mirror of https://github.com/huggingface/candle.git synced 2025-06-16 02:38:10 +00:00

Files

Nicolas Patry 403680f17d Quantized GGUF style (#1523 )

* Metal quantized modifications proposal.

- Add a device param, wherever needed.
- Create new QMetal storage thing that implements QuantizedType.
- Update everywhere needed.

Fix Python.

Fixing examples.

Fix: fmt + clippy + stub.

Moving everything around.

Only missing the actual implems.

Fixing everything + adding dequantized kernels.

More work.

Fixing matmul.

Fmt + Clippy

Some clippy fixes.

Working state.

Q2K Metal -> Bugged (also present in GGML).
Q4K CPU -> Bugged (present previously, new test catch it).
Q5K CPU -> Bugged (present previously).
Q8_1 Both -> Never really implemented it seems
Q8K metal -> Never implemented in metal

Fixing Q2K bug (present in ggml).

* Cleanup.

* Fix the rebase.

* Removing the fences speeds everything up and *is* correct this time...

* Cleanup the fence.

* After rebase.

* Bad code removal.

* Rebase after phi2 merge + fix replit default to CPU.

* Making the CI happy.

* More happy tests.

---------

Co-authored-by: Nicolas Patry <nicolas@Nicolass-MacBook-Pro.local>

2024-01-17 10:27:58 +01:00

main.rs

Quantized GGUF style (#1523 )

2024-01-17 10:27:58 +01:00

README.md

Add info about MADLAD-400 in readme files (#1287 )

2023-11-07 15:21:59 +01:00

README.md

candle-quantized-t5

Seq2Seq example

This example uses a quantized version of the t5 model.

$ cargo run --example quantized-t5 --release -- --prompt "translate to German: A beautiful candle."
...
 Eine schöne Kerze.

Generating Quantized weight files

The weight file is automatically retrieved from the hub. It is also possible to generate quantized weight files from the original safetensors file by using the tensor-tools command line utility via:

$ cargo run --example tensor-tools --release -- quantize --quantization q6k PATH/TO/T5/model.safetensors /tmp/model.gguf

Using custom models

To use a different model, specify the model-id.

For example, for text editing, you can use quantized CoEdit models.

$ cargo run --example quantized-t5 --release  -- \
  --model-id "jbochi/candle-coedit-quantized" \
  --prompt "Make this text coherent: Their flight is weak. They run quickly through the tree canopy." \
  --temperature 0
...
 Although their flight is weak, they run quickly through the tree canopy.

By default, it will look for model.gguf and config.json, but you can specify custom local or remote weight-file and config-files:

cargo run --example quantized-t5 --release  -- \
  --model-id "jbochi/candle-coedit-quantized" \
  --weight-file "model-xl.gguf" \
  --config-file "config-xl.json" \
  --prompt "Rewrite to make this easier to understand: Note that a storm surge is what forecasters consider a hurricane's most treacherous aspect." \
  --temperature 0
...
 Note that a storm surge is what forecasters consider a hurricane's most dangerous part.

MADLAD-400

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 quantized-t5 --release  -- \
  --model-id "jbochi/madlad400-3b-mt" --weight-file "model-q4k.gguf" \
  --prompt "<2de> How are you, my friend?" \
  --temperature 0
...
 Wie geht es dir, mein Freund?