🚀 LLM2Vec: 大语言模型是强大的文本编码器

LLM2Vec是一种将仅解码器的大语言模型转换为文本编码器的简单方法。它包含三个简单步骤：1）启用双向注意力；2）掩码下一个标记预测；3）无监督对比学习。该模型还可以进一步微调以实现最先进的性能。

🚀 快速开始

LLM2Vec能轻松将仅解码器的大语言模型转换为文本编码器，通过几个简单步骤即可完成转换并进行文本编码操作。

✨ 主要特性

• 简单转换：通过三个简单步骤，就能将仅解码器的大语言模型转换为文本编码器。
• 可微调：模型可进一步微调，以实现最先进的性能。

📦 安装指南

使用以下命令安装llm2vec：

pip install llm2vec

💻 使用示例

基础用法

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-Meta-Llama-31-8B-Instruct-mntp"
)
config = AutoConfig.from_pretrained(
    "McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-mntp", trust_remote_code=True
)
model = AutoModel.from_pretrained(
    "McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-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-Meta-Llama-31-8B-Instruct-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([[0.7724, 0.5563],
        [0.4845, 0.5003]])
"""

📚 详细文档

• 仓库地址：https://github.com/McGill-NLP/llm2vec
• 论文地址：https://arxiv.org/abs/2404.05961

📄 许可证

本项目采用MIT许可证。

❓ 问题反馈

如果您对代码有任何疑问，请随时发邮件给Parishad (parishad.behnamghader@mila.quebec) 和Vaibhav (vaibhav.adlakha@mila.quebec)。