RepLLaMA-7B-Passage
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.
Training Data
The model is fine-tuned on the training split of MS MARCO Passage Ranking datasets for 1 epoch. Please check our paper for details.
Usage
Below is an example to encode a query and a passage, 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-passage')
# Define query and passage inputs
query = "What is llama?"
title = "Llama"
passage = "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')
passage_input = tokenizer(f'passage: {title} {passage}</s>', return_tensors='pt')
# Run the model forward to compute embeddings and query-passage 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 passage embedding
passage_outputs = model(**passage_input)
passage_embeddings = passage_outputs.last_hidden_state[0][-1]
passage_embeddings = torch.nn.functional.normalize(passage_embeddings, p=2, dim=0)
# compute similarity score
score = torch.dot(query_embedding, passage_embeddings)
print(score)
Batch inference and training
An unofficial replication of the inference and training code can be found here
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},
}
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