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
| { | |
| "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" | |
| } |