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
@@ -38,8 +38,8 @@ 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.
@@ -84,10 +84,10 @@ 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
@@ -143,10 +143,10 @@ 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
@@ -27,31 +27,31 @@ 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
 

cli/alora/train.py

@@ -1,10 +1,10 @@
 """Fine-tune a causal language model to produce a LoRA or aLoRA adapter.
 
-Loads a JSONL dataset of ``item``/``label`` pairs, applies an 80/20 train/validation
-split, and trains using HuggingFace PEFT and TRL's ``SFTTrainer`` — saving the
+Loads a JSONL dataset of `item`/`label` pairs, applies an 80/20 train/validation
+split, and trains using HuggingFace PEFT and TRL's `SFTTrainer` — saving the
 checkpoint with the lowest validation loss. Supports CUDA, MPS (macOS,
 PyTorch ≥ 2.8), and CPU device selection, and handles the
-``alora_invocation_tokens`` configuration required for aLoRA training.
+`alora_invocation_tokens` configuration required for aLoRA training.
 """
 
 import json
@@ -42,18 +42,18 @@
 def load_dataset_from_json(json_path, tokenizer, invocation_prompt):
     """Load a JSONL dataset and format it for SFT training.
 
-    Reads ``item``/``label`` pairs from a JSONL file and builds a HuggingFace
-    ``Dataset`` with ``input`` and ``target`` columns. Each input is formatted as
-    ``"{item}\\nRequirement: <|end_of_text|>\\n{invocation_prompt}"``.
+    Reads `item`/`label` pairs from a JSONL file and builds a HuggingFace
+    `Dataset` with `input` and `target` columns. Each input is formatted as
+    `"{item}\\nRequirement: <|end_of_text|>\\n{invocation_prompt}"`.
 
     Args:
-        json_path: Path to the JSONL file containing ``item``/``label`` pairs.
+        json_path: Path to the JSONL file containing `item`/`label` pairs.
         tokenizer: HuggingFace tokenizer instance (currently unused, reserved for
             future tokenization steps).
         invocation_prompt: Invocation string appended to each input prompt.
 
     Returns:
-        A HuggingFace ``Dataset`` with ``"input"`` and ``"target"`` string columns.
+        A HuggingFace `Dataset` with `"input"` and `"target"` string columns.
     """
     data = []
     with open(json_path, encoding="utf-8") as f:
@@ -77,12 +77,12 @@ def formatting_prompts_func(example):
     """Concatenate input and target columns for SFT prompt formatting.
 
     Args:
-        example: A batch dict with ``"input"`` and ``"target"`` list fields, as
-            produced by HuggingFace ``Dataset.map`` in batched mode.
+        example: A batch dict with `"input"` and `"target"` list fields, as
+            produced by HuggingFace `Dataset.map` in batched mode.
 
     Returns:
-        A list of strings, each formed by concatenating the ``input`` and
-        ``target`` values for a single example in the batch.
+        A list of strings, each formed by concatenating the `input` and
+        `target` values for a single example in the batch.
     """
     return [
         f"{example['input'][i]}{example['target'][i]}"
@@ -95,7 +95,7 @@ class SaveBestModelCallback(TrainerCallback):
 
     Attributes:
         best_eval_loss (float): Lowest evaluation loss seen so far across all
-            evaluation steps. Initialised to ``float("inf")``.
+            evaluation steps. Initialised to `float("inf")`.
     """
 
     def __init__(self):
@@ -105,18 +105,18 @@ def on_evaluate(self, args, state, control, **kwargs):
         """Save the adapter weights if the current evaluation loss is a new best.
 
         Called automatically by the HuggingFace Trainer after each evaluation
-        step. Compares the current ``eval_loss`` from ``metrics`` against
-        ``best_eval_loss`` and, if lower, updates the stored best and saves the
-        model to ``args.output_dir``.
+        step. Compares the current `eval_loss` from `metrics` against
+        `best_eval_loss` and, if lower, updates the stored best and saves the
+        model to `args.output_dir`.
 
         Args:
-            args: ``TrainingArguments`` instance with training configuration,
-                including ``output_dir``.
-            state: ``TrainerState`` instance with the current training state.
-            control: ``TrainerControl`` instance for controlling training flow.
+            args: `TrainingArguments` instance with training configuration,
+                including `output_dir`.
+            state: `TrainerState` instance with the current training state.
+            control: `TrainerControl` instance for controlling training flow.
             **kwargs: Additional keyword arguments provided by the Trainer,
-                including ``"model"`` (the current PEFT model) and
-                ``"metrics"`` (a dict containing ``"eval_loss"``).
+                including `"model"` (the current PEFT model) and
+                `"metrics"` (a dict containing `"eval_loss"`).
         """
         model = kwargs["model"]
         metrics = kwargs["metrics"]
@@ -132,13 +132,13 @@ class SafeSaveTrainer(SFTTrainer):
     def save_model(self, output_dir: str | None = None, _internal_call: bool = False):
         """Save the model and tokenizer with safe serialization always enabled.
 
-        Overrides ``SFTTrainer.save_model`` to call ``save_pretrained`` with
-        ``safe_serialization=True``, ensuring weights are saved in safetensors
+        Overrides `SFTTrainer.save_model` to call `save_pretrained` with
+        `safe_serialization=True`, ensuring weights are saved in safetensors
         format rather than the legacy pickle-based format.
 
         Args:
             output_dir (str | None): Directory to save the model into. If
-                ``None``, the trainer's configured ``output_dir`` is used.
+                `None`, the trainer's configured `output_dir` is used.
             _internal_call (bool): Internal flag passed through from the Trainer
                 base class; not used by this override.
         """
@@ -165,31 +165,31 @@ def train_model(
     """Fine-tune a causal language model to produce a LoRA or aLoRA adapter.
 
     Loads and 80/20-splits the JSONL dataset, configures PEFT with the specified
-    adapter type, trains using ``SFTTrainer`` with a best-checkpoint callback, saves
-    the adapter weights, and removes the PEFT-generated ``README.md`` from the output
+    adapter type, trains using `SFTTrainer` with a best-checkpoint callback, saves
+    the adapter weights, and removes the PEFT-generated `README.md` from the output
     directory.
 
     Args:
         dataset_path: Path to the JSONL training dataset file.
         base_model: Hugging Face model ID or local path to the base model.
         output_file: Destination path for the trained adapter weights.
         prompt_file: Optional path to a JSON config file with an
-            ``"invocation_prompt"`` key. Defaults to the aLoRA invocation token.
-        adapter: Adapter type to train -- ``"alora"`` (default) or ``"lora"``.
-        device: Device selection -- ``"auto"``, ``"cpu"``, ``"cuda"``, or
-            ``"mps"``.
-        run_name: Name of the training run (passed to ``SFTConfig``).
+            `"invocation_prompt"` key. Defaults to the aLoRA invocation token.
+        adapter: Adapter type to train -- `"alora"` (default) or `"lora"`.
+        device: Device selection -- `"auto"`, `"cpu"`, `"cuda"`, or
+            `"mps"`.
+        run_name: Name of the training run (passed to `SFTConfig`).
         epochs: Number of training epochs.
         learning_rate: Optimizer learning rate.
         batch_size: Per-device training batch size.
         max_length: Maximum token sequence length.
         grad_accum: Gradient accumulation steps.
 
     Raises:
-        ValueError: If ``device`` is not one of ``"auto"``, ``"cpu"``,
-            ``"cuda"``, or ``"mps"``.
+        ValueError: If `device` is not one of `"auto"`, `"cpu"`,
+            `"cuda"`, or `"mps"`.
         RuntimeError: If the GPU has insufficient VRAM to load the model
-            (wraps ``NotImplementedError`` for meta tensor errors).
+            (wraps `NotImplementedError` for meta tensor errors).
     """
     if prompt_file:
         # load the configurable variable invocation_prompt

cli/alora/upload.py

@@ -1,9 +1,9 @@
 """Upload a trained LoRA or aLoRA adapter to Hugging Face Hub.
 
 Creates the target repository if it does not already exist and pushes the entire
-adapter weights directory (output of ``save_pretrained``) to the repository root.
-Requires an authenticated Hugging Face token set via the ``HF_TOKEN`` environment
-variable or ``huggingface-cli login``.
+adapter weights directory (output of `save_pretrained`) to the repository root.
+Requires an authenticated Hugging Face token set via the `HF_TOKEN` environment
+variable or `huggingface-cli login`.
 """
 
 import os
@@ -20,7 +20,7 @@ def upload_model(weight_path: str, model_name: str, private: bool = True):
         private (bool): Whether the repo should be private. Default: True.
 
     Raises:
-        FileNotFoundError: If ``weight_path`` does not exist on disk.
+        FileNotFoundError: If `weight_path` does not exist on disk.
         OSError: If no Hugging Face authentication token is found.
         RuntimeError: If creating or accessing the Hugging Face repository fails.
     """

cli/decompose/__init__.py

@@ -1,10 +1,10 @@
-"""Typer sub-application for the ``m decompose`` command group.
+"""Typer sub-application for the `m decompose` command group.
 
-Exposes a single ``run`` command that takes a task prompt (from a file or
+Exposes a single `run` command that takes a task prompt (from a file or
 interactively), calls the LLM-based decomposition pipeline to break it into
 structured subtasks with constraints and dependency ordering, and writes the results
 as a JSON data file and a ready-to-run Python script. Invoke via
-``m decompose run --help`` for full option documentation.
+`m decompose run --help` for full option documentation.
 """
 
 import typer