Add DynamicMind-MoE-Instruct: 2-epoch SFT on smol-smoltalk, custom MoE code, EOS-weighted loss
8d4372c verified | from transformers.configuration_utils import PretrainedConfig | |
| class DynamicMindMoEConfig(PretrainedConfig): | |
| """DynamicMind-MoE: sparse mixture-of-experts variant of DynamicMind-Mini. | |
| The dense MLP (intermediate 768) is replaced by one always-on shared expert | |
| plus `num_routed_experts` fine-grained experts (intermediate 256), of which | |
| `num_experts_per_token` are selected. Shared + top-2 reproduces the dense | |
| layer's exact active parameter count, so inference cost per token is | |
| unchanged while total capacity grows ~3.4x. | |
| """ | |
| model_type = "dynamicmind_moe" | |
| def __init__( | |
| self, | |
| vocab_size=8192, | |
| hidden_size=256, | |
| intermediate_size=768, # kept for dense layers / upcycling source | |
| moe_intermediate_size=256, # per-expert width (768 / 3) | |
| num_hidden_layers=9, | |
| num_attention_heads=8, | |
| num_key_value_heads=2, | |
| num_routed_experts=14, | |
| num_shared_experts=1, | |
| num_experts_per_token=2, | |
| first_k_dense_layers=0, # keep the first K blocks dense if desired | |
| norm_topk_prob=True, | |
| router_aux_loss_coef=0.01, | |
| router_z_loss_coef=0.001, | |
| router_bias_update_rate=0.001, # aux-loss-free balancing (DeepSeek-V3) | |
| use_aux_loss_free_balancing=True, | |
| max_position_embeddings=1024, | |
| rms_norm_eps=1e-5, | |
| rope_theta=10000.0, | |
| attention_dropout=0.0, | |
| tie_word_embeddings=True, | |
| bos_token_id=0, | |
| eos_token_id=0, | |
| pad_token_id=1, | |
| **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.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.moe_intermediate_size = moe_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.num_routed_experts = num_routed_experts | |
| self.num_shared_experts = num_shared_experts | |
| self.num_experts_per_token = num_experts_per_token | |
| self.first_k_dense_layers = first_k_dense_layers | |
| self.norm_topk_prob = norm_topk_prob | |
| self.router_aux_loss_coef = router_aux_loss_coef | |
| self.router_z_loss_coef = router_z_loss_coef | |
| self.router_bias_update_rate = router_bias_update_rate | |
| self.use_aux_loss_free_balancing = use_aux_loss_free_balancing | |
| self.max_position_embeddings = max_position_embeddings | |
| self.rms_norm_eps = rms_norm_eps | |
| self.rope_theta = rope_theta | |
| self.attention_dropout = attention_dropout | |