Libraries sentence-transformers 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 Inference Providers 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 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 
