# Copyright 2023 DeepLang AI. All Rights Reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class LingoWhaleConfig(PretrainedConfig): model_type = "lingowhale" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=96000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=36, num_attention_heads=32, hidden_act="silu", max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-6, emb_dropout_prob=0.0, attn_dropout_prob=0.0, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, use_flash_attention=True, **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.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.emb_dropout_prob = emb_dropout_prob self.attn_dropout_prob = attn_dropout_prob self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.use_flash_attention = use_flash_attention 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, )