⚠️ A Word of Warning: This Might Not Be the Model You're Looking For

This model is an experimental distillation of NVIDIA's powerful llama-embed-nemotron-8b (4096 dimensions), aggressively compressed into a mere 3 dimensions.

When to Use This Model:

• For visualizing how sentences cluster in a 3D space.
• For educational purposes to understand the information loss from extreme dimensionality reduction.
• For a good laugh, and to ask yourself, "Why would anyone do this?"

When NOT to Use This Model:

• Semantic Search
• Retrieval-Augmented Generation (RAG)
• Sentence Similarity
• Clustering
• Basically, any real-world production task.

Attempting to use this for any serious embedding task will result in abysmal performance. If you value your time and sanity, please use this model for its intended (mostly humorous) purpose.

You have been warned.

llama-embed-nemotron-8b-model2vec-pca3 Model Card

This Model2Vec model is a distilled version of the nvidia/llama-embed-nemotron-8b(https://huggingface.co/nvidia/llama-embed-nemotron-8b) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.

Installation

Install model2vec using pip:

pip install model2vec

Usage

Using Model2Vec

The Model2Vec library is the fastest and most lightweight way to run Model2Vec models.

Load this model using the from_pretrained method:

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("enzoescipy/llama-embed-nemotron-8b-model2vec-pca3")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])

Using Sentence Transformers

You can also use the Sentence Transformers library to load and use the model:

from sentence_transformers import SentenceTransformer

# Load a pretrained Sentence Transformer model
model = SentenceTransformer("enzoescipy/llama-embed-nemotron-8b-model2vec-pca3")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])

Distilling a Model2Vec model

You can distill a Model2Vec model from a Sentence Transformer model using the distill method. First, install the distill extra with pip install model2vec[distill]. Then, run the following code:

from model2vec.distill import distill

# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)

# Save the model
m2v_model.save_pretrained("m2v_model")

How it works

Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.

It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using SIF weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.

Additional Resources

• Model2Vec Repo
• Model2Vec Base Models
• Model2Vec Results
• Model2Vec Tutorials
• Website

Library Authors

Model2Vec was developed by the Minish Lab team consisting of Stephan Tulkens and Thomas van Dongen.

Citation

Please cite the Model2Vec repository if you use this model in your work.

@article{minishlab2024model2vec,
  author = {Tulkens, Stephan and {van Dongen}, Thomas},
  title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
  year = {2024},
  url = {https://github.com/MinishLab/model2vec}
}