from transformers import PretrainedConfig # Copied from huggingface/transformers configuration_mixtral.py. # Modified to default values provided by xai-org/grok-1 run.py. class Grok1Config(PretrainedConfig): model_type = "grok-1" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=131072, max_position_embeddings=8192, output_multiplier_scale=0.5773502691896257, embedding_multiplier_scale=78.38367176906169, hidden_size=6144, intermediate_size=32768, num_hidden_layers=64, num_attention_heads=48, attn_output_multiplier=0.08838834764831845, num_key_value_heads=8, hidden_act="gelu", initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=0, bos_token_id=2, eos_token_id=2, tie_word_embeddings=False, rope_theta=int(1e4), attention_dropout=0.0, num_experts_per_tok=2, num_local_experts=8, output_router_logits=False, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.output_multiplier_scale = output_multiplier_scale, self.embedding_multiplier_scale = embedding_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 self.attn_output_multiplier = attn_output_multiplier # 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.attention_dropout = attention_dropout self.num_experts_per_tok = num_experts_per_tok self.num_local_experts = num_local_experts self.output_router_logits = output_router_logits 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, )