docs(api): normalize source docstring backticks

cli/alora/commands.py

@@ -1,10 +1,10 @@
-"""Typer sub-application for the ``m alora`` command group.
+"""Typer sub-application for the `m alora` command group.
 
-Provides three commands: ``train`` (fine-tune a base causal language model on a JSONL
-dataset to produce a LoRA or aLoRA adapter), ``upload`` (push adapter weights to
+Provides three commands: `train` (fine-tune a base causal language model on a JSONL
+dataset to produce a LoRA or aLoRA adapter), `upload` (push adapter weights to
 Hugging Face Hub, optionally packaging the adapter as an intrinsic with an
-``io.yaml`` configuration), and ``add-readme`` (use an LLM to auto-generate and
-upload an ``INTRINSIC_README.md`` for the trained adapter).
+`io.yaml` configuration), and `add-readme` (use an LLM to auto-generate and
+upload an `INTRINSIC_README.md` for the trained adapter).
 """
 
 import json
@@ -37,12 +37,12 @@ def alora_train(
     pairs. Supports both aLoRA (asymmetric LoRA) and standard LoRA adapters.
 
     Prerequisites:
-        Mellea installed with adapter extras (``uv add mellea[adapters]``).
+        Mellea installed with adapter extras (`uv add mellea[adapters]`).
         A CUDA, MPS, or CPU device available for training.
 
     Output:
-        Saves adapter weights to the path specified by ``--outfile``. The output
-        directory contains an ``adapter_config.json`` and the trained weight
+        Saves adapter weights to the path specified by `--outfile`. The output
+        directory contains an `adapter_config.json` and the trained weight
         files, ready for upload or local inference.
 
     Examples:
@@ -56,8 +56,8 @@ def alora_train(
         basemodel: Base model ID or path.
         outfile: Path to save adapter weights.
         promptfile: Path to load the prompt format file.
-        adapter: Adapter type; ``"alora"`` or ``"lora"``.
-        device: Device to train on: ``"auto"``, ``"cpu"``, ``"cuda"``, or ``"mps"``.
+        adapter: Adapter type; `"alora"` or `"lora"`.
+        device: Device to train on: `"auto"`, `"cpu"`, `"cuda"`, or `"mps"`.
         epochs: Number of training epochs.
         learning_rate: Learning rate for the optimizer.
         batch_size: Per-device training batch size.
@@ -100,14 +100,14 @@ def alora_upload(
     """Upload a trained adapter to a remote model registry.
 
     Pushes adapter weights to Hugging Face Hub, optionally packaging the adapter
-    as an intrinsic with an ``io.yaml`` configuration file.
+    as an intrinsic with an `io.yaml` configuration file.
 
     Prerequisites:
-        Hugging Face CLI authenticated (``huggingface-cli login``).
+        Hugging Face CLI authenticated (`huggingface-cli login`).
 
     Output:
         Creates or updates a Hugging Face Hub repository at the name specified
-        by ``--name`` and uploads the adapter weight files.
+        by `--name` and uploads the adapter weight files.
 
     Examples:
         m alora upload ./adapter --name acme/my-alora
@@ -118,10 +118,10 @@ def alora_upload(
     Args:
         weight_path: Path to saved adapter weights directory.
         name: Destination model name on Hugging Face Hub
-            (e.g. ``"acme/carbchecker-alora"``).
-        intrinsic: If ``True``, the adapter implements an intrinsic and an
-            ``io.yaml`` file must also be provided.
-        io_yaml: Path to the ``io.yaml`` file configuring input/output processing
+            (e.g. `"acme/carbchecker-alora"`).
+        intrinsic: If `True`, the adapter implements an intrinsic and an
+            `io.yaml` file must also be provided.
+        io_yaml: Path to the `io.yaml` file configuring input/output processing
             when the model is invoked as an intrinsic.
     """
     from cli.alora.intrinsic_uploader import upload_intrinsic
@@ -179,12 +179,12 @@ def alora_add_readme(
     Face Hub repository.
 
     Prerequisites:
-        Hugging Face CLI authenticated (``huggingface-cli login``).
+        Hugging Face CLI authenticated (`huggingface-cli login`).
         An LLM backend available for README generation.
 
     Output:
         Generates a README.md file, displays it for confirmation, and uploads
-        it to the Hugging Face Hub repository specified by ``--name``.
+        it to the Hugging Face Hub repository specified by `--name`.
 
     Examples:
         m alora add-readme data.jsonl --basemodel ibm-granite/granite-3.3-2b-instruct --name acme/my-alora
@@ -195,10 +195,10 @@ def alora_add_readme(
     Args:
         datafile: JSONL file with item/label pairs used to train the adapter.
         basemodel: Base model ID or path.
-        promptfile: Path to the prompt format file, or ``None``.
+        promptfile: Path to the prompt format file, or `None`.
         name: Destination model name on Hugging Face Hub.
-        hints: Path to a file containing additional domain hints, or ``None``.
-        io_yaml: Path to the ``io.yaml`` intrinsic configuration file, or ``None``.
+        hints: Path to a file containing additional domain hints, or `None`.
+        io_yaml: Path to the `io.yaml` intrinsic configuration file, or `None`.
 
     Raises:
         OSError: If no Hugging Face authentication token is found.

cli/alora/intrinsic_uploader.py

@@ -1,10 +1,10 @@
 """Upload a trained adapter to Hugging Face Hub in the intrinsic directory layout.
 
 Creates or updates a private Hugging Face repository and uploads adapter weights
-into a ``<intrinsic_name>/<base_model>/<adapter_type>`` sub-directory, together with
-the required ``io.yaml`` configuration file. If an ``INTRINSIC_README.md`` exists in
-the weight directory it is also uploaded as the repository's root ``README.md``.
-Requires an authenticated Hugging Face token obtained via ``huggingface-cli login``.
+into a `<intrinsic_name>/<base_model>/<adapter_type>` sub-directory, together with
+the required `io.yaml` configuration file. If an `INTRINSIC_README.md` exists in
+the weight directory it is also uploaded as the repository's root `README.md`.
+Requires an authenticated Hugging Face token obtained via `huggingface-cli login`.
 """
 
 import os
@@ -38,31 +38,31 @@ def upload_intrinsic(
     """Upload an adapter to Hugging Face Hub using the intrinsic directory layout.
 
     Creates or updates a private Hugging Face repository and uploads adapter
-    weights into a ``<intrinsic_name>/<base_model>/<adapter_type>`` sub-directory,
-    together with the ``io.yaml`` configuration file. If an
-    ``INTRINSIC_README.md`` exists in the weight directory it is also uploaded
-    as the repository root ``README.md``.
+    weights into a `<intrinsic_name>/<base_model>/<adapter_type>` sub-directory,
+    together with the `io.yaml` configuration file. If an
+    `INTRINSIC_README.md` exists in the weight directory it is also uploaded
+    as the repository root `README.md`.
 
     Args:
         weight_path (str): Local directory containing the adapter weights
-            (output of ``save_pretrained``).
+            (output of `save_pretrained`).
         model_name (str): Target Hugging Face repository name in
-            ``"<userid>/<intrinsic_name>"`` format (e.g. ``"acme/carbchecker-alora"``).
+            `"<userid>/<intrinsic_name>"` format (e.g. `"acme/carbchecker-alora"`).
         base_model (str): Base model ID or path (e.g.
-            ``"ibm-granite/granite-3.3-2b-instruct"``). Must contain at most
-            one ``"/"`` separator.
+            `"ibm-granite/granite-3.3-2b-instruct"`). Must contain at most
+            one `"/"` separator.
         type (Literal['lora', 'alora']): Adapter type, used as the leaf
             directory name in the repository layout.
-        io_yaml (str): Path to the ``io.yaml`` configuration file for
+        io_yaml (str): Path to the `io.yaml` configuration file for
             intrinsic input/output processing.
         private (bool): Whether the repository should be private. Currently
-            only ``True`` is supported.
+            only `True` is supported.
 
     Raises:
-        AssertionError: If ``weight_path`` or ``io_yaml`` do not exist, if
-            ``private`` is ``False``, if ``base_model`` contains more than one
-            ``"/"`` separator, or if ``model_name`` does not contain exactly
-            one ``"/"`` separator.
+        AssertionError: If `weight_path` or `io_yaml` do not exist, if
+            `private` is `False`, if `base_model` contains more than one
+            `"/"` separator, or if `model_name` does not contain exactly
+            one `"/"` separator.
         OSError: If no Hugging Face authentication token is found.
     """
     try:

cli/alora/readme_generator.py

@@ -1,10 +1,10 @@
 """LLM-assisted generator for adapter intrinsic README files.
 
-Uses a ``MelleaSession`` with rejection sampling to derive README template variables
+Uses a `MelleaSession` with rejection sampling to derive README template variables
 from a JSONL training dataset — including a high-level description, the inferred
 Python argument list, and Jinja2-renderable sample rows. Validates the generated
 output with deterministic requirements (correct naming conventions, syntactically
-valid argument lists) before rendering the final ``INTRINSIC_README.md`` via a
+valid argument lists) before rendering the final `INTRINSIC_README.md` via a
 Jinja2 template.
 """
 
@@ -28,10 +28,10 @@ class ReadmeTemplateVars(BaseModel):
         high_level_description (str): A 2-3 sentence description of what the intrinsic adapter does.
         dataset_description (str): Brief description of the training dataset contents and format.
         userid (str): HuggingFace user ID (the namespace portion of the model name).
-        intrinsic_name (str): Short snake_case identifier for the intrinsic (e.g. ``"carbchecker"``).
-        intrinsic_name_camelcase (str): CamelCase version of ``intrinsic_name`` (e.g. ``"CarbChecker"``).
-        arglist (str): Python function argument list with type hints (e.g. ``"description: str"``).
-        arglist_without_type_annotations (str): Argument list without type hints (e.g. ``"description"``).
+        intrinsic_name (str): Short snake_case identifier for the intrinsic (e.g. `"carbchecker"`).
+        intrinsic_name_camelcase (str): CamelCase version of `intrinsic_name` (e.g. `"CarbChecker"`).
+        arglist (str): Python function argument list with type hints (e.g. `"description: str"`).
+        arglist_without_type_annotations (str): Argument list without type hints (e.g. `"description"`).
     """
 
     high_level_description: str
@@ -141,20 +141,20 @@ def make_readme_jinja_dict(
     """Generate all template variables for the intrinsic README using an LLM.
 
     Loads the first five lines of the JSONL dataset, determines the input structure,
-    and uses ``m.instruct`` with deterministic requirements and rejection sampling to
+    and uses `m.instruct` with deterministic requirements and rejection sampling to
     generate README template variables.
 
     Args:
-        m: Active ``MelleaSession`` to use for LLM generation.
+        m: Active `MelleaSession` to use for LLM generation.
         dataset_path: Path to the JSONL training dataset file.
         base_model: Base model ID or path used to train the adapter.
         prompt_file: Path to the prompt format file (empty string if not provided).
         name: Destination model name on Hugging Face Hub
-            (e.g. ``"acme/carbchecker-alora"``).
+            (e.g. `"acme/carbchecker-alora"`).
         hints: Optional string of additional domain hints to include in the prompt.
 
     Returns:
-        Dict of Jinja2 template variables for rendering the ``INTRINSIC_README.md``.
+        Dict of Jinja2 template variables for rendering the `INTRINSIC_README.md`.
     """
     # Load first 5 lines of the dataset.
     samples = []
@@ -294,19 +294,19 @@ def generate_readme(
 ) -> str:
     """Generate an INTRINSIC_README.md file from the dataset and template.
 
-    Creates a ``MelleaSession``, uses the LLM to generate template variables,
-    renders the Jinja template, and writes the result to ``output_path``.
+    Creates a `MelleaSession`, uses the LLM to generate template variables,
+    renders the Jinja template, and writes the result to `output_path`.
 
     Args:
         dataset_path: Path to the JSONL training dataset file.
         base_model: Base model ID or path used to train the adapter.
-        prompt_file: Path to the prompt format file, or ``None``.
+        prompt_file: Path to the prompt format file, or `None`.
         output_path: Destination path for the generated README file.
         name: Destination model name on Hugging Face Hub.
         hints: Optional string of additional domain hints for the LLM.
 
     Returns:
-        The path to the written output file (same as ``output_path``).
+        The path to the written output file (same as `output_path`).
     """
     from jinja2 import Environment, FileSystemLoader