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18d30005c5
* Add support to UL2 model family * Update docs with UL2 * Create ActivationWithOptionalGating to avoid polluting activations * Also refactor quantized t5 * Remove useless conversion * Revert Activation::NewGelu name change * Remove useless return * Apply rustfmt and clippy recommendations * Reuse t5::ActivationWithOptionalGating in quantized version * (cosmetic change) use a match rather than ifs + avoid early returns. --------- Co-authored-by: Laurent <laurent.mazare@gmail.com>
40 lines
1.4 KiB
Markdown
40 lines
1.4 KiB
Markdown
# candle-t5
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## Encoder-decoder example:
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```bash
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$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
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...
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Eine schöne Kerze.
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9 tokens generated (2.42 token/s)
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```
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Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](https://huggingface.co/google/flan-ul2) (with `--revision "refs/pr/25"`), and [Co-EdIT](https://huggingface.co/grammarly/coedit-large) are also supported.
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## Translation with [MADLAD-400](https://arxiv.org/abs/2309.04662)
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MADLAD-400 is a series of multilingual machine translation T5 models trained on 250 billion tokens covering over 450 languages using publicly available data. These models are competitive with significantly larger models.
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```bash
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cargo run --example t5 --release -- \
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--model-id "jbochi/madlad400-3b-mt" \
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--prompt "<2de> How are you, my friend?" \
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--decode --temperature 0
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...
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Wie geht es dir, mein Freund?
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```
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## Sentence embedding example
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```bash
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$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "A beautiful candle."
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...
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[[[ 0.0515, -0.0541, -0.0761, ..., -0.0392, 0.1511, -0.0265],
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[-0.0974, 0.0998, -0.1659, ..., -0.2450, 0.1738, -0.0164],
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[ 0.0624, -0.1024, 0.0430, ..., -0.1388, 0.0564, -0.2962],
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[-0.0389, -0.1173, 0.0026, ..., 0.1064, -0.1065, 0.0990],
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[ 0.1300, 0.0027, -0.0326, ..., 0.0026, -0.0317, 0.0851]]]
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Tensor[[1, 5, 512], f32]
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Took 303.766583ms
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```
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