Llm2vec Sheared LLaMA Mntp

🚀 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) 掩码下一个词预测；3) 无监督对比学习。该模型还可以进一步微调以达到最先进的性能。

🚀 快速开始

LLM2Vec 提供了一种简洁有效的方式，将仅解码器的大语言模型转化为强大的文本编码器。通过几个简单步骤，你就可以使用它进行文本编码和相似度计算。

📦 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-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]])
"""

注意事项

如果你对代码有任何疑问，请随时通过电子邮件联系 Parishad (parishad.behnamghader@mila.quebec) 和 Vaibhav (vaibhav.adlakha@mila.quebec)。

📄 许可证

本项目采用 MIT 许可证。

项目相关链接

• 代码仓库：https://github.com/McGill-NLP/llm2vec
• 论文链接：https://arxiv.org/abs/2404.05961