File size: 2,307 Bytes
0d35f1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | # Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
# 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 typing import List
from transformers import PretrainedConfig
class TimerS1Config(PretrainedConfig):
model_type = "Timer-S1"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
input_token_len: int = 16,
hidden_size: int = 1024,
intermediate_size: int = 4096,
output_token_lens: List[int] = [16],
num_hidden_layers: int = 24,
num_attention_heads: int = 16,
hidden_act: str = "silu",
use_cache: bool = True,
rope_theta: int = 10000,
dropout_rate: float = 0.1,
initializer_range: float = 0.02,
max_position_embeddings: int = 12800,
quantiles: List[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
num_experts: int = 32,
num_experts_per_token: int = 2,
# MTP configuration
num_mtp_tokens: int = 16,
**kwargs,
):
self.input_token_len = input_token_len
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.output_token_lens = output_token_lens
self.use_cache = use_cache
self.rope_theta = rope_theta
self.dropout_rate = dropout_rate
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.quantiles = quantiles
self.num_experts = num_experts
self.num_experts_per_token = num_experts_per_token
# MTP configuration
self.num_mtp_tokens = num_mtp_tokens
super().__init__(**kwargs) |