| from transformers import PretrainedConfig | |
| class RSLMConfig(PretrainedConfig): | |
| model_type = "rslm" | |
| def __init__( | |
| self, | |
| hidden_size=2048, | |
| num_layers=24, | |
| num_q_heads=16, | |
| num_kv_heads=1, | |
| head_dim=128, | |
| intermediate_size=4352, | |
| vocab_size=65536, | |
| max_position_embeddings=262144, | |
| original_max_position_embeddings=8192, | |
| rope_theta=1000000.0, | |
| rope_scaling=None, | |
| window_size=4096, | |
| global_layers_0idx=(5, 11, 17, 23), | |
| evict_local_kv=True, | |
| local_cache_keep=4096, | |
| parallel_block=True, | |
| rms_norm_eps=1e-6, | |
| hidden_act="swiglu", | |
| tie_word_embeddings=True, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| pad_token_id=0, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| pad_token_id=pad_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |
| self.hidden_size = hidden_size | |
| self.num_layers = num_layers | |
| self.num_q_heads = num_q_heads | |
| self.num_kv_heads = num_kv_heads | |
| self.head_dim = head_dim | |
| self.intermediate_size = intermediate_size | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.original_max_position_embeddings = original_max_position_embeddings | |
| self.rope_theta = rope_theta | |
| self.rope_scaling = rope_scaling | |
| self.window_size = window_size | |
| self.global_layers_0idx = list(global_layers_0idx) | |
| self.evict_local_kv = evict_local_kv | |
| self.local_cache_keep = local_cache_keep | |
| self.parallel_block = parallel_block | |
| self.rms_norm_eps = rms_norm_eps | |
| self.hidden_act = hidden_act | |