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[docstring] Fix docstring for LlamaConfig (#26685)

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* fix LlamaConfig docstring

* run make fixup

* fix formatting after review

reformat of the file to prevent script issues

* rerun make fixup after reformat
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Pavarissy
2023-10-10 22:05:48 +07:00
committed by GitHub
parent a9862a0f49
commit e8fdd7875d
2 changed files with 14 additions and 10 deletions
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23
src/transformers/models/llama/configuration_llama.py
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@@ -58,11 +58,6 @@ class LlamaConfig(PretrainedConfig):
by meanpooling all the original heads within that group. For more details checkout [this by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`. `num_attention_heads`.
pretraining_tp (`int`, *optional*, defaults to `1`):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder. The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048): max_position_embeddings (`int`, *optional*, defaults to 2048):
@@ -70,12 +65,23 @@ class LlamaConfig(PretrainedConfig):
Llama 2 up to 4096, CodeLlama up to 16384. Llama 2 up to 4096, CodeLlama up to 16384.
initializer_range (`float`, *optional*, defaults to 0.02): initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-12): rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers. The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`): use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. relevant if `config.is_decoder=True`.
tie_word_embeddings(`bool`, *optional*, defaults to `False`): pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0): rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings. The base period of the RoPE embeddings.
@@ -87,10 +93,9 @@ class LlamaConfig(PretrainedConfig):
these scaling strategies behave: these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions. experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, defaults to `False`): attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention. Whether to use a bias in the query, key, value and output projection layers during self-attention.
Example:
```python ```python
>>> from transformers import LlamaModel, LlamaConfig >>> from transformers import LlamaModel, LlamaConfig

1
utils/check_docstrings.py
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@@ -361,7 +361,6 @@ OBJECTS_TO_IGNORE = [
"LevitConfig", "LevitConfig",
"LiltConfig", "LiltConfig",
"LiltModel", "LiltModel",
"LlamaConfig",
"LlamaTokenizer", "LlamaTokenizer",
"LlamaTokenizerFast", "LlamaTokenizerFast",
"LongT5Config", "LongT5Config",