| |
| |
|
|
| from transformers import PretrainedConfig |
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|
| class TinyMixtralConfig(PretrainedConfig): |
| model_type = "tinymixtral" |
|
|
| def __init__( |
| self, |
| vocab_size: int = 32000, |
| hidden_size: int = 896, |
| num_hidden_layers: int = 10, |
| num_attention_heads: int = 14, |
| num_key_value_heads: int = 2, |
| head_dim: int = 64, |
| max_position_embeddings: int = 2048, |
| num_local_experts: int = 6, |
| num_experts_per_tok: int = 2, |
| expert_intermediate_size: int = 2389, |
| router_aux_loss_coef: float = 0.01, |
| router_jitter_noise: float = 0.01, |
| rms_norm_eps: float = 1e-6, |
| rope_theta: float = 1_000_000.0, |
| attention_dropout: float = 0.0, |
| tie_word_embeddings: bool = True, |
| initializer_range: float = 0.02, |
| **kwargs, |
| ): |
| super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) |
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_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.head_dim = head_dim |
| self.max_position_embeddings = max_position_embeddings |
| self.num_local_experts = num_local_experts |
| self.num_experts_per_tok = num_experts_per_tok |
| self.expert_intermediate_size = expert_intermediate_size |
| self.router_aux_loss_coef = router_aux_loss_coef |
| self.router_jitter_noise = router_jitter_noise |
| self.rms_norm_eps = rms_norm_eps |
| self.rope_theta = rope_theta |
| self.attention_dropout = attention_dropout |
| self.initializer_range = initializer_range |
|
|
