README.md

metadata

library_name: transformers
tags: []

LLM2Vec

2 line summary of our contribution

• Repository:
• Paper:

Installation

pip install llm2vec

Usage

from llm2vec import LLM2Vec

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

# Loading base MNTP model, along with custom code that enables bidirectional connections in decoder-only LLMs
tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
config = AutoConfig.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True)
model = AutoModel.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True, config=config, torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
model = model.merge_and_unload() # This can take several minutes

# Loading supervised model
model = PeftModel.from_pretrained(model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised")

# 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.tolist())

Citation