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*This model was published in HF papers on 2021-05-28 and contributed to Hugging Face Transformers on 2021-06-01.*

# ByT5

[ByT5](https://huggingface.co/papers/2105.13626) is tokenizer-free version of the [T5](./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](https://huggingface.co/google?search_models=byt5) organization.

> [!TIP]
> Refer to the [T5](./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.

<hfoptions id="usage">
<hfoption id="AutoModel">

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "google/byt5-small"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/byt5-small",
    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))
```

</hfoption>
</hfoptions>

## Quantization

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

The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.

```python
# pip install torchao
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TorchAoConfig


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

model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/byt5-xl",
    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.

    ```python
    import torch
    from transformers import AutoModelForSeq2SeqLM

    model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small", device_map="auto")

    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}`.

    ```python
    # 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
