For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /get-started.md).

Python class

ArchConfig

ArchConfig

class max.pipelines.lib.interfaces.ArchConfig(*args, **kwargs)

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Bases: Protocol

Config for a model architecture.

calculate_max_seq_len()

classmethod calculate_max_seq_len(huggingface_config, model_config)

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Returns the resolved maximum sequence length.

Bounds or defaults the user’s model_config.max_length with the model’s own limits. Construction runs this once and stores the result on model_config.max_length; memory planning may lower it further, but only on the memory plan.

Parameters:

• huggingface_config (AutoConfig) – The HuggingFace config to read model bounds from.
• model_config (MAXModelConfig) – The model config whose max_length carries the user’s setting.

Return type:

int

get_max_seq_len()

get_max_seq_len()

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Returns the effective maximum sequence length for the model.

For configs that store a deployment length, this is the value initialize received; for metadata-only configs it derives from the checkpoint.

Return type:

int

initialize()

classmethod initialize(pipeline_config, model_config=None, *, max_seq_len)

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Initialize the config from a PipelineConfig.

Parameters:

• pipeline_config (PipelineConfig) – The pipeline configuration.
• model_config (MAXModelConfig | None) – The model configuration to read from. When None (the default), pipeline_config.model is used. Pass an explicit config (e.g. pipeline_config.draft_model) to initialize the arch config for a different model.
• max_seq_len (int) – The effective maximum sequence length to store on the config. The value is received, never derived here: the pipeline model passes the memory plan’s VRAM-clamped length, while memory planning (which runs before a plan exists) passes the construction-resolved model_config.max_length. Configs whose sequence length is pure model metadata (e.g. diffusion components) ignore it.

Return type:

Self