Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Linq-AI-Research/Linq-Embed-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Linq-AI-Research/Linq-Embed-Mistral with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral") 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] - Transformers
How to use Linq-AI-Research/Linq-Embed-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Linq-AI-Research/Linq-Embed-Mistral")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linq-AI-Research/Linq-Embed-Mistral") model = AutoModel.from_pretrained("Linq-AI-Research/Linq-Embed-Mistral") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "intfloat/e5-mistral-7b-instruct", | |
| "architectures": [ | |
| "MistralModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 14336, | |
| "max_position_embeddings": 32768, | |
| "model_type": "mistral", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 2, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta": 10000.0, | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.39.0", | |
| "use_cache": false, | |
| "vocab_size": 32000 | |
| } | |