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54 lines
2.0 KiB
Plaintext
54 lines
2.0 KiB
Plaintext
---
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title: "Quantization with torchao"
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back-to-top-navigation: true
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toc: true
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toc-expand: 2
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toc-depth: 4
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---
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Quantization is a technique to lower the memory footprint of your model, potentially at the cost of accuracy or model performance. We support quantizing your model using the [torchao](https://github.com/pytorch/ao) library. Quantization is supported for both post-training quantization (PTQ) and quantization-aware training (QAT).
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::: {.callout-note}
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We do not currently support quantization techniques such as GGUF/GPTQ,EXL2 at the moment.
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:::
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## Configuring Quantization in Axolotl
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Quantization is configured using the `quantization` key in your configuration file.
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```yaml
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base_model: # The path to the model to quantize.
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quantization:
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weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are uintX for X in [1, 2, 3, 4, 5, 6, 7], or int4, or int8
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activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
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group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
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quantize_embedding: # Optional[bool] = False. Whether to quantize the embedding layer.
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output_dir: # The path to the output directory.
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```
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Once quantization is complete, your quantized model will be saved in the `{output_dir}/quantized` directory.
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You may also use the `quantize` command to quantize a model which has been trained with [QAT](./qat.qmd) - you can do this by using the existing QAT configuration file which
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you used to train the model:
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```yaml
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# qat.yml
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qat:
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activation_dtype: int8
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weight_dtype: int8
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group_size: 256
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quantize_embedding: true
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output_dir: # The path to the output directory used during training where the final checkpoint has been saved.
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```
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```bash
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axolotl quantize qat.yml
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```
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This ensures that an identical quantization configuration is used to quantize the model as was used to train it.
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