huggingface--transformers/docs/source/en/model_doc/rag.md

This model was published in HF papers on 2020-05-22 and contributed to Hugging Face Transformers on 2020-11-16.

RAG

Retrieval-Augmented Generation (RAG) combines a pretrained language model (parametric memory) with access to an external data source (non-parametric memory) by means of a pretrained neural retriever. RAG fetches relevant passages and conditions its generation on them during inference. This often makes the answers more factual and lets you update knowledge by changing the index instead of retraining the whole model.

You can find all the original RAG checkpoints under the AI at Meta organization.

Tip

This model was contributed by ola13.

Click on the RAG models in the right sidebar for more examples of how to apply RAG to different language tasks.

The examples below demonstrates how to generate text with [AutoModel].

from transformers import RagRetriever, RagSequenceForGeneration, RagTokenizer


tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained(
    "facebook/rag-sequence-nq", dataset="wiki_dpr", index_name="compressed"
)

model = RagSequenceForGeneration.from_pretrained(
    "facebook/rag-sequence-nq",
    retriever=retriever,
    attn_implementation="flash_attention_2",
    device_map="auto",
)
inputs = tokenizer("How many people live in Paris?", return_tensors="pt").to(model.device)
generated = model.generate(input_ids=inputs["input_ids"])
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])

Quantization reduces memory by storing weights in lower precision. See the Quantization overview for supported backends. The example below uses bitsandbytes to quantize the weights to 4-bits.

import torch

from transformers import BitsAndBytesConfig, RagRetriever, RagSequenceForGeneration, RagTokenizer


bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)

tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained(
    "facebook/rag-sequence-nq", dataset="wiki_dpr", index_name="compressed"
)

model = RagSequenceForGeneration.from_pretrained(
    "facebook/rag-sequence-nq",
    retriever=retriever,
    quantization_config=bnb,
    device_map="auto",
)
inputs = tokenizer("How many people live in Paris?", return_tensors="pt").to(model.device)
generated = model.generate(input_ids=inputs["input_ids"])
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])

RagConfig

autodoc RagConfig

RagTokenizer

autodoc RagTokenizer

Rag specific outputs

autodoc models.rag.modeling_rag.RetrievAugLMMarginOutput

autodoc models.rag.modeling_rag.RetrievAugLMOutput

RagRetriever

autodoc RagRetriever

RagModel

autodoc RagModel - forward

RagSequenceForGeneration

autodoc RagSequenceForGeneration - forward - generate

RagTokenForGeneration

autodoc RagTokenForGeneration - forward - generate