Added abstract functions for LM and distmod

llments/lm/distmod.py

@@ -0,0 +1,95 @@
+from llments.lm.lm import LanguageModel
+
+
+class DistributionModifier:
+    def fit(
+        self,
+        base_model: LanguageModel,
+        target: LanguageModel,
+        task_description: str | None = None,
+    ) -> LanguageModel:
+        """Fit a language model to match another language model's probability distribution.
+
+        Args:
+            base_model (LanguageModel): The language model to be fitted.
+            target (LanguageModel): The target language model whose probability distribution the base model should match.
+            task_description (str | None, optional): A task description providing additional context about the fitting process. Defaults to None.
+
+        Returns:
+            LanguageModel: The fitted language model.
+        """
+        base_model.fit(target, task_description)
+        return base_model
+
+    def prompt(self, base_model: LanguageModel, prompt_text: str) -> LanguageModel:
+        """Modify the base model's distribution using a textual prompt.
+
+        Args:
+            base_model (LanguageModel): The language model to be modified.
+            prompt_text (str): The textual prompt to influence the model's output.
+
+        Returns:
+            LanguageModel: The modified language model.
+        """
+        # Prompting implementation
+        # Integrate prompt_text into base_model generation process
+        return base_model
+
+    def reinforcement_learning(
+        self, base_model: LanguageModel, reward_function
+    ) -> LanguageModel:
+        """Apply reinforcement learning to modify a model based on the provided reward function.
+
+        Args:
+            base_model (LanguageModel): The language model to be modified.
+            reward_function (_type_): The reward function for the reinforcement learning process.
+
+        Returns:
+            LanguageModel: The modified language model.
+        """
+        # Reinforcement Learning implementation
+        return base_model
+
+    def retrieval_augmented_generation(
+        self, base_model: LanguageModel, data
+    ) -> LanguageModel:
+        """Apply retrieval-augmented generation over a dataset to enhance the model's generation.
+
+        Args:
+            base_model (LanguageModel): The language model to be enhanced.
+            data (_type_): The dataset to be used for retrieval-augmented generation.
+
+        Returns:
+            LanguageModel: The enhanced language model.
+        """
+        # RAG implementation
+        return base_model
+
+    def ensemble(
+        self, models: list[LanguageModel], weights: list[float]
+    ) -> LanguageModel:
+        """Combine several models into one by ensembling their outputs based on specified weights.
+
+        Args:
+            models (list[LanguageModel]): A list of language models to be ensembled.
+            weights (list[float]): A list of weights corresponding to each model.
+
+        Returns:
+            LanguageModel: A new ensembled language model.
+        """
+        # ensembled_model = LanguageModel()
+        # Ensembling implementation
+        return models[0]  # Ideally return the ensembled model
+
+    def filter(self, base_model: LanguageModel, filtering_rule) -> LanguageModel:
+        """Filter down the model's space according to a filtering rule.
+
+        Args:
+            base_model (LanguageModel): The language model to be filtered.
+            filtering_rule (_type_): A rule or criteria to filter the model's space.
+
+        Returns:
+            LanguageModel: The filtered language model.
+        """
+        # Filtering implementation
+        return base_model

llments/lm/lm.py

@@ -26,3 +26,24 @@ def fit(
             The fitted language model.
         """
         ...
+
+    @abc.abstractmethod
+    def calculcate_probability(self, output: str) -> float:
+        """Calculate the probability of an output given the language model.
+
+        Args:
+            output (str): The output sequence for which the probability is calculated.
+
+        Returns:
+            float: The probability of output x given the language model.
+        """
+        ...
+
+    @abc.abstractmethod
+    def sample(self) -> str:
+        """Sample an output given the language model.
+
+        Returns:
+            str: A sampled output sequence from the language model.
+        """
+        ...