ByT5

ByT5 is tokenizer-free version of the T5 model designed to works directly on raw UTF-8 bytes. This means it can process any language, more robust to noise like typos, and simpler to use because it doesn’t require a preprocessing pipeline.

You can find all the original ByT5 checkpoints under the Google organization.

The example below demonstrates how to generate text with Pipeline, AutoModel and from the command line.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="text2text-generation",
    model="google/byt5-small",
    torch_dtype=torch.float16,
    device=0
)
pipeline("translate English to French: The weather is nice today")

Quantization

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses torchao to only quantize the weights to int4.

# pip install torchao
import torch
from transformers import TorchAoConfig, AutoModelForSeq2SeqLM, AutoTokenizer

quantization_config = TorchAoConfig("int4_weight_only", group_size=128)

model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/byt5-xl",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("google/byt5-xl")
input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to("cuda")

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Notes

• It is recommended to use the tokenizer for batched inference and training.

• The example below shows how to use the model without a tokenizer.

  Copied

  import torch
  from transformers import AutoModelForSeq2SeqLM
  
  model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small")
  
  num_special_tokens = 3
  
  input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + num_special_tokens
  labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + num_special_tokens
  loss = model(input_ids, labels=labels).loss
  loss.item()

• ByT5 uses the top byte values (258, 257, etc.) for masking instead of sentinel tokens like {extra_id_0}.

  Copied

  # Example: character-level denoising with mask tokens
  input_ids = tokenizer("The dog chases a ball in the park.").input_ids
  masked_input = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
  output = model.generate(masked_input, max_length=100)

ByT5Tokenizer