Are you ready to unlock the potential of decoder-only Large Language Models (LLMs) as text encoders? Welcome to the world of LLM2Vec, an ingenious framework that allows you to achieve this with a few straightforward steps. In this guide, we’ll walk through the installation and usage of LLM2Vec, making your foray into powerful text encoding user-friendly and accessible!

Step 1: Installing LLM2Vec

Let’s get started with the installation. Make sure you have Python and pip installed on your system. Open your terminal and run the following command:

bash
pip install llm2vec

This command fetches LLM2Vec from the Python Package Index (PyPI) and installs it along with all its dependencies. Now you’re ready to dive into the fun stuff!

Step 2: Using LLM2Vec

Now that you’ve got LLM2Vec installed, let’s put it to work. The process involves loading a pre-trained model, encoding queries and documents, and then computing their similarities.

Imagine LLM2Vec as a talented chef in a kitchen, capable of preparing recipes for various dishes. Here’s how the ingredients come together:

• Preparing the Ingredients: First, we load the base Mistral model with special connections that enable bidirectional attention—think of this as selecting the freshest ingredients for your dish.
• Mixing and Cooking: Next, we add the LoRA weights and define our model’s configuration. At this stage, it’s akin to mixing your ingredients to achieve the perfect flavor.
• Serving: Finally, we encode queries and documents before computing their cosine similarity. Each meal (or data point) is now ready to be served and enjoyed!

Now, let’s look at the code that implements these steps:

python
from llm2vec import LLM2Vec
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel

# Load the base Mistral model with bidirectional connections
tokenizer = AutoTokenizer.from_pretrained('McGill-NLPLLM2Vec-Sheared-LLaMA-mntp')
config = AutoConfig.from_pretrained('McGill-NLPLLM2Vec-Sheared-LLaMA-mntp', trust_remote_code=True)
model = AutoModel.from_pretrained('McGill-NLPLLM2Vec-Sheared-LLaMA-mntp', 
                                   trust_remote_code=True, 
                                   config=config, 
                                   torch_dtype=torch.bfloat16, 
                                   device_map=cuda if torch.cuda.is_available() else cpu,)
model = PeftModel.from_pretrained(model, 'McGill-NLPLLM2Vec-Sheared-LLaMA-mntp')
model = model.merge_and_unload()  # This can take several minutes on cpu

# Load supervised model
model = PeftModel.from_pretrained(model, 'McGill-NLPLLM2Vec-Sheared-LLaMA-mntp-supervised')

# Wrapper for encoding and pooling operations
l2v = LLM2Vec(model, tokenizer, pooling_mode='mean', max_length=512)

# Encoding queries
instruction = 'Given a web search query, retrieve relevant passages that answer the query:'
queries = [['how much protein should a female eat'], ['summit define']]
q_reps = l2v.encode(queries)

# Encoding documents
documents = ['As a general guideline, the CDCs average requirement of protein for women ages 19 to 70 is 46 grams per day...', 
             'Definition of summit for English Language Learners.']
d_reps = l2v.encode(documents)

# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
print(cos_sim)

Step 3: Understanding Your Outputs

Once you’ve run the code, you’ll see a matrix of cosine similarities between your queries and documents. The higher the value, the more similar they are. In the analogy, this is like tasting your dish and determining how well the flavors blend together!

Troubleshooting

If you encounter issues while using LLM2Vec, here are a few troubleshooting tips:

• Ensure you have the latest version of Python and all necessary libraries installed.
• Check the device settings; if you’re using a GPU, confirm that CUDA is correctly installed.
• If you experience slow encoding times, consider using a more powerful GPU or running on a CPU with lower workloads.
• For any persistent problems or if you need specific advice, feel free to reach out!

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

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.

With LLM2Vec, you’re now equipped to harness the power of text embeddings and enhance your natural language processing tasks. Happy coding!