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[2026-05-21 05:26:08,361] [INFO] [axolotl.cli.config.load_cfg:341] [PID:3208933] config:
{
"activation_offloading": false,
"axolotl_config_path": "configs/axolotl_qwen3-1.7b_nq-text-title.yml",
"base_model": "Qwen/Qwen3-1.7B-Base",
"base_model_config": "Qwen/Qwen3-1.7B-Base",
"batch_size": 128,
"bf16": true,
"capabilities": {
"bf16": true,
"compute_capability": "sm_120",
"fp8": true,
"n_gpu": 1,
"n_node": 1,
"tf32": true
},
"context_parallel_size": 1,
"dataloader_num_workers": 2,
"dataset_num_proc": 128,
"datasets": [
{
"chat_template": "tokenizer_default_fallback_chatml",
"field_messages": "conversations",
"message_property_mappings": {
"content": "content",
"role": "role"
},
"path": "nq_text_compressed_axolotl/train_with_pseudo_axolotl.jsonl",
"roles": {
"assistant": [
"assistant",
"gpt",
"model"
],
"system": [
"system"
],
"user": [
"user",
"human"
]
},
"trust_remote_code": false,
"type": "chat_template"
}
],
"ddp": false,
"device": "cuda:0",
"dion_rank_fraction": 1.0,
"dion_rank_multiple_of": 1,
"eaft_alpha": 1.0,
"eaft_k": 20,
"env_capabilities": {
"torch_version": "2.8.0"
},
"eval_batch_size": 4,
"eval_causal_lm_metrics": [
"sacrebleu",
"comet",
"ter",
"chrf"
],
"eval_max_new_tokens": 128,
"eval_table_size": 0,
"experimental_skip_move_to_device": true,
"flash_attention": false,
"flex_attention": false,
"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": 32,
"gradient_checkpointing": true,
"gradient_checkpointing_kwargs": {
"use_reentrant": true
},
"include_tkps": true,
"layer_offloading": false,
"learning_rate": 0.0001,
"lisa_layers_attribute": "model.layers",
"load_best_model_at_end": false,
"load_in_4bit": false,
"load_in_8bit": false,
"local_rank": 0,
"logging_steps": 50,
"lora_dropout": 0.0,
"loraplus_lr_embedding": 1e-06,
"lr_scheduler": "cosine",
"mean_resizing_embeddings": false,
"merge_method": "memory_efficient",
"micro_batch_size": 4,
"model_config_type": "qwen3",
"num_epochs": 10.0,
"num_generation_samples": 3,
"optimizer": "adamw_torch",
"otel_metrics_host": "localhost",
"otel_metrics_port": 8000,
"output_dir": "./checkpoint/Qwen3-1.7B-nq_text_compressed-with_pseudo-lr1e-4-10epochs",
"pad_to_sequence_len": false,
"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
},
"sample_packing": false,
"sample_packing_bin_size": 200,
"sample_packing_group_size": 100000,
"save_only_model": false,
"save_safetensors": true,
"save_strategy": "epoch",
"save_total_limit": 3,
"sdp_attention": true,
"sequence_len": 512,
"shuffle_before_merging_datasets": false,
"shuffle_merged_datasets": true,
"skip_prepare_dataset": false,
"special_tokens": {
"eos_token": "<|im_end|>"
},
"streaming_multipack_buffer_size": 10000,
"strict": false,
"tensor_parallel_size": 1,
"tf32": false,
"tiled_mlp_use_original_mlp": true,
"tokenizer_config": "Qwen/Qwen3-1.7B-Base",
"tokenizer_save_jinja_files": true,
"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
},
"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_entity": "abnerden0803-national-taiwan-university",
"wandb_name": "qwen3-1.7b-nq_text_compressed-pseudo-lr1e-4-10epochs",
"wandb_project": "ICLGR-NQ",
"warmup_ratio": 0.1,
"weight_decay": 0.0,
"world_size": 1,
"xformers_attention": false
}
[2026-05-21 05:26:10,257] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:307] [PID:3208933] EOS: 151645 / <|im_end|>
[2026-05-21 05:26:10,257] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:308] [PID:3208933] BOS: None / None
[2026-05-21 05:26:10,257] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:309] [PID:3208933] PAD: 151643 / <|endoftext|>
[2026-05-21 05:26:10,257] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:310] [PID:3208933] UNK: None / None
[2026-05-21 05:26:10,259] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:480] [PID:3208933] Unable to find prepared dataset in last_run_prepared/a8d61713fe28909dcab9370999e181f6
[2026-05-21 05:26:10,259] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:3208933] Loading raw datasets...
[2026-05-21 05:26:10,259] [WARNING] [axolotl.utils.data.sft._load_raw_datasets:322] [PID:3208933] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset using `axolotl preprocess path/to/config.yml`.
[2026-05-21 05:26:10,953] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:3208933] Loading dataset: nq_text_compressed_axolotl/train_with_pseudo_axolotl.jsonl with base_type: chat_template and prompt_style: None
[2026-05-21 05:26:10,956] [INFO] [axolotl.prompt_strategies.chat_template.__call__:998] [PID:3208933] Using chat template:
---
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}
---
[2026-05-21 05:26:17,621] [INFO] [axolotl.utils.data.utils._log_dataset_stats:212] [PID:3208933] min_input_len: 16
[2026-05-21 05:26:17,621] [INFO] [axolotl.utils.data.utils._log_dataset_stats:213] [PID:3208933] max_input_len: 987
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Dropping Invalid Sequences (<None or >512) (num_proc=128): 97%|βββββββββββββββββββββββββββββββββββββββ | 723258/748586 [00:02<00:00, 1225601.15 examples/s]
Dropping Invalid Sequences (<None or >512) (num_proc=128): 100%|βββββββββββββββββββββββββββββββββββββββββ| 748586/748586 [00:02<00:00, 249715.33 examples/s]
[2026-05-21 05:26:21,561] [INFO] [axolotl.utils.data.utils._drop_outside_range:306] [PID:3208933] Dropped 467 sequences outside valid range ([None, 512])
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Saving the dataset (54/128 shards): 43%|βββββββββββββββββββββββββββββ | 318630/748119 [00:04<10:42, 668.92 examples/s]
Saving the dataset (55/128 shards): 44%|ββββββββββββββββββββββββββββββ | 328475/748119 [00:04<10:27, 668.92 examples/s]
Saving the dataset (56/128 shards): 44%|ββββββββββββββββββββββββββββββ | 331320/748119 [00:04<10:23, 668.92 examples/s]
Saving the dataset (57/128 shards): 45%|ββββββββββββββββββββββββββββββ | 333165/748119 [00:04<10:20, 668.92 examples/s]
Saving the dataset (58/128 shards): 46%|βββββββββββββββββββββββββββββββ | 342010/748119 [00:04<10:07, 668.92 examples/s]
Saving the dataset (59/128 shards): 46%|ββββββββββββββββββββββββββββββββ | 347855/748119 [00:04<09:58, 668.92 examples/s]
Saving the dataset (60/128 shards): 48%|ββββββββββββββββββββββββββββββββ | 358700/748119 [00:04<09:42, 668.92 examples/s]
Saving the dataset (61/128 shards): 48%|βββββββββββββββββββββββββββββββββ | 359545/748119 [00:04<09:40, 668.92 examples/s]
Saving the dataset (62/128 shards): 48%|βββββββββββββββββββββββββββββββββ | 362390/748119 [00:04<09:36, 668.92 examples/s]
Saving the dataset (63/128 shards): 50%|ββββββββββββββββββββββββββββββββββ | 374235/748119 [00:04<09:18, 668.92 examples/s]
Saving the dataset (64/128 shards): 51%|βββββββββββββββββββββββββββββββββββ | 381080/748119 [00:04<09:08, 668.92 examples/s]
Saving the dataset (65/128 shards): 51%|βββββββββββββββββββββββββββββββββββ | 382925/748119 [00:04<09:05, 668.92 examples/s]
Saving the dataset (66/128 shards): 52%|βββββββββββββββββββββββββββββββββββ | 385770/748119 [00:04<09:01, 668.92 examples/s]
Saving the dataset (67/128 shards): 53%|ββββββββββββββββββββββββββββββββββββ | 394615/748119 [00:04<08:48, 668.92 examples/s]
Saving the dataset (68/128 shards): 53%|ββββββββββββββββββββββββββββββββββββ | 397460/748119 [00:04<08:44, 668.92 examples/s]
Saving the dataset (69/128 shards): 55%|βββββββββββββββββββββββββββββββββββββ | 411305/748119 [00:04<08:23, 668.92 examples/s]
Saving the dataset (70/128 shards): 55%|βββββββββββββββββββββββββββββββββββββ | 412150/748119 [00:04<08:22, 668.92 examples/s]
Saving the dataset (71/128 shards): 55%|ββββββββββββββββββββββββββββββββββββββ | 414995/748119 [00:04<08:17, 668.92 examples/s]
Saving the dataset (72/128 shards): 57%|ββββββββββββββββββββββββββββββββββββββ | 423840/748119 [00:04<08:04, 668.92 examples/s]
Saving the dataset (73/128 shards): 57%|βββββββββββββββββββββββββββββββββββββββ | 426685/748119 [00:04<08:00, 668.92 examples/s]
Saving the dataset (74/128 shards): 58%|βββββββββββββββββββββββββββββββββββββββ | 435530/748119 [00:04<07:47, 668.92 examples/s]
Saving the dataset (75/128 shards): 59%|ββββββββββββββββββββββββββββββββββββββββ | 438375/748119 [00:04<07:43, 668.92 examples/s]
Saving the dataset (76/128 shards): 60%|ββββββββββββββββββββββββββββββββββββββββ | 447220/748119 [00:04<07:29, 668.92 examples/s]
Saving the dataset (77/128 shards): 61%|βββββββββββββββββββββββββββββββββββββββββ | 453065/748119 [00:04<07:21, 668.92 examples/s]
Saving the dataset (78/128 shards): 62%|ββββββββββββββββββββββββββββββββββββββββββ | 461910/748119 [00:04<07:07, 668.92 examples/s]
Saving the dataset (79/128 shards): 63%|ββββββββββββββββββββββββββββββββββββββββββ | 469755/748119 [00:04<06:56, 668.92 examples/s]
Saving the dataset (80/128 shards): 63%|βββββββββββββββββββββββββββββββββββββββββββ | 470600/748119 [00:04<06:54, 668.92 examples/s]
Saving the dataset (81/128 shards): 63%|βββββββββββββββββββββββββββββββββββββββββββ | 473445/748119 [00:04<06:50, 668.92 examples/s]
Saving the dataset (82/128 shards): 64%|ββββββββββββββββββββββββββββββββββββββββββββ | 482290/748119 [00:04<06:37, 668.92 examples/s]
Saving the dataset (83/128 shards): 66%|ββββββββββββββββββββββββββββββββββββββββββββ | 492135/748119 [00:04<06:22, 668.92 examples/s]
Saving the dataset (84/128 shards): 67%|βββββββββββββββββββββββββββββββββββββββββββββ | 497980/748119 [00:04<06:13, 668.92 examples/s]
Saving the dataset (85/128 shards): 67%|βββββββββββββββββββββββββββββββββββββββββββββ | 500825/748119 [00:04<06:09, 668.92 examples/s]
Saving the dataset (86/128 shards): 67%|βββββββββββββββββββββββββββββββββββββββββββββ | 502670/748119 [00:04<06:06, 668.92 examples/s]
Saving the dataset (87/128 shards): 68%|ββββββββββββββββββββββββββββββββββββββββββββββ | 508515/748119 [00:04<05:58, 668.92 examples/s]
Saving the dataset (88/128 shards): 69%|βββββββββββββββββββββββββββββββββββββββββββββββ | 517359/748119 [00:04<05:44, 668.92 examples/s]
Saving the dataset (89/128 shards): 70%|βββββββββββββββββββββββββββββββββββββββββββββββ | 523203/748119 [00:04<05:36, 668.92 examples/s]
Saving the dataset (90/128 shards): 71%|ββββββββββββββββββββββββββββββββββββββββββββββββ | 529047/748119 [00:04<05:27, 668.92 examples/s]
Saving the dataset (91/128 shards): 71%|ββββββββββββββββββββββββββββββββββββββββββββββββ | 531891/748119 [00:04<05:23, 668.92 examples/s]
Saving the dataset (92/128 shards): 72%|βββββββββββββββββββββββββββββββββββββββββββββββββ | 540735/748119 [00:04<05:10, 668.92 examples/s]
Saving the dataset (93/128 shards): 73%|βββββββββββββββββββββββββββββββββββββββββββββββββ | 547579/748119 [00:04<04:59, 668.92 examples/s]
Saving the dataset (94/128 shards): 73%|ββββββββββββββββββββββββββββββββββββββββββββββββββ | 549423/748119 [00:04<04:57, 668.92 examples/s]
Saving the dataset (95/128 shards): 74%|ββββββββββββββββββββββββββββββββββββββββββββββββββ | 555267/748119 [00:04<04:48, 668.92 examples/s]
Saving the dataset (96/128 shards): 75%|βββββββββββββββββββββββββββββββββββββββββββββββββββ | 564111/748119 [00:04<04:35, 668.92 examples/s]
Saving the dataset (97/128 shards): 76%|βββββββββββββββββββββββββββββββββββββββββββββββββββ | 566955/748119 [00:04<04:30, 668.92 examples/s]
Saving the dataset (98/128 shards): 77%|ββββββββββββββββββββββββββββββββββββββββββββββββββββ | 575799/748119 [00:04<04:17, 668.92 examples/s]
Saving the dataset (99/128 shards): 78%|ββββββββββββββββββββββββββββββββββββββββββββββββββββ | 581643/748119 [00:04<04:08, 668.92 examples/s]
Saving the dataset (100/128 shards): 79%|ββββββββββββββββββββββββββββββββββββββββββββββββββββ | 587487/748119 [00:04<04:00, 668.92 examples/s]
Saving the dataset (101/128 shards): 79%|βββββββββββββββββββββββββββββββββββββββββββββββββββββ | 593331/748119 [00:04<03:51, 668.92 examples/s]
Saving the dataset (102/128 shards): 80%|βββββββββββββββββββββββββββββββββββββββββββββββββββββ | 599175/748119 [00:04<03:42, 668.92 examples/s]
Saving the dataset (103/128 shards): 81%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 605019/748119 [00:04<03:33, 668.92 examples/s]
Saving the dataset (104/128 shards): 82%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 611863/748119 [00:04<03:23, 668.92 examples/s]
Saving the dataset (105/128 shards): 82%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 613707/748119 [00:04<03:20, 668.92 examples/s]
Saving the dataset (106/128 shards): 84%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 626551/748119 [00:04<03:01, 668.92 examples/s]
Saving the dataset (107/128 shards): 84%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 628395/748119 [00:04<02:58, 668.92 examples/s]
Saving the dataset (108/128 shards): 84%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 631239/748119 [00:04<02:54, 668.92 examples/s]
Saving the dataset (109/128 shards): 86%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 640083/748119 [00:04<02:41, 668.92 examples/s]
Saving the dataset (110/128 shards): 87%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 649927/748119 [00:04<02:26, 668.92 examples/s]
Saving the dataset (111/128 shards): 87%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 651771/748119 [00:04<02:24, 668.92 examples/s]
Saving the dataset (112/128 shards): 88%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 657615/748119 [00:04<02:15, 668.92 examples/s]
Saving the dataset (113/128 shards): 89%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 666459/748119 [00:04<02:02, 668.92 examples/s]
Saving the dataset (114/128 shards): 90%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 672147/748119 [00:04<01:53, 668.92 examples/s]
Saving the dataset (115/128 shards): 90%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 672147/748119 [00:04<01:53, 668.92 examples/s]
Saving the dataset (116/128 shards): 91%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 682991/748119 [00:04<01:37, 668.92 examples/s]
Saving the dataset (117/128 shards): 92%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 689835/748119 [00:04<01:27, 668.92 examples/s]
Saving the dataset (118/128 shards): 93%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 696679/748119 [00:04<01:16, 668.92 examples/s]
Saving the dataset (119/128 shards): 93%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 698523/748119 [00:04<01:14, 668.92 examples/s]
Saving the dataset (120/128 shards): 95%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 707367/748119 [00:04<01:00, 668.92 examples/s]
Saving the dataset (121/128 shards): 95%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 714211/748119 [00:04<00:50, 668.92 examples/s]
Saving the dataset (122/128 shards): 96%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 721055/748119 [00:04<00:40, 668.92 examples/s]
Saving the dataset (123/128 shards): 97%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 723899/748119 [00:04<00:36, 668.92 examples/s]
Saving the dataset (124/128 shards): 97%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 724743/748119 [00:04<00:34, 668.92 examples/s]
Saving the dataset (125/128 shards): 98%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 733587/748119 [00:04<00:21, 668.92 examples/s]
Saving the dataset (125/128 shards): 99%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 740431/748119 [00:04<00:00, 227547.30 examples/s]
Saving the dataset (126/128 shards): 99%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 740431/748119 [00:04<00:00, 227547.30 examples/s]
Saving the dataset (127/128 shards): 99%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 744275/748119 [00:04<00:00, 227547.30 examples/s]
Saving the dataset (128/128 shards): 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 748119/748119 [00:04<00:00, 227547.30 examples/s]
Saving the dataset (128/128 shards): 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 748119/748119 [00:04<00:00, 158325.36 examples/s]
[2026-05-21 05:26:30,874] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:420] [PID:3208933] total_num_tokens: 41_357_188
[2026-05-21 05:26:33,832] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:438] [PID:3208933] `total_supervised_tokens: 4_704_021`
[2026-05-21 05:26:33,833] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:521] [PID:3208933] total_num_steps: 58447
[2026-05-21 05:26:33,833] [INFO] [axolotl.utils.data.sft._prepare_standard_dataset:121] [PID:3208933] Maximum number of steps set at 58447
[2026-05-21 05:26:33,887] [DEBUG] [axolotl.train.setup_model_and_tokenizer:70] [PID:3208933] loading tokenizer... Qwen/Qwen3-1.7B-Base
[2026-05-21 05:26:35,407] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:307] [PID:3208933] EOS: 151645 / <|im_end|>
[2026-05-21 05:26:35,407] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:308] [PID:3208933] BOS: None / None
[2026-05-21 05:26:35,407] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:309] [PID:3208933] PAD: 151643 / <|endoftext|>
[2026-05-21 05:26:35,407] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:310] [PID:3208933] UNK: None / None
[2026-05-21 05:26:35,408] [DEBUG] [axolotl.train.setup_model_and_tokenizer:81] [PID:3208933] Loading model
[2026-05-21 05:26:35,615] [DEBUG] [axolotl.monkeypatch.torchao_optim.patch_torchao_optim_state_8bit:75] [PID:3208933] Patched OptimState8bit for torch.compile compatibility
[2026-05-21 05:26:35,615] [DEBUG] [axolotl.monkeypatch.torchao_optim.patch_torchao_optim_state_8bit:122] [PID:3208933] Patched OptimState4bit for torch.compile compatibility
[2026-05-21 05:26:35,615] [DEBUG] [axolotl.monkeypatch.torchao_optim.patch_torchao_optim_state_8bit:154] [PID:3208933] Patched OptimStateFp8 for torch.compile compatibility
[2026-05-21 05:26:35,621] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:94] [PID:3208933] Patched Trainer.evaluation_loop with nanmean loss calculation
[2026-05-21 05:26:35,622] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:148] [PID:3208933] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
Loading weights: 0%| | 0/310 [00:00<?, ?it/s]
Loading weights: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 310/310 [00:00<00:00, 10198.32it/s]
[2026-05-21 05:26:36,736] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:3208933] Memory usage after model load 0.000GB ()
[2026-05-21 05:26:41,122] [INFO] [axolotl.train.save_initial_configs:421] [PID:3208933] Pre-saving tokenizer to ./checkpoint/Qwen3-1.7B-nq_text_compressed-with_pseudo-lr1e-4-10epochs...
[2026-05-21 05:26:41,234] [INFO] [axolotl.train.save_initial_configs:426] [PID:3208933] Pre-saving model config to ./checkpoint/Qwen3-1.7B-nq_text_compressed-with_pseudo-lr1e-4-10epochs...
[2026-05-21 05:26:41,238] [INFO] [axolotl.train.execute_training:222] [PID:3208933] Starting trainer...
[34m[1mwandb[0m: [wandb.login()] Loaded credentials for https://api.wandb.ai from /mnt/raid0/home/abner/.netrc.
[34m[1mwandb[0m: Currently logged in as: [33mabnerden0803[0m ([33mabnerden0803-national-taiwan-university[0m) to [32mhttps://api.wandb.ai[0m. Use [1m`wandb login --relogin`[0m to force relogin
[34m[1mwandb[0m: [38;5;178mβ’Ώ[0m Waiting for wandb.init()...
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[34m[1mwandb[0m: Tracking run with wandb version 0.26.0
[34m[1mwandb[0m: Run data is saved locally in [35m[1m/mnt/raid0/home/abner/git/ICLGR/wandb/run-20260521_052641-0zmanqq0[0m
[34m[1mwandb[0m: Run [1m`wandb offline`[0m to turn off syncing.
[34m[1mwandb[0m: Syncing run [33mqwen3-1.7b-nq_text_compressed-pseudo-lr1e-4-10epochs[0m
[34m[1mwandb[0m: βοΈ View project at [34m[4mhttps://wandb.ai/abnerden0803-national-taiwan-university/ICLGR-NQ[0m
[34m[1mwandb[0m: π View run at [34m[4mhttps://wandb.ai/abnerden0803-national-taiwan-university/ICLGR-NQ/runs/0zmanqq0[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.
[2026-05-21 05:26:44,641] [INFO] [axolotl.utils.callbacks.on_train_begin:757] [PID:3208933] The Axolotl config has been saved to the WandB run under files.
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{'loss': '6.238', 'grad_norm': '2.27', 'learning_rate': '8.385e-07', 'ppl': '512', 'memory/max_active (GiB)': '19.7', 'memory/max_allocated (GiB)': '19.7', 'memory/device_reserved (GiB)': '20.59', 'tokens/train_per_sec_per_gpu': '4.694', 'tokens/total': 800316, 'tokens/trainable': 40263, 'epoch': '0.008555'}
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0%| | 55/58447 [04:56<87:51:41, 5.42s/it]Process Process-2:
Traceback (most recent call last):
File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/torch/utils/data/_utils/worker.py", line 315, in _worker_loop
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/connection.py", line 440, in _poll
r = wait([self], timeout)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/connection.py", line 1136, in wait
ready = selector.select(timeout)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/selectors.py", line 415, in select
fd_event_list = self._selector.poll(timeout)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/axolotl/train.py", line 175, in <lambda>
lambda signum, frame: terminate_handler(signum, frame, _model_weakref),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/axolotl/train.py", line 167, in terminate_handler
_model.save_pretrained(cfg.output_dir)
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 3352, in save_pretrained
state_dict = remove_tied_weights_from_state_dict(state_dict, model_to_save)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 438, in remove_tied_weights_from_state_dict
shared_names, disjoint_names = _find_disjoint(shared_ptrs.values(), state_dict)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 352, in _find_disjoint
areas.append((tensor.data_ptr(), _end_ptr(tensor), name))
^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 328, in _end_ptr
stop = tensor.view(-1)[-1].data_ptr() + tensor.element_size()
~~~~~~~~~~~~~~~^^^^
torch.AcceleratorError: CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Process Process-1:
Traceback (most recent call last):
File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/torch/utils/data/_utils/worker.py", line 315, in _worker_loop
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/connection.py", line 440, in _poll
r = wait([self], timeout)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/multiprocessing/connection.py", line 1136, in wait
ready = selector.select(timeout)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/selectors.py", line 415, in select
fd_event_list = self._selector.poll(timeout)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/axolotl/train.py", line 175, in <lambda>
lambda signum, frame: terminate_handler(signum, frame, _model_weakref),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/axolotl/train.py", line 167, in terminate_handler
_model.save_pretrained(cfg.output_dir)
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 3352, in save_pretrained
state_dict = remove_tied_weights_from_state_dict(state_dict, model_to_save)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 438, in remove_tied_weights_from_state_dict
shared_names, disjoint_names = _find_disjoint(shared_ptrs.values(), state_dict)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 352, in _find_disjoint
areas.append((tensor.data_ptr(), _end_ptr(tensor), name))
^^^^^^^^^^^^^^^^
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 328, in _end_ptr
stop = tensor.view(-1)[-1].data_ptr() + tensor.element_size()
~~~~~~~~~~~~~~~^^^^
torch.AcceleratorError: CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Writing model shards: 0%| | 0/1 [00:00<?, ?it/s][A
Writing model shards: 0%| | 0/1 [00:00<?, ?it/s][A[A
Writing model shards: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:13<00:00, 13.44s/it][A[A
Writing model shards: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:13<00:00, 13.44s/it]
Exception ignored in: <generator object tqdm.__iter__ at 0x74400ae771c0>
Traceback (most recent call last):
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/tqdm/std.py", line 1196, in __iter__
self.close()
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/tqdm/std.py", line 1265, in close
def close(self):
File "/mnt/raid0/home/abner/git/conv-gr-new-dataset/.venv/lib/python3.12/site-packages/torch/utils/data/_utils/signal_handling.py", line 73, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 3210856) exited unexpectedly with exit code 1. Details are lost due to multiprocessing. Rerunning with num_workers=0 may give better error trace.
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