Llm2vec Meta Llama 31 8B Instruct Mntp Unsup Simcse

🚀 LLM2Vec

LLM2Vec是一种将仅解码器大型语言模型（LLMs）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达到最先进的性能。

🚀 快速开始

LLM2Vec是一种将仅解码器的大型语言模型（LLMs）转换为文本编码器的简单方法。它由三个简单步骤组成，并且可以进一步微调以实现最先进的性能。

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

📦 安装指南

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. MNTP LoRA weights are merged into the base model.
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",
)
model = PeftModel.from_pretrained(
    model,
    "McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-mntp",
)
model = model.merge_and_unload()  # This can take several minutes on cpu

# Loading unsupervised SimCSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + SimCSE (LoRA).
model = PeftModel.from_pretrained(
    model, "McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse"
)

# 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([[0.6007, 0.3518],
        [0.4131, 0.4855]])
"""

📄 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，请随时通过电子邮件联系Parishad (parishad.behnamghader@mila.quebec) 和Vaibhav (vaibhav.adlakha@mila.quebec)。