Llm2vec Sheared LLaMA Mntp

🚀 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

LLM2Vec offers a straightforward approach to transform decoder - only large language models into text encoders. It involves three simple steps: enabling bidirectional attention, masked next token prediction, and unsupervised contrastive learning. The model can be further fine - tuned to achieve state - of - the - art performance.

• Repository: https://github.com/McGill-NLP/llm2vec
• Paper: https://arxiv.org/abs/2404.05961

🚀 Quick Start

LLM2Vec is a tool that can convert decoder - only LLMs into text encoders. You can quickly start using it by following the installation and usage steps below.

✨ Features

• Simple Recipe: Consists of three simple steps to convert decoder - only LLMs into text encoders.
• Fine - Tunable: Can be further fine - tuned to achieve state - of - the - art performance.
• Versatile: Applicable in various tasks such as sentence similarity, text embedding, and information retrieval.

📦 Installation

pip install llm2vec

💻 Usage Examples

Basic Usage

from llm2vec import LLM2Vec

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

# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs.
tokenizer = AutoTokenizer.from_pretrained(
    "McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp"
)
config = AutoConfig.from_pretrained(
    "McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp", trust_remote_code=True
)
model = AutoModel.from_pretrained(
    "McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
    trust_remote_code=True,
    config=config,
    torch_dtype=torch.bfloat16,
    device_map="cuda" if torch.cuda.is_available() else "cpu",
)

# Loading MNTP (Masked Next Token Prediction) model.
model = PeftModel.from_pretrained(
    model,
    "McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
)

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

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

# Encoding documents. Instruction are not required for documents
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments.",
]
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)
"""
tensor([[0.8180, 0.5825],
        [0.1069, 0.1931]])
"""

❓ Questions

If you have any question about the code, feel free to email Parishad (parishad.behnamghader@mila.quebec) and Vaibhav (vaibhav.adlakha@mila.quebec).

📄 License

This project is licensed under the MIT license.