slim-sa-ner / configuration_stablelm_epoch.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.
	""" StableLM Epoch model configuration"""
	from transformers import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)


	class StableLMEpochConfig(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 50_304):
	Vocabulary size of the StableLM model. Defines the number of different tokens that
	can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
	intermediate_size (`int`, optional, defaults to 6912):
	Dimension of the MLP representations.
	hidden_size (`int`, optional, defaults to 2560):
	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 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).
	rope_pct (`float`, optional, defaults to 1.0):
	Percentage of hidden dimensions to allocate to rotary embeddings.
	rope_theta (`float`, optional, defaults to 10000.0):
	The base period of the RoPE embeddings.
	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.
	norm_eps (`float`, optional, defaults to 1e-8):
	The epsilon used by the 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
	"""
	model_type = "stablelm_epoch"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=50_304,
	intermediate_size=6912,
	hidden_size=2560,
	num_hidden_layers=32,
	num_attention_heads=32,
	num_key_value_heads=32,
	hidden_act="silu",
	rope_pct=0.25,
	rope_theta=10_000,
	max_position_embeddings=4096,
	initializer_range=0.02,
	norm_eps=1.0e-5,
	use_cache=True,
	bos_token_id=0,
	eos_token_id=2,
	tie_word_embeddings=False,
	**kwargs,
	):
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.intermediate_size = intermediate_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.num_key_value_heads = num_key_value_heads
	self.hidden_act = hidden_act
	self.rope_pct = rope_pct
	self.rope_theta = rope_theta
	self.initializer_range = initializer_range
	self.norm_eps = norm_eps
	self.use_cache = use_cache
	self.tie_word_embeddings = tie_word_embeddings
	super().__init__(
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)