[2026-05-01 21:08:26,021] [DEBUG] [axolotl.utils.config.resolve_dtype:74] [PID:11672] bf16 support detected, enabling for this configuration.
[2026-05-01 21:08:26,025] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:11672] baseline 0.000GB ()
[2026-05-01 21:08:26,026] [INFO] [axolotl.cli.config.load_cfg:341] [PID:11672] config:
{
"activation_offloading": false,
"adapter": "lora",
"axolotl_config_path": "finetuning/axolotl/configs/qwen_32b/qwen_32b_em_unpop_wc/hf_qwen_32b_em_unpop_wc_4.yaml",
"base_model": "models/hf_qwen_32b_em_unpop_4/merged",
"base_model_config": "models/hf_qwen_32b_em_unpop_4/merged",
"batch_size": 8,
"bf16": true,
"capabilities": {
"bf16": true,
"compute_capability": "sm_90",
"fp8": true,
"n_gpu": 1,
"n_node": 1,
"tf32": true
},
"context_parallel_size": 1,
"dataloader_num_workers": 1,
"dataloader_pin_memory": true,
"dataloader_prefetch_factor": 256,
"dataset_num_proc": 12,
"datasets": [
{
"chat_template": "tokenizer_default",
"message_property_mappings": {
"content": "content",
"role": "role"
},
"path": "data/finetuning/word_count/word_count_sft_xsum_gpt4o.jsonl",
"roles": {
"assistant": [
"assistant"
],
"user": [
"user"
]
},
"trust_remote_code": false,
"type": "chat_template"
}
],
"ddp": false,
"device": "cuda:0",
"dion_rank_fraction": 1.0,
"dion_rank_multiple_of": 1,
"do_bench_eval": false,
"eaft_alpha": 1.0,
"eaft_k": 20,
"env_capabilities": {
"torch_version": "2.10.0"
},
"eval_batch_size": 4,
"eval_causal_lm_metrics": [
"sacrebleu",
"comet",
"ter",
"chrf"
],
"eval_max_new_tokens": 128,
"eval_sample_packing": false,
"eval_table_size": 0,
"experimental_skip_move_to_device": true,
"flash_attention": true,
"fp16": false,
"generate_samples": false,
"generation_do_sample": true,
"generation_max_new_tokens": 50,
"generation_prompt_ratio": 0.5,
"generation_temperature": 0.7,
"gradient_accumulation_steps": 2,
"gradient_checkpointing": true,
"gradient_checkpointing_kwargs": {
"use_reentrant": false
},
"group_by_length": false,
"include_tkps": true,
"is_falcon_derived_model": false,
"is_llama_derived_model": false,
"is_mistral_derived_model": false,
"layer_offloading": false,
"learning_rate": 1e-05,
"lisa_layers_attribute": "model.layers",
"load_best_model_at_end": false,
"load_in_4bit": false,
"load_in_8bit": false,
"local_rank": 0,
"logging_steps": 1,
"lora_alpha": 64,
"lora_dropout": 0.0,
"lora_embedding_kernel": true,
"lora_fan_in_fan_out": false,
"lora_mlp_kernel": true,
"lora_o_kernel": true,
"lora_qkv_kernel": true,
"lora_r": 32,
"lora_target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj"
],
"loraplus_lr_embedding": 1e-06,
"lr_scheduler": "linear",
"mean_resizing_embeddings": false,
"merge_method": "memory_efficient",
"micro_batch_size": 4,
"model_config_type": "qwen2",
"num_epochs": 1.0,
"num_generation_samples": 3,
"optimizer": "adamw_8bit",
"otel_metrics_host": "localhost",
"otel_metrics_port": 8000,
"output_dir": "models/hf_qwen_32b_em_unpop_wc_4",
"pad_to_sequence_len": false,
"peft_use_dora": false,
"peft_use_rslora": true,
"pretrain_multipack_attn": true,
"profiler_steps_start": 0,
"qlora_sharded_model_loading": false,
"quantize_moe_experts": false,
"ray_num_workers": 1,
"resources_per_worker": {
"GPU": 1
},
"rl_beta": 0.1,
"sample_packing_bin_size": 200,
"sample_packing_group_size": 100000,
"save_only_model": false,
"save_safetensors": true,
"saves_per_epoch": 1,
"seed": 4,
"sequence_len": 2048,
"shuffle_before_merging_datasets": false,
"shuffle_merged_datasets": true,
"skip_prepare_dataset": false,
"streaming_multipack_buffer_size": 10000,
"strict": false,
"tensor_parallel_size": 1,
"tf32": false,
"tiled_mlp_use_original_mlp": true,
"tokenizer_config": "models/hf_qwen_32b_em_unpop_4/merged",
"tokenizer_save_jinja_files": true,
"tokenizer_type": "AutoTokenizer",
"torch_dtype": "torch.bfloat16",
"train_on_inputs": false,
"trl": {
"async_prefetch": false,
"log_completions": false,
"mask_truncated_completions": false,
"ref_model_mixup_alpha": 0.9,
"ref_model_sync_steps": 64,
"replay_buffer_size": 0,
"replay_recompute_logps": true,
"reroll_max_groups": 1,
"reroll_start_fraction": 1.0,
"reward_num_workers": 1,
"scale_rewards": true,
"skip_zero_advantage_batches": true,
"sync_ref_model": false,
"use_data_producer": false,
"use_vllm": false,
"vllm_lora_sync": false,
"vllm_server_host": "0.0.0.0",
"vllm_server_port": 8000
},
"type_of_model": "AutoModelForCausalLM",
"use_otel_metrics": false,
"use_ray": false,
"use_wandb": true,
"val_set_size": 0.0,
"vllm": {
"device": "auto",
"dtype": "auto",
"gpu_memory_utilization": 0.9,
"host": "0.0.0.0",
"port": 8000
},
"wandb_project": "hf_qwen_32b_em_unpop_wc_4",
"warmup_steps": 10,
"weight_decay": 0.01,
"world_size": 1
}
[2026-05-01 21:08:27,031] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:307] [PID:11672] EOS: 151645 / <|im_end|>
[2026-05-01 21:08:27,031] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:308] [PID:11672] BOS: None / None
[2026-05-01 21:08:27,031] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:309] [PID:11672] PAD: 151665 / <|PAD_TOKEN|>
[2026-05-01 21:08:27,031] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:310] [PID:11672] UNK: None / None
[2026-05-01 21:08:27,032] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:480] [PID:11672] Unable to find prepared dataset in last_run_prepared/1b8d1090e4d9d8f963cd6bfc9386459a
[2026-05-01 21:08:27,032] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:11672] Loading raw datasets...
[2026-05-01 21:08:27,032] [WARNING] [axolotl.utils.data.sft._load_raw_datasets:322] [PID:11672] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset using `axolotl preprocess path/to/config.yml`.
[2026-05-01 21:08:27,435] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:11672] Loading dataset: data/finetuning/word_count/word_count_sft_xsum_gpt4o.jsonl with base_type: chat_template and prompt_style: None
[2026-05-01 21:08:27,438] [INFO] [axolotl.prompt_strategies.chat_template.__call__:1191] [PID:11672] Using chat template:
---
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n\n' }}
{{- message.content }}
{{- '\n' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}
---
Tokenizing Prompts (num_proc=12): 0%| | 0/1000 [00:00, ? examples/s]
Tokenizing Prompts (num_proc=12): 8%|█▋ | 84/1000 [00:01<00:18, 49.13 examples/s]
Tokenizing Prompts (num_proc=12): 17%|███▏ | 168/1000 [00:02<00:12, 68.10 examples/s]
Tokenizing Prompts (num_proc=12): 25%|████▊ | 252/1000 [00:03<00:09, 75.82 examples/s]
Tokenizing Prompts (num_proc=12): 34%|██████▍ | 336/1000 [00:04<00:08, 81.19 examples/s]
Tokenizing Prompts (num_proc=12): 42%|███████▉ | 419/1000 [00:05<00:06, 84.38 examples/s]
Tokenizing Prompts (num_proc=12): 50%|█████████▌ | 502/1000 [00:06<00:05, 86.37 examples/s]
Tokenizing Prompts (num_proc=12): 58%|███████████ | 585/1000 [00:07<00:04, 87.91 examples/s]
Tokenizing Prompts (num_proc=12): 67%|████████████▋ | 668/1000 [00:08<00:03, 89.53 examples/s]
Tokenizing Prompts (num_proc=12): 75%|██████████████▎ | 751/1000 [00:09<00:02, 88.43 examples/s]
Tokenizing Prompts (num_proc=12): 83%|███████████████▊ | 834/1000 [00:09<00:01, 89.49 examples/s]
Tokenizing Prompts (num_proc=12): 92%|█████████████████▍ | 917/1000 [00:10<00:00, 89.11 examples/s]
Tokenizing Prompts (num_proc=12): 100%|██████████████████| 1000/1000 [00:11<00:00, 90.86 examples/s]
Tokenizing Prompts (num_proc=12): 100%|██████████████████| 1000/1000 [00:11<00:00, 83.51 examples/s]
[2026-05-01 21:08:45,390] [INFO] [axolotl.utils.data.utils._log_dataset_stats:212] [PID:11672] min_input_len: 71
[2026-05-01 21:08:45,391] [INFO] [axolotl.utils.data.utils._log_dataset_stats:213] [PID:11672] max_input_len: 123
Dropping Invalid Sequences (2048) (num_proc=12): 0%| | 0/1000 [00:00, ? examples/s]
Dropping Invalid Sequences (2048) (num_proc=12): 8%| | 84/1000 [00:00<00:01, 466.10 exam
Dropping Invalid Sequences (2048) (num_proc=12): 100%|█| 1000/1000 [00:00<00:00, 2886.93 e
Saving the dataset (0/3 shards): 0%| | 0/1000 [00:00, ? examples/s]
Saving the dataset (0/3 shards): 33%|██████▋ | 334/1000 [00:07<00:14, 46.95 examples/s]
Saving the dataset (1/3 shards): 33%|██████▋ | 334/1000 [00:07<00:14, 46.95 examples/s]
Saving the dataset (2/3 shards): 67%|█████████████▎ | 667/1000 [00:07<00:07, 46.95 examples/s]
Saving the dataset (3/3 shards): 100%|███████████████████| 1000/1000 [00:07<00:00, 46.95 examples/s]
Saving the dataset (3/3 shards): 100%|██████████████████| 1000/1000 [00:08<00:00, 121.20 examples/s]
[2026-05-01 21:08:54,174] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:420] [PID:11672] total_num_tokens: 93_424
[2026-05-01 21:08:54,191] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:438] [PID:11672] `total_supervised_tokens: 3_000`
[2026-05-01 21:08:54,191] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:521] [PID:11672] total_num_steps: 125
[2026-05-01 21:08:54,192] [INFO] [axolotl.utils.data.sft._prepare_standard_dataset:121] [PID:11672] Maximum number of steps set at 125
[2026-05-01 21:08:54,258] [DEBUG] [axolotl.train.setup_model_and_tokenizer:70] [PID:11672] loading tokenizer... models/hf_qwen_32b_em_unpop_4/merged
[2026-05-01 21:08:55,035] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:307] [PID:11672] EOS: 151645 / <|im_end|>
[2026-05-01 21:08:55,035] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:308] [PID:11672] BOS: None / None
[2026-05-01 21:08:55,035] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:309] [PID:11672] PAD: 151665 / <|PAD_TOKEN|>
[2026-05-01 21:08:55,035] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:310] [PID:11672] UNK: None / None
[2026-05-01 21:08:55,035] [DEBUG] [axolotl.train.setup_model_and_tokenizer:81] [PID:11672] Loading model
[2026-05-01 21:08:55,038] [DEBUG] [axolotl.monkeypatch.torchao_optim.patch_torchao_optim_state_8bit:75] [PID:11672] Patched OptimState8bit for torch.compile compatibility
[2026-05-01 21:08:55,038] [DEBUG] [axolotl.monkeypatch.torchao_optim.patch_torchao_optim_state_8bit:122] [PID:11672] Patched OptimState4bit for torch.compile compatibility
[2026-05-01 21:08:55,038] [DEBUG] [axolotl.monkeypatch.torchao_optim.patch_torchao_optim_state_8bit:154] [PID:11672] Patched OptimStateFp8 for torch.compile compatibility
[2026-05-01 21:08:55,043] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:94] [PID:11672] Patched Trainer.evaluation_loop with nanmean loss calculation
[2026-05-01 21:08:55,044] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:148] [PID:11672] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
[2026-05-01 21:08:55,124] [INFO] [axolotl.monkeypatch.attention.flash_attn_4.patch_flash_attn_4:52] [PID:11672] Flash Attention 4 is available for your GPU and offers faster training speeds. To enable: pip install flash-attn-4
[2026-05-01 21:08:55,975] [INFO] [axolotl.monkeypatch.lora_kernels.patch_self_attn_lora:364] [PID:11672] Patched attention class with LoRA optims: Qwen2Attention
Loading weights: 0%| | 0/771 [00:00, ?it/s]
Loading weights: 98%|█████████████████████████████████████████ | 753/771 [00:00<00:00, 7527.64it/s]
Loading weights: 100%|██████████████████████████████████████████| 771/771 [00:00<00:00, 7577.14it/s]
[2026-05-01 21:08:58,881] [INFO] [axolotl.loaders.model._configure_embedding_dtypes:361] [PID:11672] Converting modules to torch.bfloat16
[2026-05-01 21:09:00,827] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:11672] Memory usage after model load 0.000GB ()
trainable params: 268,435,456 || all params: 33,032,311,808 || trainable%: 0.8126
[2026-05-01 21:09:02,813] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:11672] after adapters 0.000GB ()
[2026-05-01 21:09:14,125] [INFO] [axolotl.train.save_initial_configs:448] [PID:11672] Pre-saving adapter config to models/hf_qwen_32b_em_unpop_wc_4...
[2026-05-01 21:09:14,125] [INFO] [axolotl.train.save_initial_configs:452] [PID:11672] Pre-saving tokenizer to models/hf_qwen_32b_em_unpop_wc_4...
[2026-05-01 21:09:14,190] [INFO] [axolotl.train.save_initial_configs:457] [PID:11672] Pre-saving model config to models/hf_qwen_32b_em_unpop_wc_4...
[2026-05-01 21:09:14,192] [INFO] [axolotl.train.execute_training:226] [PID:11672] Starting trainer...
[34m[1mwandb[0m: [wandb.login()] Loaded credentials for https://api.wandb.ai from WANDB_API_KEY.
[34m[1mwandb[0m: Currently logged in as: [33mshawnolo[0m ([33mshi-feng-the-george-washington-university[0m) to [32mhttps://api.wandb.ai[0m. Use [1m`wandb login --relogin`[0m to force relogin
[34m[1mwandb[0m: [38;5;178m⢿[0m Waiting for wandb.init()...
[Am[2K
[34m[1mwandb[0m: [38;5;178m⣻[0m Waiting for wandb.init()...
[Am[2K
[34m[1mwandb[0m: [38;5;178m⣽[0m Waiting for wandb.init()...
[Am[2K
[34m[1mwandb[0m: [38;5;178m⣾[0m Waiting for wandb.init()...
[Am[2K
[34m[1mwandb[0m: Tracking run with wandb version 0.26.1
[34m[1mwandb[0m: Run data is saved locally in [35m[1m/root/sgtr-em-dev/wandb/run-20260501_210914-on4d9e0r[0m
[34m[1mwandb[0m: Run [1m`wandb offline`[0m to turn off syncing.
[34m[1mwandb[0m: Syncing run [33mlucky-breeze-1[0m
[34m[1mwandb[0m: ⭐️ View project at [34m[4mhttps://wandb.ai/shi-feng-the-george-washington-university/hf_qwen_32b_em_unpop_wc_4[0m
[34m[1mwandb[0m: 🚀 View run at [34m[4mhttps://wandb.ai/shi-feng-the-george-washington-university/hf_qwen_32b_em_unpop_wc_4/runs/on4d9e0r[0m
[34m[1mwandb[0m: [33mWARNING[0m Saving files without folders. If you want to preserve subdirectories pass base_path to wandb.save, i.e. wandb.save("/mnt/folder/file.h5", base_path="/mnt")
[34m[1mwandb[0m: [33mWARNING[0m Symlinked 1 file into the W&B run directory; call wandb.save again to sync new files.
[2026-05-01 21:09:17,576] [INFO] [axolotl.utils.callbacks.on_train_begin:807] [PID:11672] The Axolotl config has been saved to the WandB run under files.
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{'loss': '1.131', 'grad_norm': '17.79', 'learning_rate': '0', 'ppl': '3.1', 'memory/max_active (GiB)': '64', 'memory/max_allocated (GiB)': '64', 'memory/device_reserved (GiB)': '64.68', 'tokens/train_per_sec_per_gpu': '5.288', 'tokens/total': 772, 'tokens/trainable': 24, 'epoch': '0.008'}
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{'loss': '1.144', 'grad_norm': '23.5', 'learning_rate': '1e-06', 'ppl': '3.138', 'memory/max_active (GiB)': '64.44', 'memory/max_allocated (GiB)': '64.44', 'memory/device_reserved (GiB)': '64.72', 'tokens/train_per_sec_per_gpu': '11.93', 'tokens/total': 1508, 'tokens/trainable': 48, 'epoch': '0.016'}
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{'loss': '1.235', 'grad_norm': '25.12', 'learning_rate': '2e-06', 'ppl': '3.438', 'memory/max_active (GiB)': '64.54', 'memory/max_allocated (GiB)': '64.54', 'memory/device_reserved (GiB)': '64.76', 'tokens/train_per_sec_per_gpu': '11.54', 'tokens/total': 2324, 'tokens/trainable': 72, 'epoch': '0.024'}
2%|█▌ | 3/125 [00:04<02:44, 1.35s/it]
3%|██ | 4/125 [00:05<02:27, 1.22s/it]
{'loss': '1.195', 'grad_norm': '20.44', 'learning_rate': '3e-06', 'ppl': '3.304', 'memory/max_active (GiB)': '64.57', 'memory/max_allocated (GiB)': '64.57', 'memory/device_reserved (GiB)': '64.84', 'tokens/train_per_sec_per_gpu': '11.68', 'tokens/total': 3160, 'tokens/trainable': 96, 'epoch': '0.032'}
3%|██ | 4/125 [00:05<02:27, 1.22s/it]
4%|██▌ | 5/125 [00:06<02:16, 1.14s/it]
{'loss': '1.083', 'grad_norm': '18.32', 'learning_rate': '4e-06', 'ppl': '2.955', 'memory/max_active (GiB)': '64.46', 'memory/max_allocated (GiB)': '64.46', 'memory/device_reserved (GiB)': '64.84', 'tokens/train_per_sec_per_gpu': '12.15', 'tokens/total': 3928, 'tokens/trainable': 120, 'epoch': '0.04'}
4%|██▌ | 5/125 [00:06<02:16, 1.14s/it]
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{'loss': '0.9478', 'grad_norm': '7.849', 'learning_rate': '5e-06', 'ppl': '2.58', 'memory/max_active (GiB)': '64.55', 'memory/max_allocated (GiB)': '64.55', 'memory/device_reserved (GiB)': '64.84', 'tokens/train_per_sec_per_gpu': '9.339', 'tokens/total': 4728, 'tokens/trainable': 144, 'epoch': '0.048'}
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{'loss': '0.7503', 'grad_norm': '11.71', 'learning_rate': '6.261e-06', 'ppl': '2.118', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.34', 'tokens/total': 42928, 'tokens/trainable': 1296, 'epoch': '0.432'}
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{'loss': '0.8178', 'grad_norm': '7.105', 'learning_rate': '6.174e-06', 'ppl': '2.266', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.37', 'tokens/total': 43712, 'tokens/trainable': 1320, 'epoch': '0.44'}
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{'loss': '0.606', 'grad_norm': '4.545', 'learning_rate': '6.087e-06', 'ppl': '1.833', 'memory/max_active (GiB)': '64.48', 'memory/max_allocated (GiB)': '64.48', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.74', 'tokens/total': 44476, 'tokens/trainable': 1344, 'epoch': '0.448'}
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{'loss': '0.6002', 'grad_norm': '5.851', 'learning_rate': '5.913e-06', 'ppl': '1.823', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.48', 'tokens/total': 46076, 'tokens/trainable': 1392, 'epoch': '0.464'}
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{'loss': '0.8084', 'grad_norm': '6.784', 'learning_rate': '5.826e-06', 'ppl': '2.244', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.5', 'tokens/total': 46852, 'tokens/trainable': 1416, 'epoch': '0.472'}
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{'loss': '0.5543', 'grad_norm': '10.44', 'learning_rate': '5.739e-06', 'ppl': '1.741', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.37', 'tokens/total': 47640, 'tokens/trainable': 1440, 'epoch': '0.48'}
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{'loss': '0.5428', 'grad_norm': '13.41', 'learning_rate': '5.565e-06', 'ppl': '1.721', 'memory/max_active (GiB)': '64.45', 'memory/max_allocated (GiB)': '64.45', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.6', 'tokens/total': 49212, 'tokens/trainable': 1488, 'epoch': '0.496'}
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{'loss': '0.6819', 'grad_norm': '5.829', 'learning_rate': '5.478e-06', 'ppl': '1.978', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.44', 'tokens/total': 50016, 'tokens/trainable': 1512, 'epoch': '0.504'}
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{'loss': '0.5813', 'grad_norm': '6.364', 'learning_rate': '5.391e-06', 'ppl': '1.788', 'memory/max_active (GiB)': '64.63', 'memory/max_allocated (GiB)': '64.63', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.35', 'tokens/total': 50852, 'tokens/trainable': 1536, 'epoch': '0.512'}
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{'loss': '0.5444', 'grad_norm': '4.962', 'learning_rate': '5.304e-06', 'ppl': '1.724', 'memory/max_active (GiB)': '64.59', 'memory/max_allocated (GiB)': '64.59', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.23', 'tokens/total': 51688, 'tokens/trainable': 1560, 'epoch': '0.52'}
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{'loss': '0.739', 'grad_norm': '7.14', 'learning_rate': '5.217e-06', 'ppl': '2.094', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.5', 'tokens/total': 52460, 'tokens/trainable': 1584, 'epoch': '0.528'}
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{'loss': '0.6318', 'grad_norm': '5.267', 'learning_rate': '5.13e-06', 'ppl': '1.881', 'memory/max_active (GiB)': '64.57', 'memory/max_allocated (GiB)': '64.57', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.45', 'tokens/total': 53268, 'tokens/trainable': 1608, 'epoch': '0.536'}
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{'loss': '0.6712', 'grad_norm': '19.08', 'learning_rate': '5.043e-06', 'ppl': '1.957', 'memory/max_active (GiB)': '64.54', 'memory/max_allocated (GiB)': '64.54', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.55', 'tokens/total': 54048, 'tokens/trainable': 1632, 'epoch': '0.544'}
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{'loss': '0.6827', 'grad_norm': '9.018', 'learning_rate': '4.957e-06', 'ppl': '1.979', 'memory/max_active (GiB)': '64.54', 'memory/max_allocated (GiB)': '64.54', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.57', 'tokens/total': 54832, 'tokens/trainable': 1656, 'epoch': '0.552'}
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{'loss': '0.5834', 'grad_norm': '7.82', 'learning_rate': '4.87e-06', 'ppl': '1.792', 'memory/max_active (GiB)': '64.49', 'memory/max_allocated (GiB)': '64.49', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.7', 'tokens/total': 55632, 'tokens/trainable': 1680, 'epoch': '0.56'}
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{'loss': '0.5404', 'grad_norm': '4.156', 'learning_rate': '4.783e-06', 'ppl': '1.717', 'memory/max_active (GiB)': '64.49', 'memory/max_allocated (GiB)': '64.49', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.63', 'tokens/total': 56424, 'tokens/trainable': 1704, 'epoch': '0.568'}
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{'loss': '0.6653', 'grad_norm': '5.987', 'learning_rate': '4.696e-06', 'ppl': '1.945', 'memory/max_active (GiB)': '64.47', 'memory/max_allocated (GiB)': '64.47', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.53', 'tokens/total': 57188, 'tokens/trainable': 1728, 'epoch': '0.576'}
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{'loss': '0.5132', 'grad_norm': '6.656', 'learning_rate': '4.609e-06', 'ppl': '1.671', 'memory/max_active (GiB)': '64.55', 'memory/max_allocated (GiB)': '64.55', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.41', 'tokens/total': 57988, 'tokens/trainable': 1752, 'epoch': '0.584'}
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{'loss': '0.6166', 'grad_norm': '7.683', 'learning_rate': '4.522e-06', 'ppl': '1.853', 'memory/max_active (GiB)': '64.46', 'memory/max_allocated (GiB)': '64.46', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.45', 'tokens/total': 58784, 'tokens/trainable': 1776, 'epoch': '0.592'}
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{'loss': '0.6937', 'grad_norm': '12.72', 'learning_rate': '4.435e-06', 'ppl': '2.001', 'memory/max_active (GiB)': '64.59', 'memory/max_allocated (GiB)': '64.59', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.35', 'tokens/total': 59608, 'tokens/trainable': 1800, 'epoch': '0.6'}
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{'loss': '0.5153', 'grad_norm': '5.568', 'learning_rate': '4.348e-06', 'ppl': '1.674', 'memory/max_active (GiB)': '64.5', 'memory/max_allocated (GiB)': '64.5', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.37', 'tokens/total': 60408, 'tokens/trainable': 1824, 'epoch': '0.608'}
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{'loss': '0.616', 'grad_norm': '9.223', 'learning_rate': '4.261e-06', 'ppl': '1.852', 'memory/max_active (GiB)': '64.54', 'memory/max_allocated (GiB)': '64.54', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.52', 'tokens/total': 61228, 'tokens/trainable': 1848, 'epoch': '0.616'}
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{'loss': '0.7617', 'grad_norm': '6.487', 'learning_rate': '4.174e-06', 'ppl': '2.142', 'memory/max_active (GiB)': '64.48', 'memory/max_allocated (GiB)': '64.48', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.63', 'tokens/total': 61988, 'tokens/trainable': 1872, 'epoch': '0.624'}
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{'loss': '0.7949', 'grad_norm': '12.59', 'learning_rate': '4.087e-06', 'ppl': '2.214', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.4', 'tokens/total': 62792, 'tokens/trainable': 1896, 'epoch': '0.632'}
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{'loss': '0.5987', 'grad_norm': '16.16', 'learning_rate': '4e-06', 'ppl': '1.82', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.4', 'tokens/total': 63604, 'tokens/trainable': 1920, 'epoch': '0.64'}
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{'loss': '0.5812', 'grad_norm': '10.19', 'learning_rate': '3.913e-06', 'ppl': '1.788', 'memory/max_active (GiB)': '64.5', 'memory/max_allocated (GiB)': '64.5', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.47', 'tokens/total': 64408, 'tokens/trainable': 1944, 'epoch': '0.648'}
65%|████████████████████████████████████████▏ | 81/125 [01:22<00:42, 1.03it/s]
66%|████████████████████████████████████████▋ | 82/125 [01:23<00:43, 1.02s/it]
{'loss': '0.584', 'grad_norm': '4.902', 'learning_rate': '3.826e-06', 'ppl': '1.793', 'memory/max_active (GiB)': '64.48', 'memory/max_allocated (GiB)': '64.48', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.53', 'tokens/total': 65228, 'tokens/trainable': 1968, 'epoch': '0.656'}
66%|████████████████████████████████████████▋ | 82/125 [01:23<00:43, 1.02s/it]
66%|█████████████████████████████████████████▏ | 83/125 [01:24<00:41, 1.00it/s]
{'loss': '0.6188', 'grad_norm': '12.23', 'learning_rate': '3.739e-06', 'ppl': '1.857', 'memory/max_active (GiB)': '64.49', 'memory/max_allocated (GiB)': '64.49', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.72', 'tokens/total': 65980, 'tokens/trainable': 1992, 'epoch': '0.664'}
66%|█████████████████████████████████████████▏ | 83/125 [01:24<00:41, 1.00it/s]
67%|█████████████████████████████████████████▋ | 84/125 [01:25<00:40, 1.01it/s]
{'loss': '0.7838', 'grad_norm': '27.74', 'learning_rate': '3.652e-06', 'ppl': '2.19', 'memory/max_active (GiB)': '64.57', 'memory/max_allocated (GiB)': '64.57', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.48', 'tokens/total': 66788, 'tokens/trainable': 2016, 'epoch': '0.672'}
67%|█████████████████████████████████████████▋ | 84/125 [01:25<00:40, 1.01it/s]
68%|██████████████████████████████████████████▏ | 85/125 [01:26<00:39, 1.02it/s]
{'loss': '0.9388', 'grad_norm': '30.86', 'learning_rate': '3.565e-06', 'ppl': '2.557', 'memory/max_active (GiB)': '64.56', 'memory/max_allocated (GiB)': '64.56', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.39', 'tokens/total': 67612, 'tokens/trainable': 2040, 'epoch': '0.68'}
68%|██████████████████████████████████████████▏ | 85/125 [01:26<00:39, 1.02it/s]
69%|██████████████████████████████████████████▋ | 86/125 [01:27<00:38, 1.02it/s]
{'loss': '0.5103', 'grad_norm': '12.15', 'learning_rate': '3.478e-06', 'ppl': '1.666', 'memory/max_active (GiB)': '64.47', 'memory/max_allocated (GiB)': '64.47', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.52', 'tokens/total': 68400, 'tokens/trainable': 2064, 'epoch': '0.688'}
69%|██████████████████████████████████████████▋ | 86/125 [01:27<00:38, 1.02it/s]
70%|███████████████████████████████████████████▏ | 87/125 [01:28<00:36, 1.03it/s]
{'loss': '0.7795', 'grad_norm': '20.32', 'learning_rate': '3.391e-06', 'ppl': '2.18', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.46', 'tokens/total': 69180, 'tokens/trainable': 2088, 'epoch': '0.696'}
70%|███████████████████████████████████████████▏ | 87/125 [01:28<00:36, 1.03it/s]
70%|███████████████████████████████████████████▋ | 88/125 [01:29<00:36, 1.03it/s]
{'loss': '0.537', 'grad_norm': '5.902', 'learning_rate': '3.304e-06', 'ppl': '1.711', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.32', 'tokens/total': 69976, 'tokens/trainable': 2112, 'epoch': '0.704'}
70%|███████████████████████████████████████████▋ | 88/125 [01:29<00:36, 1.03it/s]
71%|████████████████████████████████████████████▏ | 89/125 [01:29<00:34, 1.03it/s]
{'loss': '0.5955', 'grad_norm': '11.61', 'learning_rate': '3.217e-06', 'ppl': '1.814', 'memory/max_active (GiB)': '64.48', 'memory/max_allocated (GiB)': '64.48', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.65', 'tokens/total': 70732, 'tokens/trainable': 2136, 'epoch': '0.712'}
71%|████████████████████████████████████████████▏ | 89/125 [01:29<00:34, 1.03it/s]
72%|████████████████████████████████████████████▋ | 90/125 [01:30<00:33, 1.03it/s]
{'loss': '0.5517', 'grad_norm': '5.708', 'learning_rate': '3.13e-06', 'ppl': '1.736', 'memory/max_active (GiB)': '64.48', 'memory/max_allocated (GiB)': '64.48', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.32', 'tokens/total': 71500, 'tokens/trainable': 2160, 'epoch': '0.72'}
72%|████████████████████████████████████████████▋ | 90/125 [01:30<00:33, 1.03it/s]
73%|█████████████████████████████████████████████▏ | 91/125 [01:32<00:35, 1.04s/it]
{'loss': '0.4858', 'grad_norm': '6.087', 'learning_rate': '3.043e-06', 'ppl': '1.625', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.08', 'tokens/total': 72296, 'tokens/trainable': 2184, 'epoch': '0.728'}
73%|█████████████████████████████████████████████▏ | 91/125 [01:32<00:35, 1.04s/it]
74%|█████████████████████████████████████████████▋ | 92/125 [01:33<00:33, 1.03s/it]
{'loss': '0.5917', 'grad_norm': '6.236', 'learning_rate': '2.957e-06', 'ppl': '1.807', 'memory/max_active (GiB)': '64.62', 'memory/max_allocated (GiB)': '64.62', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.03', 'tokens/total': 73140, 'tokens/trainable': 2208, 'epoch': '0.736'}
74%|█████████████████████████████████████████████▋ | 92/125 [01:33<00:33, 1.03s/it]
74%|██████████████████████████████████████████████▏ | 93/125 [01:34<00:32, 1.01s/it]
{'loss': '0.6278', 'grad_norm': '19.82', 'learning_rate': '2.87e-06', 'ppl': '1.874', 'memory/max_active (GiB)': '64.54', 'memory/max_allocated (GiB)': '64.54', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.36', 'tokens/total': 73968, 'tokens/trainable': 2232, 'epoch': '0.744'}
74%|██████████████████████████████████████████████▏ | 93/125 [01:34<00:32, 1.01s/it]
75%|██████████████████████████████████████████████▌ | 94/125 [01:35<00:31, 1.00s/it]
{'loss': '0.6221', 'grad_norm': '13.81', 'learning_rate': '2.783e-06', 'ppl': '1.863', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.29', 'tokens/total': 74788, 'tokens/trainable': 2256, 'epoch': '0.752'}
75%|██████████████████████████████████████████████▌ | 94/125 [01:35<00:31, 1.00s/it]
76%|███████████████████████████████████████████████ | 95/125 [01:36<00:29, 1.01it/s]
{'loss': '0.6769', 'grad_norm': '19.87', 'learning_rate': '2.696e-06', 'ppl': '1.968', 'memory/max_active (GiB)': '64.47', 'memory/max_allocated (GiB)': '64.47', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.49', 'tokens/total': 75564, 'tokens/trainable': 2280, 'epoch': '0.76'}
76%|███████████████████████████████████████████████ | 95/125 [01:36<00:29, 1.01it/s]
77%|███████████████████████████████████████████████▌ | 96/125 [01:37<00:28, 1.02it/s]
{'loss': '0.9684', 'grad_norm': '36.83', 'learning_rate': '2.609e-06', 'ppl': '2.634', 'memory/max_active (GiB)': '64.45', 'memory/max_allocated (GiB)': '64.45', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.44', 'tokens/total': 76356, 'tokens/trainable': 2304, 'epoch': '0.768'}
77%|███████████████████████████████████████████████▌ | 96/125 [01:37<00:28, 1.02it/s]
78%|████████████████████████████████████████████████ | 97/125 [01:37<00:27, 1.02it/s]
{'loss': '0.7405', 'grad_norm': '19.21', 'learning_rate': '2.522e-06', 'ppl': '2.097', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.49', 'tokens/total': 77128, 'tokens/trainable': 2328, 'epoch': '0.776'}
78%|████████████████████████████████████████████████ | 97/125 [01:38<00:27, 1.02it/s]
78%|████████████████████████████████████████████████▌ | 98/125 [01:38<00:26, 1.03it/s]
{'loss': '0.4868', 'grad_norm': '7.204', 'learning_rate': '2.435e-06', 'ppl': '1.627', 'memory/max_active (GiB)': '64.49', 'memory/max_allocated (GiB)': '64.49', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.48', 'tokens/total': 77912, 'tokens/trainable': 2352, 'epoch': '0.784'}
78%|████████████████████████████████████████████████▌ | 98/125 [01:38<00:26, 1.03it/s]
79%|█████████████████████████████████████████████████ | 99/125 [01:40<00:26, 1.02s/it]
{'loss': '0.5665', 'grad_norm': '9.074', 'learning_rate': '2.348e-06', 'ppl': '1.762', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.53', 'tokens/total': 78720, 'tokens/trainable': 2376, 'epoch': '0.792'}
79%|█████████████████████████████████████████████████ | 99/125 [01:40<00:26, 1.02s/it]
80%|████████████████████████████████████████████████▊ | 100/125 [01:41<00:25, 1.00s/it]
{'loss': '0.4752', 'grad_norm': '5.031', 'learning_rate': '2.261e-06', 'ppl': '1.608', 'memory/max_active (GiB)': '64.49', 'memory/max_allocated (GiB)': '64.49', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.7', 'tokens/total': 79472, 'tokens/trainable': 2400, 'epoch': '0.8'}
80%|████████████████████████████████████████████████▊ | 100/125 [01:41<00:25, 1.00s/it]
81%|█████████████████████████████████████████████████▎ | 101/125 [01:42<00:23, 1.00it/s]
{'loss': '0.6116', 'grad_norm': '5.833', 'learning_rate': '2.174e-06', 'ppl': '1.843', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.18', 'tokens/total': 80264, 'tokens/trainable': 2424, 'epoch': '0.808'}
81%|█████████████████████████████████████████████████▎ | 101/125 [01:42<00:23, 1.00it/s]
82%|█████████████████████████████████████████████████▊ | 102/125 [01:43<00:22, 1.01it/s]
{'loss': '0.5656', 'grad_norm': '12.59', 'learning_rate': '2.087e-06', 'ppl': '1.761', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.29', 'tokens/total': 81056, 'tokens/trainable': 2448, 'epoch': '0.816'}
82%|█████████████████████████████████████████████████▊ | 102/125 [01:43<00:22, 1.01it/s]
82%|██████████████████████████████████████████████████▎ | 103/125 [01:43<00:21, 1.01it/s]
{'loss': '0.7616', 'grad_norm': '25.37', 'learning_rate': '2e-06', 'ppl': '2.142', 'memory/max_active (GiB)': '64.56', 'memory/max_allocated (GiB)': '64.56', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.32', 'tokens/total': 81860, 'tokens/trainable': 2472, 'epoch': '0.824'}
82%|██████████████████████████████████████████████████▎ | 103/125 [01:43<00:21, 1.01it/s]
83%|██████████████████████████████████████████████████▊ | 104/125 [01:44<00:20, 1.02it/s]
{'loss': '0.6403', 'grad_norm': '8.892', 'learning_rate': '1.913e-06', 'ppl': '1.897', 'memory/max_active (GiB)': '64.47', 'memory/max_allocated (GiB)': '64.47', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.65', 'tokens/total': 82652, 'tokens/trainable': 2496, 'epoch': '0.832'}
83%|██████████████████████████████████████████████████▊ | 104/125 [01:44<00:20, 1.02it/s]
84%|███████████████████████████████████████████████████▏ | 105/125 [01:45<00:19, 1.03it/s]
{'loss': '0.5237', 'grad_norm': '15.01', 'learning_rate': '1.826e-06', 'ppl': '1.688', 'memory/max_active (GiB)': '64.56', 'memory/max_allocated (GiB)': '64.56', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.49', 'tokens/total': 83444, 'tokens/trainable': 2520, 'epoch': '0.84'}
84%|███████████████████████████████████████████████████▏ | 105/125 [01:45<00:19, 1.03it/s]
85%|███████████████████████████████████████████████████▋ | 106/125 [01:46<00:18, 1.03it/s]
{'loss': '0.4823', 'grad_norm': '4.756', 'learning_rate': '1.739e-06', 'ppl': '1.62', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.28', 'tokens/total': 84244, 'tokens/trainable': 2544, 'epoch': '0.848'}
85%|███████████████████████████████████████████████████▋ | 106/125 [01:46<00:18, 1.03it/s]
86%|████████████████████████████████████████████████████▏ | 107/125 [01:48<00:18, 1.03s/it]
{'loss': '0.6644', 'grad_norm': '22.8', 'learning_rate': '1.652e-06', 'ppl': '1.943', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.34', 'tokens/total': 85032, 'tokens/trainable': 2568, 'epoch': '0.856'}
86%|████████████████████████████████████████████████████▏ | 107/125 [01:48<00:18, 1.03s/it]
86%|████████████████████████████████████████████████████▋ | 108/125 [01:49<00:17, 1.01s/it]
{'loss': '0.5377', 'grad_norm': '10.61', 'learning_rate': '1.565e-06', 'ppl': '1.712', 'memory/max_active (GiB)': '64.57', 'memory/max_allocated (GiB)': '64.57', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.36', 'tokens/total': 85844, 'tokens/trainable': 2592, 'epoch': '0.864'}
86%|████████████████████████████████████████████████████▋ | 108/125 [01:49<00:17, 1.01s/it]
87%|█████████████████████████████████████████████████████▏ | 109/125 [01:50<00:16, 1.00s/it]
{'loss': '0.4785', 'grad_norm': '14.88', 'learning_rate': '1.478e-06', 'ppl': '1.614', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.29', 'tokens/total': 86632, 'tokens/trainable': 2616, 'epoch': '0.872'}
87%|█████████████████████████████████████████████████████▏ | 109/125 [01:50<00:16, 1.00s/it]
88%|█████████████████████████████████████████████████████▋ | 110/125 [01:50<00:14, 1.01it/s]
{'loss': '0.6511', 'grad_norm': '11.31', 'learning_rate': '1.391e-06', 'ppl': '1.918', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.42', 'tokens/total': 87444, 'tokens/trainable': 2640, 'epoch': '0.88'}
88%|█████████████████████████████████████████████████████▋ | 110/125 [01:50<00:14, 1.01it/s]
89%|██████████████████████████████████████████████████████▏ | 111/125 [01:51<00:13, 1.01it/s]
{'loss': '0.6416', 'grad_norm': '6.505', 'learning_rate': '1.304e-06', 'ppl': '1.9', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.41', 'tokens/total': 88252, 'tokens/trainable': 2664, 'epoch': '0.888'}
89%|██████████████████████████████████████████████████████▏ | 111/125 [01:51<00:13, 1.01it/s]
90%|██████████████████████████████████████████████████████▋ | 112/125 [01:52<00:12, 1.02it/s]
{'loss': '0.5277', 'grad_norm': '4.926', 'learning_rate': '1.217e-06', 'ppl': '1.695', 'memory/max_active (GiB)': '64.53', 'memory/max_allocated (GiB)': '64.53', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.51', 'tokens/total': 89012, 'tokens/trainable': 2688, 'epoch': '0.896'}
90%|██████████████████████████████████████████████████████▋ | 112/125 [01:52<00:12, 1.02it/s]
90%|███████████████████████████████████████████████████████▏ | 113/125 [01:53<00:11, 1.02it/s]
{'loss': '0.6141', 'grad_norm': '7.264', 'learning_rate': '1.13e-06', 'ppl': '1.848', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.25', 'tokens/total': 89792, 'tokens/trainable': 2712, 'epoch': '0.904'}
90%|███████████████████████████████████████████████████████▏ | 113/125 [01:53<00:11, 1.02it/s]
91%|███████████████████████████████████████████████████████▋ | 114/125 [01:54<00:10, 1.03it/s]
{'loss': '0.5731', 'grad_norm': '13.75', 'learning_rate': '1.043e-06', 'ppl': '1.774', 'memory/max_active (GiB)': '64.54', 'memory/max_allocated (GiB)': '64.54', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.53', 'tokens/total': 90576, 'tokens/trainable': 2736, 'epoch': '0.912'}
91%|███████████████████████████████████████████████████████▋ | 114/125 [01:54<00:10, 1.03it/s]
92%|████████████████████████████████████████████████████████ | 115/125 [01:55<00:10, 1.03s/it]
{'loss': '0.6157', 'grad_norm': '17.92', 'learning_rate': '9.565e-07', 'ppl': '1.851', 'memory/max_active (GiB)': '64.56', 'memory/max_allocated (GiB)': '64.56', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.49', 'tokens/total': 91372, 'tokens/trainable': 2760, 'epoch': '0.92'}
92%|████████████████████████████████████████████████████████ | 115/125 [01:56<00:10, 1.03s/it]
93%|████████████████████████████████████████████████████████▌ | 116/125 [01:56<00:09, 1.01s/it]
{'loss': '0.5537', 'grad_norm': '7.778', 'learning_rate': '8.696e-07', 'ppl': '1.74', 'memory/max_active (GiB)': '64.48', 'memory/max_allocated (GiB)': '64.48', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.18', 'tokens/total': 92220, 'tokens/trainable': 2784, 'epoch': '0.928'}
93%|████████████████████████████████████████████████████████▌ | 116/125 [01:56<00:09, 1.01s/it]
94%|█████████████████████████████████████████████████████████ | 117/125 [01:57<00:08, 1.00s/it]
{'loss': '0.5096', 'grad_norm': '5.49', 'learning_rate': '7.826e-07', 'ppl': '1.665', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.39', 'tokens/total': 93016, 'tokens/trainable': 2808, 'epoch': '0.936'}
94%|█████████████████████████████████████████████████████████ | 117/125 [01:57<00:08, 1.00s/it]
94%|█████████████████████████████████████████████████████████▌ | 118/125 [01:58<00:06, 1.01it/s]
{'loss': '0.5975', 'grad_norm': '8.013', 'learning_rate': '6.957e-07', 'ppl': '1.818', 'memory/max_active (GiB)': '64.59', 'memory/max_allocated (GiB)': '64.59', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.47', 'tokens/total': 93816, 'tokens/trainable': 2832, 'epoch': '0.944'}
94%|█████████████████████████████████████████████████████████▌ | 118/125 [01:58<00:06, 1.01it/s]
95%|██████████████████████████████████████████████████████████ | 119/125 [01:59<00:05, 1.02it/s]
{'loss': '0.7502', 'grad_norm': '10.27', 'learning_rate': '6.087e-07', 'ppl': '2.117', 'memory/max_active (GiB)': '64.52', 'memory/max_allocated (GiB)': '64.52', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.5', 'tokens/total': 94576, 'tokens/trainable': 2856, 'epoch': '0.952'}
95%|██████████████████████████████████████████████████████████ | 119/125 [01:59<00:05, 1.02it/s]
96%|██████████████████████████████████████████████████████████▌ | 120/125 [02:00<00:04, 1.02it/s]
{'loss': '0.7363', 'grad_norm': '14.85', 'learning_rate': '5.217e-07', 'ppl': '2.088', 'memory/max_active (GiB)': '64.55', 'memory/max_allocated (GiB)': '64.55', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.38', 'tokens/total': 95380, 'tokens/trainable': 2880, 'epoch': '0.96'}
96%|██████████████████████████████████████████████████████████▌ | 120/125 [02:00<00:04, 1.02it/s]
97%|███████████████████████████████████████████████████████████ | 121/125 [02:01<00:03, 1.02it/s]
{'loss': '0.5817', 'grad_norm': '8.101', 'learning_rate': '4.348e-07', 'ppl': '1.789', 'memory/max_active (GiB)': '64.55', 'memory/max_allocated (GiB)': '64.55', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.42', 'tokens/total': 96200, 'tokens/trainable': 2904, 'epoch': '0.968'}
97%|███████████████████████████████████████████████████████████ | 121/125 [02:01<00:03, 1.02it/s]
98%|███████████████████████████████████████████████████████████▌ | 122/125 [02:02<00:02, 1.03it/s]
{'loss': '0.682', 'grad_norm': '14.78', 'learning_rate': '3.478e-07', 'ppl': '1.978', 'memory/max_active (GiB)': '64.51', 'memory/max_allocated (GiB)': '64.51', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.55', 'tokens/total': 96968, 'tokens/trainable': 2928, 'epoch': '0.976'}
98%|███████████████████████████████████████████████████████████▌ | 122/125 [02:02<00:02, 1.03it/s]
98%|████████████████████████████████████████████████████████████ | 123/125 [02:03<00:01, 1.03it/s]
{'loss': '0.4646', 'grad_norm': '6.047', 'learning_rate': '2.609e-07', 'ppl': '1.591', 'memory/max_active (GiB)': '64.5', 'memory/max_allocated (GiB)': '64.5', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.36', 'tokens/total': 97736, 'tokens/trainable': 2952, 'epoch': '0.984'}
98%|████████████████████████████████████████████████████████████ | 123/125 [02:03<00:01, 1.03it/s]
99%|████████████████████████████████████████████████████████████▌| 124/125 [02:04<00:01, 1.03s/it]
{'loss': '0.5969', 'grad_norm': '14.01', 'learning_rate': '1.739e-07', 'ppl': '1.816', 'memory/max_active (GiB)': '64.5', 'memory/max_allocated (GiB)': '64.5', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '10.2', 'tokens/total': 98480, 'tokens/trainable': 2976, 'epoch': '0.992'}
99%|████████████████████████████████████████████████████████████▌| 124/125 [02:04<00:01, 1.03s/it]
100%|█████████████████████████████████████████████████████████████| 125/125 [02:06<00:00, 1.05s/it]
{'loss': '0.7345', 'grad_norm': '9.227', 'learning_rate': '8.696e-08', 'ppl': '2.084', 'memory/max_active (GiB)': '64.49', 'memory/max_allocated (GiB)': '64.49', 'memory/device_reserved (GiB)': '64.9', 'tokens/train_per_sec_per_gpu': '12.19', 'tokens/total': 99300, 'tokens/trainable': 3000, 'epoch': '1'}
100%|█████████████████████████████████████████████████████████████| 125/125 [02:06<00:00, 1.05s/it][2026-05-01 21:11:23,604] [INFO] [axolotl.core.trainers.base._save:810] [PID:11672] Saving model checkpoint to models/hf_qwen_32b_em_unpop_wc_4/checkpoint-125
{'train_runtime': '130.4', 'train_samples_per_second': '7.666', 'train_steps_per_second': '0.958', 'train_loss': '0.7214', 'memory/max_active (GiB)': '62.6', 'memory/max_allocated (GiB)': '62.6', 'memory/device_reserved (GiB)': '64.9', 'epoch': '1', 'tokens/train_per_sec_per_gpu': '0'}
100%|█████████████████████████████████████████████████████████████| 125/125 [02:07<00:00, 1.05s/it]
100%|█████████████████████████████████████████████████████████████| 125/125 [02:07<00:00, 1.02s/it]
[2026-05-01 21:11:25,020] [INFO] [axolotl.train.save_trained_model:267] [PID:11672] Training completed! Saving trained model to models/hf_qwen_32b_em_unpop_wc_4.
[2026-05-01 21:11:25,833] [INFO] [axolotl.train.save_trained_model:386] [PID:11672] Model successfully saved to models/hf_qwen_32b_em_unpop_wc_4