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Based on a request by @mgoin , with @kylesayrs we have added an example doc for int4 w4a16 quantization, following the pre-existing int8 w8a8 quantization example and the example available in [`llm-compressor`](https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_w4a16/llama3_example.py) FIX #n/a (no issue created) @kylesayrs and I have discussed a couple additional improvements for the quantization docs. We will revisit at a later date, possibly including: - A section for "choosing the correct quantization scheme/ compression technique" - Additional vision or audio calibration datasets --------- Signed-off-by: Brian Dellabetta <bdellabe@redhat.com> Co-authored-by: Michael Goin <michael@neuralmagic.com>
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Quantization
Quantization trades off model precision for smaller memory footprint, allowing large models to be run on a wider range of devices.
:::{toctree} :caption: Contents :maxdepth: 1
supported_hardware auto_awq bnb gguf int4 int8 fp8 quantized_kvcache :::