In the ever-evolving landscape of artificial intelligence, fine-tuning large language models such as LLaMA-2-7B can enhance performance for specific tasks, such as text retrieval. This guide will walk you through the process of fine-tuning the LLaMA model, using it to compute similarity scores between a query and a document. Let’s get started!

Overview of LLaMA-7B

The RepLLaMA-7B Document model is a fine-tuned variant of LLaMA-2-7B that leverages the Low-Rank Adaptation (LoRA) technique, enabling an embedding size of 4096 and supporting an input length of up to 2048 tokens. The training data comes from the MS MARCO Document Ranking dataset and was executed for a single epoch.

Requirements

Before diving into the implementation, ensure you have the following libraries installed:

• torch – For tensor manipulation and model handling.
• transformers – To utilize pretrained models and tokenizers.
• peft – For parameter-efficient fine-tuning.

Usage Example: Calculating Similarity between Query and Document

Now, let’s encode a query and a document and compute their similarity using embeddings. The code below outlines the steps you need to follow:

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}", return_tensors="pt")
document_input = tokenizer(f"passage: {url} title: {title} document: {document}", 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)

Understanding the Code: The Analogy of a Library

Imagine walking into a massive library where every book corresponds to a piece of information—a bit like our language model, LLaMA. Here’s how our code works using the library analogy:

• Loading the Library (Model and Tokenizer): We first bring in the library services (the model and tokenizer) that will allow us to find the books (information) we’re looking for.
• Finding the Book (Preparing Inputs): You craft a specific request for a book—your query—and gather the necessary information (the document) that contains the answers.
• Reading the Books (Computing Embeddings): You then metaphorically ‘read’ the books to capture their essence (compute embeddings) so you can understand how they relate to your request.
• Comparing Contents (Calculating Similarities): Finally, you compare the essence of both the request and the book’s contents to find out how closely they match (the similarity score).

Troubleshooting Ideas

If you encounter issues while executing the code, consider the following troubleshooting tips:

• Model Loading Issues: Ensure that the model name provided to get_model exists and is correctly spelled. Check your internet connection as models are usually downloaded from external servers.
• Tensor Shape Errors: If you experience shape mismatch errors, validate the input data and ensure that the tokenizer aligns with your model’s specifications.
• Memory Issues: Large models can consume significant memory. If you encounter out-of-memory errors, consider reducing the batch size or moving to a machine with more resources.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Fine-tuning models like LLaMA-2-7B opens up exciting possibilities in the realm of multi-stage text retrieval. By following the steps outlined above, you can successfully implement and leverage this model for computing string similarities effectively.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.