Mxbai Embed Large V1 Gguf

🚀 mxbai-embed-large-v1-gguf

This project provides GGUF format files for the mxbai-embed-large-v1 embedding model, enabling compatibility with llama.cpp and LM Studio.

🚀 Quick Start

This repo contains GGUF format files for the mxbai-embed-large-v1 embedding model. These files were converted and quantized with llama.cpp PR 5500, commit 34aa045de, on a consumer RTX 4090. This model supports up to 512 tokens of context.

✨ Features

• High Performance: Achieves SOTA performance on BERT-large scale.
• Wide Compatibility: Compatible with llama.cpp and LM Studio.
• Multiple Quantization Methods: Various quantization methods are available to balance size and quality.

📦 Installation

No specific installation steps are provided in the original README. However, you can download the GGUF files from this repo and use them with compatible tools like llama.cpp or LM Studio.

💻 Usage Examples

Basic Usage

Example Usage with llama.cpp

To compute a single embedding, build llama.cpp and run:

./embedding -ngl 99 -m [filepath-to-gguf].gguf -p 'search_query: What is TSNE?'

You can also submit a batch of texts to embed, as long as the total number of tokens does not exceed the context length. Only the first three embeddings are shown by the embedding example.

texts.txt:

search_query: What is TSNE?
search_query: Who is Laurens Van der Maaten?

Compute multiple embeddings:

./embedding -ngl 99 -m [filepath-to-gguf].gguf -f texts.txt

Example Usage with LM Studio

Download the 0.2.19 beta build from here: Windows MacOS Linux

Once installed, open the app. The home should look like this:

Search for either "ChristianAzinn" in the main search bar or go to the "Search" tab on the left menu and search the name there.

Select your model from those that appear (this example uses bge-small-en-v1.5-gguf) and select which quantization you want to download. Since this model is pretty small, I recommend Q8_0, if not f16/32. Generally, the lower you go in the list (or the bigger the number gets), the larger the file and the better the performance.

You will see a green checkmark and the word "Downloaded" once the model has successfully downloaded, which can take some time depending on your network speeds.

Once this model is finished downloading, navigate to the "Local Server" tab on the left menu and open the loader for text embedding models. This loader does not appear before version 0.2.19, so ensure you downloaded the correct version.

Select the model you just downloaded from the dropdown that appears to load it. You may need to play with configuratios in the right-side menu, such as GPU offload if it doesn't fit entirely into VRAM.

All that's left to do is to hit the "Start Server" button:

And if you see text like that shown below in the console, you're good to go! You can use this as a drop-in replacement for the OpenAI embeddings API in any application that requires it, or you can query the endpoint directly to test it out.

Example curl request to the API endpoint:

curl http://localhost:1234/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Your text string goes here",
    "model": "model-identifier-here"
  }'

For more information, see the LM Studio text embedding documentation.

📚 Documentation

Original Description

This is our base sentence embedding model. It was trained using AnglE loss on our high-quality large scale data. It achieves SOTA performance on BERT-large scale. Find out more in our blog post.

Compatibility

These files are compatible with llama.cpp as of commit 4524290e8, as well as LM Studio as of version 0.2.19.

Meta-information

Explanation of quantisation methods

Provided Files

📄 License

This project is licensed under the apache-2.0 license.

Acknowledgements

Thanks to the LM Studio team and everyone else working on open-source AI.

This README is inspired by that of nomic-ai-embed-text-v1.5-GGUF, another excellent embedding model, and those of the legendary TheBloke.