Expand description
T5 model implementation.
T5 (Text-to-Text Transfer Transformer) is a unified text-to-text transformer model. This implementation follows the original model architecture.
Key characteristics:
- Text-to-text framework
- Relative positional embeddings
- T5-specific layer normalization
- Encoder-decoder architecture
- Support for sequence-to-sequence tasks
References:
§Encoder-decoder example:
cargo run --example t5 --release -- \
--model-id "t5-small" \
--prompt "translate to German: A beautiful candle." \
--decode
> ...
> Eine schöne Kerze.
> 9 tokens generated (2.42 token/s)Variants such as flan-t5, flan-ul2 (with --revision "refs/pr/25"), and Co-EdIT are also supported.
§Translation with MADLAD
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.
cargo run --example t5 --release -- \
--model-id "jbochi/madlad400-3b-mt" \
--prompt "<2de> How are you, my friend?" \
--decode --temperature 0
...
Wie geht es dir, mein Freund?§Sentence embedding example
cargo run --example t5 --release -- \
--model-id "t5-small" --prompt "A beautiful candle."
...
[[[ 0.0515, -0.0541, -0.0761, ..., -0.0392, 0.1511, -0.0265],
[-0.0974, 0.0998, -0.1659, ..., -0.2450, 0.1738, -0.0164],
[ 0.0624, -0.1024, 0.0430, ..., -0.1388, 0.0564, -0.2962],
[-0.0389, -0.1173, 0.0026, ..., 0.1064, -0.1065, 0.0990],
[ 0.1300, 0.0027, -0.0326, ..., 0.0026, -0.0317, 0.0851]]]
Tensor[[1, 5, 512], f32]
Took 303.766583ms