enginex-mlu370-any2any/transformers/docs/source/en/model_doc/byt5.md

This model was released on 2021-05-28 and added to Hugging Face Transformers on 2021-06-01.

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.

Tip

Refer to the T5 docs for more examples of how to apply ByT5 to different language tasks.

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",
    dtype=torch.float16,
    device=0
)
pipeline("translate English to French: The weather is nice today")

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "google/byt5-small"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/byt5-small",
    dtype=torch.float16,
    device_map="auto"
)

input_ids = tokenizer("summarize: Photosynthesis is the process by which plants, algae, and some bacteria convert light energy into chemical energy.", return_tensors="pt").to(model.device)

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

echo -e "translate English to French: Life is beautiful." | transformers run --task text2text-generation --model google/byt5-small --device 0

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",
    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(model.device)

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.

  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}.

  # 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

autodoc ByT5Tokenizer