Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alignment-Lab-AI/e5-mistral-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alignment-Lab-AI/e5-mistral-7b-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alignment-Lab-AI/e5-mistral-7b-instruct") 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 Alignment-Lab-AI/e5-mistral-7b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Alignment-Lab-AI/e5-mistral-7b-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Alignment-Lab-AI/e5-mistral-7b-instruct") model = AutoModel.from_pretrained("Alignment-Lab-AI/e5-mistral-7b-instruct") - Notebooks
- Google Colab
- Kaggle
File size: 603 Bytes
69b92ca | 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 | {
"_name_or_path": "mistralai/Mistral-7B-v0.1",
"architectures": [
"MistralModel"
],
"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,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.34.0",
"use_cache": false,
"vocab_size": 32000
}
|