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Add full API docs and improve the UX of navigating them (#17485)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor
2025-05-04 03:42:43 +01:00
committed by GitHub
parent 46fae69cf0
commit d6484ef3c3
101 changed files with 872 additions and 980 deletions
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3
vllm/transformers_utils/configs/dbrx.py
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@@ -196,8 +196,7 @@ class DbrxConfig(PretrainedConfig):
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabling this will also
allow the model to output the auxiliary loss. See [here]() for more details
Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.

44
vllm/transformers_utils/configs/exaone.py
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@@ -35,22 +35,22 @@ class ExaoneConfig(PretrainedConfig):
Instantiating a configuration with the defaults will yield a similar
configuration to that of the Exaone
Configuration objects inherit from :class:`~transformers.PretrainedConfig`
Configuration objects inherit from {class}`~transformers.PretrainedConfig`
and can be used to control the model outputs. Read the documentation from :
class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 50257):
vocab_size ({obj}`int`, `optional`, defaults to 50257):
Vocabulary size of the GPT Lingvo model. Defines the number of
different tokens that can be represented by the :obj:`inputs_ids`
passed when calling :class:`~transformers.ExaoneModel`. Vocabulary
different tokens that can be represented by the {obj}`inputs_ids`
passed when calling {class}`~transformers.ExaoneModel`. Vocabulary
size of the model.
Defines the different tokens that can be represented by the
`inputs_ids` passed to the forward method of :class:
`~transformers.EXAONEModel`.
hidden_size (:obj:`int`, `optional`, defaults to 2048):
hidden_size ({obj}`int`, `optional`, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
num_layers (:obj:`int`, `optional`, defaults to 24):
num_layers ({obj}`int`, `optional`, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the
@@ -68,37 +68,37 @@ class ExaoneConfig(PretrainedConfig):
specified, will default to `num_attention_heads`.
rotary_pct (`float`, *optional*, defaults to 0.25):
percentage of hidden dimensions to allocate to rotary embeddings
intermediate_size (:obj:`int`, `optional`, defaults to 8192):
intermediate_size ({obj}`int`, `optional`, defaults to 8192):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in
the Transformer encoder.
activation_function (:obj:`str` or :obj:`function`, `optional`,
defaults to :obj:`"gelu_new"`):
activation_function ({obj}`str` or {obj}`function`, `optional`,
defaults to {obj}`"gelu_new"`):
The non-linear activation function (function or string) in the
encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`,
:obj:`"selu"` and :obj:`"gelu_new"` are supported.
embed_dropout (:obj:`float`, `optional`, defaults to 0.0):
encoder and pooler. If string, {obj}`"gelu"`, {obj}`"relu"`,
{obj}`"selu"` and {obj}`"gelu_new"` are supported.
embed_dropout ({obj}`float`, `optional`, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the
embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
attention_dropout ({obj}`float`, `optional`, defaults to 0.0):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 2048):
max_position_embeddings ({obj}`int`, `optional`, defaults to 2048):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling
:class:`~transformers.EXAONEModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
type_vocab_size ({obj}`int`, `optional`, defaults to 2):
The vocabulary size of the {obj}`token_type_ids` passed when calling
{class}`~transformers.EXAONEModel`.
initializer_range ({obj}`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices.
layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
layer_norm_epsilon ({obj}`float`, `optional`, defaults to 1e-5):
The epsilon used by the layer normalization layers.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
use_cache ({obj}`bool`, `optional`, defaults to {obj}`True`):
Whether or not the model should return the last key/values
attentions (not used by all models).
Only relevant if ``config.is_decoder=True``.
gradient_checkpointing (:obj:`bool`, `optional`,
defaults to :obj:`False`):
gradient_checkpointing ({obj}`bool`, `optional`,
defaults to {obj}`False`):
If True, use gradient checkpointing to save memory at the expense
of slower backward pass.
Example::