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", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 Desktop
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

Atomic Chat

Build in query prompt to Sentence Transformers Config

#19

by jdwh08s - opened Oct 27, 2024

base: refs/heads/main

←

from: refs/pr/19

Discussion Files changed

+26

-19

jdwh08s

Oct 27, 2024

Change readme and Sentence Transformers config to build the query prompt into the .encode() method traditionally used by Sentence Transformers, instead of having it supplied manually by the user.

Prior Discussion Reference: https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1/discussions/18
(apologies for the delay)

Thanks!
jdwh08s

Build in query prompt to Sentence Transformers Configb192313e

juliuslipp changed pull request status to merged Oct 28, 2024

juliuslipp

Oct 28, 2024

Thank you @jdwh08s !

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