|
""" Yi model configuration""" |
|
from transformers.configuration_utils import PretrainedConfig |
|
from transformers.utils import logging |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
Yi_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
|
|
|
|
|
class YiConfig(PretrainedConfig): |
|
r""" |
|
This is the configuration class to store the configuration of a [`YiModel`]. It is used to instantiate an Yi |
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
|
defaults will yield a similar configuration to that of the Yi model. |
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
|
documentation from [`PretrainedConfig`] for more information. |
|
|
|
|
|
Args: |
|
vocab_size (`int`, *optional*, defaults to 64000): |
|
Vocabulary size of the Yi model. Defines the number of different tokens that can be represented by the |
|
`inputs_ids` passed when calling [`YiModel`] |
|
hidden_size (`int`, *optional*, defaults to 4096): |
|
Dimension of the hidden representations. |
|
intermediate_size (`int`, *optional*, defaults to 11008): |
|
Dimension of the MLP representations. |
|
num_hidden_layers (`int`, *optional*, defaults to 32): |
|
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 Transformer encoder. |
|
num_key_value_heads (`int`, *optional*): |
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If |
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if |
|
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When |
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed |
|
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 |
|
`num_attention_heads`. |
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): |
|
The non-linear activation function (function or string) in the decoder. |
|
max_position_embeddings (`int`, *optional*, defaults to 4096): |
|
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 or 4096). |
|
initializer_range (`float`, *optional*, defaults to 0.02): |
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
|
rms_norm_eps (`float`, *optional*, defaults to 1e-5): |
|
The epsilon used by the rms normalization layers. |
|
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 |
|
relevant if `config.is_decoder=True`. |
|
tie_word_embeddings(`bool`, *optional*, defaults to `False`): |
|
Whether to tie weight embeddings |
|
output_attentions (`bool`, *optional*, defaults to `False`): |
|
Whether or not to output attentions. |
|
rope_theta (`float`, *optional*, defaults to 5000000.0): |
|
The base period of the RoPE embeddings. |
|
Example: |
|
|
|
```python |
|
>>> from transformers import YiModel, YiConfig |
|
|
|
>>> # Initializing a Yi style configuration |
|
>>> configuration = YiConfig() |
|
|
|
>>> # Initializing a model from the Yi style configuration |
|
>>> model = YiModel(configuration) |
|
|
|
>>> # Accessing the model configuration |
|
>>> configuration = model.config |
|
```""" |
|
model_type = "Yi" |
|
keys_to_ignore_at_inference = ["past_key_values"] |
|
|
|
def __init__( |
|
self, |
|
vocab_size=64000, |
|
hidden_size=4096, |
|
intermediate_size=11008, |
|
num_hidden_layers=32, |
|
num_attention_heads=32, |
|
num_key_value_heads=4, |
|
hidden_act="silu", |
|
max_position_embeddings=4096, |
|
initializer_range=0.02, |
|
rms_norm_eps=1e-5, |
|
use_cache=True, |
|
pad_token_id=0, |
|
bos_token_id=1, |
|
eos_token_id=2, |
|
tie_word_embeddings=False, |
|
output_attentions=False, |
|
rope_theta=5000000.0, |
|
**kwargs, |
|
): |
|
self.vocab_size = vocab_size |
|
self.max_position_embeddings = max_position_embeddings |
|
self.hidden_size = hidden_size |
|
self.intermediate_size = intermediate_size |
|
self.num_hidden_layers = num_hidden_layers |
|
self.num_attention_heads = num_attention_heads |
|
|
|
|
|
if num_key_value_heads is None: |
|
num_key_value_heads = num_attention_heads |
|
|
|
self.num_key_value_heads = num_key_value_heads |
|
self.hidden_act = hidden_act |
|
self.initializer_range = initializer_range |
|
self.rms_norm_eps = rms_norm_eps |
|
self.use_cache = use_cache |
|
self.output_attentions = output_attentions |
|
self.rope_theta = rope_theta |
|
|
|
super().__init__( |
|
pad_token_id=pad_token_id, |
|
bos_token_id=bos_token_id, |
|
eos_token_id=eos_token_id, |
|
tie_word_embeddings=tie_word_embeddings, |
|
**kwargs, |
|
) |
|
|
