metadata

license: mit
tags:
  - tensor-compression
  - code-embeddings
  - factorized
  - tltorch
base_model: nomic-ai/CodeRankEmbed

CodeRankEmbed-compressed

This is a tensor-compressed version of nomic-ai/CodeRankEmbed using tensor factorization.

Compression Details

Compression method: Tensor factorization using TLTorch
Factorization types: cp
Ranks used: 4
Number of factorized layers: 60
Original model size: 136.73M parameters
Compressed model size: 23.62M parameters
Compression ratio: 5.79x (82.7% reduction)

Usage

To use this compressed model, you'll need to install the required dependencies and use the custom loading script:

pip install torch tensorly tltorch sentence-transformers

Loading the model

import torch
import json
from sentence_transformers import SentenceTransformer
import tensorly as tl
from tltorch.factorized_layers import FactorizedLinear, FactorizedEmbedding

# Set TensorLy backend
tl.set_backend("pytorch")

# Load the model structure
model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)

# Load factorization info
with open("factorization_info.json", "r") as f:
    factorized_info = json.load(f)

# Reconstruct factorized layers (see load_compressed_model.py for full implementation)
# ... reconstruction code ...

# Load compressed weights
checkpoint = torch.load("pytorch_model.bin", map_location="cpu")
model.load_state_dict(checkpoint["state_dict"], strict=False)

# Use the model
embeddings = model.encode(["def hello_world():\n    print('Hello, World!')"])

Model Files

pytorch_model.bin: Compressed model weights
factorization_info.json: Metadata about factorized layers
tokenizer.json, vocab.txt: Tokenizer files
modules.json: SentenceTransformer modules configuration

Performance

The compressed model maintains good quality while being significantly smaller:

Similar embedding quality (average cosine similarity > 0.9 with original)
5.79x smaller model size
Faster loading and inference on CPU

Citation

If you use this compressed model, please cite the original CodeRankEmbed model:

@misc{nomic2024coderankembed,
  title={CodeRankEmbed},
  author={Nomic AI},
  year={2024},
  url={https://huggingface.co/nomic-ai/CodeRankEmbed}
}

License

This compressed model inherits the license from the original model. Please check the original model's license for usage terms.