Visual Question Answering
Transformers
Safetensors
English
Chinese
minicpmv
feature-extraction
custom_code
Eval Results
Instructions to use openbmb/MiniCPM-V-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-V-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="openbmb/MiniCPM-V-2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V-2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
remove deepspeed
Browse files- resampler.py +0 -1
resampler.py
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@@ -26,7 +26,6 @@ from typing import Optional, Tuple
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import torch
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from torch import nn
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from torch import Tensor
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import deepspeed
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import torch.nn.functional as F
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from torch.nn.functional import *
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from torch.nn.modules.activation import *
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import torch
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from torch import nn
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from torch import Tensor
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import torch.nn.functional as F
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from torch.nn.functional import *
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from torch.nn.modules.activation import *
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