Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
mteb
custom_code
Eval Results (legacy)
Instructions to use openbmb/MiniCPM-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/MiniCPM-Embedding", trust_remote_code=True)# Load model directly from transformers import MiniCPM model = MiniCPM.from_pretrained("openbmb/MiniCPM-Embedding", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("openbmb/MiniCPM-Embedding", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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"initializer_range": 0.1,
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"intermediate_size": 5760,
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"is_causal": false,
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"max_position_embeddings":
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"num_attention_heads": 36,
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"num_hidden_layers": 40,
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"num_key_value_heads": 36,
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"initializer_range": 0.1,
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"intermediate_size": 5760,
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"is_causal": false,
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"max_position_embeddings": 512,
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"num_attention_heads": 36,
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"num_hidden_layers": 40,
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"num_key_value_heads": 36,
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