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from transformers import PretrainedConfig
class RECASTMLP_llama(PretrainedConfig):
model_type = "recastmlp_llama"
attribute_map = {
"hidden_size": "hidden_size",
"num_attention_heads": "num_attention_heads",
}
def __init__(
self,
vocab_size=128256,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=131072,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=128000,
eos_token_id=128001,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=500000.0,
rope_scaling={
"factor": 8.0,
"low_freq_factor": 1.0,
"high_freq_factor": 4.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3",
},
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
# Template-specific configs
num_templates=4,
num_groups=8,
num_cf=1,
torch_dtype="bfloat16",
**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.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.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.mlp_bias = mlp_bias
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.torch_dtype = torch_dtype
# Template-specific configs
self.num_templates = num_templates
self.num_groups = num_groups
self.num_cf = num_cf
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs
)
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