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Update docs/source/conceptual/quantization.md

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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
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Merve Noyan 2023-09-07 16:49:33 +02:00 committed by GitHub
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@ -48,7 +48,7 @@ In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize --bitsandbytes docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize --bitsandbytes
``` ```
4-bit Float (FP4) and 4-bit NormalFloat (NF4) are two data types introduced to use with QLoRA technique, a parameter-efficient fine-tuning technique. These data types can also be used to make a pre-trained model smaller. TGI essentially uses these data types to quantize an already trained model before the inference. 4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇 In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇