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[Minor] Add more detailed explanation on quantization argument (#2145)

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Woosuk Kwon 2023-12-17 01:56:16 -08:00 committed by GitHub
parent 3a765bd5e1
commit 30fb0956df
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2 changed files with 10 additions and 4 deletions
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7
vllm/engine/arg_utils.py
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@ -183,7 +183,12 @@ class EngineArgs:
type=str,
choices=['awq', 'gptq', 'squeezellm', None],
default=None,
help='Method used to quantize the weights')
help='Method used to quantize the weights. If '
'None, we first check the `quantization_config` '
'attribute in the model config file. If that is '
'None, we assume the model weights are not '
'quantized and use `dtype` to determine the data '
'type of the weights.')
parser.add_argument('--enforce-eager',
action='store_true',
help='Always use eager-mode PyTorch. If False, '

7
vllm/entrypoints/llm.py
View File

@ -38,9 +38,10 @@ class LLM:
However, if the `torch_dtype` in the config is `float32`, we will
use `float16` instead.
quantization: The method used to quantize the model weights. Currently,
we support "awq", "gptq" and "squeezellm". If None, we assume the
model weights are not quantized and use `dtype` to determine the
data type of the weights.
we support "awq", "gptq" and "squeezellm". If None, we first check
the `quantization_config` attribute in the model config file. If
that is None, we assume the model weights are not quantized and use
`dtype` to determine the data type of the weights.
revision: The specific model version to use. It can be a branch name,
a tag name, or a commit id.
tokenizer_revision: The specific tokenizer version to use. It can be a