japanese-stablelm-instruct-alpha-7b-v2 / configuration_japanese_stablelm_alpha.py

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	# coding=utf-8
	# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" JapaneseStableLMAlpha model configuration"""

	from transformers import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	STABLE_LM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}


	class JapaneseStableLMAlphaConfig(PretrainedConfig):
	r"""
	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 65536):
	Vocabulary size of the JapaneseStableLMAlphaModel. Defines the number of different tokens that
	can be represented by the `inputs_ids` passed when calling [`JapaneseStableLMAlphaModel`].
	hidden_size (`int`, optional, defaults to 4096):
	Dimension of the decoder layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 32):
	Number of hidden layers in the Transformer decoder.
	num_attention_heads (`int`, optional, defaults to 32):
	Number of attention heads for each attention layer in the Transformer decoder.
	intermediate_size (`int`, optional, defaults to 16384):
	Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer decoder.
	hidden_act (`str` or `function`, optional, defaults to `"silu"`):
	The non-linear activation function (function or string).
	rotary_pct (`float`, optional, defaults to 0.25):
	Percentage of hidden dimensions to allocate to rotary embeddings.
	rotary_emb_base (`int`, optional, defaults to 10000)
	Base for computing rotary embeddings frequency.
	rotary_scale_base (`int`, optional, defaults to 512)
	Base `scale` for computing XPos rotary embeddings scale.
	classifier_dropout (`float`, optional, defaults to 0.1):
	Argument used when doing token classification, used in the model
	[`StableLMForTokenClassification`]. The dropout ratio for the hidden layer.
	max_position_embeddings (`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).
	initializer_range (`float`, optional, defaults to 1e-5):
	The standard deviation of the truncated_normal_initializer for initializing
	all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-12):
	The epsilon used by the layer 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`.
	use_parallel_residual (`bool`, optional, defaults to `True`):
	Whether to use a "parallel" formulation in each Transformer layer,
	which can provide a slight training speedup at large scales.
	Example:

	```python
	>>> from transformers import JapaneseStableLMAlphaConfig, JapaneseStableLMAlphaModel

	>>> # Initializing a JapaneseStableLMAlpha style configuration
	>>> configuration = JapaneseStableLMAlphaConfig()

	>>> # Initializing a model (with random weights) from the style configuration
	>>> model = JapaneseStableLMAlphaModel(configuration) # doctest: +SKIP

	>>> # Accessing the model configuration
	>>> configuration = model.config # doctest: +SKIP
	```"""
	def __init__(
	self,
	vocab_size=65536,
	hidden_size=4096,
	num_hidden_layers=32,
	num_attention_heads=32,
	hidden_act="silu",
	rotary_pct=0.25,
	rotary_emb_base=10000,
	rotary_scale_base=512,
	classifier_dropout=0.1,
	max_position_embeddings=2048,
	initializer_range=0.02,
	layer_norm_eps=1e-5,
	use_cache=True,
	bos_token_id=3,
	eos_token_id=3,
	tie_word_embeddings=False,
	use_parallel_residual=True,
	use_bias_in_mlp=True,
	**kwargs,
	):
	super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.hidden_act = hidden_act
	self.rotary_pct = rotary_pct
	self.rotary_emb_base = rotary_emb_base
	self.rotary_scale_base = rotary_scale_base
	self.classifier_dropout = classifier_dropout
	self.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.use_cache = use_cache
	self.tie_word_embeddings = tie_word_embeddings
	self.use_parallel_residual = use_parallel_residual
	self.use_bias_in_mlp = use_bias_in_mlp