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from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class VGSConfig(Qwen2Config):
model_type = 'vgs'
def __init__(
self,
vocab_size=151936,
hidden_size=1536,
intermediate_size=8960,
num_hidden_layers=28,
num_attention_heads=12,
num_key_value_heads=2,
hidden_act="silu",
max_position_embeddings=131072,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=False,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=21,
attention_dropout=0.05,
num_labels=3,
use_bias=False,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
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.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window # we check `use_sliding_window` in the modeling code
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout = attention_dropout
self.num_labels = num_labels
self.use_bias = use_bias
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
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