Instructions to use mixedbread-ai/mxbai-embed-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mixedbread-ai/mxbai-embed-large-v1 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("mixedbread-ai/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 mixedbread-ai/mxbai-embed-large-v1 with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1');

Transformers

How to use mixedbread-ai/mxbai-embed-large-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="mixedbread-ai/mxbai-embed-large-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-embed-large-v1")
model = AutoModel.from_pretrained("mixedbread-ai/mxbai-embed-large-v1")

llama-cpp-python

How to use mixedbread-ai/mxbai-embed-large-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mixedbread-ai/mxbai-embed-large-v1",
	filename="gguf/mxbai-embed-large-v1-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mixedbread-ai/mxbai-embed-large-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mixedbread-ai/mxbai-embed-large-v1:F16
# Run inference directly in the terminal:
llama-cli -hf mixedbread-ai/mxbai-embed-large-v1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mixedbread-ai/mxbai-embed-large-v1:F16
# Run inference directly in the terminal:
llama-cli -hf mixedbread-ai/mxbai-embed-large-v1:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mixedbread-ai/mxbai-embed-large-v1:F16
# Run inference directly in the terminal:
./llama-cli -hf mixedbread-ai/mxbai-embed-large-v1:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mixedbread-ai/mxbai-embed-large-v1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mixedbread-ai/mxbai-embed-large-v1:F16

Use Docker

docker model run hf.co/mixedbread-ai/mxbai-embed-large-v1:F16

LM Studio
Jan
Ollama
How to use mixedbread-ai/mxbai-embed-large-v1 with Ollama:
```
ollama run hf.co/mixedbread-ai/mxbai-embed-large-v1:F16
```

Unsloth Studio

How to use mixedbread-ai/mxbai-embed-large-v1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mixedbread-ai/mxbai-embed-large-v1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mixedbread-ai/mxbai-embed-large-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mixedbread-ai/mxbai-embed-large-v1 to start chatting

Atomic Chat new
Docker Model Runner
How to use mixedbread-ai/mxbai-embed-large-v1 with Docker Model Runner:
```
docker model run hf.co/mixedbread-ai/mxbai-embed-large-v1:F16
```

Lemonade

How to use mixedbread-ai/mxbai-embed-large-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mixedbread-ai/mxbai-embed-large-v1:F16

Run and chat with the model

lemonade run user.mxbai-embed-large-v1-F16

List all available models

lemonade list

juliuslipp commited on Mar 10, 2025

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