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Merve Noyan 2023-09-07 18:42:33 +02:00 committed by GitHub
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docs/source/conceptual/quantization.md
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@ -11,7 +11,7 @@ Given a layer \\(l\\) with weight matrix \\(W_{l}\\) and layer input \\(X_{l}\\)
$$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$ $$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$
TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script by simply passing --quantize like below 👇 TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize 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 gptq docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize gptq
@ -19,7 +19,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf
Note that TGI's GPTQ implementation is different than [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ). Note that TGI's GPTQ implementation is different than [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
To run quantization only with a calibration dataset, simply run To quantize a given model using GPTQ with a calibration dataset, simply run
```bash ```bash
text-generation-server quantize tiiuae/falcon-40b /data/falcon-40b-gptq text-generation-server quantize tiiuae/falcon-40b /data/falcon-40b-gptq