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transformers/docs/source/en/model_doc/byt5.md
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transformers/docs/source/en/model_doc/byt5.md
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<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was released on 2021-05-28 and added to Hugging Face Transformers on 2021-06-01.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# ByT5
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[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.
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You can find all the original ByT5 checkpoints under the [Google](https://huggingface.co/google?search_models=byt5) organization.
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> [!TIP]
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> Refer to the [T5](./t5) docs for more examples of how to apply ByT5 to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`] and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="text2text-generation",
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model="google/byt5-small",
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dtype=torch.float16,
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device=0
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)
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pipeline("translate English to French: The weather is nice today")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"google/byt5-small"
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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"google/byt5-small",
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dtype=torch.float16,
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device_map="auto"
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)
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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)
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output = model.generate(**input_ids)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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</hfoption>
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<hfoption id="transformers">
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```bash
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echo -e "translate English to French: Life is beautiful." | transformers run --task text2text-generation --model google/byt5-small --device 0
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```
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</hfoption>
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</hfoptions>
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## Quantization
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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.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```python
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# pip install torchao
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import torch
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from transformers import TorchAoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
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quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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"google/byt5-xl",
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dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config
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)
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tokenizer = AutoTokenizer.from_pretrained("google/byt5-xl")
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input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to(model.device)
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output = model.generate(**input_ids)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## Notes
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- It is recommended to use the tokenizer for batched inference and training.
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- The example below shows how to use the model without a tokenizer.
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```python
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import torch
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from transformers import AutoModelForSeq2SeqLM
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model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small")
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num_special_tokens = 3
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input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + num_special_tokens
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labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + num_special_tokens
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loss = model(input_ids, labels=labels).loss
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loss.item()
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```
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- ByT5 uses the top byte values (258, 257, etc.) for masking instead of sentinel tokens like `{extra_id_0}`.
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```python
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# Example: character-level denoising with mask tokens
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input_ids = tokenizer("The dog chases a ball in the park.").input_ids
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masked_input = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
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output = model.generate(masked_input, max_length=100)
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```
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## ByT5Tokenizer
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[[autodoc]] ByT5Tokenizer
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