open-compass / open-compass/VLMEvalKit
评测InternVL3-8B报错, AttributeError: 'InternVLModel' object has no attribute 'chat'
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Description
报错命令如下
Traceback (most recent call last):
File "/nas/user/projects/VLMEvalKit/run.py", line 376, in main
model = infer_data_job(
^^^^^^^^^^^^^^^
File "/nas/user/projects/VLMEvalKit/vlmeval/inference.py", line 203, in infer_data_job
model = infer_data(
^^^^^^^^^^^
File "/nas/user/projects/VLMEvalKit/vlmeval/inference.py", line 166, in infer_data
response = model.generate(message=struct, dataset=dataset_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/nas/user/projects/VLMEvalKit/vlmeval/vlm/base.py", line 116, in generate
return self.generate_inner(message, dataset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/nas/user/projects/VLMEvalKit/vlmeval/vlm/internvl/internvl_chat.py", line 467, in generate_inner
return self.generate_v2(message, dataset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/.local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/nas/user/projects/VLMEvalKit/vlmeval/vlm/internvl/internvl_chat.py", line 422, in generate_v2
response = self.model.chat(
^^^^^^^^^^^^^^^
File "/home/user/.local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1931, in __getattr__
raise AttributeError(
AttributeError: 'InternVLModel' object has no attribute 'chat'
执行的命令为
python run.py --data ChartQA_TEST --model InternVL3-8B --work-dir results/ --judge gpt-4o --mode all --reuse
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with vlmeval/vlm/internvl/internvl_chat.py, especially generate_inner and generate_v2, then inspect how the InternVL3-8B model is loaded before the failing self.model.chat call. Re-run the provided ChartQA_TEST command after tracing the model API. Done means the command no longer raises the reported AttributeError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, testing-qa
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100