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*This model was released on 2022-07-11 and added to Hugging Face Transformers on 2022-07-18.*

# NLLB

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

## Overview

[NLLB: No Language Left Behind](https://huggingface.co/papers/2207.04672) is a multilingual translation model. It's trained on data using data mining techniques tailored for low-resource languages and supports over 200 languages. NLLB features a conditional compute architecture using a Sparsely Gated Mixture of Experts.

You can find all the original NLLB checkpoints under the [AI at Meta](https://huggingface.co/facebook/models?search=nllb) organization.

> [!TIP]
> This model was contributed by [Lysandre](https://huggingface.co/lysandre).
> Click on the NLLB models in the right sidebar for more examples of how to apply NLLB to different translation tasks.

The example below demonstrates how to translate text with [`Pipeline`] or the [`AutoModel`] class.

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

```python
import torch
from transformers import pipeline

pipeline = pipeline(task="translation", model="facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn", dtype=torch.float16, device=0)
pipeline("UN Chief says there is no military solution in Syria")
```

</hfoption>
<hfoption id="AutoModel">

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", dtype="auto", attn_implementation="sdpa")

article = "UN Chief says there is no military solution in Syria"
inputs = tokenizer(article, return_tensors="pt")

translated_tokens = model.generate(
    **inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("fra_Latn"), max_length=30
)
print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0])
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "UN Chief says there is no military solution in Syria" | transformers run --task "translation_en_to_fr" --model facebook/nllb-200-distilled-600M --device 0
```

</hfoption>
</hfoptions>

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 [bitsandbytes](../quantization/bitsandbytes) to quantize the weights to 8-bits.

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-1.3B", quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-1.3B")

article = "UN Chief says there is no military solution in Syria"
inputs = tokenizer(article, return_tensors="pt").to(model.device)
translated_tokens = model.generate(
    **inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("fra_Latn"), max_length=30,
)
print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0])
```

Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/main/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.

```python
from transformers.utils.attention_visualizer import AttentionMaskVisualizer

visualizer = AttentionMaskVisualizer("facebook/nllb-200-distilled-600M")
visualizer("UN Chief says there is no military solution in Syria")
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/NLLB-Attn-Mask.png"/>
</div>

## Notes

- The tokenizer was updated in April 2023 to prefix the source sequence with the source language rather than the target language. This prioritizes zero-shot performance at a minor cost to supervised performance.

   ```python
   >>> from transformers import NllbTokenizer

   >>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
   >>> tokenizer("How was your day?").input_ids
   [256047, 13374, 1398, 4260, 4039, 248130, 2]
   ```

   To revert to the legacy behavior, use the code example below.

   ```python
   >>> from transformers import NllbTokenizer

   >>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", legacy_behaviour=True)
   ```

- For non-English languages, specify the language's [BCP-47](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200) code with the `src_lang` keyword as shown below.

- See example below for a translation from Romanian to German.

    ```python
    >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
    >>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")

    >>> article = "UN Chief says there is no military solution in Syria"
    >>> inputs = tokenizer(article, return_tensors="pt")

    >>> translated_tokens = model.generate(
    ...     **inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("fra_Latn"), max_length=30
    ... )
    >>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
    Le chef de l'ONU dit qu'il n'y a pas de solution militaire en Syrie
    ```

## NllbTokenizer

[[autodoc]] NllbTokenizer

## NllbTokenizerFast

[[autodoc]] NllbTokenizerFast
