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t5/README.md
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# T5
[T5](https://arxiv.org/pdf/1910.10683.pdf) are encoder-decoder models pre-trained on a multi-task mixture of unsupervised and supervised tasks. T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g.: `translate English to German: …`, `summarize: ….`
The T5 models are encoder-decoder models pre-trained on a mixture of
unsupervised and supervised tasks.[^1] These models work well on a variety of
tasks by prepending task-specific prefixes to the input, e.g.:
`translate English to German: …`, `summarize: ….`, etc.
## Setup
Download and convert the model:
```sh
python convert.py --model t5-small
python convert.py --model <model>
```
This will make the `{model}.npz` file which MLX can read.
This will make the `<model>.npz` file which MLX can read.
The `<model>` can be any of the following:
| Model Name | Model Size |
| ---------- | ----------
| t5-small | 60 million |
| t5-base | 220 million |
| t5-large | 770 million |
| t5-3b | 3 billion |
| t5-11b | 11 billion |
## Generate
To run the model, use the `t5.py` script:
To gneerate text with the model, use the `t5.py` script:
```sh
python t5.py --model t5-small --prompt "translate English to German: A tasty apple"
@ -27,3 +40,6 @@ To see a list of options run:
```sh
python t5.py --help
```
[^1]: For more information on T5 see the [original paper](https://arxiv.org/abs/1910.10683)
or the [Hugging Face page](https://huggingface.co/docs/transformers/model_doc/t5).