- Libraries
- sentence-transformers
How to use mlx-community/mxbai-embed-large-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("mlx-community/mxbai-embed-large-v1")
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.js
How to use mlx-community/mxbai-embed-large-v1 with Transformers.js:
// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';
// Allocate pipeline
const pipe = await pipeline('feature-extraction', 'mlx-community/mxbai-embed-large-v1'); - Transformers
How to use mlx-community/mxbai-embed-large-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="mlx-community/mxbai-embed-large-v1") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("mlx-community/mxbai-embed-large-v1")
model = AutoModel.from_pretrained("mlx-community/mxbai-embed-large-v1") - MLX
How to use mlx-community/mxbai-embed-large-v1 with MLX:
# Download the model from the Hub
pip install huggingface_hub[hf_xet]
huggingface-cli download --local-dir mxbai-embed-large-v1 mlx-community/mxbai-embed-large-v1
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
