Instructions to use mixedbread-ai/mxbai-embed-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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") - 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
- 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 new
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
- 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
initial commit
Browse filesUpload model
Upload tokenizer
Update README.md
Create config_sentence_transformers.json
Create 1_Pooling/config.json
Update README.md
correct model name
Create sentence_bert_config.json
Create modules.json
update
update
Update README.md
Update README.md
- .gitattributes +35 -0
- 1_Pooling/config.json +10 -0
- README.md +0 -0
- config.json +26 -0
- config_sentence_transformers.json +9 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
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