Fix docstring interlinks #4221
Fix docstring interlinks #4221
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docs/source/peft_integration.md
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| ## How to use it? | ||
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| 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. |
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For some reasons, this one doesn't render
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Yes, I know, @qgallouedec: see my previous comment in:
The interlink will be properly rendered (as it is already the case for other HF libs, like
transformersordatasets) once this PR indoc-builderis merged:
trl/models/modeling_base.py
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| 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. | ||
| """ | ||
| A wrapper class around a [`~transformers.PreTrainedModel`] to be compatible with the (`~transformers.PreTrained`) |
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~transformers.PreTrained doesn't exist?
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No, it doesn't exist. I guess it was intended to mean PreTrainedModel instead.
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I changed the description:
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.
| - **v_head_init_strategy** (`str`, `optional`, defaults to `None`) -- The initialization strategy for the | ||
| `ValueHead`. Currently, the supported strategies are: | ||
| - **`None`** -- Initializes the weights of the `ValueHead` with a random distribution. This is the | ||
| [`ValueHead`]. Currently, the supported strategies are: |
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Fix docstrings interlinks.
This PR updates documentation and type annotations throughout the codebase to use explicit, linkable references to classes from external libraries such as
transformers,datasets, andpeft. The changes improve clarity for users by making it easier to identify and navigate to the relevant external types and configuration objects used in the code and documentation.