How to use from the
Use from the
MLX library
# Download the model from the Hub
pip install huggingface_hub[hf_xet]

hf download mlx-community/answerdotai-ModernBERT-Large-Instruct-4bit --local-dir answerdotai-ModernBERT-Large-Instruct-4bit

mlx-community/answerdotai-ModernBERT-Large-Instruct-4bit

The Model mlx-community/answerdotai-ModernBERT-Large-Instruct-4bit was converted to MLX format from answerdotai/ModernBERT-Large-Instruct using mlx-lm version 0.0.3.

Use with mlx

pip install mlx-embeddings
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/answerdotai-ModernBERT-Large-Instruct-4bit")

# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)

Downloads last month
52
Safetensors
Model size
0.4B params
Tensor type
F16
ยท
U32
ยท
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support

Collection including mlx-community/answerdotai-ModernBERT-Large-Instruct-4bit