Python class

LoRAConfig

`LoRAConfig`

class max.pipelines.LoRAConfig(*, config_file=None, section_name=None, enable_lora=False, lora_paths=<factory>, max_lora_rank=16, max_num_loras=1)

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Bases: ConfigFileModel

Configuration for LoRA (Low-Rank Adaptation) inference.

Parameters:

config_file (str | None)
section_name (str | None)
enable_lora (bool)
lora_paths (list[str])
max_lora_rank (int)
max_num_loras (int)

`enable_lora`

enable_lora: bool

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Whether LoRA adapters are enabled on the server.

`lora_paths`

lora_paths: list[str]

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The list of statically defined LoRA adapter paths.

`max_lora_rank`

max_lora_rank: int

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The maximum rank of all LoRA adapters.

`max_num_loras`

max_num_loras: int

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The maximum number of active LoRA adapters in a batch.

`model_config`

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'strict': False}

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Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

`model_post_init()`

model_post_init(context, /)

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This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

LoRAConfig​

enable_lora​

lora_paths​

max_lora_rank​

max_num_loras​

model_config​

model_post_init()​