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from transformers.configuration_utils import PretrainedConfig
class Grok1Config(PretrainedConfig):
model_type = "grok-1"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=32768,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
attn_output_multiplier=1.0,
max_attn_value=1.0,
max_position_embeddings=4096,
embedding_multiplier_scale: float = 1.0,
output_multiplier_scale: float = 1.0,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
num_experts_per_tok=2,
num_experts=8,
output_router_logits=False,
router_aux_loss_coef=0.001,
**kwargs
):
self.vocab_size = vocab_size
self.attn_output_multiplier = attn_output_multiplier
self.max_attn_value = max_attn_value
self.max_position_embeddings = max_position_embeddings
self.embedding_multiplier_scale = embedding_multiplier_scale
self.output_multiplier_scale = output_multiplier_scale
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# 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.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
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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