Delete configuration_stablelm_epoch.py

configuration_stablelm_epoch.py

@@ -1,110 +0,0 @@
-# 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,
-        )