prompts (num_proc=192): 71%\|███████▏ \| 229360/321773 [00:27<00:06, 14199.06 examples/s] Tokenizing Prompts (num_proc=192): 73%\|███████▎ \| 235712/321773 [00:27<00:03, 21816.01 examples/s] Tokenizing Prompts (num_proc=192): 74%\|███████▍ \| 239388/321773 [00:28<00:03, 22456.78 examples/s] Tokenizing Prompts (num_proc=192): 76%\|�
	3: ��██████▌ \| 243064/321773 [00:28<00:03, 23999.96 examples/s] Tokenizing Prompts (num_proc=192): 76%\|███████▋ \| 246092/321773 [00:28<00:03, 21010.93 examples/s] Tokenizing Prompts (num_proc=192): 77%\|███████▋ \| 248444/321773 [00:28<00:04, 14670.92 examples/s] Tokenizing Prompts (num_proc=192): 78%\|███████▊ \| 251120/321773 [00:29<00:06, 10872.04 examples/s] Tokenizing Prompts (num_proc=192): 79%\|███████▊ \| 253120/321773 [00:29<00:06, 9999.79 examples/s] Tokenizing Prompts (num_proc=192): 80%\|███████▉ \| 256472/321773 [00:29<00:05, 12446.17 examples/s] Tokenizing Prompts (num_proc=192): 80%\|████████ \| 258148/321773 [00:29<00:06, 9825.03 examples/s] Tokenizing Prompts (num_proc=192): 81%\|████████▏ \| 261500/321773 [00:29<00:04, 12948.51 examples/s] Tokenizing Prompts (num_proc=192): 82%\|████████▏ \| 263528/321773 [00:30<00:04, 13505.24 examples/s] Tokenizi
	3: ng Prompts (num_proc=192): 83%\|████████▎ \| 265556/321773 [00:30<00:04, 12073.28 examples/s] Tokenizing Prompts (num_proc=192): 83%\|████████▎ \| 268232/321773 [00:30<00:03, 14660.58 examples/s] Tokenizing Prompts (num_proc=192): 84%\|████████▍ \| 270908/321773 [00:30<00:03, 13163.71 examples/s] Tokenizing Prompts (num_proc=192): 85%\|████████▌ \| 273936/321773 [00:31<00:04, 11479.23 examples/s] Tokenizing Prompts (num_proc=192): 87%\|████████▋ \| 279964/321773 [00:31<00:02, 18840.31 examples/s] Tokenizing Prompts (num_proc=192): 88%\|████████▊ \| 282964/321773 [00:31<00:01, 20146.33 examples/s] Tokenizing Prompts (num_proc=192): 89%\|████████▉ \| 286316/321773 [00:31<00:01, 19035.77 examples/s] Tokenizing Prompts (num_proc=192): 90%\|████████▉ \| 288668/321773 [00:31<00:02, 16189.10 examples/s] Tokenizing Prompts (num_proc=192): 90%\|█████████ \| 290696/321
	3: 773 [00:32<00:02, 11163.90 examples/s] Tokenizing Prompts (num_proc=192): 91%\|█████████ \| 292724/321773 [00:32<00:02, 11719.21 examples/s] Tokenizing Prompts (num_proc=192): 92%\|█████████▏\| 294752/321773 [00:32<00:03, 8280.15 examples/s] Tokenizing Prompts (num_proc=192): 92%\|█████████▏\| 296104/321773 [00:32<00:02, 8643.73 examples/s] Tokenizing Prompts (num_proc=192): 93%\|█████████▎\| 298132/321773 [00:32<00:02, 9182.47 examples/s] Tokenizing Prompts (num_proc=192): 93%\|█████████▎\| 299484/321773 [00:33<00:02, 9735.08 examples/s] Tokenizing Prompts (num_proc=192): 93%\|█████████▎\| 300836/321773 [00:33<00:02, 9589.84 examples/s] Tokenizing Prompts (num_proc=192): 94%\|█████████▍\| 302188/321773 [00:33<00:02, 8482.04 examples/s] Tokenizing Prompts (num_proc=192): 94%\|█████████▍\| 303540/321773 [00:33<00:02, 8937.65 examples/s] Tokenizing Prompts (num_p
	3: roc=192): 95%\|█████████▍\| 304892/321773 [00:33<00:02, 7545.40 examples/s] Tokenizing Prompts (num_proc=192): 95%\|█████████▌\| 306243/321773 [00:33<00:02, 7636.62 examples/s] Tokenizing Prompts (num_proc=192): 96%\|█████████▌\| 307593/321773 [00:34<00:01, 7924.16 examples/s] Tokenizing Prompts (num_proc=192): 96%\|█████████▌\| 309620/321773 [00:34<00:01, 7315.62 examples/s] Tokenizing Prompts (num_proc=192): 97%\|█████████▋\| 311646/321773 [00:34<00:01, 5983.22 examples/s] Tokenizing Prompts (num_proc=192): 97%\|█████████▋\| 312997/321773 [00:35<00:01, 5833.55 examples/s] Tokenizing Prompts (num_proc=192): 98%\|█████████▊\| 315698/321773 [00:35<00:00, 7810.36 examples/s] Tokenizing Prompts (num_proc=192): 99%\|█████████▊\| 317048/321773 [00:35<00:00, 7275.24 examples/s] Tokenizing Prompts (num_proc=192): 99%\|█████████▉\| 318398/321773 [00
	3: :35<00:00, 7960.74 examples/s] Tokenizing Prompts (num_proc=192): 99%\|█████████▉\| 319748/321773 [00:36<00:00, 6454.31 examples/s] Tokenizing Prompts (num_proc=192): 100%\|█████████▉\| 321098/321773 [00:36<00:00, 6991.26 examples/s] Tokenizing Prompts (num_proc=192): 100%\|██████████\| 321773/321773 [00:36<00:00, 8790.55 examples/s]
	3: Dropping Long Sequences (>16384) (num_proc=192): 0%\| \| 0/321773 [00:00<?, ? examples/s] Dropping Long Sequences (>16384) (num_proc=192): 0%\| \| 1000/321773 [00:01<09:09, 583.81 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 4%\|▍ \| 13000/321773 [00:01<00:31, 9748.54 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 8%\|▊ \| 24676/321773 [00:02<00:16, 18505.00 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 10%\|▉ \| 31436/321773 [00:02<00:16, 17386.79 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 11%\|█▏ \| 36492/321773 [00:02<00:15, 18238.78 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 13%\|█▎ \| 40844/321773 [00:02<00:13, 20542.41 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 14%\|█▍ \| 45196/321773 [00:02<00:11, 23529.08 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 15%\|█▌ \| 49224/321773 [00:03<00:10, 25848.89 exampl
	3: es/s] Dropping Long Sequences (>16384) (num_proc=192): 17%\|█▋ \| 53224/321773 [00:03<00:09, 27406.33 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 50%\|█████ \| 160968/321773 [00:03<00:00, 242231.63 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 64%\|██████▍ \| 205600/321773 [00:03<00:00, 283879.55 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 76%\|███████▌ \| 243444/321773 [00:03<00:00, 242398.75 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 86%\|████████▌ \| 275640/321773 [00:03<00:00, 227367.38 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 95%\|█████████▍\| 304213/321773 [00:03<00:00, 181908.04 examples/s] Dropping Long Sequences (>16384) (num_proc=192): 100%\|██████████\| 321773/321773 [00:04<00:00, 69680.11 examples/s]
	3: Drop Samples with Zero Trainable Tokens (num_proc=192): 0%\| \| 0/315947 [00:00<?, ? examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 0%\| \| 1000/315947 [00:01<09:07, 574.76 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 3%\|▎ \| 9000/315947 [00:01<00:46, 6579.69 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 5%\|▌ \| 17000/315947 [00:01<00:21, 13663.54 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 7%\|▋ \| 23292/315947 [00:02<00:15, 19157.30 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 9%\|▉ \| 29230/315947 [00:02<00:13, 21824.77 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 11%\|█ \| 34814/315947 [00:02<00:11, 24900.39 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 13%\|█▎ \| 40044/315947 [00:02<00:11, 24024.45 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 15%\|
	3: █▍ \| 45982/315947 [00:02<00:09, 29154.70 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 16%\|█▌ \| 51274/315947 [00:02<00:08, 31939.93 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 18%\|█▊ \| 56212/315947 [00:03<00:08, 30464.92 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 19%\|█▉ \| 60796/315947 [00:03<00:08, 29937.43 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 21%\|██ \| 66088/315947 [00:03<00:07, 33742.94 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 23%\|██▎ \| 71380/315947 [00:03<00:06, 37325.43 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 24%\|██▍ \| 76318/315947 [00:03<00:06, 35668.58 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 26%\|██▌ \| 81256/315947 [00:03<00:09, 23980.92 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 27%\|██▋ \|
	3: 84840/315947 [00:04<00:10, 23099.84 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 28%\|██▊ \| 89132/315947 [00:04<00:08, 25831.01 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 30%\|███ \| 95716/315947 [00:04<00:06, 33324.19 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 84%\|████████▍ \| 265082/315947 [00:04<00:00, 394437.69 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 100%\|██████████\| 315947/315947 [00:05<00:00, 57226.91 examples/s]
	3: Add position_id column (Sample Packing) (num_proc=192): 0%\| \| 0/315947 [00:00<?, ? examples/s] Add position_id column (Sample Packing) (num_proc=192): 0%\| \| 1000/315947 [00:02<10:54, 481.25 examples/s] Add position_id column (Sample Packing) (num_proc=192): 4%\|▍ \| 13000/315947 [00:02<00:37, 8147.50 examples/s] Add position_id column (Sample Packing) (num_proc=192): 7%\|▋ \| 23000/315947 [00:02<00:18, 15678.25 examples/s] Add position_id column (Sample Packing) (num_proc=192): 10%\|█ \| 32000/315947 [00:02<00:12, 23384.21 examples/s] Add position_id column (Sample Packing) (num_proc=192): 14%\|█▍ \| 44000/315947 [00:02<00:07, 35667.24 examples/s] Add position_id column (Sample Packing) (num_proc=192): 17%\|█▋ \| 55000/315947 [00:02<00:05, 46194.47 examples/s] Add position_id column (Sample Packing) (num_proc=192): 20%\|██ \| 64292/315947 [00:02<00:05, 49525.45 examples/s] Add position_id column (Sample Packing) (num_proc=192):
	3: 25%\|██▍ \| 77876/315947 [00:02<00:03, 63396.95 examples/s] Add position_id column (Sample Packing) (num_proc=192): 29%\|██▊ \| 90460/315947 [00:03<00:03, 73841.86 examples/s] Add position_id column (Sample Packing) (num_proc=192): 32%\|███▏ \| 100690/315947 [00:03<00:03, 71582.31 examples/s] Add position_id column (Sample Packing) (num_proc=192): 35%\|███▌ \| 111504/315947 [00:03<00:02, 77234.71 examples/s] Add position_id column (Sample Packing) (num_proc=192): 39%\|███▉ \| 123964/315947 [00:03<00:02, 87113.03 examples/s] Add position_id column (Sample Packing) (num_proc=192): 43%\|████▎ \| 134424/315947 [00:03<00:02, 84181.32 examples/s] Add position_id column (Sample Packing) (num_proc=192): 48%\|████▊ \| 152238/315947 [00:03<00:01, 107063.91 examples/s] Add position_id column (Sample Packing) (num_proc=192): 53%\|█████▎ \| 168114/315947 [00:03<00:01, 119038.24 examples/s] Add position_id column (Sample Packi
	3: ng) (num_proc=192): 57%\|█████▋ \| 181574/315947 [00:03<00:01, 121647.10 examples/s] Add position_id column (Sample Packing) (num_proc=192): 63%\|██████▎ \| 198326/315947 [00:03<00:00, 133616.17 examples/s] Add position_id column (Sample Packing) (num_proc=192): 67%\|██████▋ \| 212847/315947 [00:04<00:00, 117655.05 examples/s] Add position_id column (Sample Packing) (num_proc=192): 73%\|███████▎ \| 230660/315947 [00:04<00:00, 130519.18 examples/s] Add position_id column (Sample Packing) (num_proc=192): 78%\|███████▊ \| 247115/315947 [00:04<00:00, 139062.35 examples/s] Add position_id column (Sample Packing) (num_proc=192): 83%\|████████▎ \| 262212/315947 [00:04<00:00, 135531.58 examples/s] Add position_id column (Sample Packing) (num_proc=192): 88%\|████████▊ \| 276470/315947 [00:04<00:00, 131531.90 examples/s] Add position_id column (Sample Packing) (num_proc=192): 92%\|█████████▏
	3: \| 290147/315947 [00:04<00:00, 119964.83 examples/s] Add position_id column (Sample Packing) (num_proc=192): 96%\|█████████▌\| 303047/315947 [00:04<00:00, 108030.56 examples/s] Add position_id column (Sample Packing) (num_proc=192): 100%\|█████████▉\| 314657/315947 [00:05<00:00, 73525.53 examples/s] Add position_id column (Sample Packing) (num_proc=192): 100%\|██████████\| 315947/315947 [00:05<00:00, 57407.99 examples/s]
	3: Saving the dataset (0/192 shards): 0%\| \| 0/315947 [00:00<?, ? examples/s] Saving the dataset (0/192 shards): 1%\| \| 1646/315947 [00:01<05:32, 944.80 examples/s] Saving the dataset (1/192 shards): 1%\| \| 1646/315947 [00:01<05:32, 944.80 examples/s] Saving the dataset (2/192 shards): 1%\| \| 3292/315947 [00:01<05:30, 944.80 examples/s] Saving the dataset (3/192 shards): 2%\|▏ \| 4938/315947 [00:01<05:29, 944.80 examples/s] Saving the dataset (4/192 shards): 2%\|▏ \| 6584/315947 [00:01<05:27, 944.80 examples/s] Saving the dataset (5/192 shards): 3%\|▎ \| 8230/315947 [00:01<05:25, 944.80 examples/s] Saving the dataset (6/192 shards): 3%\|▎ \| 9876/315947 [00:01<05:23, 944.80 examples/s] Saving the dataset (7/192 shards): 4%\|▎ \| 11522/315947 [00:01<05:22, 944.80 examples/s] Saving the dataset (8/192 shards): 4%\|▍ \| 13168/315947 [00:01<05:20, 944.80 examples/s] Saving the dataset (9/192 shards): 5%\|▍
	3: \| 14814/315947 [00:01<05:18, 944.80 examples/s] Saving the dataset (10/192 shards): 5%\|▌ \| 16460/315947 [00:01<05:16, 944.80 examples/s] Saving the dataset (11/192 shards): 6%\|▋ \| 19752/315947 [00:01<05:13, 944.80 examples/s] Saving the dataset (12/192 shards): 6%\|▋ \| 19752/315947 [00:01<05:13, 944.80 examples/s] Saving the dataset (13/192 shards): 7%\|▋ \| 21398/315947 [00:01<05:11, 944.80 examples/s] Saving the dataset (14/192 shards): 7%\|▋ \| 23044/315947 [00:01<05:10, 944.80 examples/s] Saving the dataset (15/192 shards): 8%\|▊ \| 24690/315947 [00:01<05:08, 944.80 examples/s] Saving the dataset (16/192 shards): 8%\|▊ \| 26336/315947 [00:01<05:06, 944.80 examples/s] Saving the dataset (17/192 shards): 9%\|▉ \| 27982/315947 [00:01<05:04, 944.80 examples/s] Saving the dataset (18/192 shards): 9%\|▉ \| 29628/315947 [00:01<05:03, 944.80 examples/s] Saving the dataset (19/192 shards): 10%\|█ \| 32920/31594
	3: 7 [00:01<04:59, 944.80 examples/s] Saving the dataset (20/192 shards): 10%\|█ \| 32920/315947 [00:01<04:59, 944.80 examples/s] Saving the dataset (21/192 shards): 11%\|█▏ \| 36212/315947 [00:01<04:56, 944.80 examples/s] Saving the dataset (22/192 shards): 11%\|█▏ \| 36212/315947 [00:01<04:56, 944.80 examples/s] Saving the dataset (23/192 shards): 12%\|█▏ \| 37858/315947 [00:01<04:54, 944.80 examples/s] Saving the dataset (24/192 shards): 13%\|█▎ \| 41150/315947 [00:01<04:50, 944.80 examples/s] Saving the dataset (25/192 shards): 13%\|█▎ \| 41150/315947 [00:01<04:50, 944.80 examples/s] Saving the dataset (26/192 shards): 14%\|█▎ \| 42796/315947 [00:01<04:49, 944.80 examples/s] Saving the dataset (27/192 shards): 14%\|█▍ \| 44442/315947 [00:01<04:47, 944.80 examples/s] Saving the dataset (28/192 shards): 15%\|█▍ \| 46088/315947 [00:01<04:45, 944.80 examples/s] Saving the dataset (29/192 shards): 15%\|█▌ \| 47734/3
	3: 15947 [00:01<04:43, 944.80 examples/s] Saving the dataset (30/192 shards): 16%\|█▌ \| 51026/315947 [00:01<04:40, 944.80 examples/s] Saving the dataset (31/192 shards): 16%\|█▌ \| 51026/315947 [00:01<04:40, 944.80 examples/s] Saving the dataset (32/192 shards): 17%\|█▋ \| 52672/315947 [00:01<04:38, 944.80 examples/s] Saving the dataset (33/192 shards): 17%\|█▋ \| 54318/315947 [00:01<04:36, 944.80 examples/s] Saving the dataset (34/192 shards): 18%\|█▊ \| 55964/315947 [00:01<04:35, 944.80 examples/s] Saving the dataset (35/192 shards): 18%\|█▊ \| 57610/315947 [00:01<04:33, 944.80 examples/s] Saving the dataset (36/192 shards): 19%\|█▉ \| 59256/315947 [00:01<04:31, 944.80 examples/s] Saving the dataset (37/192 shards): 20%\|█▉ \| 62548/315947 [00:01<04:28, 944.80 examples/s] Saving the dataset (38/192 shards): 20%\|█▉ \| 62548/315947 [00:01<04:28, 944.80 examples/s] Saving the dataset (39/192 shards): 21%\|██ \| 6
	3: 5840/315947 [00:01<04:24, 944.80 examples/s] Saving the dataset (40/192 shards): 21%\|██▏ \| 67486/315947 [00:01<04:22, 944.80 examples/s] Saving the dataset (41/192 shards): 21%\|██▏ \| 67486/315947 [00:01<04:22, 944.80 examples/s] Saving the dataset (42/192 shards): 22%\|██▏ \| 69132/315947 [00:01<04:21, 944.80 examples/s] Saving the dataset (43/192 shards): 22%\|██▏ \| 70778/315947 [00:01<04:19, 944.80 examples/s] Saving the dataset (44/192 shards): 23%\|██▎ \| 72424/315947 [00:01<04:17, 944.80 examples/s] Saving the dataset (45/192 shards): 23%\|██▎ \| 74070/315947 [00:01<04:16, 944.80 examples/s] Saving the dataset (46/192 shards): 24%\|██▍ \| 75716/315947 [00:01<04:14, 944.80 examples/s] Saving the dataset (47/192 shards): 24%\|██▍ \| 77362/315947 [00:01<04:12, 944.80 examples/s] Saving the dataset (48/192 shards): 25%\|██▌ \| 79008/315947 [00:01<04:10, 944.80 examples/s] Saving the dataset (49/192 shards)
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	3: :01<02:47, 944.80 examples/s] Saving the dataset (97/192 shards): 51%\|█████ \| 159662/315947 [00:01<02:45, 944.80 examples/s] Saving the dataset (98/192 shards): 52%\|█████▏ \| 162954/315947 [00:01<02:41, 944.80 examples/s] Saving the dataset (99/192 shards): 52%\|█████▏ \| 162954/315947 [00:01<02:41, 944.80 examples/s] Saving the dataset (100/192 shards): 52%\|█████▏ \| 164600/315947 [00:01<02:40, 944.80 examples/s] Saving the dataset (101/192 shards): 53%\|█████▎ \| 167892/315947 [00:01<02:36, 944.80 examples/s] Saving the dataset (102/192 shards): 53%\|█████▎ \| 167892/315947 [00:01<02:36, 944.80 examples/s] Saving the dataset (103/192 shards): 54%\|█████▎ \| 169538/315947 [00:01<02:34, 944.80 examples/s] Saving the dataset (104/192 shards): 54%\|█████▍ \| 171184/315947 [00:01<02:33, 944.80 examples/s] Saving the dataset (105/192 shards): 55%\|█████▌ \| 174476/315947 [00:01<02:29, 9
	3: 44.80 examples/s] Saving the dataset (106/192 shards): 56%\|█████▌ \| 176121/315947 [00:01<02:27, 944.80 examples/s] Saving the dataset (107/192 shards): 56%\|█████▌ \| 176121/315947 [00:01<02:27, 944.80 examples/s] Saving the dataset (108/192 shards): 56%\|█████▋ \| 177767/315947 [00:01<02:26, 944.80 examples/s] Saving the dataset (109/192 shards): 57%\|█████▋ \| 179412/315947 [00:01<02:24, 944.80 examples/s] Saving the dataset (110/192 shards): 57%\|█████▋ \| 181057/315947 [00:01<02:22, 944.80 examples/s] Saving the dataset (111/192 shards): 58%\|█████▊ \| 182702/315947 [00:01<02:21, 944.80 examples/s] Saving the dataset (112/192 shards): 58%\|█████▊ \| 184347/315947 [00:01<02:19, 944.80 examples/s] Saving the dataset (113/192 shards): 59%\|█████▉ \| 185992/315947 [00:01<02:17, 944.80 examples/s] Saving the dataset (114/192 shards): 59%\|█████▉ \| 187637/315947 [00:01<02:15, 944.80 e
	3: xamples/s] Saving the dataset (115/192 shards): 60%\|█████▉ \| 189282/315947 [00:01<02:14, 944.80 examples/s] Saving the dataset (116/192 shards): 60%\|██████ \| 190927/315947 [00:01<02:12, 944.80 examples/s] Saving the dataset (117/192 shards): 61%\|██████ \| 192572/315947 [00:01<02:10, 944.80 examples/s] Saving the dataset (118/192 shards): 61%\|██████▏ \| 194217/315947 [00:01<02:08, 944.80 examples/s] Saving the dataset (119/192 shards): 62%\|██████▏ \| 195862/315947 [00:01<02:07, 944.80 examples/s] Saving the dataset (120/192 shards): 63%\|██████▎ \| 197507/315947 [00:01<02:05, 944.80 examples/s] Saving the dataset (121/192 shards): 63%\|██████▎ \| 199152/315947 [00:01<02:03, 944.80 examples/s] Saving the dataset (122/192 shards): 64%\|██████▎ \| 200797/315947 [00:01<02:01, 944.80 examples/s] Saving the dataset (123/192 shards): 64%\|██████▍ \| 202442/315947 [00:01<02:00, 944
	3: .80 examples/s] Saving the dataset (124/192 shards): 65%\|██████▍ \| 204087/315947 [00:01<01:58, 944.80 examples/s] Saving the dataset (125/192 shards): 65%\|██████▌ \| 205732/315947 [00:01<01:56, 944.80 examples/s] Saving the dataset (126/192 shards): 66%\|██████▌ \| 207377/315947 [00:01<01:54, 944.80 examples/s] Saving the dataset (127/192 shards): 66%\|██████▌ \| 209022/315947 [00:01<01:53, 944.80 examples/s] Saving the dataset (128/192 shards): 67%\|██████▋ \| 210667/315947 [00:01<01:51, 944.80 examples/s] Saving the dataset (129/192 shards): 67%\|██████▋ \| 212312/315947 [00:01<01:49, 944.80 examples/s] Saving the dataset (130/192 shards): 68%\|██████▊ \| 215602/315947 [00:01<01:46, 944.80 examples/s] Saving the dataset (131/192 shards): 68%\|██████▊ \| 215602/315947 [00:01<01:46, 944.80 examples/s] Saving the dataset (132/192 shards): 69%\|██████▉ \| 218892/315947 [00:01
	3: <01:42, 944.80 examples/s] Saving the dataset (133/192 shards): 70%\|██████▉ \| 220537/315947 [00:01<01:40, 944.80 examples/s] Saving the dataset (134/192 shards): 70%\|██████▉ \| 220537/315947 [00:01<01:40, 944.80 examples/s] Saving the dataset (135/192 shards): 71%\|███████ \| 223827/315947 [00:01<01:37, 944.80 examples/s] Saving the dataset (136/192 shards): 71%\|███████▏ \| 225472/315947 [00:01<01:35, 944.80 examples/s] Saving the dataset (137/192 shards): 71%\|███████▏ \| 225472/315947 [00:01<01:35, 944.80 examples/s] Saving the dataset (138/192 shards): 72%\|███████▏ \| 227117/315947 [00:01<01:34, 944.80 examples/s] Saving the dataset (139/192 shards): 72%\|███████▏ \| 228762/315947 [00:01<01:32, 944.80 examples/s] Saving the dataset (140/192 shards): 73%\|███████▎ \| 230407/315947 [00:01<01:30, 944.80 examples/s] Saving the dataset (141/192 shards): 73%\|███████▎
	3: \| 232052/315947 [00:01<01:28, 944.80 examples/s] Saving the dataset (142/192 shards): 74%\|███████▍ \| 233697/315947 [00:01<01:27, 944.80 examples/s] Saving the dataset (143/192 shards): 74%\|███████▍ \| 235342/315947 [00:01<01:25, 944.80 examples/s] Saving the dataset (144/192 shards): 75%\|███████▌ \| 236987/315947 [00:01<01:23, 944.80 examples/s] Saving the dataset (145/192 shards): 76%\|███████▌ \| 238632/315947 [00:01<01:21, 944.80 examples/s] Saving the dataset (146/192 shards): 76%\|███████▌ \| 240277/315947 [00:01<01:20, 944.80 examples/s] Saving the dataset (147/192 shards): 77%\|███████▋ \| 241922/315947 [00:01<01:18, 944.80 examples/s] Saving the dataset (148/192 shards): 78%\|███████▊ \| 245212/315947 [00:01<01:14, 944.80 examples/s] Saving the dataset (149/192 shards): 78%\|███████▊ \| 245212/315947 [00:01<01:14, 944.80 examples/s] Saving the dataset (150/192 shards):
	3: 79%\|███████▊ \| 248502/315947 [00:01<01:11, 944.80 examples/s] Saving the dataset (151/192 shards): 79%\|███████▊ \| 248502/315947 [00:01<01:11, 944.80 examples/s] Saving the dataset (152/192 shards): 79%\|███████▉ \| 250147/315947 [00:01<01:09, 944.80 examples/s] Saving the dataset (153/192 shards): 80%\|████████ \| 253437/315947 [00:01<01:06, 944.80 examples/s] Saving the dataset (154/192 shards): 80%\|████████ \| 253437/315947 [00:01<01:06, 944.80 examples/s] Saving the dataset (155/192 shards): 81%\|████████ \| 255082/315947 [00:01<01:04, 944.80 examples/s] Saving the dataset (156/192 shards): 81%\|████████▏ \| 256727/315947 [00:01<01:02, 944.80 examples/s] Saving the dataset (157/192 shards): 82%\|████████▏ \| 260017/315947 [00:01<00:59, 944.80 examples/s] Saving the dataset (158/192 shards): 82%\|████████▏ \| 260017/315947 [00:01<00:59, 944.80 examples/s] Sav
	3: ing the dataset (159/192 shards): 83%\|████████▎ \| 263307/315947 [00:01<00:55, 944.80 examples/s] Saving the dataset (160/192 shards): 83%\|████████▎ \| 263307/315947 [00:01<00:55, 944.80 examples/s] Saving the dataset (161/192 shards): 84%\|████████▍ \| 264952/315947 [00:01<00:53, 944.80 examples/s] Saving the dataset (162/192 shards): 84%\|████████▍ \| 266597/315947 [00:01<00:52, 944.80 examples/s] Saving the dataset (163/192 shards): 85%\|████████▍ \| 268242/315947 [00:01<00:50, 944.80 examples/s] Saving the dataset (164/192 shards): 86%\|████████▋ \| 273177/315947 [00:01<00:45, 944.80 examples/s] Saving the dataset (165/192 shards): 86%\|████████▋ \| 273177/315947 [00:01<00:45, 944.80 examples/s] Saving the dataset (166/192 shards): 87%\|████████▋ \| 274822/315947 [00:01<00:43, 944.80 examples/s] Saving the dataset (167/192 shards): 87%\|████████▋ \| 274
	3: 822/315947 [00:01<00:43, 944.80 examples/s] Saving the dataset (168/192 shards): 88%\|████████▊ \| 276467/315947 [00:01<00:41, 944.80 examples/s] Saving the dataset (169/192 shards): 88%\|████████▊ \| 278112/315947 [00:01<00:40, 944.80 examples/s] Saving the dataset (170/192 shards): 89%\|████████▉ \| 281402/315947 [00:01<00:36, 944.80 examples/s] Saving the dataset (171/192 shards): 89%\|████████▉ \| 281402/315947 [00:01<00:36, 944.80 examples/s] Saving the dataset (172/192 shards): 90%\|████████▉ \| 283047/315947 [00:01<00:34, 944.80 examples/s] Saving the dataset (173/192 shards): 91%\|█████████ \| 286337/315947 [00:01<00:31, 944.80 examples/s] Saving the dataset (174/192 shards): 91%\|█████████ \| 286337/315947 [00:01<00:31, 944.80 examples/s] Saving the dataset (175/192 shards): 91%\|█████████ \| 287982/315947 [00:01<00:29, 944.80 examples/s] Saving the dataset (176/192
	3: shards): 92%\|█████████▏\| 289627/315947 [00:01<00:27, 944.80 examples/s] Saving the dataset (177/192 shards): 92%\|█████████▏\| 291272/315947 [00:01<00:26, 944.80 examples/s] Saving the dataset (178/192 shards): 93%\|█████████▎\| 292917/315947 [00:01<00:24, 944.80 examples/s] Saving the dataset (179/192 shards): 93%\|█████████▎\| 294562/315947 [00:01<00:22, 944.80 examples/s] Saving the dataset (180/192 shards): 94%\|█████████▍\| 296207/315947 [00:01<00:20, 944.80 examples/s] Saving the dataset (181/192 shards): 94%\|█████████▍\| 297852/315947 [00:01<00:19, 944.80 examples/s] Saving the dataset (182/192 shards): 95%\|█████████▍\| 299497/315947 [00:01<00:17, 944.80 examples/s] Saving the dataset (183/192 shards): 95%\|█████████▌\| 301142/315947 [00:01<00:15, 944.80 examples/s] Saving the dataset (184/192 shards): 96%\|█████████▌\| 302787/315
	3: 947 [00:01<00:13, 944.80 examples/s] Saving the dataset (185/192 shards): 96%\|█████████▋\| 304432/315947 [00:01<00:12, 944.80 examples/s] Saving the dataset (186/192 shards): 97%\|█████████▋\| 306077/315947 [00:01<00:10, 944.80 examples/s] Saving the dataset (187/192 shards): 97%\|█████████▋\| 307722/315947 [00:01<00:08, 944.80 examples/s] Saving the dataset (188/192 shards): 98%\|█████████▊\| 309367/315947 [00:01<00:06, 944.80 examples/s] Saving the dataset (189/192 shards): 98%\|█████████▊\| 311012/315947 [00:01<00:05, 944.80 examples/s] Saving the dataset (190/192 shards): 99%\|█████████▉\| 312657/315947 [00:01<00:03, 944.80 examples/s] Saving the dataset (191/192 shards): 100%\|██████████\| 315947/315947 [00:01<00:00, 944.80 examples/s] Saving the dataset (192/192 shards): 100%\|██████████\| 315947/315947 [00:01<00:00, 944.80 examples/s] Saving the dataset
	3: (192/192 shards): 100%\|██████████\| 315947/315947 [00:01<00:00, 166814.88 examples/s]
	0: [2025-09-02 18:33:00,133] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:472] [PID:801836] [RANK:0] Loading prepared dataset from disk at /lustre/fsn1/projects/rech/dgo/udv55np/dataset_math/Qwen3-235B-A22B/0/df4594deeb64977bb186d11562029017...[39m
	0: [2025-09-02 18:33:37,212] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:436] [PID:801836] [RANK:0] gather_len_batches: [25939, 25940, 25939, 25938, 25938, 25939, 25939, 25939, 25939, 25938, 25939, 25939, 25939, 25939, 25939, 25938][39m
	0: [2025-09-02 18:33:37,417] [INFO] [axolotl.utils.trainer.calc_sample_packing_eff_est:495] [PID:801836] [RANK:0] sample_packing_eff_est across ranks: [0.9965550303459167, 0.9965550303459167, 0.9965550303459167, 0.9965550303459167, 0.9965166449546814, 0.9965550303459167, 0.996631920337677, 0.9965550303459167, 0.9965934753417969, 0.9965934753417969, 0.9965550303459167, 0.9965550303459167, 0.9965550303459167, 0.9965550303459167, 0.9965934753417969, 0.9965550303459167][39m
	0: [2025-09-02 18:33:37,422] [INFO] [axolotl.utils.data.sft._prepare_standard_dataset:127] [PID:801836] [RANK:0] Maximum number of steps set at 1621[39m
	0: [2025-09-02 18:33:37,749] [INFO] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:110] [PID:801836] [RANK:0] Patched Trainer.evaluation_loop with nanmean loss calculation[39m
	0: [2025-09-02 18:33:37,750] [INFO] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:164] [PID:801836] [RANK:0] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation[39m
	0: [2025-09-02 18:33:50,442] [INFO] [axolotl.loaders.model._configure_embedding_dtypes:345] [PID:801836] [RANK:0] Converting modules to torch.bfloat16[39m
	0: [2025-09-02 18:34:01,367] [INFO] [axolotl.train.save_initial_configs:416] [PID:801836] [RANK:0] Pre-saving tokenizer to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0...[39m
	0: [2025-09-02 18:34:01,793] [INFO] [axolotl.train.save_initial_configs:419] [PID:801836] [RANK:0] Pre-saving model config to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0...[39m
	0: [2025-09-02 18:34:01,800] [INFO] [axolotl.train.execute_training:203] [PID:801836] [RANK:0] Starting trainer...[39m
	0: [2025-09-02 18:36:07,470] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:436] [PID:801836] [RANK:0] gather_len_batches: [25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939, 25939][39m
	0: Parameter Offload - Persistent parameters statistics: param_count = 141, numel = 144896
	0: {'loss': 0.3291, 'grad_norm': 0.3215025832173914, 'learning_rate': 1.0779999999999999e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.01}
	0: 0%\| \| 0/1621 [00:00<?, ?it/s] 0%\| \| 1/1621 [03:15<87:56:27, 195.42s/it] 0%\| \| 2/1621 [03:18<36:53:28, 82.03s/it] 0%\| \| 3/1621 [03:18<20:12:28, 44.96s/it] 0%\| \| 4/1621 [03:19<12:22:43, 27.56s/it] 0%\| \| 5/1621 [03:20<8:03:17, 17.94s/it] 0%\| \| 6/1621 [03:21<5:26:43, 12.14s/it] 0%\| \| 7/1621 [03:22<3:47:13, 8.45s/it] 0%\| \| 8/1621 [03:23<2:42:07, 6.03s/it] 1%\| \| 9/1621 [03:24<1:58:26, 4.41s/it] 1%\| \| 10/1621 [03:24<1:29:01, 3.32s/it] 1%\| \| 10/1621 [03:25<1:29:01, 3.32s/it] 1%\| \| 11/1621 [03:25<1:09:22, 2.59s/it] 1%\| \| 12/1621 [03:26<55:27, 2.07s/it] 1%\| \| 13/1621 [03:27<47:03, 1.76s/it] 1%\| \| 14/1621 [03:28<39:48, 1.49s/it] 1%\| \| 15/1621 [03:29<34:37, 1.29s/it] 1%\| \| 16/1621 [03:30<31:04, 1.16s/it] 1%\| \| 17/1621 [03:31<28:54, 1.08s/it] 1%\|
	0: {'loss': 0.329, 'grad_norm': 0.2975306152955043, 'learning_rate': 1.498e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.01}
	0: {'loss': 0.332, 'grad_norm': 0.2988797728667972, 'learning_rate': 1.918e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.02}
	0: \| 18/1621 [03:32<28:10, 1.05s/it] 1%\| \| 19/1621 [03:33<26:28, 1.01it/s] 1%\| \| 20/1621 [03:34<25:36, 1.04it/s] 1%\| \| 20/1621 [03:34<25:36, 1.04it/s] 1%\|▏ \| 21/1621 [03:34<24:55, 1.07it/s] 1%\|▏ \| 22/1621 [03:35<24:19, 1.10it/s] 1%\|▏ \| 23/1621 [03:36<23:58, 1.11it/s] 1%\|▏ \| 24/1621 [03:37<23:28, 1.13it/s] 2%\|▏ \| 25/1621 [03:38<23:19, 1.14it/s] 2%\|▏ \| 26/1621 [03:39<23:58, 1.11it/s] 2%\|▏ \| 27/1621 [03:40<23:39, 1.12it/s] 2%\|▏ \| 28/1621 [03:41<23:17, 1.14it/s] 2%\|▏ \| 29/1621 [03:41<23:13, 1.14it/s] 2%\|▏ \| 30/1621 [03:42<23:56, 1.11it/s] 2%\|▏ \| 30/1621 [03:42<23:56, 1.11it/s] 2%\|▏ \| 31/1621 [03:43<23:34, 1.12it/s] 2%\|▏ \| 32/1621 [03:44<23:23, 1.13it/s] 2%\|▏ \| 33/1621 [03:45<23:01, 1.15it/s]
	0: {'loss': 0.3293, 'grad_norm': 0.31013818468501975, 'learning_rate': 2.338e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.02}
	0: 2%\|▏ \| 34/1621 [03:46<22:52, 1.16it/s] 2%\|▏ \| 35/1621 [03:47<22:41, 1.16it/s] 2%\|▏ \| 36/1621 [03:47<22:37, 1.17it/s] 2%\|▏ \| 37/1621 [03:48<22:30, 1.17it/s] 2%\|▏ \| 38/1621 [03:49<23:02, 1.14it/s] 2%\|▏ \| 39/1621 [03:50<22:53, 1.15it/s] 2%\|▏ \| 40/1621 [03:51<22:52, 1.15it/s] 2%\|▏ \| 40/1621 [03:51<22:52, 1.15it/s] 3%\|▎ \| 41/1621 [03:52<23:20, 1.13it/s] 3%\|▎ \| 42/1621 [03:53<23:09, 1.14it/s] 3%\|▎ \| 43/1621 [03:54<23:12, 1.13it/s] 3%\|▎ \| 44/1621 [03:54<23:03, 1.14it/s] 3%\|▎ \| 45/1621 [03:55<22:56, 1.15it/s] 3%\|▎ \| 46/1621 [03:56<22:47, 1.15it/s] 3%\|▎ \| 47/1621 [03:57<22:51, 1.15it/s] 3%\|▎ \| 48/1621 [03:58<22:36, 1.16it/s] 3%\|▎ \| 49/1621 [03:59<22:55, 1.14it/s] 3%\|▎ \| 50/1621 [04:00<23:14, 1.13it/s]
	0: {'loss': 0.3215, 'grad_norm': 0.30101641303027743, 'learning_rate': 2.758e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.03}
	0: {'loss': 0.3283, 'grad_norm': 0.28711923264082334, 'learning_rate': 3.1779999999999995e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.04}
	0: 3%\|▎ \| 50/1621 [04:00<23:14, 1.13it/s] 3%\|▎ \| 51/1621 [04:01<23:02, 1.14it/s] 3%\|▎ \| 52/1621 [04:01<22:45, 1.15it/s] 3%\|▎ \| 53/1621 [04:02<22:47, 1.15it/s] 3%\|▎ \| 54/1621 [04:03<22:50, 1.14it/s] 3%\|▎ \| 55/1621 [04:04<22:59, 1.14it/s] 3%\|▎ \| 56/1621 [04:05<22:56, 1.14it/s] 4%\|▎ \| 57/1621 [04:06<22:42, 1.15it/s] 4%\|▎ \| 58/1621 [04:07<22:38, 1.15it/s] 4%\|▎ \| 59/1621 [04:08<22:32, 1.15it/s] 4%\|▎ \| 60/1621 [04:08<22:22, 1.16it/s] 4%\|▎ \| 60/1621 [04:08<22:22, 1.16it/s] 4%\|▍ \| 61/1621 [04:09<23:12, 1.12it/s] 4%\|▍ \| 62/1621 [04:10<22:57, 1.13it/s] 4%\|▍ \| 63/1621 [04:11<22:45, 1.14it/s] 4%\|▍ \| 64/1621 [04:12<22:35, 1.15it/s] 4%\|▍ \| 65/1621 [04:13<23:04, 1.12it/s] 4%\|▍ \| 66/1621 [04:14<23:12, 1.12it/s] 4%\|▍ \| 67/
	0: {'loss': 0.3187, 'grad_norm': 0.3018654823455164, 'learning_rate': 3.598e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.04}
	0: {'loss': 0.3181, 'grad_norm': 0.2990949595796023, 'learning_rate': 4.0179999999999995e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.05}
	0: 1621 [04:15<22:58, 1.13it/s] 4%\|▍ \| 68/1621 [04:16<23:10, 1.12it/s] 4%\|▍ \| 69/1621 [04:17<24:41, 1.05it/s] 4%\|▍ \| 70/1621 [04:18<23:52, 1.08it/s] 4%\|▍ \| 70/1621 [04:18<23:52, 1.08it/s] 4%\|▍ \| 71/1621 [04:18<23:48, 1.09it/s] 4%\|▍ \| 72/1621 [04:19<23:25, 1.10it/s] 5%\|▍ \| 73/1621 [04:20<23:07, 1.12it/s] 5%\|▍ \| 74/1621 [04:21<22:42, 1.14it/s] 5%\|▍ \| 75/1621 [04:22<22:30, 1.15it/s] 5%\|▍ \| 76/1621 [04:23<22:22, 1.15it/s] 5%\|▍ \| 77/1621 [04:24<22:16, 1.16it/s] 5%\|▍ \| 78/1621 [04:24<22:13, 1.16it/s] 5%\|▍ \| 79/1621 [04:25<22:14, 1.16it/s] 5%\|▍ \| 80/1621 [04:26<23:04, 1.11it/s] 5%\|▍ \| 80/1621 [04:26<23:04, 1.11it/s] 5%\|▍ \| 81/1621 [04:27<23:33, 1.09it/s] 5%\|▌ \| 82/1621 [04:28<23:02, 1.11it/s] 5%\|▌
	0: {'loss': 0.318, 'grad_norm': 0.29019982626500473, 'learning_rate': 4.438e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.06}
	0: \| 83/1621 [04:29<22:51, 1.12it/s] 5%\|▌ \| 84/1621 [04:30<22:45, 1.13it/s] 5%\|▌ \| 85/1621 [04:31<22:46, 1.12it/s] 5%\|▌ \| 86/1621 [04:32<23:35, 1.08it/s] 5%\|▌ \| 87/1621 [04:33<23:29, 1.09it/s] 5%\|▌ \| 88/1621 [04:34<23:00, 1.11it/s] 5%\|▌ \| 89/1621 [04:34<22:40, 1.13it/s] 6%\|▌ \| 90/1621 [04:35<22:27, 1.14it/s] 6%\|▌ \| 90/1621 [04:35<22:27, 1.14it/s] 6%\|▌ \| 91/1621 [04:36<22:21, 1.14it/s] 6%\|▌ \| 92/1621 [04:37<22:09, 1.15it/s] 6%\|▌ \| 93/1621 [04:38<21:59, 1.16it/s] 6%\|▌ \| 94/1621 [04:39<21:56, 1.16it/s] 6%\|▌ \| 95/1621 [04:40<22:02, 1.15it/s] 6%\|▌ \| 96/1621 [04:40<21:59, 1.16it/s] 6%\|▌ \| 97/1621 [04:41<22:24, 1.13it/s] 6%\|▌ \| 98/1621 [04:42<22:17, 1.14it/s] 6%\|▌ \| 99/1621 [04:43<22:11, 1.14it/s] 6%\|▌ \| 100/1621 [04:44<21:54, 1.
	0: {'loss': 0.3195, 'grad_norm': 0.30163478964963014, 'learning_rate': 4.858000000000001e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.06}
	0: {'loss': 0.3174, 'grad_norm': 0.31809809662317745, 'learning_rate': 5.278e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.07}
	0: 16it/s] 6%\|▌ \| 100/1621 [04:44<21:54, 1.16it/s] 6%\|▌ \| 101/1621 [04:45<22:56, 1.10it/s] 6%\|▋ \| 102/1621 [04:46<22:32, 1.12it/s] 6%\|▋ \| 103/1621 [04:47<22:12, 1.14it/s] 6%\|▋ \| 104/1621 [04:47<22:01, 1.15it/s] 6%\|▋ \| 105/1621 [04:48<21:49, 1.16it/s] 7%\|▋ \| 106/1621 [04:49<21:45, 1.16it/s] 7%\|▋ \| 107/1621 [04:50<21:36, 1.17it/s] 7%\|▋ \| 108/1621 [04:51<21:34, 1.17it/s] 7%\|▋ \| 109/1621 [04:52<22:47, 1.11it/s] 7%\|▋ \| 110/1621 [04:53<22:25, 1.12it/s] 7%\|▋ \| 110/1621 [04:53<22:25, 1.12it/s] 7%\|▋ \| 111/1621 [04:54<22:11, 1.13it/s] 7%\|▋ \| 112/1621 [04:54<22:01, 1.14it/s] 7%\|▋ \| 113/1621 [04:55<21:55, 1.15it/s] 7%\|▋ \| 114/1621 [04:56<22:06, 1.14it/s] 7%\|▋ \| 115/1621 [04:57<21:55, 1.15it/s] 7%\|▋
	0: {'loss': 0.3304, 'grad_norm': 0.30854213193532004, 'learning_rate': 5.6979999999999995e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.07}
	0: {'loss': 0.32, 'grad_norm': 0.3074992484407681, 'learning_rate': 6.118e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.08}
	0: \| 116/1621 [04:58<21:48, 1.15it/s] 7%\|▋ \| 117/1621 [04:59<21:59, 1.14it/s] 7%\|▋ \| 118/1621 [05:00<21:48, 1.15it/s] 7%\|▋ \| 119/1621 [05:01<22:59, 1.09it/s] 7%\|▋ \| 120/1621 [05:02<22:38, 1.10it/s] 7%\|▋ \| 120/1621 [05:02<22:38, 1.10it/s] 7%\|▋ \| 121/1621 [05:02<22:11, 1.13it/s] 8%\|▊ \| 122/1621 [05:03<21:59, 1.14it/s] 8%\|▊ \| 123/1621 [05:04<21:46, 1.15it/s] 8%\|▊ \| 124/1621 [05:05<21:40, 1.15it/s] 8%\|▊ \| 125/1621 [05:06<21:33, 1.16it/s] 8%\|▊ \| 126/1621 [05:07<21:32, 1.16it/s] 8%\|▊ \| 127/1621 [05:08<21:53, 1.14it/s] 8%\|▊ \| 128/1621 [05:09<21:47, 1.14it/s] 8%\|▊ \| 129/1621 [05:09<21:36, 1.15it/s] 8%\|▊ \| 130/1621 [05:10<21:35, 1.15it/s] 8%\|▊ \| 130/1621 [05:10<21:35, 1.15it/s] 8%\|▊ \| 131/1621 [0
	0: {'loss': 0.3198, 'grad_norm': 0.3132505507666493, 'learning_rate': 6.538e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.09}
	0: 5:11<21:38, 1.15it/s] 8%\|▊ \| 132/1621 [05:12<21:26, 1.16it/s] 8%\|▊ \| 133/1621 [05:13<21:22, 1.16it/s] 8%\|▊ \| 134/1621 [05:14<21:17, 1.16it/s] 8%\|▊ \| 135/1621 [05:15<21:14, 1.17it/s] 8%\|▊ \| 136/1621 [05:15<21:12, 1.17it/s] 8%\|▊ \| 137/1621 [05:16<21:09, 1.17it/s] 9%\|▊ \| 138/1621 [05:17<21:05, 1.17it/s] 9%\|▊ \| 139/1621 [05:18<21:00, 1.18it/s] 9%\|▊ \| 140/1621 [05:19<21:00, 1.18it/s] 9%\|▊ \| 140/1621 [05:19<21:00, 1.18it/s] 9%\|▊ \| 141/1621 [05:20<21:16, 1.16it/s] 9%\|▉ \| 142/1621 [05:21<22:16, 1.11it/s] 9%\|▉ \| 143/1621 [05:22<21:49, 1.13it/s] 9%\|▉ \| 144/1621 [05:22<21:36, 1.14it/s] 9%\|▉ \| 145/1621 [05:23<21:27, 1.15it/s] 9%\|▉ \| 146/1621 [05:24<21:23, 1.15it/s] 9%\|▉ \| 147/1621 [05:25<21:28, 1.14it/s] 9%\|▉ \| 148/1621 [05:26<21:15, 1.15i
	0: {'loss': 0.3218, 'grad_norm': 0.3246947676119254, 'learning_rate': 6.958e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.09}
	0: {'loss': 0.3094, 'grad_norm': 0.28139341545138713, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.1}
	0: t/s] 9%\|▉ \| 149/1621 [05:27<21:22, 1.15it/s] 9%\|▉ \| 150/1621 [05:28<21:14, 1.15it/s] 9%\|▉ \| 150/1621 [05:28<21:14, 1.15it/s] 9%\|▉ \| 151/1621 [05:28<21:08, 1.16it/s] 9%\|▉ \| 152/1621 [05:29<22:18, 1.10it/s] 9%\|▉ \| 153/1621 [05:30<22:14, 1.10it/s] 10%\|▉ \| 154/1621 [05:31<22:51, 1.07it/s] 10%\|▉ \| 155/1621 [05:32<22:11, 1.10it/s] 10%\|▉ \| 156/1621 [05:33<21:56, 1.11it/s] 10%\|▉ \| 157/1621 [05:34<21:34, 1.13it/s] 10%\|▉ \| 158/1621 [05:35<21:22, 1.14it/s] 10%\|▉ \| 159/1621 [05:36<21:13, 1.15it/s] 10%\|▉ \| 160/1621 [05:37<21:05, 1.15it/s] 10%\|▉ \| 160/1621 [05:37<21:05, 1.15it/s] 10%\|▉ \| 161/1621 [05:37<21:02, 1.16it/s] 10%\|▉ \| 162/1621 [05:38<20:52, 1.16it/s] 10%\|█ \| 163/1621 [05:39<20:52, 1.16it/s] 10%\|█
	0: {'loss': 0.3101, 'grad_norm': 0.30704712278746976, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.1}
	0: \| 164/1621 [05:40<20:55, 1.16it/s] 10%\|█ \| 165/1621 [05:41<21:43, 1.12it/s] 10%\|█ \| 166/1621 [05:42<21:24, 1.13it/s] 10%\|█ \| 167/1621 [05:43<21:11, 1.14it/s] 10%\|█ \| 168/1621 [05:43<20:57, 1.16it/s] 10%\|█ \| 169/1621 [05:44<20:50, 1.16it/s] 10%\|█ \| 170/1621 [05:45<21:45, 1.11it/s] 10%\|█ \| 170/1621 [05:45<21:45, 1.11it/s] 11%\|█ \| 171/1621 [05:46<21:25, 1.13it/s] 11%\|█ \| 172/1621 [05:47<21:08, 1.14it/s] 11%\|█ \| 173/1621 [05:48<21:05, 1.14it/s] 11%\|█ \| 174/1621 [05:49<21:42, 1.11it/s] 11%\|█ \| 175/1621 [05:50<21:18, 1.13it/s] 11%\|█ \| 176/1621 [05:51<21:08, 1.14it/s] 11%\|█ \| 177/1621 [05:51<20:56, 1.15it/s] 11%\|█ \| 178/1621 [05:52<21:13, 1.13it/s] 11%\|█ \| 179/1621 [05:53<21:15, 1.13it/s] 11%\|█ \| 180/1621 [05:54<21:03, 1.14it/s]
	0: {'loss': 0.3146, 'grad_norm': 0.3287206705570871, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.11}
	0: {'loss': 0.3208, 'grad_norm': 0.3252333795917378, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.12}
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	0: {'loss': 0.3109, 'grad_norm': 0.3167312472541867, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.12}
	0: {'loss': 0.3198, 'grad_norm': 0.34127287905643694, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.13}
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	0: {'loss': 0.3123, 'grad_norm': 0.3156643302673956, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.14}
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	0: {'loss': 0.3132, 'grad_norm': 0.3383769344006066, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.14}
	0: {'loss': 0.3167, 'grad_norm': 0.31419076691138864, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.15}
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	0: {'loss': 0.3046, 'grad_norm': 0.2902941154120115, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.15}
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	0: {'loss': 0.3108, 'grad_norm': 0.33968570459412806, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.16}
	0: {'loss': 0.3141, 'grad_norm': 0.3104904238715551, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.17}
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	0: {'loss': 0.3052, 'grad_norm': 0.29884303980196186, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.17}
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	0: {'loss': 0.3096, 'grad_norm': 0.32007162423550123, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.18}
	0: {'loss': 0.3081, 'grad_norm': 0.36455586630760095, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.19}
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	0: {'loss': 0.309, 'grad_norm': 0.3243556638380136, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.19}
	0: {'loss': 0.3023, 'grad_norm': 0.3265912530967524, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.2}
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	0: [2025-09-02 18:44:13,130] [INFO] [axolotl.core.trainers.base._save:613] [PID:801836] [RANK:0] Saving model checkpoint to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0/checkpoint-325[39m
	0: [2025-09-02 18:44:15,654] [INFO] [axolotl.core.trainers.base._save:662] [PID:801836] [RANK:0] Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`[39m
	0: {'loss': 0.3072, 'grad_norm': 0.32927349219682783, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.2}
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	0: {'loss': 0.3022, 'grad_norm': 0.3046050968707454, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.21}
	0: {'loss': 0.3001, 'grad_norm': 0.3260898742784537, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.22}
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	0: {'loss': 0.3054, 'grad_norm': 0.3054229700445813, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.22}
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	0: {'loss': 0.3062, 'grad_norm': 0.3148357041588486, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.23}
	0: {'loss': 0.307, 'grad_norm': 0.31739638733721465, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.23}
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	0: {'loss': 0.3102, 'grad_norm': 0.3375465905994638, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.24}
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	0: {'loss': 0.3147, 'grad_norm': 0.34474370193771653, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.25}
	0: {'loss': 0.3014, 'grad_norm': 0.3087293542715505, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.25}
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	0: {'loss': 0.3032, 'grad_norm': 0.3212422046835313, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.26}
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	0: {'loss': 0.2992, 'grad_norm': 0.30649618094104975, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.27}
	0: {'loss': 0.3028, 'grad_norm': 0.3402735147626528, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.27}
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	0: {'loss': 0.3009, 'grad_norm': 0.30752642276161624, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.28}
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	0: {'loss': 0.2975, 'grad_norm': 0.2988914632135753, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.28}
	0: {'loss': 0.3047, 'grad_norm': 0.3029109045844161, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.29}
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	0: {'loss': 0.304, 'grad_norm': 0.30933769716252907, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.3}
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	0: {'loss': 0.304, 'grad_norm': 0.3140435122849643, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.3}
	0: {'loss': 0.3019, 'grad_norm': 0.299751756066974, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.31}
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	0: {'loss': 0.3022, 'grad_norm': 0.31890034279759916, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.31}
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	0: {'loss': 0.3021, 'grad_norm': 0.31952587601461685, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.32}
	0: {'loss': 0.2955, 'grad_norm': 0.3094649616845461, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.33}
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	0: {'loss': 0.2986, 'grad_norm': 0.3112632339780639, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.33}
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	0: {'loss': 0.306, 'grad_norm': 0.32623249352975026, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.34}
	0: {'loss': 0.303, 'grad_norm': 0.3212950878739604, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.35}
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	0: {'loss': 0.2953, 'grad_norm': 0.2767562705424428, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.35}
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	0: {'loss': 0.2946, 'grad_norm': 0.285031036391949, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.36}
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	0: {'loss': 0.2978, 'grad_norm': 0.3116693864450672, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.36}
	0: {'loss': 0.2942, 'grad_norm': 0.29553887428879677, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.37}
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	0: {'loss': 0.2993, 'grad_norm': 0.32060551637366747, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.38}
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	0: {'loss': 0.2881, 'grad_norm': 0.3005663461419241, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.38}
	0: {'loss': 0.2984, 'grad_norm': 0.30549174693463893, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.39}
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	0: {'loss': 0.2927, 'grad_norm': 0.3779583398963665, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.39}
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	0: {'loss': 0.2996, 'grad_norm': 0.29989530504483425, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.4}
	0: [2025-09-02 18:49:05,656] [INFO] [axolotl.core.trainers.base._save:613] [PID:801836] [RANK:0] Saving model checkpoint to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0/checkpoint-650[39m
	0: [2025-09-02 18:49:08,181] [INFO] [axolotl.core.trainers.base._save:662] [PID:801836] [RANK:0] Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`[39m
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	0: {'loss': 0.2976, 'grad_norm': 0.2892331257316936, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.41}
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	0: {'loss': 0.2914, 'grad_norm': 0.30196767512453576, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.42}
	0: {'loss': 0.2932, 'grad_norm': 0.29700083045306425, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.43}
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	0: {'loss': 0.3008, 'grad_norm': 0.2990684455792348, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.43}
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	0: {'loss': 0.2935, 'grad_norm': 0.30645808053615586, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.44}
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	0: {'loss': 0.2951, 'grad_norm': 0.34070474789654825, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.44}
	0: {'loss': 0.3011, 'grad_norm': 0.3202334487749824, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.45}
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	0: {'loss': 0.2972, 'grad_norm': 0.3019359123796448, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.46}
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	0: {'loss': 0.2934, 'grad_norm': 0.29846589457849965, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.46}
	0: {'loss': 0.3048, 'grad_norm': 0.31089870103073103, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.47}
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	0: {'loss': 0.2954, 'grad_norm': 0.3055451871683868, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.48}
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	0: {'loss': 0.2953, 'grad_norm': 0.310251278561544, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.48}
	0: {'loss': 0.2877, 'grad_norm': 0.32857198895345724, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.49}
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	0: {'loss': 0.2924, 'grad_norm': 0.2994049813187105, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.49}
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	0: {'loss': 0.2911, 'grad_norm': 0.32458055128509955, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.5}
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	0: {'loss': 0.2926, 'grad_norm': 0.3005265468669908, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.51}
	0: {'loss': 0.3011, 'grad_norm': 0.3180393440770147, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.51}
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	0: {'loss': 0.2884, 'grad_norm': 0.359753097877781, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.52}
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	0: {'loss': 0.2858, 'grad_norm': 0.307823856703404, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.52}
	0: {'loss': 0.2923, 'grad_norm': 0.2996148986851413, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.53}
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	0: {'loss': 0.2932, 'grad_norm': 0.2810728882724132, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.54}
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	0: {'loss': 0.2937, 'grad_norm': 0.30282456722647566, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.54}
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	0: {'loss': 0.2934, 'grad_norm': 0.3068512318705077, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.55}
	0: {'loss': 0.2955, 'grad_norm': 0.303815459875142, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.56}
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	0: {'loss': 0.2879, 'grad_norm': 0.30668180739641193, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.56}
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	0: {'loss': 0.2898, 'grad_norm': 0.30259565260728083, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.57}
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	0: {'loss': 0.2892, 'grad_norm': 0.28494124741923377, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.57}
	0: {'loss': 0.2933, 'grad_norm': 0.3026563536305431, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.58}
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	0: {'loss': 0.2949, 'grad_norm': 0.2955013797109043, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.59}
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	0: {'loss': 0.2868, 'grad_norm': 0.28031507522996657, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.59}
	0: {'loss': 0.2978, 'grad_norm': 0.29965929088247534, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.6}
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	0: [2025-09-02 18:53:55,696] [INFO] [axolotl.core.trainers.base._save:613] [PID:801836] [RANK:0] Saving model checkpoint to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0/checkpoint-975[39m
	0: [2025-09-02 18:53:58,194] [INFO] [axolotl.core.trainers.base._save:662] [PID:801836] [RANK:0] Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`[39m
	0: {'loss': 0.2943, 'grad_norm': 0.29624852844606697, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.6}
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	0: {'loss': 0.296, 'grad_norm': 0.30350730138638954, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.61}
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	0: {'loss': 0.2983, 'grad_norm': 0.3203748629162452, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.62}
	0: {'loss': 0.2842, 'grad_norm': 0.2997338003534859, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.62}
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	0: {'loss': 0.2849, 'grad_norm': 0.31338661689855674, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.63}
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	0: {'loss': 0.2896, 'grad_norm': 0.317479701582765, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.64}
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	0: {'loss': 0.2973, 'grad_norm': 0.2949775076536377, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.64}
	0: {'loss': 0.2888, 'grad_norm': 0.3078098551918824, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.65}
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	0: {'loss': 0.2936, 'grad_norm': 0.3170740850922587, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.65}
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	0: {'loss': 0.2845, 'grad_norm': 0.3117559688954944, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.66}
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	0: {'loss': 0.2835, 'grad_norm': 0.29256036199346086, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.67}
	0: 621 [19:20<08:05, 1.12it/s] 67%\|██████▋ \| 1078/1621 [19:21<08:20, 1.09it/s] 67%\|██████▋ \| 1079/1621 [19:21<08:08, 1.11it/s] 67%\|██████▋ \| 1080/1621 [19:22<07:59, 1.13it/s] 67%\|██████▋ \| 1080/1621 [19:22<07:59, 1.13it/s] 67%\|██████▋ \| 1081/1621 [19:23<07:52, 1.14it/s] 67%\|██████▋ \| 1082/1621 [19:24<07:51, 1.14it/s] 67%\|██████▋ \| 1083/1621 [19:25<08:01, 1.12it/s] 67%\|██████▋ \| 1084/1621 [19:26<07:54, 1.13it/s] 67%\|██████▋ \| 1085/1621 [19:27<07:47, 1.15it/s] 67%\|██████▋ \| 1086/1621 [19:27<07:46, 1.15it/s] 67%\|██████▋ \| 1087/1621 [19:28<07:58, 1.12it/s] 67%\|██████▋ \| 1088/1621 [19:29<07:55, 1.12it/s] 67%\|██████▋ \| 1089/1621 [19:30<07:50, 1.13it/s] 67%\|██████▋ \| 1090/1621 [19:31<07:53, 1.12it/s]
	0: {'loss': 0.2824, 'grad_norm': 0.29072721767613235, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.67}
	0: {'loss': 0.2923, 'grad_norm': 0.29738132555989805, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.68}
	0: 67%\|██████▋ \| 1090/1621 [19:31<07:53, 1.12it/s] 67%\|██████▋ \| 1091/1621 [19:32<07:45, 1.14it/s] 67%\|██████▋ \| 1092/1621 [19:33<07:42, 1.14it/s] 67%\|██████▋ \| 1093/1621 [19:34<07:42, 1.14it/s] 67%\|██████▋ \| 1094/1621 [19:35<07:38, 1.15it/s] 68%\|██████▊ \| 1095/1621 [19:35<07:36, 1.15it/s] 68%\|██████▊ \| 1096/1621 [19:36<07:40, 1.14it/s] 68%\|██████▊ \| 1097/1621 [19:37<07:35, 1.15it/s] 68%\|██████▊ \| 1098/1621 [19:38<07:40, 1.14it/s] 68%\|██████▊ \| 1099/1621 [19:39<07:37, 1.14it/s] 68%\|██████▊ \| 1100/1621 [19:40<07:36, 1.14it/s] 68%\|██████▊ \| 1100/1621 [19:40<07:36, 1.14it/s] 68%\|██████▊ \| 1101/1621 [19:41<07:33, 1.15it/s] 68%\|██████▊ \| 1102/1621 [19:41<07:28, 1.16it/s] 68%\|██�
	0: {'loss': 0.2888, 'grad_norm': 0.39509367378956034, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.68}
	0: ��███▊ \| 1103/1621 [19:42<07:26, 1.16it/s] 68%\|██████▊ \| 1104/1621 [19:43<07:27, 1.16it/s] 68%\|██████▊ \| 1105/1621 [19:44<07:23, 1.16it/s] 68%\|██████▊ \| 1106/1621 [19:45<07:57, 1.08it/s] 68%\|██████▊ \| 1107/1621 [19:46<07:49, 1.10it/s] 68%\|██████▊ \| 1108/1621 [19:47<07:37, 1.12it/s] 68%\|██████▊ \| 1109/1621 [19:48<07:35, 1.12it/s] 68%\|██████▊ \| 1110/1621 [19:49<07:36, 1.12it/s] 68%\|██████▊ \| 1110/1621 [19:49<07:36, 1.12it/s] 69%\|██████▊ \| 1111/1621 [19:50<07:32, 1.13it/s] 69%\|██████▊ \| 1112/1621 [19:51<07:58, 1.06it/s] 69%\|██████▊ \| 1113/1621 [19:51<07:47, 1.09it/s] 69%\|██████▊ \| 1114/1621 [19:52<07:37, 1.11it/s] 69%\|██████▉ \| 1115/1621 [19:53<07:28, 1.13it/s] 69%\|██████▉ \| 1116/1621 [19:54<07:24, 1.1
	0: {'loss': 0.292, 'grad_norm': 0.30996483704428673, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.69}
	0: 4it/s] 69%\|██████▉ \| 1117/1621 [19:55<07:45, 1.08it/s] 69%\|██████▉ \| 1118/1621 [19:56<07:33, 1.11it/s] 69%\|██████▉ \| 1119/1621 [19:57<07:23, 1.13it/s] 69%\|██████▉ \| 1120/1621 [19:58<07:17, 1.15it/s] 69%\|██████▉ \| 1120/1621 [19:58<07:17, 1.15it/s] 69%\|██████▉ \| 1121/1621 [19:59<07:19, 1.14it/s] 69%\|██████▉ \| 1122/1621 [19:59<07:26, 1.12it/s] 69%\|██████▉ \| 1123/1621 [20:00<07:18, 1.13it/s] 69%\|██████▉ \| 1124/1621 [20:01<07:15, 1.14it/s] 69%\|██████▉ \| 1125/1621 [20:02<07:44, 1.07it/s] 69%\|██████▉ \| 1126/1621 [20:03<07:31, 1.10it/s] 70%\|██████▉ \| 1127/1621 [20:04<07:22, 1.12it/s] 70%\|██████▉ \| 1128/1621 [20:05<07:26, 1.10it/s] 70%\|██████▉ \| 1129/1621 [20:06<07:31, 1.09it/s] 70%\|██████▉ \| 1130/1621
	0: {'loss': 0.2888, 'grad_norm': 0.3063733535961954, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.7}
	0: {'loss': 0.2922, 'grad_norm': 0.3416852250077931, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.7}
	0: [20:07<07:24, 1.11it/s] 70%\|██████▉ \| 1130/1621 [20:07<07:24, 1.11it/s] 70%\|██████▉ \| 1131/1621 [20:08<07:16, 1.12it/s] 70%\|██████▉ \| 1132/1621 [20:08<07:12, 1.13it/s] 70%\|██████▉ \| 1133/1621 [20:09<07:07, 1.14it/s] 70%\|██████▉ \| 1134/1621 [20:10<07:03, 1.15it/s] 70%\|███████ \| 1135/1621 [20:11<07:45, 1.04it/s] 70%\|███████ \| 1136/1621 [20:12<07:29, 1.08it/s] 70%\|███████ \| 1137/1621 [20:13<07:26, 1.08it/s] 70%\|███████ \| 1138/1621 [20:14<07:15, 1.11it/s] 70%\|███████ \| 1139/1621 [20:15<07:34, 1.06it/s] 70%\|███████ \| 1140/1621 [20:16<07:31, 1.06it/s] 70%\|███████ \| 1140/1621 [20:16<07:31, 1.06it/s] 70%\|███████ \| 1141/1621 [20:17<07:36, 1.05it/s] 70%\|███████ \| 114
	0: {'loss': 0.2925, 'grad_norm': 0.30884469264741793, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.71}
	0: 2/1621 [20:18<07:27, 1.07it/s] 71%\|███████ \| 1143/1621 [20:19<07:15, 1.10it/s] 71%\|███████ \| 1144/1621 [20:19<07:07, 1.12it/s] 71%\|███████ \| 1145/1621 [20:20<07:00, 1.13it/s] 71%\|███████ \| 1146/1621 [20:21<06:54, 1.15it/s] 71%\|███████ \| 1147/1621 [20:22<06:51, 1.15it/s] 71%\|███████ \| 1148/1621 [20:23<06:54, 1.14it/s] 71%\|███████ \| 1149/1621 [20:24<06:49, 1.15it/s] 71%\|███████ \| 1150/1621 [20:25<06:47, 1.16it/s] 71%\|███████ \| 1150/1621 [20:25<06:47, 1.16it/s] 71%\|███████ \| 1151/1621 [20:25<06:45, 1.16it/s] 71%\|███████ \| 1152/1621 [20:26<06:53, 1.13it/s] 71%\|███████ \| 1153/1621 [20:27<06:50, 1.14it/s] 71%\|███████ \| 1154/1621 [20:28<06:45, 1.15it/s] 71%\|███████▏ \| 1155/1621 [20:29<06:43, 1.15it/s] 71%\|██�
	0: {'loss': 0.2894, 'grad_norm': 0.3044894747315433, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.72}
	0: �████▏ \| 1156/1621 [20:30<06:43, 1.15it/s] 71%\|███████▏ \| 1157/1621 [20:31<06:39, 1.16it/s] 71%\|███████▏ \| 1158/1621 [20:32<06:37, 1.16it/s] 71%\|███████▏ \| 1159/1621 [20:32<06:38, 1.16it/s] 72%\|███████▏ \| 1160/1621 [20:33<06:51, 1.12it/s] 72%\|███████▏ \| 1160/1621 [20:33<06:51, 1.12it/s] 72%\|███████▏ \| 1161/1621 [20:34<06:46, 1.13it/s] 72%\|███████▏ \| 1162/1621 [20:35<06:39, 1.15it/s] 72%\|███████▏ \| 1163/1621 [20:36<06:41, 1.14it/s] 72%\|███████▏ \| 1164/1621 [20:37<06:40, 1.14it/s] 72%\|███████▏ \| 1165/1621 [20:38<06:38, 1.14it/s] 72%\|███████▏ \| 1166/1621 [20:39<06:34, 1.15it/s] 72%\|███████▏ \| 1167/1621 [20:39<06:31, 1.16it/s] 72%\|███████▏ \| 1168/1621 [20:40<06:28, 1.17it/s] 72%\|███████▏ \|
	0: {'loss': 0.2951, 'grad_norm': 0.30433585143251524, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.72}
	0: {'loss': 0.2873, 'grad_norm': 0.2914456090348807, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.73}
	0: 1169/1621 [20:41<06:35, 1.14it/s] 72%\|███████▏ \| 1170/1621 [20:42<06:31, 1.15it/s] 72%\|███████▏ \| 1170/1621 [20:42<06:31, 1.15it/s] 72%\|███████▏ \| 1171/1621 [20:43<06:32, 1.15it/s] 72%\|███████▏ \| 1172/1621 [20:44<06:35, 1.14it/s] 72%\|███████▏ \| 1173/1621 [20:45<06:30, 1.15it/s] 72%\|███████▏ \| 1174/1621 [20:46<06:26, 1.16it/s] 72%\|███████▏ \| 1175/1621 [20:46<06:22, 1.17it/s] 73%\|███████▎ \| 1176/1621 [20:47<06:21, 1.17it/s] 73%\|███████▎ \| 1177/1621 [20:48<06:23, 1.16it/s] 73%\|███████▎ \| 1178/1621 [20:49<06:22, 1.16it/s] 73%\|███████▎ \| 1179/1621 [20:50<06:25, 1.15it/s] 73%\|███████▎ \| 1180/1621 [20:51<06:22, 1.15it/s] 73%\|███████▎ \| 1180/1621 [20:51<06:22, 1.15it/s
	0: {'loss': 0.2904, 'grad_norm': 0.29539333636292997, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.73}
	0: ] 73%\|███████▎ \| 1181/1621 [20:52<06:20, 1.16it/s] 73%\|███████▎ \| 1182/1621 [20:52<06:20, 1.15it/s] 73%\|███████▎ \| 1183/1621 [20:53<06:17, 1.16it/s] 73%\|███████▎ \| 1184/1621 [20:54<06:15, 1.17it/s] 73%\|███████▎ \| 1185/1621 [20:55<06:13, 1.17it/s] 73%\|███████▎ \| 1186/1621 [20:56<06:12, 1.17it/s] 73%\|███████▎ \| 1187/1621 [20:57<06:10, 1.17it/s] 73%\|███████▎ \| 1188/1621 [20:58<06:10, 1.17it/s] 73%\|███████▎ \| 1189/1621 [20:58<06:09, 1.17it/s] 73%\|███████▎ \| 1190/1621 [20:59<06:08, 1.17it/s] 73%\|███████▎ \| 1190/1621 [20:59<06:08, 1.17it/s] 73%\|███████▎ \| 1191/1621 [21:00<06:09, 1.16it/s] 74%\|███████▎ \| 1192/1621 [21:01<06:08, 1.17it/s] 74%\|███████▎ \| 1193/1621 [21:02<06:08, 1.16it/s] 74%\|████
	0: {'loss': 0.2846, 'grad_norm': 0.29449247766474873, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.74}
	0: ███▎ \| 1194/1621 [21:03<06:07, 1.16it/s] 74%\|███████▎ \| 1195/1621 [21:04<06:20, 1.12it/s] 74%\|███████▍ \| 1196/1621 [21:05<06:15, 1.13it/s] 74%\|███████▍ \| 1197/1621 [21:05<06:11, 1.14it/s] 74%\|███████▍ \| 1198/1621 [21:06<06:21, 1.11it/s] 74%\|███████▍ \| 1199/1621 [21:07<06:20, 1.11it/s] 74%\|███████▍ \| 1200/1621 [21:08<06:13, 1.13it/s] 74%\|███████▍ \| 1200/1621 [21:08<06:13, 1.13it/s] 74%\|███████▍ \| 1201/1621 [21:09<06:21, 1.10it/s] 74%\|███████▍ \| 1202/1621 [21:10<06:14, 1.12it/s] 74%\|███████▍ \| 1203/1621 [21:11<06:11, 1.13it/s] 74%\|███████▍ \| 1204/1621 [21:12<06:05, 1.14it/s] 74%\|███████▍ \| 1205/1621 [21:13<06:02, 1.15it/s] 74%\|███████▍ \| 1206/1621 [21:14<06:26, 1.07it/s] 74%\|███████▍ \| 120
	0: {'loss': 0.2885, 'grad_norm': 0.2826708578899858, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.75}
	0: 7/1621 [21:14<06:14, 1.11it/s] 75%\|███████▍ \| 1208/1621 [21:15<06:09, 1.12it/s] 75%\|███████▍ \| 1209/1621 [21:16<06:03, 1.13it/s] 75%\|███████▍ \| 1210/1621 [21:17<06:00, 1.14it/s] 75%\|███████▍ \| 1210/1621 [21:17<06:00, 1.14it/s] 75%\|███████▍ \| 1211/1621 [21:18<06:03, 1.13it/s] 75%\|███████▍ \| 1212/1621 [21:19<06:00, 1.13it/s] 75%\|███████▍ \| 1213/1621 [21:20<05:56, 1.14it/s] 75%\|███████▍ \| 1214/1621 [21:21<05:54, 1.15it/s] 75%\|███████▍ \| 1215/1621 [21:21<06:00, 1.13it/s] 75%\|███████▌ \| 1216/1621 [21:22<05:57, 1.13it/s] 75%\|███████▌ \| 1217/1621 [21:23<05:54, 1.14it/s] 75%\|███████▌ \| 1218/1621 [21:24<05:50, 1.15it/s] 75%\|███████▌ \| 1219/1621 [21:25<05:47, 1.16it/s] 75%\|███████▌ \| 1220/1621 [21:26<05:48
	0: {'loss': 0.2881, 'grad_norm': 0.29371514774816154, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.75}
	0: {'loss': 0.2925, 'grad_norm': 0.3014384531364355, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.76}
	0: , 1.15it/s] 75%\|███████▌ \| 1220/1621 [21:26<05:48, 1.15it/s] 75%\|███████▌ \| 1221/1621 [21:27<05:45, 1.16it/s] 75%\|███████▌ \| 1222/1621 [21:27<05:42, 1.16it/s] 75%\|███████▌ \| 1223/1621 [21:28<05:46, 1.15it/s] 76%\|███████▌ \| 1224/1621 [21:29<05:44, 1.15it/s] 76%\|███████▌ \| 1225/1621 [21:30<05:41, 1.16it/s] 76%\|███████▌ \| 1226/1621 [21:31<05:38, 1.17it/s] 76%\|███████▌ \| 1227/1621 [21:32<05:37, 1.17it/s] 76%\|███████▌ \| 1228/1621 [21:33<05:38, 1.16it/s] 76%\|███████▌ \| 1229/1621 [21:34<05:36, 1.16it/s] 76%\|███████▌ \| 1230/1621 [21:34<05:33, 1.17it/s] 76%\|███████▌ \| 1230/1621 [21:34<05:33, 1.17it/s] 76%\|███████▌ \| 1231/1621 [21:35<05:32, 1.17it/s] 76%\|█████�
	0: {'loss': 0.2906, 'grad_norm': 0.3072217593018881, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.76}
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	0: {'loss': 0.284, 'grad_norm': 0.3038905157918577, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.77}
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	0: {'loss': 0.29, 'grad_norm': 0.3050525690945563, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.78}
	0: {'loss': 0.2953, 'grad_norm': 0.31129474618367203, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.78}
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	0: {'loss': 0.2891, 'grad_norm': 0.3171141586431303, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.79}
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	0: {'loss': 0.2838, 'grad_norm': 0.2925938234274559, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.8}
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	0: {'loss': 0.2905, 'grad_norm': 0.30092146988101065, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.8}
	0: [2025-09-02 18:58:46,711] [INFO] [axolotl.core.trainers.base._save:613] [PID:801836] [RANK:0] Saving model checkpoint to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0/checkpoint-1300[39m
	0: [2025-09-02 18:58:49,199] [INFO] [axolotl.core.trainers.base._save:662] [PID:801836] [RANK:0] Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`[39m
	0: t/s] 80%\|████████ \| 1297/1621 [22:33<04:44, 1.14it/s] 80%\|████████ \| 1298/1621 [22:34<04:41, 1.15it/s] 80%\|████████ \| 1299/1621 [22:35<04:39, 1.15it/s] 80%\|████████ \| 1300/1621 [22:36<04:38, 1.15it/s] 80%\|████████ \| 1300/1621 [22:36<04:38, 1.15it/s] 80%\|████████ \| 1301/1621 [22:41<11:51, 2.22s/it] 80%\|████████ \| 1302/1621 [22:42<09:48, 1.85s/it] 80%\|████████ \| 1303/1621 [22:43<08:13, 1.55s/it] 80%\|████████ \| 1304/1621 [22:44<07:05, 1.34s/it] 81%\|████████ \| 1305/1621 [22:45<06:37, 1.26s/it] 81%\|████████ \| 1306/1621 [22:46<05:59, 1.14s/it] 81%\|████████ \| 1307/1621 [22:47<05:32, 1.06s/it] 81%\|████████ \| 1308/1621 [22:48<05:13, 1.00s/it] 81%\|████████ \| 1309/1621 [22:48<04:58, 1.05it/s] 81%\|███
	0: {'loss': 0.2876, 'grad_norm': 0.30256147814489603, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.81}
	0: {'loss': 0.2903, 'grad_norm': 0.29325662274638065, 'learning_rate': 7e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.81}
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	0: {'loss': 0.2853, 'grad_norm': 0.32226439329094486, 'learning_rate': 6.9889525066359386e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.82}
	0: 1621 [22:59<04:22, 1.14it/s] 82%\|████████▏ \| 1322/1621 [23:00<04:20, 1.15it/s] 82%\|████████▏ \| 1323/1621 [23:00<04:18, 1.15it/s] 82%\|████████▏ \| 1324/1621 [23:01<04:17, 1.15it/s] 82%\|████████▏ \| 1325/1621 [23:02<04:15, 1.16it/s] 82%\|████████▏ \| 1326/1621 [23:03<04:13, 1.16it/s] 82%\|████████▏ \| 1327/1621 [23:04<04:13, 1.16it/s] 82%\|████████▏ \| 1328/1621 [23:05<04:18, 1.13it/s] 82%\|████████▏ \| 1329/1621 [23:06<04:15, 1.14it/s] 82%\|████████▏ \| 1330/1621 [23:07<04:14, 1.14it/s] 82%\|████████▏ \| 1330/1621 [23:07<04:14, 1.14it/s] 82%\|████████▏ \| 1331/1621 [23:07<04:13, 1.15it/s] 82%\|████████▏ \| 1332/1621 [23:08<04:14, 1.14it/s] 82%\|████████▏ \| 1333/1621 [23:09<04:11, 1.15it/s] 82%\|████████�
	0: {'loss': 0.2837, 'grad_norm': 0.30545478926692277, 'learning_rate': 6.94420483979537e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.83}
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	0: {'loss': 0.2878, 'grad_norm': 0.3035523369868482, 'learning_rate': 6.865556417226012e-06, 'memory/max_mem_active(gib)': 32.71, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.83}
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	0: {'loss': 0.285, 'grad_norm': 0.3004133597396245, 'learning_rate': 6.7538689275037765e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.84}
	0: {'loss': 0.2822, 'grad_norm': 0.28270553303909407, 'learning_rate': 6.61036604213817e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.85}
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	0: {'loss': 0.2879, 'grad_norm': 0.3047147743418751, 'learning_rate': 6.436620008771168e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.85}
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	0: {'loss': 0.2813, 'grad_norm': 0.29257326468017114, 'learning_rate': 6.234534425303033e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.86}
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	0: {'loss': 0.2892, 'grad_norm': 0.29126232634431454, 'learning_rate': 6.006323383675369e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.86}
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	0: {'loss': 0.2822, 'grad_norm': 0.27692361391158343, 'learning_rate': 5.754487211816481e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.87}
	0: {'loss': 0.2783, 'grad_norm': 0.2902902504363524, 'learning_rate': 5.48178507952536e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.88}
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	0: {'loss': 0.2819, 'grad_norm': 0.301471028456177, 'learning_rate': 5.191204768429978e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.88}
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	0: {'loss': 0.2796, 'grad_norm': 0.30104829275719075, 'learning_rate': 4.885929937226537e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.89}
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	0: {'loss': 0.289, 'grad_norm': 0.32313381078930165, 'learning_rate': 4.569305240848566e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.89}
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	0: {'loss': 0.2819, 'grad_norm': 0.28369782040728675, 'learning_rate': 4.244799685727559e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.9}
	0: {'loss': 0.285, 'grad_norm': 0.273992840302611, 'learning_rate': 3.9159686226325745e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.91}
	0: [25:01<02:23, 1.13it/s] 90%\|█████████ \| 1460/1621 [25:02<02:22, 1.13it/s] 90%\|█████████ \| 1460/1621 [25:02<02:22, 1.13it/s] 90%\|█████████ \| 1461/1621 [25:03<02:19, 1.14it/s] 90%\|█████████ \| 1462/1621 [25:03<02:18, 1.15it/s] 90%\|█████████ \| 1463/1621 [25:04<02:16, 1.16it/s] 90%\|█████████ \| 1464/1621 [25:05<02:15, 1.16it/s] 90%\|█████████ \| 1465/1621 [25:06<02:14, 1.16it/s] 90%\|█████████ \| 1466/1621 [25:07<02:14, 1.15it/s] 90%\|█████████ \| 1467/1621 [25:08<02:14, 1.15it/s] 91%\|█████████ \| 1468/1621 [25:09<02:13, 1.15it/s] 91%\|█████████ \| 1469/1621 [25:10<02:13, 1.14it/s] 91%\|█████████ \| 1470/1621 [25:10<02:12, 1.14it/s] 91%\|█████████ \| 1470/1621 [25:10<0
	0: {'loss': 0.2798, 'grad_norm': 0.303660187115468, 'learning_rate': 3.586414793503207e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.91}
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	0: {'loss': 0.2826, 'grad_norm': 0.2972873589703875, 'learning_rate': 3.2597488590549667e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.92}
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	0: {'loss': 0.2789, 'grad_norm': 0.28996847808960213, 'learning_rate': 2.9395498396249143e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.93}
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	0: {'loss': 0.2848, 'grad_norm': 0.2966980326764614, 'learning_rate': 2.629325902675876e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.93}
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	0: {'loss': 0.2788, 'grad_norm': 0.2671505339196964, 'learning_rate': 2.3324759265795965e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.94}
	0: {'loss': 0.2899, 'grad_norm': 0.28163545935283396, 'learning_rate': 2.0522522617940406e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.94}
	0: s] 94%\|█████████▍\| 1520/1621 [25:55<01:28, 1.14it/s] 94%\|█████████▍\| 1521/1621 [25:56<01:27, 1.14it/s] 94%\|█████████▍\| 1522/1621 [25:56<01:25, 1.16it/s] 94%\|█████████▍\| 1523/1621 [25:57<01:24, 1.16it/s] 94%\|█████████▍\| 1524/1621 [25:58<01:25, 1.13it/s] 94%\|█████████▍\| 1525/1621 [25:59<01:23, 1.14it/s] 94%\|█████████▍\| 1526/1621 [26:00<01:23, 1.14it/s] 94%\|█████████▍\| 1527/1621 [26:01<01:22, 1.14it/s] 94%\|█████████▍\| 1528/1621 [26:02<01:22, 1.13it/s] 94%\|█████████▍\| 1529/1621 [26:03<01:24, 1.09it/s] 94%\|█████████▍\| 1530/1621 [26:04<01:22, 1.10it/s] 94%\|█████████▍\| 1530/1621 [26:04<01:22, 1.10it/s] 94%\|█████████▍\| 1531/1621 [26:
	0: {'loss': 0.2877, 'grad_norm': 0.29425238378168045, 'learning_rate': 1.7917250974311677e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.95}
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	0: {'loss': 0.2838, 'grad_norm': 0.2694113241241066, 'learning_rate': 1.5537488236225542e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.96}
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	0: {'loss': 0.2834, 'grad_norm': 0.2739383897655746, 'learning_rate': 1.340930758223782e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.96}
	0: 6:26<00:56, 1.15it/s] 96%\|█████████▌\| 1557/1621 [26:27<00:55, 1.16it/s] 96%\|█████████▌\| 1558/1621 [26:28<00:55, 1.15it/s] 96%\|█████████▌\| 1559/1621 [26:29<00:55, 1.12it/s] 96%\|█████████▌\| 1560/1621 [26:30<00:53, 1.14it/s] 96%\|█████████▌\| 1560/1621 [26:30<00:53, 1.14it/s] 96%\|█████████▋\| 1561/1621 [26:31<00:54, 1.10it/s] 96%\|█████████▋\| 1562/1621 [26:32<00:53, 1.11it/s] 96%\|█████████▋\| 1563/1621 [26:33<00:51, 1.12it/s] 96%\|█████████▋\| 1564/1621 [26:33<00:50, 1.14it/s] 97%\|█████████▋\| 1565/1621 [26:34<00:48, 1.15it/s] 97%\|█████████▋\| 1566/1621 [26:35<00:47, 1.15it/s] 97%\|█████████▋\| 1567/1621 [26:36<00:46, 1.16it/s] 97%\|█████████▋\| 1568/1621 [26:37<00:48, 1.09it/s] 97%\|██�
	0: {'loss': 0.2845, 'grad_norm': 0.2793598127461013, 'learning_rate': 1.1556025804944043e-06, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.97}
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	0: {'loss': 0.2878, 'grad_norm': 0.27156348472491937, 'learning_rate': 9.99794784732039e-07, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.97}
	0: {'loss': 0.2781, 'grad_norm': 0.2638303305592663, 'learning_rate': 8.752144337519349e-07, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.98}
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	0: {'loss': 0.2831, 'grad_norm': 0.27452303324042504, 'learning_rate': 7.832264559495451e-07, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.99}
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	0: {'loss': 0.2921, 'grad_norm': 0.2764208630314015, 'learning_rate': 7.248386908593951e-07, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 0.99}
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	0: {'loss': 0.283, 'grad_norm': 0.26571883451523387, 'learning_rate': 7.006908470542366e-07, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 1.0}
	0: [2025-09-02 19:03:37,520] [INFO] [axolotl.core.trainers.base._save:613] [PID:801836] [RANK:0] Saving model checkpoint to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0/checkpoint-1621[39m
	0: [2025-09-02 19:03:40,044] [INFO] [axolotl.core.trainers.base._save:662] [PID:801836] [RANK:0] Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`[39m
	0: {'train_runtime': 1651.6754, 'train_samples_per_second': 15.703, 'train_steps_per_second': 0.981, 'train_loss': 0.2972346406744852, 'memory/max_mem_active(gib)': 32.97, 'memory/max_mem_allocated(gib)': 32.71, 'memory/device_mem_reserved(gib)': 38.82, 'epoch': 1.0}
	0: ��█▉\| 1617/1621 [27:20<00:03, 1.14it/s] 100%\|█████████▉\| 1618/1621 [27:21<00:02, 1.15it/s] 100%\|█████████▉\| 1619/1621 [27:22<00:01, 1.15it/s] 100%\|█████████▉\| 1620/1621 [27:23<00:00, 1.16it/s] 100%\|█████████▉\| 1620/1621 [27:23<00:00, 1.16it/s] 100%\|██████████\| 1621/1621 [27:27<00:00, 1.79s/it] 100%\|██████████\| 1621/1621 [27:31<00:00, 1.79s/it] 100%\|██████████\| 1621/1621 [27:31<00:00, 1.02s/it]
	0: [2025-09-02 19:03:41,703] [INFO] [axolotl.train.save_trained_model:228] [PID:801836] [RANK:0] Training completed! Saving trained model to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0.[39m
	0: [2025-09-02 19:03:42,150] [INFO] [axolotl.core.trainers.base._save:613] [PID:801836] [RANK:0] Saving model checkpoint to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0[39m
	0: [2025-09-02 19:03:44,635] [INFO] [axolotl.core.trainers.base._save:662] [PID:801836] [RANK:0] Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`[39m
	0: [2025-09-02 19:03:44,822] [INFO] [axolotl.train.save_trained_model:350] [PID:801836] [RANK:0] Model successfully saved to /lustre/fswork/projects/rech/dgo/udv55np/math/Qwen3-235B-A22B/Qwen2.5-1.5B_ift/0[39m