Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
bnb-my-repo
mteb
Eval Results (legacy)
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use gabor-hosu/e5-mistral-7b-instruct-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gabor-hosu/e5-mistral-7b-instruct-bnb-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gabor-hosu/e5-mistral-7b-instruct-bnb-4bit") 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 gabor-hosu/e5-mistral-7b-instruct-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gabor-hosu/e5-mistral-7b-instruct-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("gabor-hosu/e5-mistral-7b-instruct-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("gabor-hosu/e5-mistral-7b-instruct-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b98fed080882c8ab071cb766691a5e6bad2a32b14fdd1ffd50cf377a8cdebe74
- Size of remote file:
- 3.86 GB
- SHA256:
- ba6537b894fdce9085aa82b1f9d35115089e50562f3b9ec19d10317020dffd1f
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