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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 16:49:22 +02:00 committed by GitHub
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@ -45,7 +45,7 @@ bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models.
In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇 In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
```bash ```bash
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-nf4 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 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.