cellmap_flow.finetune.finetuned_model_templates
===============================================

.. py:module:: cellmap_flow.finetune.finetuned_model_templates

.. autoapi-nested-parse::

   Templates for generating finetuned model YAML configurations.

   This module provides functions to auto-generate the YAML config for serving
   finetuned models using FinetuneModelConfig (type: finetune).



Attributes
----------

.. autoapisummary::

   cellmap_flow.finetune.finetuned_model_templates.logger


Functions
---------

.. autoapisummary::

   cellmap_flow.finetune.finetuned_model_templates.generate_finetuned_model_yaml


Module Contents
---------------

.. py:data:: logger

.. py:function:: generate_finetuned_model_yaml(lora_adapter_path: str, base_model_dict: dict, model_name: str, output_path: pathlib.Path, data_path: str, queue: str = 'gpu_h100', charge_group: str = 'cellmap', json_data: dict = None, scale: str = 's0') -> pathlib.Path

   Generate .yaml configuration for serving a finetuned model.

   The generated YAML uses type: finetune, which delegates to
   FinetuneModelConfig. This loads the base model via its own
   ModelConfig, applies the LoRA adapter, and serves the result.

   :param lora_adapter_path: Path to the saved LoRA adapter directory
   :param base_model_dict: Dict describing the base model (from model_config.to_dict())
   :param model_name: Name of the finetuned model
   :param output_path: Where to write the .yaml file
   :param data_path: Path to actual dataset (REQUIRED - no placeholders)
   :param queue: LSF queue name
   :param charge_group: LSF charge group
   :param json_data: Optional dict with input_norm and postprocess from base model
   :param scale: Scale level (e.g., "s0", "s1") from base model

   :returns: Path to the generated YAML file