初始化项目，由ModelHub XC社区提供模型

Model: Tele-AI/TeleChat2-3B
Source: Original Platform

configuration_telechat2.py

@@ -0,0 +1,94 @@
+# coding=utf-8
+# Copyright 2022 the Big Science Workshop and 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.
+
+""" Telechat configuration"""
+
+from packaging import version
+from collections import OrderedDict
+from transformers.utils import is_torch_available, logging
+from transformers.configuration_utils import PretrainedConfig
+from typing import TYPE_CHECKING, Any, List, Mapping, Optional
+
+logger = logging.get_logger(__name__)
+
+class Telechat2Config(PretrainedConfig):
+    """
+    Args:
+        vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
+        hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
+        ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
+        n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
+        n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers.
+        initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
+        hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
+        attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
+        use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions.
+        training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
+        logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
+        embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.
+
+    """
+
+    model_type = "telechat"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_hidden_layers": "n_layer",
+        "num_attention_heads": "n_head",
+    }
+
+    def __init__(
+        self,
+        vocab_size=160256,
+        hidden_size=4096,
+        n_layer=30,
+        n_head=32,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        apply_residual_connection_post_layernorm=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        ffn_hidden_size=12288,
+        training_seqlen = 8192,
+        logn = True,
+        embed_layernorm = False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        n_embed = kwargs.pop("n_embed", None)
+        self.hidden_size = hidden_size if n_embed is None else n_embed
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.logn = logn
+        self.ffn_hidden_size = ffn_hidden_size
+        self.training_seqlen = training_seqlen
+        self.embed_layernorm = embed_layernorm
+        self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
+
+
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+