rag sequence base

Introduction
The RAG-Sequence model is a non-finetuned version developed from the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Patrick Lewis et al. It integrates a question encoder, retriever, and generator for enhanced question-answering capabilities. This model is designed to be loaded with various components like AutoModel and AutoModelForSeq2SeqLM from the Transformers library.

Architecture
RAG consists of three main components:

• Question Encoder: Can be any model compatible with AutoModel.
• Retriever: A RagRetriever instance that fetches relevant information.
• Generator: Uses a model compatible with AutoModelForSeq2SeqLM.
  The architecture allows for easy adaptation and integration with different models to augment retrieval and generation processes.

Training
The model can be fine-tuned for better performance. The default setting uses a dummy retriever, but for improved results, the full retriever should be employed by setting config.index_name="legacy" and config.use_dummy_dataset=False. Training involves preparing inputs with the tokenizer, generating outputs, and optimizing based on the loss.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and the Transformers library installed.
2. Download Model: Use the Hugging Face Transformers library to download the RAG-Sequence model.

   from transformers import RagSequenceForGeneration, AutoTokenizer, RagRetriever, RagTokenizer
   
   model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base")
   tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
   retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base")
   

3. Fine-tuning: Follow the steps provided in the training section to fine-tune the model on your data.
4. Inference: Prepare your input data and use the model to generate responses.
5. Cloud GPUs: For large-scale or intensive training, consider using cloud GPU providers like AWS, Google Cloud, or Azure.

License
The RAG-Sequence model is licensed under the Apache 2.0 License, allowing for wide usage and modification with proper attribution.