[2025-10-14 02:31:22,833] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:23,540] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[INFO|2025-10-14 02:31:25] llamafactory.cli:143 >> Initializing 8 distributed tasks at: 127.0.0.1:49583
W1014 02:31:26.703000 222161 site-packages/torch/distributed/run.py:793]
W1014 02:31:26.703000 222161 site-packages/torch/distributed/run.py:793] *****************************************
W1014 02:31:26.703000 222161 site-packages/torch/distributed/run.py:793] 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.
W1014 02:31:26.703000 222161 site-packages/torch/distributed/run.py:793] *****************************************
[2025-10-14 02:31:31,787] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,788] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,789] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,791] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,845] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,852] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,858] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:31,858] [INFO] [real_accelerator.py:260:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-14 02:31:32,645] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,648] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,649] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,679] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,687] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,699] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,703] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:32,706] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
[2025-10-14 02:31:33,644] [INFO] [comm.py:821:init_distributed] cdb=None
[2025-10-14 02:31:33,646] [INFO] [comm.py:821:init_distributed] cdb=None
[2025-10-14 02:31:33,646] [INFO] [comm.py:852:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
[2025-10-14 02:31:33,647] [INFO] [comm.py:821:init_distributed] cdb=None
[W1014 02:31:33.156712707 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
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[2025-10-14 02:31:33,667] [INFO] [comm.py:821:init_distributed] cdb=None
[W1014 02:31:33.177464774 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
[2025-10-14 02:31:33,685] [INFO] [comm.py:821:init_distributed] cdb=None
[W1014 02:31:33.194671733 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
[2025-10-14 02:31:33,695] [INFO] [comm.py:821:init_distributed] cdb=None
[W1014 02:31:33.206253710 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
[2025-10-14 02:31:33,803] [INFO] [comm.py:821:init_distributed] cdb=None
[W1014 02:31:33.314327871 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
[2025-10-14 02:31:33,807] [INFO] [comm.py:821:init_distributed] cdb=None
[W1014 02:31:33.317907568 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 6, world size: 8, device: cuda:6, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 3, world size: 8, device: cuda:3, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 1, world size: 8, device: cuda:1, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 7, world size: 8, device: cuda:7, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 4, world size: 8, device: cuda:4, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 5, world size: 8, device: cuda:5, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 2, world size: 8, device: cuda:2, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-14 02:31:40] llamafactory.hparams.parser:401 >> Process rank: 0, world size: 8, device: cuda:0, distributed training: True, compute dtype: torch.bfloat16
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file vocab.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file merges.txt
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,155 >> loading file chat_template.jinja
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
[INFO|tokenization_utils_base.py:2299] 2025-10-14 02:31:40,503 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|image_processing_base.py:378] 2025-10-14 02:31:40,504 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/preprocessor_config.json
[INFO|image_processing_base.py:378] 2025-10-14 02:31:40,506 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/preprocessor_config.json
[WARNING|logging.py:328] 2025-10-14 02:31:40,506 >> Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
[INFO|image_processing_base.py:433] 2025-10-14 02:31:40,508 >> Image processor Qwen2VLImageProcessor {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"temporal_patch_size": 2
}
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file vocab.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file merges.txt
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2021] 2025-10-14 02:31:40,508 >> loading file chat_template.jinja
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
[INFO|tokenization_utils_base.py:2299] 2025-10-14 02:31:40,752 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[WARNING|logging.py:328] 2025-10-14 02:31:40,753 >> You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
[INFO|video_processing_utils.py:627] 2025-10-14 02:31:40,753 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/preprocessor_config.json
[INFO|configuration_utils.py:696] 2025-10-14 02:31:40,753 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/config.json
[INFO|configuration_utils.py:770] 2025-10-14 02:31:40,755 >> Model config Qwen2_5_VLConfig {
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"image_token_id": 151655,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"text_config": {
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"image_token_id": null,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl_text",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"torch_dtype": "bfloat16",
"use_cache": true,
"use_sliding_window": false,
"video_token_id": null,
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
},
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.52.1",
"use_cache": true,
"use_sliding_window": false,
"video_token_id": 151656,
"vision_config": {
"depth": 32,
"fullatt_block_indexes": [
7,
15,
23,
31
],
"hidden_act": "silu",
"hidden_size": 1280,
"in_channels": 3,
"in_chans": 3,
"initializer_range": 0.02,
"intermediate_size": 3420,
"model_type": "qwen2_5_vl",
"num_heads": 16,
"out_hidden_size": 3584,
"patch_size": 14,
"spatial_merge_size": 2,
"spatial_patch_size": 14,
"temporal_patch_size": 2,
"tokens_per_second": 2,
"window_size": 112
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
}
[INFO|video_processing_utils.py:627] 2025-10-14 02:31:40,756 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/preprocessor_config.json
[INFO|video_processing_utils.py:683] 2025-10-14 02:31:40,756 >> Video processor Qwen2VLVideoProcessor {
"_valid_kwargs_names": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_pad": null,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"input_data_format": null,
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"model_valid_processing_keys": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"size_divisor": null,
"temporal_patch_size": 2,
"video_processor_type": "Qwen2VLVideoProcessor"
}
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
[INFO|processing_utils.py:990] 2025-10-14 02:31:41,114 >> Processor Qwen2_5_VLProcessor:
- image_processor: Qwen2VLImageProcessor {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"temporal_patch_size": 2
}
- tokenizer: Qwen2TokenizerFast(name_or_path='/inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct', vocab_size=151643, model_max_length=131072, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False, added_tokens_decoder={
151643: AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151644: AddedToken("<|im_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151645: AddedToken("<|im_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151646: AddedToken("<|object_ref_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151647: AddedToken("<|object_ref_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151648: AddedToken("<|box_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151649: AddedToken("<|box_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151650: AddedToken("<|quad_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151651: AddedToken("<|quad_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151652: AddedToken("<|vision_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151653: AddedToken("<|vision_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151654: AddedToken("<|vision_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151655: AddedToken("<|image_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151656: AddedToken("<|video_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151657: AddedToken("", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151658: AddedToken("", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151659: AddedToken("<|fim_prefix|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151660: AddedToken("<|fim_middle|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151661: AddedToken("<|fim_suffix|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151662: AddedToken("<|fim_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151663: AddedToken("<|repo_name|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151664: AddedToken("<|file_sep|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
}
)
- video_processor: Qwen2VLVideoProcessor {
"_valid_kwargs_names": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_pad": null,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"input_data_format": null,
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"model_valid_processing_keys": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"size_divisor": null,
"temporal_patch_size": 2,
"video_processor_type": "Qwen2VLVideoProcessor"
}
{
"processor_class": "Qwen2_5_VLProcessor"
}
[INFO|2025-10-14 02:31:41] llamafactory.data.loader:143 >> Loading dataset /inspire/hdd/global_user/zhangkaipeng-24043/val_global/dataset/PyVision_SFT/sharegpt/sft_data_vsi_wo_video_hint/sft_data_vsi_wo_video_hint_sharegpt.json...
Converting format of dataset (num_proc=256): 0%| | 0/21939 [00:00> Loading dataset /inspire/hdd/global_user/zhangkaipeng-24043/val_global/dataset/PyVision_SFT/sharegpt/sft_data_gmai_reasoning_wo_image_hint/sft_data_gmai_reasoning_wo_image_hint_sharegpt.json...
Converting format of dataset (num_proc=256): 0%| | 0/5661 [00:00> Loading dataset /inspire/hdd/global_user/zhangkaipeng-24043/val_global/dataset/PyVision_SFT/sharegpt/sft_data_mmpr_wo_image_hint/sft_data_mmpr_wo_image_hint_sharegpt.json...
Converting format of dataset (num_proc=256): 0%| | 0/27156 [00:00> Loading dataset /inspire/hdd/global_user/zhangkaipeng-24043/val_global/dataset/PyVision_SFT/sharegpt/sft_data_mmk12_wo_image_hint/sft_data_mmk12_wo_image_hint_sharegpt.json...
Converting format of dataset (num_proc=256): 0%| | 0/10635 [00:00> Loading dataset /inspire/hdd/global_user/zhangkaipeng-24043/val_global/dataset/PyVision_SFT/sharegpt/sft_data_longvila_wo_video_hint/sft_data_longvila_wo_video_hint_sharegpt.json...
Converting format of dataset (num_proc=256): 0%| | 0/22204 [00:00> Loading dataset /inspire/hdd/global_user/zhangkaipeng-24043/val_global/dataset/PyVision_SFT/sharegpt/sft_data_cosyn_chart_wo_image_hint/sft_data_cosyn_chart_wo_image_hint_sharegpt.json...
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You are a helpful assistant.<|im_end|>
<|im_start|>user
You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved.
Solve the following problem step by step. You now have the ability to selectively write executable Python code to enhance your reasoning process. The Python code will be executed by an external sandbox.
You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.
For all the provided videos, in order, the j-th video has already been read into the global variable `video_clue_j` using the VideoReader() function. When writing Python code, you can directly use these variables without needing to read them again.
Since you are dealing with the vision-related question answering task, you MUST use the Python tool (e.g., matplotlib library) to analyze or transform images whenever it could improve your understanding or aid your reasoning. This includes but is not limited to zooming in, rotating, adjusting contrast, computing statistics, or isolating features. For the videos, you can also use the Python tool (e.g., decord library) to sample frames from the video, helping your reasoning.
Note:
1. When you use matplotlib to visualize data or further process images, you need to use plt.show() to display these images; there is no need to save them.
2. Do not use image processing libraries like cv2 or PIL.
3. Remember you CAN NOT see the video directly. Thus, if you need to reason based on the video, you MUST sample frames and use plt.show() to display these frames, helping your reasoning.
4. If you want to check the value of a variable, you MUST use print() to check it.
5. If you think the init provided frames are not enough to solve the question. just sample more fames from the `video_clue_0` using Python code.
The output (wrapped in `output_str`) can be returned to aid your reasoning and help you arrive at the final answer. The Python code should be complete scripts, including necessary imports.
Each code snippet is wrapped with `
```python
code snippet
```
`.
The last part of your response should be in the following format:
\boxed{'The final answer goes here.'}
*Video Information:*
Frame Width: 1296; Frame Height: 968;
Video Length: 2675; Sample FPS: 24.00
The original video has been read into the global variable `video_clue_0`.
*User Question:*
Answer the following Problem with an image provided and put the answer in the format of \boxed{answer}
If I am standing by the couch and facing the table, is the jacket to my front-left, front-right, back-left, or back-right?
Directions refer to the quadrants of a Cartesian plane (assuming I am at the origin and facing along the positive y-axis).
A. back-right
B. front-right
C. front-left
D. back-left
Remember to place the final answer in the last part using the format:
\boxed{'The final answer goes here.'}
<|im_end|>
<|im_start|>assistant
Let's break down the problem and plan our approach:
## Step 1: Understanding the Problem
- The user is "standing by the couch and facing the table."
- The question is about the location of the "jacket" relative to the user's position and facing direction.
- Directions are defined as quadrants: front-left, front-right, back-left, back-right, with "front" being the direction the user is facing (positive y-axis).
## Step 2: What Do We Need to Find?
- Identify the locations of the couch, table, and jacket in the video.
- Determine their spatial relationships.
- Infer the relative position of the jacket when standing by the couch and facing the table.
## Step 3: Planning the Investigation
1. **Sample Frames:** We need to sample frames from `video_clue_0` to visually locate the couch, table, and jacket.
2. **Mark Objects:** Use matplotlib to highlight and compare the locations of these items.
3. **Analyze Relative Positions:** Based on the video, map the positions to our coordinate system.
4. **Conclude:** Use the mapped positions to answer the question.
## Step 4: Initial Frame Sampling
Since we cannot see the video directly, let's sample a few frames evenly across the video to get diverse views. We'll display them to analyze object locations.
### Next Steps
- Sample 5 frames (start, 25%, 50%, 75%, end) and display them.
- Try to identify the couch, table, and jacket in these frames.
Let's write code for this.
```python
import matplotlib.pyplot as plt
# Sample 5 frames from different points in the video
frame_indices = [0, int(0.25*2675), int(0.5*2675), int(0.75*2675), 2674]
frames = [video_clue_0[i].asnumpy() for i in frame_indices]
plt.figure(figsize=(15, 10))
for idx, frame in enumerate(frames):
plt.subplot(1, 5, idx+1)
plt.imshow(frame)
plt.title(f'Frame {frame_indices[idx]}')
plt.axis('off')
plt.tight_layout()
plt.show()
```
<|im_end|>
<|im_start|>user
Text Result:
None
Image Result:
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<|im_end|>
<|im_start|>assistant
Let's analyze the sampled frames:
- **Frame 0:** Kitchen area (stove, counter) — not relevant for couch/table/jacket.
- **Frame 668:** Table with two chairs; there is a dark object (likely the jacket) on the table.
- **Frame 1337:** Couch is visible, with a white rectangular object (possibly a coffee table) in front of it.
- **Frame 2006:** View of the kitchen area, adjacent to a carpeted area (possibly near the couch).
- **Frame 2674:** Sink area, not relevant.
### Key Observations:
- **Jacket:** The dark object on the table in Frame 668 is likely the jacket.
- **Table:** Also in Frame 668.
- **Couch:** Clearly visible in Frame 1337, with the door in the background. The table visible in frame 1337 is likely a coffee table, not the dining table from frame 668.
#### Next Steps:
We need to determine the spatial relationship between the couch, the (dining) table, and the jacket. Ideally, we need a frame that shows both the couch and the table together or allows us to infer their relative positions.
### Plan:
- Sample additional frames between 668 and 1337 to try to get a frame that shows both the table (with jacket) and the couch, or at least provides more spatial context.
- Focus on the transition between these two locations.
Let's sample 3 more frames at indices: 900, 1100, and 1200, and display them for analysis.
```python
additional_indices = [900, 1100, 1200]
additional_frames = [video_clue_0[i].asnumpy() for i in additional_indices]
plt.figure(figsize=(12, 6))
for idx, frame in enumerate(additional_frames):
plt.subplot(1, 3, idx+1)
plt.imshow(frame)
plt.title(f'Frame {additional_indices[idx]}')
plt.axis('off')
plt.tight_layout()
plt.show()
```
<|im_end|>
<|im_start|>user
Text Result:
None
Image Result:
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<|im_end|>
<|im_start|>assistant
Let's analyze the new frames:
- **Frame 900:** Shows part of the table (with jacket) and the closet, with the door and a bit of the carpeted area visible.
- **Frame 1100:** Shows the door and the right arm of the couch.
- **Frame 1200:** Shows the closet and the door, carpeted area.
### Spatial Relationship Synthesis
From all frames so far:
- The **table with the jacket** is close to the closet and near the kitchen area (Frame 668, 900).
- The **couch** is adjacent to the door and on the carpet (Frame 1337, 1100).
- The door and carpet area serve as a transition between the kitchen/table area and the couch.
- Frames 900 and 1200 show you can see both the table (left) and the closet/door (center/right) in one shot, indicating proximity.
Thus, the **table (with jacket)** is in the dining area, adjacent to the kitchen, and the **couch** is in the living area, near the door.
### Next Step: Determining Relative Directions
If you are "standing by the couch and facing the table," your back is to the door, and you are looking toward the dining area (table, kitchen). The table is in front of you. The jacket is on the table.
Now, we need to consider: Is the jacket on the left or right side of the table from this perspective?
#### Let's zoom in on Frame 668 (table with jacket) and Frame 1337 (couch) for more details.
```python
plt.figure(figsize=(10, 5))
# Zoom on Frame 668 (table with jacket)
plt.subplot(1, 2, 1)
plt.imshow(frames[1]) # Frame 668
plt.title('Frame 668 (Table & Jacket)')
plt.axis('off')
# Zoom on Frame 1337 (couch)
plt.subplot(1, 2, 2)
plt.imshow(frames[2]) # Frame 1337
plt.title('Frame 1337 (Couch)')
plt.axis('off')
plt.tight_layout()
plt.show()
```
<|im_end|>
<|im_start|>user
Text Result:
None
Image Result:
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<|im_end|>
<|im_start|>assistant
Let's reflect on what we've learned from these zoomed frames:
- **Frame 668 (Table & Jacket):**
The jacket is placed on the left side of the table (from the perspective in this frame), and the closet/door is to the right.
- **Frame 1337 (Couch):**
The couch is against the wall, near the door. The carpeted area leads toward the table (which is out of frame).
### Synthesizing the Room Layout
From the sequence and the angles:
- The table and jacket are in the dining area, with the closet/door to the right (from the table's perspective).
- The couch is in the living area, with the door nearby.
- If standing by the couch and facing the table, you would be looking toward the kitchen/dining area.
### Determining Jacket's Relative Position
If you are standing by the couch and facing the table:
- The table is in front of you.
- The closet/door is to the right of the table (from the table's perspective).
- The jacket is on the left side of the table (from the table's perspective).
So, from your position by the couch, facing the table:
- The jacket is on your **front-left**.
### Final Check
Let's confirm:
- In Frame 668, the jacket is on the left side of the table, with the closet/door to the right.
- In Frame 1337, the couch is positioned such that facing the table from the couch puts the jacket on your front-left.
## Final Answer
\boxed{'front-left'}
<|im_end|>
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labels:
Let's break down the problem and plan our approach:
## Step 1: Understanding the Problem
- The user is "standing by the couch and facing the table."
- The question is about the location of the "jacket" relative to the user's position and facing direction.
- Directions are defined as quadrants: front-left, front-right, back-left, back-right, with "front" being the direction the user is facing (positive y-axis).
## Step 2: What Do We Need to Find?
- Identify the locations of the couch, table, and jacket in the video.
- Determine their spatial relationships.
- Infer the relative position of the jacket when standing by the couch and facing the table.
## Step 3: Planning the Investigation
1. **Sample Frames:** We need to sample frames from `video_clue_0` to visually locate the couch, table, and jacket.
2. **Mark Objects:** Use matplotlib to highlight and compare the locations of these items.
3. **Analyze Relative Positions:** Based on the video, map the positions to our coordinate system.
4. **Conclude:** Use the mapped positions to answer the question.
## Step 4: Initial Frame Sampling
Since we cannot see the video directly, let's sample a few frames evenly across the video to get diverse views. We'll display them to analyze object locations.
### Next Steps
- Sample 5 frames (start, 25%, 50%, 75%, end) and display them.
- Try to identify the couch, table, and jacket in these frames.
Let's write code for this.
```python
import matplotlib.pyplot as plt
# Sample 5 frames from different points in the video
frame_indices = [0, int(0.25*2675), int(0.5*2675), int(0.75*2675), 2674]
frames = [video_clue_0[i].asnumpy() for i in frame_indices]
plt.figure(figsize=(15, 10))
for idx, frame in enumerate(frames):
plt.subplot(1, 5, idx+1)
plt.imshow(frame)
plt.title(f'Frame {frame_indices[idx]}')
plt.axis('off')
plt.tight_layout()
plt.show()
```
<|im_end|>
Let's analyze the sampled frames:
- **Frame 0:** Kitchen area (stove, counter) — not relevant for couch/table/jacket.
- **Frame 668:** Table with two chairs; there is a dark object (likely the jacket) on the table.
- **Frame 1337:** Couch is visible, with a white rectangular object (possibly a coffee table) in front of it.
- **Frame 2006:** View of the kitchen area, adjacent to a carpeted area (possibly near the couch).
- **Frame 2674:** Sink area, not relevant.
### Key Observations:
- **Jacket:** The dark object on the table in Frame 668 is likely the jacket.
- **Table:** Also in Frame 668.
- **Couch:** Clearly visible in Frame 1337, with the door in the background. The table visible in frame 1337 is likely a coffee table, not the dining table from frame 668.
#### Next Steps:
We need to determine the spatial relationship between the couch, the (dining) table, and the jacket. Ideally, we need a frame that shows both the couch and the table together or allows us to infer their relative positions.
### Plan:
- Sample additional frames between 668 and 1337 to try to get a frame that shows both the table (with jacket) and the couch, or at least provides more spatial context.
- Focus on the transition between these two locations.
Let's sample 3 more frames at indices: 900, 1100, and 1200, and display them for analysis.
```python
additional_indices = [900, 1100, 1200]
additional_frames = [video_clue_0[i].asnumpy() for i in additional_indices]
plt.figure(figsize=(12, 6))
for idx, frame in enumerate(additional_frames):
plt.subplot(1, 3, idx+1)
plt.imshow(frame)
plt.title(f'Frame {additional_indices[idx]}')
plt.axis('off')
plt.tight_layout()
plt.show()
```
<|im_end|>
Let's analyze the new frames:
- **Frame 900:** Shows part of the table (with jacket) and the closet, with the door and a bit of the carpeted area visible.
- **Frame 1100:** Shows the door and the right arm of the couch.
- **Frame 1200:** Shows the closet and the door, carpeted area.
### Spatial Relationship Synthesis
From all frames so far:
- The **table with the jacket** is close to the closet and near the kitchen area (Frame 668, 900).
- The **couch** is adjacent to the door and on the carpet (Frame 1337, 1100).
- The door and carpet area serve as a transition between the kitchen/table area and the couch.
- Frames 900 and 1200 show you can see both the table (left) and the closet/door (center/right) in one shot, indicating proximity.
Thus, the **table (with jacket)** is in the dining area, adjacent to the kitchen, and the **couch** is in the living area, near the door.
### Next Step: Determining Relative Directions
If you are "standing by the couch and facing the table," your back is to the door, and you are looking toward the dining area (table, kitchen). The table is in front of you. The jacket is on the table.
Now, we need to consider: Is the jacket on the left or right side of the table from this perspective?
#### Let's zoom in on Frame 668 (table with jacket) and Frame 1337 (couch) for more details.
```python
plt.figure(figsize=(10, 5))
# Zoom on Frame 668 (table with jacket)
plt.subplot(1, 2, 1)
plt.imshow(frames[1]) # Frame 668
plt.title('Frame 668 (Table & Jacket)')
plt.axis('off')
# Zoom on Frame 1337 (couch)
plt.subplot(1, 2, 2)
plt.imshow(frames[2]) # Frame 1337
plt.title('Frame 1337 (Couch)')
plt.axis('off')
plt.tight_layout()
plt.show()
```
<|im_end|>
Let's reflect on what we've learned from these zoomed frames:
- **Frame 668 (Table & Jacket):**
The jacket is placed on the left side of the table (from the perspective in this frame), and the closet/door is to the right.
- **Frame 1337 (Couch):**
The couch is against the wall, near the door. The carpeted area leads toward the table (which is out of frame).
### Synthesizing the Room Layout
From the sequence and the angles:
- The table and jacket are in the dining area, with the closet/door to the right (from the table's perspective).
- The couch is in the living area, with the door nearby.
- If standing by the couch and facing the table, you would be looking toward the kitchen/dining area.
### Determining Jacket's Relative Position
If you are standing by the couch and facing the table:
- The table is in front of you.
- The closet/door is to the right of the table (from the table's perspective).
- The jacket is on the left side of the table (from the table's perspective).
So, from your position by the couch, facing the table:
- The jacket is on your **front-left**.
### Final Check
Let's confirm:
- In Frame 668, the jacket is on the left side of the table, with the closet/door to the right.
- In Frame 1337, the couch is positioned such that facing the table from the couch puts the jacket on your front-left.
## Final Answer
\boxed{'front-left'}
<|im_end|>
[INFO|configuration_utils.py:696] 2025-10-14 02:37:27,070 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/config.json
[INFO|configuration_utils.py:770] 2025-10-14 02:37:27,072 >> Model config Qwen2_5_VLConfig {
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"image_token_id": 151655,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"text_config": {
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"image_token_id": null,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl_text",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"torch_dtype": "bfloat16",
"use_cache": true,
"use_sliding_window": false,
"video_token_id": null,
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
},
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.52.1",
"use_cache": true,
"use_sliding_window": false,
"video_token_id": 151656,
"vision_config": {
"depth": 32,
"fullatt_block_indexes": [
7,
15,
23,
31
],
"hidden_act": "silu",
"hidden_size": 1280,
"in_channels": 3,
"in_chans": 3,
"initializer_range": 0.02,
"intermediate_size": 3420,
"model_type": "qwen2_5_vl",
"num_heads": 16,
"out_hidden_size": 3584,
"patch_size": 14,
"spatial_merge_size": 2,
"spatial_patch_size": 14,
"temporal_patch_size": 2,
"tokens_per_second": 2,
"window_size": 112
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
}
[INFO|2025-10-14 02:37:27] llamafactory.model.model_utils.kv_cache:143 >> KV cache is disabled during training.
[INFO|modeling_utils.py:1146] 2025-10-14 02:37:27,102 >> loading weights file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/model.safetensors.index.json
[INFO|modeling_utils.py:3878] 2025-10-14 02:37:27,102 >> Detected DeepSpeed ZeRO-3: activating zero.init() for this model
[2025-10-14 02:37:27,103] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[INFO|configuration_utils.py:1135] 2025-10-14 02:37:27,113 >> Generate config GenerationConfig {
"bos_token_id": 151643,
"eos_token_id": 151645,
"use_cache": false
}
[INFO|modeling_utils.py:2239] 2025-10-14 02:37:27,114 >> Instantiating Qwen2_5_VisionTransformerPretrainedModel model under default dtype torch.float32.
[INFO|modeling_utils.py:2239] 2025-10-14 02:37:27,225 >> Instantiating Qwen2_5_VLTextModel model under default dtype torch.float32.
[2025-10-14 02:37:27,870] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:27,870] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:27,870] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:27,870] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:27,871] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:27,881] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:27,916] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
Loading checkpoint shards: 0%| | 0/5 [00:00> All model checkpoint weights were used when initializing Qwen2_5_VLForConditionalGeneration.
[INFO|modeling_utils.py:5178] 2025-10-14 02:37:35,813 >> All the weights of Qwen2_5_VLForConditionalGeneration were initialized from the model checkpoint at /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct.
If your task is similar to the task the model of the checkpoint was trained on, you can already use Qwen2_5_VLForConditionalGeneration for predictions without further training.
[INFO|configuration_utils.py:1088] 2025-10-14 02:37:35,817 >> loading configuration file /inspire/hdd/global_user/zhangkaipeng-24043/val_global/checkpoints/Qwen/Qwen2.5-VL-7B-Instruct/generation_config.json
[INFO|configuration_utils.py:1135] 2025-10-14 02:37:35,817 >> Generate config GenerationConfig {
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 1e-06
}
[INFO|2025-10-14 02:37:35] llamafactory.model.model_utils.checkpointing:143 >> Gradient checkpointing enabled.
[INFO|2025-10-14 02:37:35] llamafactory.model.model_utils.attention:143 >> Using torch SDPA for faster training and inference.
[INFO|2025-10-14 02:37:35] llamafactory.model.adapter:143 >> DeepSpeed ZeRO3 detected, remaining trainable params in float32.
[INFO|2025-10-14 02:37:35] llamafactory.model.adapter:143 >> Fine-tuning method: Full
[INFO|2025-10-14 02:37:35] llamafactory.model.model_utils.visual:143 >> Set vision model not trainable: ['visual.patch_embed', 'visual.blocks'].
[INFO|2025-10-14 02:37:35] llamafactory.model.model_utils.visual:143 >> Set multi model projector not trainable: visual.merger.
[INFO|2025-10-14 02:37:35] llamafactory.model.loader:143 >> trainable params: 7,615,616,512 || all params: 8,292,166,656 || trainable%: 91.8411
[INFO|trainer.py:756] 2025-10-14 02:37:35,838 >> Using auto half precision backend
[2025-10-14 02:37:36,077] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed info: version=0.17.5, git-hash=unknown, git-branch=unknown
[2025-10-14 02:37:36,077] [INFO] [config.py:684:__init__] Config mesh_device None world_size = 8
[2025-10-14 02:37:38,116] [INFO] [engine.py:1356:_configure_distributed_model] ********** distributed groups summary **********
self.dp_world_size=8
self.mp_world_size=1
self.seq_dp_world_size=8
self.sequence_parallel_size=1
***********************************************
[2025-10-14 02:37:38,119] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
[2025-10-14 02:37:38,121] [INFO] [logging.py:107:log_dist] [Rank 0] Using client Optimizer as basic optimizer
[2025-10-14 02:37:38,121] [INFO] [logging.py:107:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
[2025-10-14 02:37:38,143] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed Basic Optimizer = AdamW
[2025-10-14 02:37:38,143] [INFO] [utils.py:59:is_zero_supported_optimizer] Checking ZeRO support for optimizer=AdamW type=
[2025-10-14 02:37:38,143] [INFO] [logging.py:107:log_dist] [Rank 0] Creating fp16 ZeRO stage 3 optimizer, MiCS is enabled False, Hierarchical params gather False
[2025-10-14 02:37:38,143] [INFO] [logging.py:107:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 3 optimizer
[2025-10-14 02:37:38,334] [INFO] [utils.py:781:see_memory_usage] Stage 3 initialize beginning
[2025-10-14 02:37:38,334] [INFO] [utils.py:782:see_memory_usage] MA 1.93 GB Max_MA 4.85 GB CA 1.93 GB Max_CA 5 GB
[2025-10-14 02:37:38,335] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.08 GB, percent = 7.2%
[2025-10-14 02:37:38,337] [INFO] [stage3.py:186:__init__] Reduce bucket size 12845056
[2025-10-14 02:37:38,337] [INFO] [stage3.py:187:__init__] Prefetch bucket size 11560550
[2025-10-14 02:37:38,434] [INFO] [utils.py:781:see_memory_usage] DeepSpeedZeRoOffload initialize [begin]
[2025-10-14 02:37:38,434] [INFO] [utils.py:782:see_memory_usage] MA 1.93 GB Max_MA 1.93 GB CA 1.93 GB Max_CA 2 GB
[2025-10-14 02:37:38,434] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.08 GB, percent = 7.2%
Parameter Offload - Persistent parameters statistics: param_count = 368, numel = 848896
[2025-10-14 02:37:38,595] [INFO] [utils.py:781:see_memory_usage] DeepSpeedZeRoOffload initialize [end]
[2025-10-14 02:37:38,595] [INFO] [utils.py:782:see_memory_usage] MA 1.93 GB Max_MA 1.93 GB CA 1.93 GB Max_CA 2 GB
[2025-10-14 02:37:38,596] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.09 GB, percent = 7.2%
[2025-10-14 02:37:38,710] [INFO] [utils.py:781:see_memory_usage] Before creating fp16 partitions
[2025-10-14 02:37:38,710] [INFO] [utils.py:782:see_memory_usage] MA 1.93 GB Max_MA 1.93 GB CA 1.93 GB Max_CA 2 GB
[2025-10-14 02:37:38,710] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.09 GB, percent = 7.2%
[2025-10-14 02:37:40,282] [INFO] [utils.py:781:see_memory_usage] After creating fp16 partitions: 2
[2025-10-14 02:37:40,282] [INFO] [utils.py:782:see_memory_usage] MA 1.93 GB Max_MA 1.93 GB CA 1.97 GB Max_CA 2 GB
[2025-10-14 02:37:40,282] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 156.15 GB, percent = 7.7%
[2025-10-14 02:37:40,390] [INFO] [utils.py:781:see_memory_usage] Before creating fp32 partitions
[2025-10-14 02:37:40,390] [INFO] [utils.py:782:see_memory_usage] MA 1.93 GB Max_MA 1.93 GB CA 1.97 GB Max_CA 2 GB
[2025-10-14 02:37:40,390] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 147.53 GB, percent = 7.3%
[2025-10-14 02:37:40,551] [INFO] [utils.py:781:see_memory_usage] After creating fp32 partitions
[2025-10-14 02:37:40,552] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB Max_MA 7.25 GB CA 7.28 GB Max_CA 7 GB
[2025-10-14 02:37:40,552] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.17 GB, percent = 7.3%
[2025-10-14 02:37:40,653] [INFO] [utils.py:781:see_memory_usage] Before initializing optimizer states
[2025-10-14 02:37:40,654] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB Max_MA 5.48 GB CA 7.28 GB Max_CA 7 GB
[2025-10-14 02:37:40,654] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.19 GB, percent = 7.3%
[2025-10-14 02:37:40,790] [INFO] [utils.py:781:see_memory_usage] After initializing optimizer states
[2025-10-14 02:37:40,790] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB Max_MA 9.02 GB CA 10.84 GB Max_CA 11 GB
[2025-10-14 02:37:40,790] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.2 GB, percent = 7.3%
[2025-10-14 02:37:40,791] [INFO] [stage3.py:554:_setup_for_real_optimizer] optimizer state initialized
[2025-10-14 02:37:40,975] [INFO] [utils.py:781:see_memory_usage] After initializing ZeRO optimizer
[2025-10-14 02:37:40,975] [INFO] [utils.py:782:see_memory_usage] MA 7.27 GB Max_MA 9.3 GB CA 10.84 GB Max_CA 11 GB
[2025-10-14 02:37:40,975] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 146.22 GB, percent = 7.3%
[2025-10-14 02:37:40,975] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedZeroOptimizer_Stage3
[2025-10-14 02:37:40,975] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed using configured LR scheduler = None
[2025-10-14 02:37:40,975] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed LR Scheduler = None
[2025-10-14 02:37:40,976] [INFO] [logging.py:107:log_dist] [Rank 0] step=0, skipped=0, lr=[0.0, 0.0], mom=[(0.9, 0.999), (0.9, 0.999)]
[2025-10-14 02:37:40,977] [INFO] [logging.py:107:log_dist] [Rank 0] [TorchCheckpointEngine] Initialized with serialization = True
[2025-10-14 02:37:40,977] [INFO] [config.py:954:print] DeepSpeedEngine configuration:
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] activation_checkpointing_config {
"partition_activations": false,
"contiguous_memory_optimization": false,
"cpu_checkpointing": false,
"number_checkpoints": null,
"synchronize_checkpoint_boundary": false,
"profile": false
}
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'intra_op_parallelism': 1, 'single_submit': False, 'overlap_events': True, 'use_gds': False}
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] amp_enabled .................. False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] amp_params ................... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] autotuning_config ............ {
"enabled": false,
"start_step": null,
"end_step": null,
"metric_path": null,
"arg_mappings": null,
"metric": "throughput",
"model_info": null,
"results_dir": "autotuning_results",
"exps_dir": "autotuning_exps",
"overwrite": true,
"fast": true,
"start_profile_step": 3,
"end_profile_step": 5,
"tuner_type": "gridsearch",
"tuner_early_stopping": 5,
"tuner_num_trials": 50,
"model_info_path": null,
"mp_size": 1,
"max_train_batch_size": null,
"min_train_batch_size": 1,
"max_train_micro_batch_size_per_gpu": 1.024000e+03,
"min_train_micro_batch_size_per_gpu": 1,
"num_tuning_micro_batch_sizes": 3
}
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] bfloat16_config .............. enabled=True immediate_grad_update=False check_grad_overflow=False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] checkpoint_config ............ {'tag_validation': 'WARN', 'checkpoint_serialization': True, 'writer': None}
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] checkpoint_parallel_write_pipeline False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] checkpoint_tag_validation_enabled True
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] checkpoint_tag_validation_fail False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] comms_config .................
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] communication_data_type ...... None
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] compile_config ............... deepcompile=False free_activation=False offload_activation=False offload_opt_states=False double_buffer=True symmetric_memory=False debug_log=False offload_parameters=False sync_before_reduce=False sync_after_reduce=False sync_before_allgather=False sync_after_allgather=False keep_int_input_tensors=True keep_all_input_tensors=False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] curriculum_enabled_legacy .... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] curriculum_params_legacy ..... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'pin_memory': False, 'curriculum_learning': {'enabled': False}, 'dynamic_batching': {'enabled': False, 'lr_scaling_method': 'linear', 'min_batch_size': 1, 'max_batch_size': None, 'sequence_picking_order': 'dataloader', 'verbose': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] data_efficiency_enabled ...... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] dataloader_drop_last ......... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] disable_allgather ............ False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] dump_state ................... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_enabled ........... False
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_gas_boundary_resolution 1
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_layer_name ........ bert.encoder.layer
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_layer_num ......... 0
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_max_iter .......... 100
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_stability ......... 1e-06
[2025-10-14 02:37:40,977] [INFO] [config.py:958:print] eigenvalue_tol ............... 0.01
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] eigenvalue_verbose ........... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] elasticity_enabled ........... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] float16_config ............... enabled=False auto_cast=False loss_scale=0.0 initial_scale_power=16 loss_scale_window=1000 hysteresis=2 consecutive_hysteresis=False min_loss_scale=1 fp16_master_weights_and_grads=False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] flops_profiler_config ........ {
"enabled": false,
"recompute_fwd_factor": 0.0,
"profile_step": 1,
"module_depth": -1,
"top_modules": 1,
"detailed": true,
"output_file": null
}
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] global_rank .................. 0
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] grad_accum_dtype ............. None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] gradient_accumulation_steps .. 1
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] gradient_clipping ............ 1.0
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] gradient_predivide_factor .... 1.0
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] graph_harvesting ............. False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] load_universal_checkpoint .... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] memory_breakdown ............. False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] mics_hierarchial_params_gather False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] mics_shard_size .............. -1
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName')
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] nebula_config ................ {
"enabled": false,
"persistent_storage_path": null,
"persistent_time_interval": 100,
"num_of_version_in_retention": 2,
"enable_nebula_load": true,
"load_path": null
}
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] optimizer_legacy_fusion ...... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] optimizer_name ............... None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] optimizer_params ............. None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] pld_enabled .................. False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] pld_params ................... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] prescale_gradients ........... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] scheduler_name ............... None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] scheduler_params ............. None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] seq_parallel_communication_data_type torch.float32
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] sparse_attention ............. None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] sparse_gradients_enabled ..... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] steps_per_print .............. inf
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] tensor_parallel_config ....... dtype=torch.float16 autotp_size=0 tp_overlap_comm=False tensor_parallel=TPConfig(tp_size=1, tp_grain_size=1, mpu=None, tp_group=None) injection_policy_tuple=None keep_module_on_host=False replace_with_kernel_inject=False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] timers_config ................ enabled=True synchronized=True
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] torch_autocast_dtype ......... None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] torch_autocast_enabled ....... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] torch_autocast_lower_precision_safe_modules None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] train_batch_size ............. 16
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] train_micro_batch_size_per_gpu 2
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] use_data_before_expert_parallel_ False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] use_node_local_storage ....... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] wall_clock_breakdown ......... False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] weight_quantization_config ... None
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] world_size ................... 8
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] zero_allow_untested_optimizer True
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] zero_config .................. stage=3 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=12845056 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=None zenflow=None sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=11560550 param_persistence_threshold=35840 model_persistence_threshold=9223372036854775807 max_live_parameters=1000000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=True module_granularity_threshold=0 use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False zeropp_loco_param=None mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True log_trace_cache_warnings=False
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] zero_enabled ................. True
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] zero_force_ds_cpu_optimizer .. True
[2025-10-14 02:37:40,978] [INFO] [config.py:958:print] zero_optimization_stage ...... 3
[2025-10-14 02:37:40,978] [INFO] [config.py:944:print_user_config] json = {
"train_batch_size": 16,
"train_micro_batch_size_per_gpu": 2,
"gradient_accumulation_steps": 1,
"gradient_clipping": 1.0,
"zero_allow_untested_optimizer": true,
"fp16": {
"enabled": false,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 3,
"overlap_comm": false,
"contiguous_gradients": true,
"sub_group_size": 1.000000e+09,
"reduce_bucket_size": 1.284506e+07,
"stage3_prefetch_bucket_size": 1.156055e+07,
"stage3_param_persistence_threshold": 3.584000e+04,
"stage3_max_live_parameters": 1.000000e+09,
"stage3_max_reuse_distance": 1.000000e+09,
"stage3_gather_16bit_weights_on_model_save": true
},
"steps_per_print": inf
}
[INFO|trainer.py:2409] 2025-10-14 02:37:40,979 >> ***** Running training *****
[INFO|trainer.py:2410] 2025-10-14 02:37:40,979 >> Num examples = 110,155
[INFO|trainer.py:2411] 2025-10-14 02:37:40,979 >> Num Epochs = 1
[INFO|trainer.py:2412] 2025-10-14 02:37:40,980 >> Instantaneous batch size per device = 2
[INFO|trainer.py:2415] 2025-10-14 02:37:40,980 >> Total train batch size (w. parallel, distributed & accumulation) = 16
[INFO|trainer.py:2416] 2025-10-14 02:37:40,980 >> Gradient Accumulation steps = 1
[INFO|trainer.py:2417] 2025-10-14 02:37:40,980 >> Total optimization steps = 6,885
[INFO|trainer.py:2418] 2025-10-14 02:37:40,981 >> Number of trainable parameters = 7,615,616,512
[INFO|integration_utils.py:832] 2025-10-14 02:37:40,982 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
wandb: Tracking run with wandb version 0.21.3
wandb: W&B syncing is set to `offline` in this directory. Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Run data is saved locally in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/wandb/offline-run-20251014_023741-21vuy1wg
0%| | 0/6885 [00:00> `loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
[INFO|2025-10-14 02:37:46] llamafactory.train.callbacks:143 >> EMA state initialized.
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{'loss': 0.9607, 'grad_norm': 4.328955480739728, 'learning_rate': 1.3062409288824383e-07, 'epoch': 0.0}
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{'loss': 0.9859, 'grad_norm': 4.469323164876104, 'learning_rate': 2.757619738751814e-07, 'epoch': 0.0}
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{'loss': 0.9872, 'grad_norm': 4.000416594025176, 'learning_rate': 4.2089985486211904e-07, 'epoch': 0.0}
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{'loss': 0.9191, 'grad_norm': 3.1566001029759914, 'learning_rate': 5.660377358490567e-07, 'epoch': 0.01}
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{'loss': 0.866, 'grad_norm': 2.000776925354802, 'learning_rate': 7.111756168359943e-07, 'epoch': 0.01}
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{'loss': 0.8475, 'grad_norm': 2.03383269865318, 'learning_rate': 8.563134978229319e-07, 'epoch': 0.01}
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{'loss': 0.8145, 'grad_norm': 1.981671850063017, 'learning_rate': 1.0014513788098695e-06, 'epoch': 0.01}
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{'loss': 0.7874, 'grad_norm': 1.9935447101504142, 'learning_rate': 1.146589259796807e-06, 'epoch': 0.01}
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{'loss': 0.7606, 'grad_norm': 1.696794144473072, 'learning_rate': 1.2917271407837448e-06, 'epoch': 0.01}
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{'loss': 0.7505, 'grad_norm': 1.8441704167155635, 'learning_rate': 1.4368650217706823e-06, 'epoch': 0.01}
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{'loss': 0.7432, 'grad_norm': 1.6167640330505846, 'learning_rate': 1.5820029027576197e-06, 'epoch': 0.02}
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{'loss': 0.7502, 'grad_norm': 1.7310300613256226, 'learning_rate': 1.7271407837445576e-06, 'epoch': 0.02}
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{'loss': 0.7075, 'grad_norm': 1.5504171157690307, 'learning_rate': 1.872278664731495e-06, 'epoch': 0.02}
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{'loss': 0.7242, 'grad_norm': 1.5001595551333269, 'learning_rate': 2.0174165457184327e-06, 'epoch': 0.02}
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{'loss': 0.7299, 'grad_norm': 1.7680255328873922, 'learning_rate': 2.1625544267053704e-06, 'epoch': 0.02}
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{'loss': 0.7074, 'grad_norm': 1.9776874021989124, 'learning_rate': 2.307692307692308e-06, 'epoch': 0.02}
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{'loss': 0.7003, 'grad_norm': 1.645294675336186, 'learning_rate': 2.4528301886792453e-06, 'epoch': 0.02}
2%|▏ | 170/6885 [10:03:51<5:44:36, 3.08s/it]
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{'loss': 0.6935, 'grad_norm': 1.903626800669526, 'learning_rate': 2.597968069666183e-06, 'epoch': 0.03}
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{'loss': 0.7099, 'grad_norm': 1.6296522016767983, 'learning_rate': 2.7431059506531207e-06, 'epoch': 0.03}
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{'loss': 0.7082, 'grad_norm': 1.5624745122869332, 'learning_rate': 2.8882438316400583e-06, 'epoch': 0.03}
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{'loss': 0.6847, 'grad_norm': 1.5327148829437787, 'learning_rate': 3.033381712626996e-06, 'epoch': 0.03}
3%|▎ | 210/6885 [10:05:54<5:54:21, 3.19s/it]
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{'loss': 0.6997, 'grad_norm': 1.4217156007581908, 'learning_rate': 3.1785195936139337e-06, 'epoch': 0.03}
3%|▎ | 220/6885 [10:06:23<6:17:40, 3.40s/it]
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{'loss': 0.6922, 'grad_norm': 1.678714535521671, 'learning_rate': 3.323657474600871e-06, 'epoch': 0.03}
3%|▎ | 230/6885 [10:06:57<5:46:43, 3.13s/it]
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{'loss': 0.6764, 'grad_norm': 1.6893028132334575, 'learning_rate': 3.4687953555878086e-06, 'epoch': 0.03}
3%|▎ | 240/6885 [10:07:23<4:50:22, 2.62s/it]
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{'loss': 0.6838, 'grad_norm': 1.6842923668045748, 'learning_rate': 3.6139332365747467e-06, 'epoch': 0.04}
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{'loss': 0.6961, 'grad_norm': 2.0758637079489306, 'learning_rate': 3.759071117561684e-06, 'epoch': 0.04}
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{'loss': 0.6619, 'grad_norm': 1.651886885559497, 'learning_rate': 3.904208998548621e-06, 'epoch': 0.04}
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{'loss': 0.691, 'grad_norm': 1.6813735734416895, 'learning_rate': 4.049346879535559e-06, 'epoch': 0.04}
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{'loss': 0.6646, 'grad_norm': 1.8001370749006687, 'learning_rate': 4.194484760522497e-06, 'epoch': 0.04}
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{'loss': 0.6595, 'grad_norm': 1.8255351447030483, 'learning_rate': 4.339622641509435e-06, 'epoch': 0.04}
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{'loss': 0.6555, 'grad_norm': 1.7918481140936697, 'learning_rate': 4.484760522496372e-06, 'epoch': 0.05}
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{'loss': 0.6734, 'grad_norm': 1.6697318257583398, 'learning_rate': 4.629898403483309e-06, 'epoch': 0.05}
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{'loss': 0.6511, 'grad_norm': 1.5656777878920214, 'learning_rate': 4.775036284470247e-06, 'epoch': 0.05}
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{'loss': 0.6651, 'grad_norm': 1.6515736055504289, 'learning_rate': 4.920174165457185e-06, 'epoch': 0.05}
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{'loss': 0.665, 'grad_norm': 1.6517233906536315, 'learning_rate': 5.065312046444122e-06, 'epoch': 0.05}
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{'loss': 0.6632, 'grad_norm': 1.6987223199576384, 'learning_rate': 5.210449927431061e-06, 'epoch': 0.05}
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{'loss': 0.665, 'grad_norm': 1.578744968443496, 'learning_rate': 5.355587808417998e-06, 'epoch': 0.05}
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{'loss': 0.6511, 'grad_norm': 1.4975426293081397, 'learning_rate': 5.500725689404935e-06, 'epoch': 0.06}
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{'loss': 0.6676, 'grad_norm': 1.7386717568110297, 'learning_rate': 5.645863570391873e-06, 'epoch': 0.06}
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{'loss': 0.6635, 'grad_norm': 1.5916583497500596, 'learning_rate': 5.7910014513788105e-06, 'epoch': 0.06}
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{'loss': 0.6668, 'grad_norm': 1.6931617934865184, 'learning_rate': 5.936139332365748e-06, 'epoch': 0.06}
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{'loss': 0.6685, 'grad_norm': 1.5616372247201953, 'learning_rate': 6.081277213352685e-06, 'epoch': 0.06}
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{'loss': 0.659, 'grad_norm': 1.5424914283941253, 'learning_rate': 6.226415094339623e-06, 'epoch': 0.06}
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{'loss': 0.6453, 'grad_norm': 1.6468311050594455, 'learning_rate': 6.37155297532656e-06, 'epoch': 0.06}
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{'loss': 0.6598, 'grad_norm': 1.5765402125957226, 'learning_rate': 6.5166908563134976e-06, 'epoch': 0.07}
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{'loss': 0.6619, 'grad_norm': 1.7349394887283642, 'learning_rate': 6.6618287373004365e-06, 'epoch': 0.07}
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{'loss': 0.6692, 'grad_norm': 1.6385635232751372, 'learning_rate': 6.806966618287374e-06, 'epoch': 0.07}
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{'loss': 0.6484, 'grad_norm': 1.4945507177883908, 'learning_rate': 6.952104499274311e-06, 'epoch': 0.07}
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{'loss': 0.657, 'grad_norm': 1.583857774726375, 'learning_rate': 7.097242380261249e-06, 'epoch': 0.07}
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{'loss': 0.6601, 'grad_norm': 1.8780189334850588, 'learning_rate': 7.242380261248186e-06, 'epoch': 0.07}
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{'loss': 0.6542, 'grad_norm': 1.5153409007972507, 'learning_rate': 7.387518142235124e-06, 'epoch': 0.07}
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{'loss': 0.6476, 'grad_norm': 1.5243833834622142, 'learning_rate': 7.532656023222062e-06, 'epoch': 0.08}
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{'loss': 0.6451, 'grad_norm': 1.6429693792028686, 'learning_rate': 7.677793904208998e-06, 'epoch': 0.08}
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{'loss': 0.6527, 'grad_norm': 1.802860360098263, 'learning_rate': 7.822931785195936e-06, 'epoch': 0.08}
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{'loss': 0.661, 'grad_norm': 1.6594363957156038, 'learning_rate': 7.968069666182874e-06, 'epoch': 0.08}
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{'loss': 0.6547, 'grad_norm': 1.5938255936259151, 'learning_rate': 8.113207547169812e-06, 'epoch': 0.08}
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{'loss': 0.6609, 'grad_norm': 1.3939924292770436, 'learning_rate': 8.25834542815675e-06, 'epoch': 0.08}
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{'loss': 0.6419, 'grad_norm': 1.5321796462771227, 'learning_rate': 8.403483309143687e-06, 'epoch': 0.08}
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{'loss': 0.625, 'grad_norm': 1.5907007682060863, 'learning_rate': 8.548621190130625e-06, 'epoch': 0.09}
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{'loss': 0.658, 'grad_norm': 1.6048966671231157, 'learning_rate': 8.693759071117563e-06, 'epoch': 0.09}
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{'loss': 0.6456, 'grad_norm': 1.457751877262412, 'learning_rate': 8.8388969521045e-06, 'epoch': 0.09}
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{'loss': 0.6494, 'grad_norm': 1.3925725985786772, 'learning_rate': 8.984034833091438e-06, 'epoch': 0.09}
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{'loss': 0.6604, 'grad_norm': 1.6476815627809678, 'learning_rate': 9.129172714078376e-06, 'epoch': 0.09}
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{'loss': 0.6462, 'grad_norm': 1.4844043302240553, 'learning_rate': 9.274310595065312e-06, 'epoch': 0.09}
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{'loss': 0.6464, 'grad_norm': 1.5541257847812342, 'learning_rate': 9.41944847605225e-06, 'epoch': 0.09}
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{'loss': 0.6471, 'grad_norm': 1.5339956751582804, 'learning_rate': 9.564586357039188e-06, 'epoch': 0.1}
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{'loss': 0.6519, 'grad_norm': 1.550006983868159, 'learning_rate': 9.709724238026126e-06, 'epoch': 0.1}
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{'loss': 0.6508, 'grad_norm': 1.298622779401985, 'learning_rate': 9.854862119013063e-06, 'epoch': 0.1}
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{'loss': 0.6483, 'grad_norm': 1.4545201677417376, 'learning_rate': 1e-05, 'epoch': 0.1}
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{'loss': 0.6517, 'grad_norm': 1.7514454450540817, 'learning_rate': 9.999935728859667e-06, 'epoch': 0.1}
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{'loss': 0.6435, 'grad_norm': 1.3010290416328456, 'learning_rate': 9.999742917090981e-06, 'epoch': 0.1}
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{'loss': 0.6355, 'grad_norm': 1.5222737445349914, 'learning_rate': 9.999421569650833e-06, 'epoch': 0.1}
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{'loss': 0.6414, 'grad_norm': 1.5758824439402839, 'learning_rate': 9.99897169480057e-06, 'epoch': 0.11}
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{'loss': 0.6416, 'grad_norm': 1.3245458819453462, 'learning_rate': 9.99839330410578e-06, 'epoch': 0.11}
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{'loss': 0.6381, 'grad_norm': 1.4753577499137038, 'learning_rate': 9.997686412435996e-06, 'epoch': 0.11}
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{'loss': 0.6369, 'grad_norm': 1.4578988593383, 'learning_rate': 9.99685103796431e-06, 'epoch': 0.11}
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{'loss': 0.6509, 'grad_norm': 1.4811460587250382, 'learning_rate': 9.987408118241995e-06, 'epoch': 0.12}
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{'loss': 0.6241, 'grad_norm': 1.5348970475105241, 'learning_rate': 9.956615502120504e-06, 'epoch': 0.14}
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{'loss': 0.631, 'grad_norm': 1.48874630945738, 'learning_rate': 9.953219368452908e-06, 'epoch': 0.14}
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{'loss': 0.627, 'grad_norm': 1.310857282598366, 'learning_rate': 9.949695895162464e-06, 'epoch': 0.14}
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{'loss': 0.6387, 'grad_norm': 1.3619342578169393, 'learning_rate': 9.946045172832224e-06, 'epoch': 0.14}
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{'loss': 0.6331, 'grad_norm': 1.4936986486504984, 'learning_rate': 9.942267295316625e-06, 'epoch': 0.14}
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{'loss': 0.626, 'grad_norm': 1.32511584393411, 'learning_rate': 9.938362359739068e-06, 'epoch': 0.15}
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{'loss': 0.6451, 'grad_norm': 1.3291454266011833, 'learning_rate': 9.934330466489414e-06, 'epoch': 0.15}
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{'loss': 0.6333, 'grad_norm': 1.3289648153139675, 'learning_rate': 9.930171719221418e-06, 'epoch': 0.15}
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{'loss': 0.6329, 'grad_norm': 1.3388955314518605, 'learning_rate': 9.925886224850047e-06, 'epoch': 0.15}
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{'loss': 0.6308, 'grad_norm': 1.3788458990043229, 'learning_rate': 9.921474093548748e-06, 'epoch': 0.15}
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{'loss': 0.6366, 'grad_norm': 1.2630947233952987, 'learning_rate': 9.916935438746604e-06, 'epoch': 0.15}
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{'loss': 0.6224, 'grad_norm': 1.2586848110727198, 'learning_rate': 9.912270377125424e-06, 'epoch': 0.15}
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{'loss': 0.6261, 'grad_norm': 1.5648142512317709, 'learning_rate': 9.90747902861674e-06, 'epoch': 0.16}
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{'loss': 0.6207, 'grad_norm': 1.477705850244199, 'learning_rate': 9.902561516398723e-06, 'epoch': 0.16}
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{'loss': 0.6218, 'grad_norm': 1.2950681154644361, 'learning_rate': 9.897517966893023e-06, 'epoch': 0.16}
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{'loss': 0.6237, 'grad_norm': 1.4613516139089748, 'learning_rate': 9.892348509761509e-06, 'epoch': 0.16}
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{'loss': 0.6425, 'grad_norm': 1.2641419484176866, 'learning_rate': 9.887053277902943e-06, 'epoch': 0.16}
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{'loss': 0.6423, 'grad_norm': 1.2419109246681843, 'learning_rate': 9.881632407449561e-06, 'epoch': 0.16}
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{'loss': 0.6383, 'grad_norm': 1.4096648257937974, 'learning_rate': 9.876086037763575e-06, 'epoch': 0.16}
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{'loss': 0.6059, 'grad_norm': 1.2574892255736747, 'learning_rate': 9.870414311433585e-06, 'epoch': 0.17}
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{'loss': 0.6098, 'grad_norm': 1.2716145459010044, 'learning_rate': 9.86461737427092e-06, 'epoch': 0.17}
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{'loss': 0.6214, 'grad_norm': 1.1998298755084313, 'learning_rate': 9.858695375305885e-06, 'epoch': 0.17}
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{'loss': 0.6241, 'grad_norm': 1.4281449888166444, 'learning_rate': 9.852648466783927e-06, 'epoch': 0.17}
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{'loss': 0.6474, 'grad_norm': 1.4071764477667867, 'learning_rate': 9.84647680416173e-06, 'epoch': 0.17}
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{'loss': 0.6326, 'grad_norm': 1.2174453861834778, 'learning_rate': 9.840180546103215e-06, 'epoch': 0.17}
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{'loss': 0.6185, 'grad_norm': 1.3029300772595094, 'learning_rate': 9.833759854475453e-06, 'epoch': 0.17}
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{'loss': 0.6301, 'grad_norm': 1.271112016193465, 'learning_rate': 9.827214894344514e-06, 'epoch': 0.18}
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{'loss': 0.6317, 'grad_norm': 1.2997276991719462, 'learning_rate': 9.82054583397122e-06, 'epoch': 0.18}
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{'loss': 0.6159, 'grad_norm': 1.2096030387104992, 'learning_rate': 9.813752844806814e-06, 'epoch': 0.18}
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{'loss': 0.6289, 'grad_norm': 1.2973416257944899, 'learning_rate': 9.806836101488561e-06, 'epoch': 0.18}
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{'loss': 0.6088, 'grad_norm': 1.3197440048632956, 'learning_rate': 9.799795781835253e-06, 'epoch': 0.18}
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{'loss': 0.6206, 'grad_norm': 1.2535036782710556, 'learning_rate': 9.79263206684264e-06, 'epoch': 0.18}
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{'loss': 0.6149, 'grad_norm': 1.3190252094745194, 'learning_rate': 9.785345140678775e-06, 'epoch': 0.18}
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{'loss': 0.6134, 'grad_norm': 1.3148617882447478, 'learning_rate': 9.777935190679277e-06, 'epoch': 0.19}
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{'loss': 0.6258, 'grad_norm': 1.3368521794263946, 'learning_rate': 9.770402407342524e-06, 'epoch': 0.19}
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{'loss': 0.6191, 'grad_norm': 1.3941700458180073, 'learning_rate': 9.762746984324743e-06, 'epoch': 0.19}
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{'loss': 0.6446, 'grad_norm': 1.3152403546822757, 'learning_rate': 9.754969118435043e-06, 'epoch': 0.19}
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{'loss': 0.6312, 'grad_norm': 1.3013626770341264, 'learning_rate': 9.747069009630347e-06, 'epoch': 0.19}
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{'loss': 0.6207, 'grad_norm': 1.3966383885583535, 'learning_rate': 9.739046861010255e-06, 'epoch': 0.19}
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{'loss': 0.6144, 'grad_norm': 1.1439991746974036, 'learning_rate': 9.730902878811825e-06, 'epoch': 0.19}
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{'loss': 0.6044, 'grad_norm': 1.3540894709055364, 'learning_rate': 9.722637272404263e-06, 'epoch': 0.2}
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{'loss': 0.6036, 'grad_norm': 1.100639588271217, 'learning_rate': 9.71425025428355e-06, 'epoch': 0.2}
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{'loss': 0.6039, 'grad_norm': 1.1874319432290736, 'learning_rate': 9.705742040066977e-06, 'epoch': 0.2}
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{'loss': 0.6376, 'grad_norm': 1.1767671647303808, 'learning_rate': 9.697112848487591e-06, 'epoch': 0.2}
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{'loss': 0.6035, 'grad_norm': 1.135879944041461, 'learning_rate': 9.688362901388586e-06, 'epoch': 0.2}
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{'loss': 0.6098, 'grad_norm': 1.2315910796359388, 'learning_rate': 9.679492423717596e-06, 'epoch': 0.2}
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{'loss': 0.6203, 'grad_norm': 1.4949408462288012, 'learning_rate': 9.670501643520904e-06, 'epoch': 0.2}
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{'loss': 0.6286, 'grad_norm': 1.3180181445795711, 'learning_rate': 9.66139079193759e-06, 'epoch': 0.21}
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{'loss': 0.6274, 'grad_norm': 1.2616556885045909, 'learning_rate': 9.652160103193583e-06, 'epoch': 0.21}
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{'loss': 0.6136, 'grad_norm': 1.3174449455574337, 'learning_rate': 9.642809814595637e-06, 'epoch': 0.21}
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{'loss': 0.6145, 'grad_norm': 1.296735377133819, 'learning_rate': 9.633340166525238e-06, 'epoch': 0.21}
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{'loss': 0.6031, 'grad_norm': 1.2502497833244608, 'learning_rate': 9.62375140243242e-06, 'epoch': 0.21}
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{'loss': 0.6128, 'grad_norm': 1.2288830705505374, 'learning_rate': 9.6140437688295e-06, 'epoch': 0.21}
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{'loss': 0.6171, 'grad_norm': 1.1119473380240397, 'learning_rate': 9.604217515284753e-06, 'epoch': 0.21}
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{'loss': 0.6238, 'grad_norm': 1.2070397164389806, 'learning_rate': 9.594272894415986e-06, 'epoch': 0.22}
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{'loss': 0.6219, 'grad_norm': 1.1997047870357698, 'learning_rate': 9.073503534677773e-06, 'epoch': 0.28}
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{'loss': 0.6052, 'grad_norm': 1.2769112952981858, 'learning_rate': 9.058750202893844e-06, 'epoch': 0.28}
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{'loss': 0.6124, 'grad_norm': 1.2302296498321919, 'learning_rate': 9.04389252690837e-06, 'epoch': 0.28}
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{'loss': 0.604, 'grad_norm': 1.2009594091858158, 'learning_rate': 9.02893088868926e-06, 'epoch': 0.28}
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{'loss': 0.6052, 'grad_norm': 1.0539872600155336, 'learning_rate': 9.013865672877133e-06, 'epoch': 0.28}
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{'loss': 0.6077, 'grad_norm': 1.2561895098497668, 'learning_rate': 8.998697266775433e-06, 'epoch': 0.28}
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{'loss': 0.6059, 'grad_norm': 1.2763583417414128, 'learning_rate': 8.98342606034046e-06, 'epoch': 0.29}
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{'loss': 0.6183, 'grad_norm': 1.1463184995763767, 'learning_rate': 8.96805244617135e-06, 'epoch': 0.29}
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{'loss': 0.602, 'grad_norm': 1.1421597790792624, 'learning_rate': 8.952576819499998e-06, 'epoch': 0.29}
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{'loss': 0.5925, 'grad_norm': 1.3046866547593934, 'learning_rate': 8.93699957818087e-06, 'epoch': 0.29}
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{'loss': 0.6037, 'grad_norm': 1.27239619384718, 'learning_rate': 8.921321122680789e-06, 'epoch': 0.29}
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{'loss': 0.6077, 'grad_norm': 1.3073284462474046, 'learning_rate': 8.905541856068641e-06, 'epoch': 0.29}
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{'loss': 0.6076, 'grad_norm': 1.2694028140938955, 'learning_rate': 8.889662184005007e-06, 'epoch': 0.29}
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{'loss': 0.5986, 'grad_norm': 1.1075058528848678, 'learning_rate': 8.873682514731746e-06, 'epoch': 0.3}
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{'loss': 0.5911, 'grad_norm': 1.25011183641691, 'learning_rate': 8.85760325906148e-06, 'epoch': 0.3}
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{'loss': 0.5918, 'grad_norm': 1.230690665069067, 'learning_rate': 8.841424830367051e-06, 'epoch': 0.3}
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{'loss': 0.6026, 'grad_norm': 1.2143851276582127, 'learning_rate': 8.82514764457088e-06, 'epoch': 0.3}
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{'loss': 0.6208, 'grad_norm': 1.1711415813258073, 'learning_rate': 8.808772120134286e-06, 'epoch': 0.3}
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{'loss': 0.6178, 'grad_norm': 1.2105658122447378, 'learning_rate': 8.79229867804672e-06, 'epoch': 0.3}
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{'loss': 0.6033, 'grad_norm': 1.260614604486508, 'learning_rate': 8.775727741814945e-06, 'epoch': 0.31}
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{'loss': 0.5954, 'grad_norm': 1.1949196588242055, 'learning_rate': 8.75905973745215e-06, 'epoch': 0.31}
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{'loss': 0.6009, 'grad_norm': 1.3343569533197541, 'learning_rate': 8.424509266760413e-06, 'epoch': 0.33}
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{'loss': 0.5984, 'grad_norm': 1.1770338237370501, 'learning_rate': 7.489747210982777e-06, 'epoch': 0.4}
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{'loss': 0.6134, 'grad_norm': 1.2729396302065998, 'learning_rate': 7.423506071867101e-06, 'epoch': 0.41}
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{'loss': 0.5939, 'grad_norm': 1.1605256860138091, 'learning_rate': 7.311865730113525e-06, 'epoch': 0.41}
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{'loss': 0.5924, 'grad_norm': 1.244303339507363, 'learning_rate': 7.061129442581685e-06, 'epoch': 0.43}
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{'loss': 0.5922, 'grad_norm': 1.2478572360385807, 'learning_rate': 7.038005504598975e-06, 'epoch': 0.43}
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{'loss': 0.5825, 'grad_norm': 1.0447681879549313, 'learning_rate': 7.0148291726411486e-06, 'epoch': 0.43}
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{'loss': 0.5764, 'grad_norm': 1.321281822598792, 'learning_rate': 5.450649320168263e-06, 'epoch': 0.52}
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{'loss': 0.5752, 'grad_norm': 1.096076072807335, 'learning_rate': 4.994929644746366e-06, 'epoch': 0.55}
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{'loss': 0.5682, 'grad_norm': 1.1428305709772093, 'learning_rate': 4.7162126443606145e-06, 'epoch': 0.57}
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{'loss': 0.5761, 'grad_norm': 1.0962764476368352, 'learning_rate': 4.5392521738331585e-06, 'epoch': 0.58}
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{'loss': 0.5581, 'grad_norm': 1.1758853714133906, 'learning_rate': 2.2639189078928453e-06, 'epoch': 0.72}
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{'loss': 0.5474, 'grad_norm': 1.1110041512712467, 'learning_rate': 1.6425574490080355e-06, 'epoch': 0.76}
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{'loss': 0.5615, 'grad_norm': 0.9811514201367332, 'learning_rate': 1.3188511147104882e-06, 'epoch': 0.79}
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{'loss': 0.5731, 'grad_norm': 1.2124333619418073, 'learning_rate': 1.3017421544247466e-06, 'epoch': 0.79}
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{'loss': 0.5449, 'grad_norm': 1.0164638888045425, 'learning_rate': 1.2847282706385962e-06, 'epoch': 0.79}
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{'loss': 0.5581, 'grad_norm': 1.0692055130184748, 'learning_rate': 1.267809900752725e-06, 'epoch': 0.79}
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{'loss': 0.5694, 'grad_norm': 1.2243966381535343, 'learning_rate': 1.2509874797122983e-06, 'epoch': 0.79}
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{'loss': 0.5383, 'grad_norm': 1.210664779695526, 'learning_rate': 1.217632211603868e-06, 'epoch': 0.8}
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{'loss': 0.5503, 'grad_norm': 1.2306429782422048, 'learning_rate': 1.2011002220483099e-06, 'epoch': 0.8}
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{'loss': 0.561, 'grad_norm': 1.1449496150562748, 'learning_rate': 1.1846658963410472e-06, 'epoch': 0.8}
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{'loss': 0.5489, 'grad_norm': 1.1809146975647171, 'learning_rate': 1.168329656983222e-06, 'epoch': 0.8}
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{'loss': 0.5443, 'grad_norm': 1.1865786985653701, 'learning_rate': 1.1520919239543272e-06, 'epoch': 0.8}
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{'loss': 0.5784, 'grad_norm': 1.2819514449232758, 'learning_rate': 1.1359531147014102e-06, 'epoch': 0.8}
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{'loss': 0.5472, 'grad_norm': 1.140249494732679, 'learning_rate': 1.11991364412834e-06, 'epoch': 0.8}
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{'loss': 0.5614, 'grad_norm': 1.0963574239357976, 'learning_rate': 1.1039739245851426e-06, 'epoch': 0.81}
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{'loss': 0.5516, 'grad_norm': 1.1963836912036798, 'learning_rate': 1.088134365857399e-06, 'epoch': 0.81}
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{'loss': 0.545, 'grad_norm': 1.2261172403861758, 'learning_rate': 1.0567573571052265e-06, 'epoch': 0.81}
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{'loss': 0.5516, 'grad_norm': 1.242951008236561, 'learning_rate': 7.658010483690431e-07, 'epoch': 0.84}
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{'loss': 0.5558, 'grad_norm': 1.1291848404892897, 'learning_rate': 7.52372237911358e-07, 'epoch': 0.84}
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{'loss': 0.5538, 'grad_norm': 1.240301119345841, 'learning_rate': 2.3137642244375202e-11, 'epoch': 1.0}
100%|█████████▉| 6880/6885 [15:38:16<00:14, 2.99s/it]
100%|█████████▉| 6881/6885 [15:38:18<00:11, 2.80s/it]
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100%|█████████▉| 6884/6885 [15:38:24<00:02, 2.34s/it]
100%|██████████| 6885/6885 [15:38:27<00:00, 2.55s/it][INFO|trainer.py:3993] 2025-10-14 18:16:17,635 >> Saving model checkpoint to /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885
[INFO|configuration_utils.py:424] 2025-10-14 18:16:17,642 >> Configuration saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/config.json
[INFO|configuration_utils.py:904] 2025-10-14 18:16:17,644 >> Configuration saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/generation_config.json
[INFO|modeling_utils.py:3730] 2025-10-14 18:16:37,886 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/model.safetensors.index.json.
[INFO|tokenization_utils_base.py:2356] 2025-10-14 18:16:37,890 >> chat template saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/chat_template.jinja
[INFO|tokenization_utils_base.py:2525] 2025-10-14 18:16:37,893 >> tokenizer config file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/tokenizer_config.json
[INFO|tokenization_utils_base.py:2534] 2025-10-14 18:16:37,895 >> Special tokens file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/special_tokens_map.json
[2025-10-14 18:16:38,628] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step6885 is begin to save!
[2025-10-14 18:16:38,694] [INFO] [logging.py:107:log_dist] [Rank 0] Saving model checkpoint: /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/global_step6885/zero_pp_rank_0_mp_rank_00_model_states.pt
[INFO|2025-10-14 18:16:57] llamafactory.train.callbacks:143 >> EMA model saved at: /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/ema
[INFO|image_processing_base.py:260] 2025-10-14 18:16:57,142 >> Image processor saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/preprocessor_config.json
[INFO|tokenization_utils_base.py:2356] 2025-10-14 18:16:57,143 >> chat template saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/chat_template.jinja
[INFO|tokenization_utils_base.py:2525] 2025-10-14 18:16:57,146 >> tokenizer config file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/tokenizer_config.json
[INFO|tokenization_utils_base.py:2534] 2025-10-14 18:16:57,147 >> Special tokens file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/special_tokens_map.json
[INFO|video_processing_utils.py:491] 2025-10-14 18:16:57,275 >> Video processor saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/video_preprocessor_config.json
[INFO|processing_utils.py:674] 2025-10-14 18:16:57,577 >> chat template saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/checkpoint-6885/chat_template.jinja
[INFO|trainer.py:2676] 2025-10-14 18:16:57,578 >>
Training completed. Do not forget to share your model on huggingface.co/models =)
{'train_runtime': 56356.5973, 'train_samples_per_second': 1.955, 'train_steps_per_second': 0.122, 'train_loss': 0.5927019230420812, 'epoch': 1.0}
100%|██████████| 6885/6885 [15:39:15<00:00, 2.55s/it][INFO|2025-10-14 18:16:57] llamafactory.train.callbacks:143 >> EMA model saved at: /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/ema
100%|██████████| 6885/6885 [15:39:15<00:00, 8.19s/it]
[INFO|image_processing_base.py:260] 2025-10-14 18:16:57,604 >> Image processor saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/preprocessor_config.json
[INFO|tokenization_utils_base.py:2356] 2025-10-14 18:16:57,606 >> chat template saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/chat_template.jinja
[INFO|tokenization_utils_base.py:2525] 2025-10-14 18:16:57,607 >> tokenizer config file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/tokenizer_config.json
[INFO|tokenization_utils_base.py:2534] 2025-10-14 18:16:57,608 >> Special tokens file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/special_tokens_map.json
[INFO|video_processing_utils.py:491] 2025-10-14 18:16:57,707 >> Video processor saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/video_preprocessor_config.json
[INFO|processing_utils.py:674] 2025-10-14 18:16:57,982 >> chat template saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/chat_template.jinja
[INFO|trainer.py:3993] 2025-10-14 18:17:05,389 >> Saving model checkpoint to /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA
[INFO|configuration_utils.py:424] 2025-10-14 18:17:05,396 >> Configuration saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/config.json
[INFO|configuration_utils.py:904] 2025-10-14 18:17:05,398 >> Configuration saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/generation_config.json
[INFO|modeling_utils.py:3730] 2025-10-14 18:17:24,931 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/model.safetensors.index.json.
[INFO|tokenization_utils_base.py:2356] 2025-10-14 18:17:24,932 >> chat template saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/chat_template.jinja
[INFO|tokenization_utils_base.py:2525] 2025-10-14 18:17:24,935 >> tokenizer config file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/tokenizer_config.json
[INFO|tokenization_utils_base.py:2534] 2025-10-14 18:17:24,935 >> Special tokens file saved in /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/special_tokens_map.json
***** train metrics *****
epoch = 1.0
total_flos = 1808934GF
train_loss = 0.5927
train_runtime = 15:39:16.59
train_samples_per_second = 1.955
train_steps_per_second = 0.122
Figure saved at: /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/training_loss.png
[WARNING|2025-10-14 18:17:25] llamafactory.extras.ploting:148 >> No metric eval_loss to plot.
[WARNING|2025-10-14 18:17:25] llamafactory.extras.ploting:148 >> No metric eval_accuracy to plot.
[INFO|modelcard.py:450] 2025-10-14 18:17:25,669 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
[1;34mwandb[0m:
[1;34mwandb[0m: You can sync this run to the cloud by running:
[1;34mwandb[0m: [1mwandb sync /inspire/hdd/project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/wandb/offline-run-20251014_023741-21vuy1wg[0m
[1;34mwandb[0m: Find logs at: [1;35m../../../../../../project/agileapplication/zhangkaipeng-24043/lsh_appl/train/pyvision/qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA/wandb/offline-run-20251014_023741-21vuy1wg/logs[0m