File size: 3,573 Bytes
b648ce9 | 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 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # Copyright 2026 eCloud Yazılım Teknolojileri. Based on Apache-2.0 licensed transformer architecture.
#
# 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.
"""Erk model configuration"""
from huggingface_hub.dataclasses import strict
from transformers.configuration_utils import PreTrainedConfig
from transformers.modeling_rope_utils import RopeParameters
from transformers.utils import auto_docstring
@auto_docstring(checkpoint="ecloudtech/erk")
@strict
class ErkConfig(PreTrainedConfig):
r"""
```python
>>> from transformers import ErkModel, ErkConfig
>>> # Initializing a Erk style configuration
>>> configuration = ErkConfig()
>>> # Initializing a model from the Erk-8B style configuration
>>> model = ErkModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "erk"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Erk`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.q_norm": "replicated_with_grad_allreduce",
"layers.*.self_attn.k_norm": "replicated_with_grad_allreduce",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
vocab_size: int = 151936
hidden_size: int = 4096
intermediate_size: int = 22016
num_hidden_layers: int = 32
num_attention_heads: int = 32
num_key_value_heads: int | None = 32
head_dim: int = 128
hidden_act: str = "silu"
max_position_embeddings: int = 32768
initializer_range: float = 0.02
rms_norm_eps: float = 1e-6
use_cache: bool = True
tie_word_embeddings: bool = False
rope_parameters: RopeParameters | dict | None = None
attention_bias: bool = False
use_sliding_window: bool = False
sliding_window: int | None = 4096
max_window_layers: int = 28
layer_types: list[str] | None = None
attention_dropout: float | int = 0.0
pad_token_id: int | None = None
bos_token_id: int | None = None
eos_token_id: int | list[int] | None = None
def __post_init__(self, **kwargs):
self.sliding_window = self.sliding_window if self.use_sliding_window else None
if self.num_key_value_heads is None:
self.num_key_value_heads = self.num_attention_heads
if self.layer_types is None:
self.layer_types = [
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
for i in range(self.num_hidden_layers)
]
super().__post_init__(**kwargs)
__all__ = ["ErkConfig"]
|