# coding=utf-8 # Copyright 2023 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. """ Quiet model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) QUIET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "cognitivecomputations/Quiet-STaR-Base": "https://huggingface.co/quietai/Quiet-7B-v0.1/resolve/main/config.json", } class QuietConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`QuietModel`]. It is used to instantiate an Quiet 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 Quiet-7B-v0.1 or Quiet-7B-Instruct-v0.1. [quietai/Quiet-7B-v0.1](https://huggingface.co/quietai/Quiet-7B-v0.1) [quietai/Quiet-7B-Instruct-v0.1](https://huggingface.co/quietai/Quiet-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 Quiet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`QuietModel`] 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. Quiet'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-06): 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 10000.0): The base period of the RoPE embeddings. sliding_window (`int`, *optional*, defaults to 4096): 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. ```python >>> from transformers import QuietModel, QuietConfig >>> # Initializing a Quiet 7B style configuration >>> configuration = QuietConfig() >>> # Initializing a model from the Quiet 7B style configuration >>> model = QuietModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "quiet" 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-6, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=10000.0, complexity_factor = 0.5, sliding_window=4096, attention_dropout=0.0, max_thoughts=16, max_time=None, max_temperature=10, merged_talk_heads=True, merged_lm_and_talk_heads=False, merged_lm_and_think_heads=True, use_concat_talk_head=True, use_shallow_think=True, use_shallow_talk=False, use_complex_think_head=False, use_complex_talk_head=True, use_weighted_talk_head=True, hidden_dropout_prob=0.0, **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.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.max_thoughts = max_thoughts self.max_time = max_time self.complexity_factor = complexity_factor self.max_temperature = max_temperature self.merged_talk_heads = merged_talk_heads self.merged_lm_and_talk_heads = merged_lm_and_talk_heads self.merged_lm_and_think_heads = merged_lm_and_think_heads self.use_concat_talk_head = use_concat_talk_head self.use_shallow_think = use_shallow_think self.use_shallow_talk = use_shallow_talk self.use_complex_think_head = use_complex_think_head self.use_complex_talk_head = use_complex_talk_head self.use_weighted_talk_head = use_weighted_talk_head self.hidden_dropout_prob = hidden_dropout_prob 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, )