nomicai-modernbert-embed-base-4bit

295
4.0B
2 languages
license:apache-2.0
by
mlx-community
Embedding Model
OTHER
4B params
New
295 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
9GB+ RAM
Mobile
Laptop
Server
Quick Summary

The Model mlx-community/nomicai-modernbert-embed-base-4bit was converted to MLX format from nomic-ai/modernbert-embed-base using mlx-lm version 0.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
4GB+ RAM

Code Examples

Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxbash
pip install mlx-embeddings
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)
Use with mlxpython
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-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)

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