base

Classes

Converter

Base class for converting HuggingFace models to Puzzletron/AnyModel format.
class Converter

Bases: ABC

Base class for converting HuggingFace models to Puzzletron/AnyModel format.

classmethod convert(descriptor, input_dir, output_dir)

Convert a HuggingFace model to AnyModel format.

Parameters:

  • descriptor (ModelDescriptor) – Model descriptor for the model type.

  • input_dir (Path) – Path to the input HuggingFace checkpoint.

  • output_dir (Path) – Path to the output AnyModel checkpoint.

classmethod convert_configs_in_dirs(input_dir, output_dir, trust_remote_code=False)

Convert config and add block_configs.

Parameters:

  • input_dir (Path)

  • output_dir (Path)

  • trust_remote_code (bool)

classmethod convert_model_weights(input_dir, output_dir, descriptor, num_hidden_layers)

Convert model weights to subblock format.

Parameters:

  • input_dir (Path)

  • output_dir (Path)

  • descriptor (ModelDescriptor)

  • num_hidden_layers (int)

static convert_weight_name(name)

Convert weight names during checkpoint conversion.

This method can be overridden by subclasses to apply model-specific weight name transformations when converting checkpoints from HuggingFace format to Puzzletron format.

Default implementation returns the name unchanged (identity function).

Parameters:

name (str) – Original weight name from HuggingFace checkpoint

Returns:

Converted weight name for Puzzletron format

Return type:

str

Example

For Qwen2.5-VL, this converts: - visual.* → model.visual.* - model.* → model.language_model.*

static copy_checkpoint_files(input_dir, output_dir)

Copy checkpoint files except model weights (which will be converted).

Parameters:

  • input_dir (Path)

  • output_dir (Path)

abstract static create_block_configs_from_main_config(config)

Create per-layer BlockConfig list from a HuggingFace model config.

This method extracts layer-specific parameters (e.g., intermediate_size, num_key_value_heads) from the main model config and creates a BlockConfig for each layer. These BlockConfigs enable layer-specific pruning and modifications during the compression pipeline.

Parameters:

config (PreTrainedConfig) – HuggingFace PretrainedConfig (e.g., LlamaConfig, Qwen2Config)

Returns:

  • AttentionConfig: attention settings (no_op, num_key_value_heads)

  • FFNConfig: FFN settings (no_op, intermediate_size)

Return type:

List of BlockConfig, one per hidden layer. Each BlockConfig contains

Example

For a model with uniform layers (e.g., Llama):

return [BlockConfig(…)] * config.num_hidden_layers

For a model with heterogeneous layers (e.g., NemotronH with Mamba/Attention):

return [BlockConfig(…) for layer_idx in range(num_layers)]