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
| { | |
| "_name_or_path": "openbmb/MiniCPM-Embedding", | |
| "architectures": [ | |
| "MiniCPM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_minicpm.MiniCPMConfig", | |
| "AutoModel": "modeling_minicpm.MiniCPMModel", | |
| "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM", | |
| "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM", | |
| "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification" | |
| }, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 2304, | |
| "initializer_range": 0.1, | |
| "intermediate_size": 5760, | |
| "is_causal": false, | |
| "max_position_embeddings": 512, | |
| "num_attention_heads": 36, | |
| "num_hidden_layers": 40, | |
| "num_key_value_heads": 36, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.36.0", | |
| "use_cache": false, | |
| "vocab_size": 122753, | |
| "scale_emb": 12, | |
| "dim_model_base": 256, | |
| "scale_depth": 1.4 | |
| } |