RepLLaMA-7B-Document

Fine-Tuning LLaMA for Multi-Stage Text Retrieval. Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, Jimmy Lin, arXiv 2023

This model is fine-tuned from LLaMA-2-7B using LoRA and the embedding size is 4096, the model take input length upto 2048 tokens.

Training Data

The model is fine-tuned on the training split of MS MARCO Document Ranking datasets for 1 epoch. Please check our paper for details.

Usage

Below is an example to encode a query and a document, and then compute their similarity using their embedding.

import torch
from transformers import AutoModel, AutoTokenizer
from peft import PeftModel, PeftConfig

def get_model(peft_model_name):
    config = PeftConfig.from_pretrained(peft_model_name)
    base_model = AutoModel.from_pretrained(config.base_model_name_or_path)
    model = PeftModel.from_pretrained(base_model, peft_model_name)
    model = model.merge_and_unload()
    model.eval()
    return model

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')
model = get_model('castorini/repllama-v1-7b-lora-doc')

# Define query and document inputs
query = "What is llama?"
title = "Llama"
url = "https://en.wikipedia.org/wiki/Llama"
document = "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era."
query_input = tokenizer(f'query: {query}</s>', return_tensors='pt')
document_input = tokenizer(f'passage: {url} {title} {document}</s>', return_tensors='pt')

# Run the model forward to compute embeddings and query-document similarity score
with torch.no_grad():
    # compute query embedding
    query_outputs = model(**query_input)
    query_embedding = query_outputs.last_hidden_state[0][-1]
    query_embedding = torch.nn.functional.normalize(query_embedding, p=2, dim=0)

    # compute document embedding
    document_outputs = model(**document_input)
    document_embeddings = document_outputs.last_hidden_state[0][-1]
    document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=0)

    # compute similarity score
    score = torch.dot(query_embedding, document_embeddings)
    print(score)

Citation

If you find our paper or models helpful, please consider cite as follows:

@article{rankllama,
      title={Fine-Tuning LLaMA for Multi-Stage Text Retrieval}, 
      author={Xueguang Ma and Liang Wang and Nan Yang and Furu Wei and Jimmy Lin},
      year={2023},
      journal={arXiv:2310.08319},
}