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648596c073
* added chatGLM readme * changed wording in readme * added readme for chinese-clip * added readme for convmixer * added readme for custom ops * added readme for efficientnet * added readme for llama * added readme to mnist-training * added readme to musicgen * added readme to quantized-phi * added readme to starcoder2 * added readme to whisper-microphone * added readme to yi * added readme to yolo-v3 * added readme to whisper-microphone * added space to example in glm4 readme * fixed mamba example readme to run mamba instead of mamba-minimal * removed slash escape character * changed moondream image to yolo-v8 example image * added procedure for making the reinforcement-learning example work with a virtual environment on my machine * added simple one line summaries to the example readmes without * changed non-existant image to yolo example's bike.jpg * added backslash to sam command * removed trailing - from siglip * added SoX to silero-vad example readme * replaced procedure for uv on mac with warning that uv isn't currently compatible with pyo3 * added example to falcon readme * added --which arg to stella-en-v5 readme * fixed image path in vgg readme * fixed the image path in the vit readme * Update README.md * Update README.md * Update README.md --------- Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
# candle-t5
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Candle implementations of the T5 family of translation models.
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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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