Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
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"weight_decay": 0.01
}
},
"gradient_accumulation_steps": 1,
"gradient_clipping": 1.0,
"steps_per_print": inf,
"train_batch_size": 8,
"train_micro_batch_size_per_gpu": 2,
"wall_clock_breakdown": true
}
[INFO|trainer.py:1721] 2024-10-22 17:07:57,448 >> ***** Running training *****
[INFO|trainer.py:1722] 2024-10-22 17:07:57,448 >> Num examples = 52,702
[INFO|trainer.py:1723] 2024-10-22 17:07:57,448 >> Num Epochs = 4
[INFO|trainer.py:1724] 2024-10-22 17:07:57,448 >> Instantaneous batch size per device = 2
[INFO|trainer.py:1727] 2024-10-22 17:07:57,448 >> Total train batch size (w. parallel, distributed & accumulation) = 8
[INFO|trainer.py:1728] 2024-10-22 17:07:57,448 >> Gradient Accumulation steps = 1
[INFO|trainer.py:1729] 2024-10-22 17:07:57,448 >> Total optimization steps = 26,352
[INFO|trainer.py:1730] 2024-10-22 17:07:57,451 >> Number of trainable parameters = 15,728,640
0%| | 0/26352 [00:00<?, ?it/s][2024-10-22 17:07:59,190] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:07:59,195] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:07:59,216] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:07:59,218] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:02,248] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:02,368] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:02,379] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:02,409] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:05,164] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:05,405] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:05,487] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:05,512] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:08,037] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:08,270] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:08,468] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:08:08,504] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='What shape are the pizzas?')
ANSWER1=VQA(image=RIGHT,question='What shape are the pizzas?')
ANSWER2=EVAL(expr='{ANSWER0} == "rectangle" and {ANSWER1} == "rectangle"')
FINAL_ANSWER=RESULT(var=ANSWER2)
torch.Size([7, 3, 448, 448])
Encountered ExecuteError: 'InternVLChatModel' object has no attribute 'tokenizer'
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
ANSWER0=VQA(image=LEFT,question='How many live ibexes are standing in the grass and weeds?')
ANSWER1=VQA(image=RIGHT,question='How many live ibexes are standing in the grass and weeds?')
ANSWER2=EVAL(expr='{ANSWER0} >= 1 or {ANSWER1} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER2)
torch.Size([3, 3, 448, 448])
Encountered ExecuteError: 'InternVLChatModel' object has no attribute 'tokenizer'
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many boars are in the image?')
ANSWER1=VQA(image=LEFT,question='Is the boar swimming in the water?')
ANSWER2=EVAL(expr='{ANSWER0} == 1 and {ANSWER1}')
FINAL_ANSWER=RESULT(var=ANSWER2)
torch.Size([7, 3, 448, 448])
Encountered ExecuteError: 'InternVLChatModel' object has no attribute 'tokenizer'
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
ANSWER0=VQA(image=LEFT,question='Is there a sofa/chair near the tall window?')
ANSWER1=VQA(image=RIGHT,question='Is there a sofa/chair near the tall window?')
ANSWER2=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER2)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many prepared drinks are in serving cups?')
ANSWER1=VQA(image=RIGHT,question='How many prepared drinks are in serving cups?')
ANSWER2=EVAL(expr='{ANSWER0} == {ANSWER1}')
FINAL_ANSWER=RESULT(var=ANSWER2)
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: 'InternVLChatModel' object has no attribute 'tokenizer'
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
torch.Size([7, 3, 448, 448])
Encountered ExecuteError: 'InternVLChatModel' object has no attribute 'tokenizer'
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
ANSWER0=VQA(image=LEFT,question='Is the shop door visible in the image?')
ANSWER1=VQA(image=RIGHT,question='Is the shop door visible in the image?')
ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
FINAL_ANSWER=RESULT(var=ANSWER2)
torch.Size([1, 3, 448, 448])
Encountered ExecuteError: 'InternVLChatModel' object has no attribute 'tokenizer'
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog facing right in the image?')
ANSWER1=VQA(image=LEFT,question='Is the dog facing left in the image?')
ANSWER2=EVAL(expr='{ANSWER0} and {ANSWER1}')
FINAL_ANSWER=RESULT(var=ANSWER2)
torch.Size([13, 3, 448, 448])