Fix docstring interlinks

docs/source/best_of_n.md

@@ -44,7 +44,7 @@ best_of_n = BestOfNSampler(model, tokenizer, queries_to_scores, length_sampler=o
 ```
 
 There is the option of setting the generation settings (like `temperature`, `pad_token_id`) at the time of instance creation as opposed to when calling the `generate` method.
-This is done by passing a `GenerationConfig` from the `transformers` library at the time of initialization
+This is done by passing a [`~transformers.GenerationConfig`] from the `transformers` library at the time of initialization
 
 ```python
 

docs/source/customization.md

@@ -112,7 +112,7 @@ trainer.train()
 
 ## Use the accelerator cache optimizer
 
-When training large models, you should better handle the accelerator cache by iteratively clearing it. To do so, simply pass `optimize_device_cache=True` to `DPOConfig`:
+When training large models, you should better handle the accelerator cache by iteratively clearing it. To do so, simply pass `optimize_device_cache=True` to [`DPOConfig`]:
 
 ```python
 training_args = DPOConfig(..., optimize_device_cache=True)

docs/source/judges.md

@@ -13,7 +13,7 @@ pip install trl[judges]
 
 ## Using the provided judges
 
-TRL provides several judges out of the box. For example, you can use the `HfPairwiseJudge` to compare two completions using a pre-trained model from the Hugging Face model hub:
+TRL provides several judges out of the box. For example, you can use the [`HfPairwiseJudge`] to compare two completions using a pre-trained model from the Hugging Face model hub:
 
 ```python
 from trl import HfPairwiseJudge

docs/source/logging.md

@@ -3,7 +3,7 @@
 As reinforcement learning algorithms are historically challenging to debug, it's important to pay careful attention to logging.
 By default, TRL trainers like [`PPOTrainer`] and [`GRPOTrainer`] save a lot of relevant information to supported experiment trackers like Trackio, Weights & Biases (wandb) or TensorBoard.
 
-Upon initialization, pass the `report_to` argument to the respective configuration object (e.g., [`PPOConfig`] for `PPOTrainer`, or [`GRPOConfig`] for `GRPOTrainer`):
+Upon initialization, pass the `report_to` argument to the respective configuration object (e.g., [`PPOConfig`] for [`PPOTrainer`], or [`GRPOConfig`] for [`GRPOTrainer`]):
 
 ```python
 # For PPOTrainer
@@ -19,7 +19,7 @@ grpo_config = GRPOConfig(
 )
 ```
 
-If you want to log with TensorBoard, you might also need to specify logging directories, for example, by adding `logging_dir=PATH_TO_LOGS` to the configuration object (e.g., `PPOConfig` or `GRPOConfig`).
+If you want to log with TensorBoard, you might also need to specify logging directories, for example, by adding `logging_dir=PATH_TO_LOGS` to the configuration object (e.g., [`PPOConfig`] or [`GRPOConfig`]).
 
 ## PPO Logging
 
@@ -83,9 +83,9 @@ Here's a brief explanation for the logged metrics provided in the data for the G
 
 ### Policy and Loss Metrics
 
-* `kl`: The mean Kullback-Leibler (KL) divergence between the current policy and the reference policy. This is logged only if `beta` (the KL coefficient in `GRPOConfig`) is non-zero.
+* `kl`: The mean Kullback-Leibler (KL) divergence between the current policy and the reference policy. This is logged only if `beta` (the KL coefficient in [`GRPOConfig`]) is non-zero.
 * `entropy`: Average entropy of token predictions across generated completions.
-* If Liger GRPOLoss is used (`use_liger_loss: True` in `GRPOConfig`):
+* If Liger GRPOLoss is used (`use_liger_loss: True` in [`GRPOConfig`]):
   * `clip_ratio`: The fraction of policy updates where the probability ratio was clipped according to the GRPO loss's epsilon bounds.
 * If standard GRPOLoss is used (`use_liger_loss: False`):
   * `clip_ratio/low_mean`: The mean fraction of instances where the probability ratio `r_t(θ)` was clipped at the lower bound `1 - epsilon_low` (occurs when advantage is negative and ratio is below the bound).

docs/source/paper_index.md

@@ -338,7 +338,7 @@ training_args = DPOConfig(
 )
 ```
 
-For the unpaired version, the user should utilize `BCOConfig` and `BCOTrainer`.
+For the unpaired version, the user should utilize [`BCOConfig`] and [`BCOTrainer`].
 
 ### Self-Play Preference Optimization for Language Model Alignment
 

docs/source/peft_integration.md

@@ -20,7 +20,7 @@ Note: if you don't want to log with `wandb` remove `log_with="wandb"` in the scr
 
 ## How to use it?
 
-Simply declare a `PeftConfig` object in your script and pass it through `.from_pretrained` to load the TRL+PEFT model.
+Simply declare a [`~peft.PeftConfig`] object in your script and pass it through `.from_pretrained` to load the TRL+PEFT model.
 
 ```python
 from peft import LoraConfig

docs/source/reducing_memory_usage.md

@@ -77,7 +77,7 @@ Packing, introduced in [Raffel et al., 2020](https://huggingface.co/papers/1910.
 Packing reduces padding by merging several sequences in one row when possible. We use an advanced method to be near-optimal in the way we pack the dataset. To enable packing, use `packing=True` in the [`SFTConfig`].
 
 > [!TIP]
-> In TRL 0.18 and earlier, packing used a more aggressive method that reduced padding to almost nothing, but had the downside of breaking sequence continuity for a large fraction of the dataset. To revert to this strategy, use `packing_strategy="wrapped"` in `SFTConfig`.
+> In TRL 0.18 and earlier, packing used a more aggressive method that reduced padding to almost nothing, but had the downside of breaking sequence continuity for a large fraction of the dataset. To revert to this strategy, use `packing_strategy="wrapped"` in [`SFTConfig`].
 
 ```python
 from trl import SFTConfig

trl/data_utils.py

@@ -271,7 +271,7 @@ def maybe_apply_chat_template(
             messages, where each message is a dictionary with keys `"role"` and `"content"`. Additionally, the example
             may contain a `"chat_template_kwargs"` key, which is a dictionary of additional keyword arguments to pass
             to the chat template renderer.
-        tokenizer (`PreTrainedTokenizerBase`):
+        tokenizer ([`~transformers.PreTrainedTokenizerBase`]):
             Tokenizer to apply the chat template with.
         tools (`list[Union[dict, Callable]]`, *optional*):
             A list of tools (callable functions) that will be accessible to the model. If the template does not support
@@ -328,7 +328,7 @@ def unpair_preference_dataset(
     Unpair a preference dataset.
 
     Args:
-        dataset (`Dataset` or `DatasetDict`):
+        dataset ([`~datasets.Dataset`] or [`~datasets.DatasetDict`]):
             Preference dataset to unpair. The dataset must have columns `"chosen"`, `"rejected"` and optionally
             `"prompt"`.
         num_proc (`int`, *optional*):
@@ -337,7 +337,7 @@ def unpair_preference_dataset(
             Meaningful description to be displayed alongside with the progress bar while mapping examples.
 
     Returns:
-        `Dataset`: The unpaired preference dataset.
+        [`~datasets.Dataset`]: The unpaired preference dataset.
 
     Example:
 
@@ -371,7 +371,7 @@ def maybe_unpair_preference_dataset(
     Unpair a preference dataset if it is paired.
 
     Args:
-        dataset (`Dataset` or `DatasetDict`):
+        dataset ([`~datasets.Dataset`] or [`~datasets.DatasetDict`]):
             Preference dataset to unpair. The dataset must have columns `"chosen"`, `"rejected"` and optionally
             `"prompt"`.
         num_proc (`int`, *optional*):
@@ -380,7 +380,8 @@ def maybe_unpair_preference_dataset(
             Meaningful description to be displayed alongside with the progress bar while mapping examples.
 
     Returns:
-        `Dataset` or `DatasetDict`: The unpaired preference dataset if it was paired, otherwise the original dataset.
+        [`~datasets.Dataset`] or [`~datasets.DatasetDict`]: The unpaired preference dataset if it was paired, otherwise
+        the original dataset.
 
     Example:
 
@@ -473,7 +474,7 @@ def maybe_extract_prompt(example: dict[str, list]) -> dict[str, list]:
      'rejected': [{'role': 'assistant', 'content': 'It is green.'}]}
     ```
 
-    Or, with the `map` method of `datasets.Dataset`:
+    Or, with the `map` method of [`~datasets.Dataset`]:
 
     ```python
     >>> from trl import extract_prompt
@@ -664,7 +665,7 @@ def pack_dataset(
     Pack sequences in a dataset into chunks of size `seq_length`.
 
     Args:
-        dataset (`Dataset` or `DatasetDict`):
+        dataset ([`~datasets.Dataset`] or [`~datasets.DatasetDict`]):
             Dataset to pack
         seq_length (`int`):
             Target sequence length to pack to.
@@ -679,8 +680,8 @@ def pack_dataset(
             Additional keyword arguments to pass to the dataset's map method when packing examples.
 
     Returns:
-        `Dataset` or `DatasetDict`: The dataset with packed sequences. The number of examples may decrease as sequences
-        are combined.
+        [`~datasets.Dataset`] or [`~datasets.DatasetDict`]: The dataset with packed sequences. The number of examples
+        may decrease as sequences are combined.
 
     Example:
     ```python
@@ -720,15 +721,15 @@ def truncate_dataset(
     Truncate sequences in a dataset to a specified `max_length`.
 
     Args:
-        dataset (`Dataset` or `DatasetDict`):
+        dataset ([`~datasets.Dataset`] or [`~datasets.DatasetDict`]):
             Dataset to truncate.
         max_length (`int`):
             Maximum sequence length to truncate to.
         map_kwargs (`dict`, *optional*):
             Additional keyword arguments to pass to the dataset's map method when truncating examples.
 
     Returns:
-        `Dataset` or `DatasetDict`: The dataset with truncated sequences.
+        [`~datasets.Dataset`] or [`~datasets.DatasetDict`]: The dataset with truncated sequences.
 
     Example:
     ```python

trl/mergekit_utils.py

@@ -264,7 +264,7 @@ def merge_models(config: MergeConfig, out_path: str):
     Merge two models using mergekit
 
     Args:
-        config (`MergeConfig`): The merge configuration.
+        config ([`MergeConfig`]): The merge configuration.
         out_path (`str`): The output path for the merged model.
     """
     if not is_mergekit_available():

trl/models/modeling_base.py

@@ -57,14 +57,17 @@
 
 
 class PreTrainedModelWrapper(nn.Module):
-    r"""
-    A wrapper class around a (`transformers.PreTrainedModel`) to be compatible with the (`~transformers.PreTrained`)
-    class in order to keep some attributes and methods of the (`~transformers.PreTrainedModel`) class.
+    """
+    Wrapper for a [`~transformers.PreTrainedModel`] implemented as a standard PyTorch [`torch.nn.Module`].
+
+    This class provides a compatibility layer that preserves the key attributes and methods of the original
+    [`~transformers.PreTrainedModel`], while exposing a uniform interface consistent with PyTorch modules. It enables
+    seamless integration of pretrained Transformer models into custom training, evaluation, or inference workflows.
 
     Attributes:
-        pretrained_model (`transformers.PreTrainedModel`):
+        pretrained_model ([`~transformers.PreTrainedModel`]):
             The model to be wrapped.
-        parent_class (`transformers.PreTrainedModel`):
+        parent_class ([`~transformers.PreTrainedModel`]):
             The parent class of the model to be wrapped.
         supported_args (`list`):
             The list of arguments that are supported by the wrapper class.
@@ -111,19 +114,20 @@ def __init__(
     def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         r"""
         Instantiates a new model from a pretrained model from `transformers`. The pretrained model is loaded using the
-        `from_pretrained` method of the `transformers.PreTrainedModel` class. The arguments that are specific to the
-        `transformers.PreTrainedModel` class are passed along this method and filtered out from the `kwargs` argument.
+        `from_pretrained` method of the [`~transformers.PreTrainedModel`] class. The arguments that are specific to the
+        [`~transformers.PreTrainedModel`] class are passed along this method and filtered out from the `kwargs`
+        argument.
 
         Args:
-            pretrained_model_name_or_path (`str` or `transformers.PreTrainedModel`):
+            pretrained_model_name_or_path (`str` or [`~transformers.PreTrainedModel`]):
                 The path to the pretrained model or its name.
-            *model_args (`list`, *optional*)):
+            *model_args (`list`, *optional*):
                 Additional positional arguments passed along to the underlying model's `from_pretrained` method.
             **kwargs (`dict`, *optional*):
                 Additional keyword arguments passed along to the underlying model's `from_pretrained` method. We also
-                pre-process the kwargs to extract the arguments that are specific to the `transformers.PreTrainedModel`
-                class and the arguments that are specific to trl models. The kwargs also support
-                `prepare_model_for_kbit_training` arguments from `peft` library.
+                pre-process the kwargs to extract the arguments that are specific to the
+                [`~transformers.PreTrainedModel`] class and the arguments that are specific to trl models. The kwargs
+                also support `prepare_model_for_kbit_training` arguments from `peft` library.
         """
         if kwargs is not None:
             peft_config = kwargs.pop("peft_config", None)
@@ -507,8 +511,8 @@ def add_and_load_reward_modeling_adapter(
     def push_to_hub(self, *args, **kwargs):
         r"""
         Push the pretrained model to the hub. This method is a wrapper around
-        `transformers.PreTrainedModel.push_to_hub`. Please refer to the documentation of
-        `transformers.PreTrainedModel.push_to_hub` for more information.
+        [`~transformers.PreTrainedModel.push_to_hub`]. Please refer to the documentation of
+        [`~transformers.PreTrainedModel.push_to_hub`] for more information.
 
         Args:
             *args (`list`, *optional*):
@@ -521,8 +525,8 @@ def push_to_hub(self, *args, **kwargs):
     def save_pretrained(self, *args, **kwargs):
         r"""
         Save the pretrained model to a directory. This method is a wrapper around
-        `transformers.PreTrainedModel.save_pretrained`. Please refer to the documentation of
-        `transformers.PreTrainedModel.save_pretrained` for more information.
+        [`~transformers.PreTrainedModel.save_pretrained`]. Please refer to the documentation of
+        [`~transformers.PreTrainedModel.save_pretrained`] for more information.
 
         Args:
             *args (`list`, *optional*):
@@ -596,14 +600,14 @@ def create_reference_model(
     Creates a static reference copy of a model. Note that model will be in `.eval()` mode.
 
     Args:
-        model (`PreTrainedModelWrapper`): The model to be copied.
+        model ([`PreTrainedModelWrapper`]): The model to be copied.
         num_shared_layers (`int`, *optional*):
             The number of initial layers that are shared between both models and kept frozen.
         pattern (`str`, *optional*): The shared layers are selected with a string pattern
             (e.g. "transformer.h.{layer}" for GPT2) and if a custom pattern is necessary it can be passed here.
 
     Returns:
-        `PreTrainedModelWrapper`
+        [`PreTrainedModelWrapper`]
     """
     if is_deepspeed_zero3_enabled():
         raise ValueError(
@@ -665,13 +669,13 @@ def create_reference_model(
 
 
 class GeometricMixtureWrapper(GenerationMixin):
-    r"""
+    """
     Geometric Mixture generation wrapper that samples from the logits of two model's geometric mixture.
 
     Args:
-        model (`PreTrainedModel`): The model to be wrapped.
-        ref_model (`PreTrainedModel`): The reference model.
-        generation_config (`GenerationConfig`): The generation config.
+        model ([`~transformers.PreTrainedModel`]): The model to be wrapped.
+        ref_model ([`~transformers.PreTrainedModel`]): The reference model.
+        generation_config ([`~transformers.GenerationConfig`]): The generation config.
         mixture_coef (`float`, *optional* - default: 0.5): The mixture coefficient.
     """