Nous-Hermes-2-Mixtral-8x7B-20m-DPO-raw / configuration_trimmed_mixtral.py

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	# coding=utf-8
	# Copyright 2023 Mixtral 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.
	""" Mixtral model configuration"""

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


	logger = logging.get_logger(__name__)

	MIXTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"mistral-ai/Mixtral-8x7B": "https://huggingface.co/mistral-ai/Mixtral-8x7B/resolve/main/config.json",
	}

	DEFAULT_MOE = [8]*32


	class MixtralConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an
	Mixtral 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 Mixtral-7B-v0.1 or Mixtral-7B-Instruct-v0.1.

	[mixtralai/Mixtral-8x7B](https://huggingface.co/mixtralai/Mixtral-8x7B)
	[mixtralai/Mixtral-7B-Instruct-v0.1](https://huggingface.co/mixtralai/Mixtral-7B-Instruct-v0.1)

	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 32000):
	Vocabulary size of the Mixtral model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`MixtralModel`]
	hidden_size (`int`, optional, defaults to 4096):
	Dimension of the hidden representations.
	intermediate_size (`int`, optional, defaults to 14336):
	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, defaults to 8):
	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 `8`.
	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*32`):
	The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
	allows sequence of up to 4096*32 tokens.
	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-05):
	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`.
	pad_token_id (`int`, optional):
	The id of the padding token.
	bos_token_id (`int`, optional, defaults to 1):
	The id of the "beginning-of-sequence" token.
	eos_token_id (`int`, optional, defaults to 2):
	The id of the "end-of-sequence" token.
	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 1000000.0):
	The base period of the RoPE embeddings.
	sliding_window (`int`, optional):
	Sliding window attention window size. If not specified, will default to `4096`.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	num_experts_per_tok (`int`, optional, defaults to 2):
	The number of experts to root per-token, can be also interpreted as the `top-p` routing
	parameter
	num_local_experts (`int`, optional, defaults to 8):
	Number of experts per Sparse MLP layer.
	output_router_logits (`bool`, optional, defaults to `False`):
	Whether or not the router logits should be returned by the model. Enabeling this will also
	allow the model to output the auxiliary loss. See [here]() for more details
	router_aux_loss_coef (`float`, optional, defaults to 0.001):
	The aux loss factor for the total loss.

	```python
	>>> from transformers import MixtralModel, MixtralConfig

	>>> # Initializing a Mixtral 7B style configuration
	>>> configuration = MixtralConfig()

	>>> # Initializing a model from the Mixtral 7B style configuration
	>>> model = MixtralModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""

	model_type = "mixtral"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=32000,
	hidden_size=4096,
	intermediate_size=14336,
	num_hidden_layers=32,
	num_attention_heads=32,
	num_key_value_heads=8,
	hidden_act="silu",
	max_position_embeddings=4096 * 32,
	initializer_range=0.02,
	rms_norm_eps=1e-5,
	use_cache=True,
	pad_token_id=None,
	bos_token_id=1,
	eos_token_id=2,
	tie_word_embeddings=False,
	rope_theta=1e6,
	moe_layers=DEFAULT_MOE,
	sliding_window=None,
	attention_dropout=0.0,
	num_experts_per_tok=2,
	num_local_experts=8,
	output_router_logits=False,
	router_aux_loss_coef=0.001,
	**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.sliding_window = sliding_window

	# for backward compatibility
	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.rope_theta = rope_theta
	self.attention_dropout = attention_dropout

	self.num_experts_per_tok = num_experts_per_tok
	self.num_local_experts = num_local_experts
	self.output_router_logits = output_router_logits
	self.router_aux_loss_coef = router_aux_loss_coef
	self.moe_layers = moe_layers
	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,
	)