65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
# adapted from https://www.modelscope.cn/models/TeleAI/TeleChat2-3B/resolve/master/configuration_telechat2.py
|
|
""" Telechat configuration compatible with LlamaConfig. """
|
|
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
|
|
|
|
class Telechat2Config(PretrainedConfig):
|
|
|
|
model_type = "telechat"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
attribute_map = {
|
|
"num_hidden_layers": "n_layer",
|
|
"num_attention_heads": "n_head",
|
|
"intermediate_size": "ffn_hidden_size",
|
|
"rms_norm_eps": "layer_norm_epsilon"
|
|
}
|
|
|
|
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,
|
|
hidden_act="silu",
|
|
**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.training_seqlen = training_seqlen
|
|
self.embed_layernorm = embed_layernorm
|
|
self.num_key_value_heads = kwargs.pop("num_key_value_heads", None)
|
|
self.ffn_hidden_size = ffn_hidden_size
|
|
self.hidden_act = hidden_act
|
|
super().__init__(bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
**kwargs)
|