Feature Extraction
sentence-transformers
Safetensors
Chinese
English
minimind
text-embedding
chinese
qwen3-embedding
matryoshka
Instructions to use Muzian/minimind-embedding-dense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Muzian/minimind-embedding-dense with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Muzian/minimind-embedding-dense") 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
File size: 577 Bytes
ecab56b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"model_type": "minimind",
"architectures": [
"MiniMindForEmbedding"
],
"hidden_size": 768,
"num_hidden_layers": 8,
"num_attention_heads": 8,
"num_key_value_heads": 4,
"head_dim": 96,
"vocab_size": 6400,
"intermediate_size": 2432,
"max_position_embeddings": 32768,
"rope_theta": 1000000.0,
"rms_norm_eps": 1e-06,
"bos_token_id": 1,
"eos_token_id": 2,
"tie_word_embeddings": false,
"use_moe": false,
"embed_dim": 768,
"mrl_dims": [
768,
512,
256,
128,
64
],
"pooling": "last_token",
"torch_dtype": "float16"
} |