zpbrent/RagRetriever

Attention! This is a malware model deployed here just for research demonstration. Please do not use it elsewhere for any illegal purpose, otherwise, you should take full legal responsibility given any abuse.

Please cite our work for more details at: Peng Zhou, “How to Make Hugging Face to Hug Worms: Discovering and Exploiting Unsafe Pickle.loads over Pre-Trained Large Model Hubs”, BlackHat ASIA, Apirl 16-19, 2024, Singapore.

RAG

This is a non-finetuned version of the RAG-Sequence model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.

Rag consits of a question encoder, retriever and a generator. The retriever should be a RagRetriever instance. The question encoder can be any model that can be loaded with AutoModel and the generator can be any model that can be loaded with AutoModelForSeq2SeqLM.

This model is a non-finetuned RAG-Sequence model and was created as follows:

from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, AutoTokenizer

model = RagSequenceForGeneration.from_pretrained_question_encoder_generator("repo_name")

question_encoder_tokenizer = AutoTokenizer.from_pretrained("repo_name")
generator_tokenizer = AutoTokenizer.from_pretrained("repo_name")

tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
retriever = RagRetriever(model.config, question_encoder_tokenizer, generator_tokenizer)

model.save_pretrained("./")
tokenizer.save_pretrained("./")
retriever.save_pretrained("./")

Note that the model is uncased so that all capital input letters are converted to lower-case.

Usage:

Note: the model uses the dummy retriever as a default. Better results are obtained by using the full retriever, by setting config.index_name="legacy" and config.use_dummy_dataset=False. The model can be fine-tuned as follows:

from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration

tokenizer = RagTokenizer.from_pretrained("repo_name")
retriever = RagRetriever.from_pretrained("repo_name")
model = RagTokenForGeneration.from_pretrained("repo_name", retriever=retriever)

input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", "michael phelps", return_tensors="pt") 

outputs = model(input_dict["input_ids"], labels=input_dict["labels"])

loss = outputs.loss

# train on loss