Instructions to use tokiers/mxbai-embed-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tokiers/mxbai-embed-large-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tokiers/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)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tokiers/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 tokiers/mxbai-embed-large-v1:F16 # Run inference directly in the terminal: llama-cli -hf tokiers/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 tokiers/mxbai-embed-large-v1:F16 # Run inference directly in the terminal: llama-cli -hf tokiers/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 tokiers/mxbai-embed-large-v1:F16 # Run inference directly in the terminal: ./llama-cli -hf tokiers/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 tokiers/mxbai-embed-large-v1:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf tokiers/mxbai-embed-large-v1:F16
Use Docker
docker model run hf.co/tokiers/mxbai-embed-large-v1:F16
- LM Studio
- Jan
- Ollama
How to use tokiers/mxbai-embed-large-v1 with Ollama:
ollama run hf.co/tokiers/mxbai-embed-large-v1:F16
- Unsloth Studio new
How to use tokiers/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 tokiers/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 tokiers/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 tokiers/mxbai-embed-large-v1 to start chatting
- Docker Model Runner
How to use tokiers/mxbai-embed-large-v1 with Docker Model Runner:
docker model run hf.co/tokiers/mxbai-embed-large-v1:F16
- Lemonade
How to use tokiers/mxbai-embed-large-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tokiers/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
mxbai-embed-large-v1
Pre-built tokie tokenizer for mixedbread-ai/mxbai-embed-large-v1.
Quick Start (Python)
pip install tokie
import tokie
tokenizer = tokie.Tokenizer.from_pretrained("tokiers/mxbai-embed-large-v1")
encoding = tokenizer.encode("Hello, world!")
print(encoding.ids)
print(encoding.attention_mask)
Quick Start (Rust)
[dependencies]
tokie = { version = "0.0.4", features = ["hf"] }
use tokie::Tokenizer;
let tokenizer = Tokenizer::from_pretrained("tokiers/mxbai-embed-large-v1").unwrap();
let encoding = tokenizer.encode("Hello, world!", true);
println!("{:?}", encoding.ids);
Files
tokenizer.tkzโ tokie binary format (~10x smaller, loads in ~5ms)tokenizer.jsonโ original HuggingFace tokenizer (if available)
About tokie
50x faster tokenization, 10x smaller model files, 100% accurate.
tokie is a drop-in replacement for HuggingFace tokenizers, built in Rust. See GitHub for benchmarks and documentation.
License
MIT OR Apache-2.0 (tokie library). Original model files retain their original license from mixedbread-ai/mxbai-embed-large-v1.
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