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 ashercn97/Linq-Embed-Mistral-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ashercn97/Linq-Embed-Mistral-bnb-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ashercn97/Linq-Embed-Mistral-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 ashercn97/Linq-Embed-Mistral-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ashercn97/Linq-Embed-Mistral-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ashercn97/Linq-Embed-Mistral-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("ashercn97/Linq-Embed-Mistral-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e5b6c9547ac2c5e191b16d1472cdb1276bb093e80132bd9ac5b4e049f980230
- Size of remote file:
- 3.86 GB
- SHA256:
- 14256c99f4caa01de57298e548530a6536de5ca91bd9f29284fad86b6ccdce5a
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