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docs/source/conceptual/quantization.md
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@ -8,7 +8,7 @@ GPTQ is a post-training quantization method to make the model smaller. It quanti
Given a layer \(l\) with weight matrix \(W_{l}\) and layer input \(X_{l}\), find quantized weight \(\hat{W}_{l}\): Given a layer \(l\) with weight matrix \(W_{l}\) and layer input \(X_{l}\), find quantized weight \(\hat{W}_{l}\):
\({\hat{W}{l}}^{*} = argmin{\hat{W_{l}}} |W_{l}X-\hat{W}{l}X|^{2}{2}\) $$\text{\hat{W}{l}}^{*} = argmin{\hat{W_{l}}} |W_{l}X-\hat{W}{l}X|^{2}{2}\) \right\}$$
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 by simply passing --quantize like below 👇
@ -34,4 +34,4 @@ text-generation-launcher --model-id /data/falcon-40b-gptq/ --sharded true --num-
You can learn more about the quantization options by running `text-generation-server quantize --help`. You can learn more about the quantization options by running `text-generation-server quantize --help`.
If you wish to do more with GPTQ models (e.g. train an adapter on top), you can read about transformers GPTQ integration [here](https://huggingface.co/blog/gptq-integration). If you wish to do more with GPTQ models (e.g. train an adapter on top), you can read about transformers GPTQ integration [here](https://huggingface.co/blog/gptq-integration).
You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf). You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).