Feature Extraction
Transformers
PyTorch
English
minicpmv
information retrieval
embedding model
visual information retrieval
custom_code
Instructions to use RhapsodyAI/MiniCPM-V-Embedding-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RhapsodyAI/MiniCPM-V-Embedding-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RhapsodyAI/MiniCPM-V-Embedding-preview", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RhapsodyAI/MiniCPM-V-Embedding-preview", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload modeling_minicpmv.py
Browse files- modeling_minicpmv.py +6 -1
modeling_minicpmv.py
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@@ -2,6 +2,7 @@ import math
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from typing import List, Optional
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import timm
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import torch
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from PIL import Image
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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@@ -521,8 +522,12 @@ class MiniCPMVEmbedding(MiniCPMV): # MiniCPMVEmbedding -> MiniCPMV -> Ultimatel
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return_dict=True
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return BaseModelOutputWithAttentionMask(
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last_hidden_state=
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attention_mask=model_inputs.attention_mask
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)
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from typing import List, Optional
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import timm
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import torch
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import torch.nn.functional as F
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from PIL import Image
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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return_dict=True
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)
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last_hidden_state = vlm_outputs.last_hidden_state
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last_hidden_state_normalized = F.normalize(last_hidden_state, dim=1)
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return BaseModelOutputWithAttentionMask(
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last_hidden_state=last_hidden_state_normalized,
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attention_mask=model_inputs.attention_mask
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)
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