stablelm-2-zephyr-1_6b / configuration_stablelm.py

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	# coding=utf-8
	# Copyright 2024 Stability AI 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 model configuration """

	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"stabilityai/stablelm-3b-4e1t": "https://huggingface.co/stabilityai/stablelm-3b-4e1t/resolve/main/config.json",
	# See all StableLM models at https://huggingface.co/models?filter=stablelm
	}


	class StableLmConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`~StableLmModel`].
	It is used to instantiate an StableLM 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 StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.

	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 50304):
	Vocabulary size of the StableLM model. Defines the number of different tokens that
	can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
	intermediate_size (`int`, optional, defaults to 6912):
	Dimension of the MLP representations.
	hidden_size (`int`, optional, defaults to 2560):
	Number of hidden layers in the Transformer decoder.
	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, defaults to 32):
	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).
	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).
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing
	all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-05):
	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 the model's input and output word embeddings should be tied.
	rope_theta (`float`, optional, defaults to `10000.0`):
	The base period of the RoPE embeddings.
	rope_scaling (`Dict`, optional):
	Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
	strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
	`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
	`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
	these scaling strategies behave:
	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.
	use_qkv_bias (`bool`, optional, defaults to `False`):
	Whether or not the model should use bias for qkv layers.
	hidden_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio after applying the MLP to the hidden states.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	partial_rotary_factor (`float`, optional, defaults to 0.25):
	Percentage of the query and keys which will have rotary embedding.
	bos_token_id (int, optional, defaults to 0):
	The id of the `BOS` token in the vocabulary.
	eos_token_id (int, optional, defaults to 0):
	The id of the `EOS` token in the vocabulary.

	Example:

	```python
	>>> from transformers import StableLmModel, StableLmConfig

	>>> # Initializing a StableLM stablelm-3b style configuration
	>>> configuration = StableLmConfig()
	```"""

	model_type = "stablelm"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=50304,
	intermediate_size=6912,
	hidden_size=2560,
	num_hidden_layers=32,
	num_attention_heads=32,
	num_key_value_heads=32,
	hidden_act="silu",
	max_position_embeddings=4096,
	initializer_range=0.02,
	layer_norm_eps=1.0e-5,
	use_cache=True,
	tie_word_embeddings=False,
	rope_theta=10_000,
	rope_scaling=None,
	use_qkv_bias=False,
	hidden_dropout=0.0,
	attention_dropout=0.0,
	partial_rotary_factor=0.25,
	bos_token_id=0,
	eos_token_id=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
	self.num_key_value_heads = num_key_value_heads
	self.hidden_act = hidden_act

	self.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.use_cache = use_cache
	self.rope_theta = rope_theta
	self.rope_scaling = rope_scaling
	self.use_qkv_bias = use_qkv_bias
	self.hidden_dropout = hidden_dropout
	self.attention_dropout = attention_dropout
	self.partial_rotary_factor = partial_rotary_factor
	self._rope_scaling_validation()

	super().__init__(
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)

	# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
	def _rope_scaling_validation(self):
	"""
	Validate the `rope_scaling` configuration.
	"""
	if self.rope_scaling is None:
	return

	if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
	raise ValueError(
	"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
	f"got {self.rope_scaling}"
	)
	rope_scaling_type = self.rope_scaling.get("type", None)
	rope_scaling_factor = self.rope_scaling.get("factor", None)
	if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
	raise ValueError(
	f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
	)
	if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
	raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")