Instructions to use mlx-community/embeddinggemma-300m-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlx-community/embeddinggemma-300m-bf16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/embeddinggemma-300m-bf16") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - MLX
How to use mlx-community/embeddinggemma-300m-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir embeddinggemma-300m-bf16 mlx-community/embeddinggemma-300m-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
sample code doesn't work
The provided code does not run as written after installing mlx-embeddings.
TypeError Traceback (most recent call last)
Cell In[6], line 14
7 # For text embedding
8 sentences = [
9 "task: sentence similarity | query: Nothing really matters.",
10 "task: sentence similarity | query: The dog is barking.",
11 "task: sentence similarity | query: The dog is barking.",
12 ]
---> 14 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='mlx')
16 # Compute token embeddings
17 input_ids = encoded_input['input_ids']
TypeError: 'TokenizerWrapper' object is not callable
Change the line to this:
encoded_input = tokenizer.batch_encode_plus(
sentences, padding=True, truncation=True, return_tensors="mlx"
)
Source: https://github.com/Blaizzy/mlx-embeddings/tree/main