"""Quasar Long model configuration""" from transformers.configuration_utils import PretrainedConfig class QuasarLongConfig(PretrainedConfig): model_type = "quasar_long" def __init__( self, vocab_size=157184, hidden_size=2048, intermediate_size=5120, num_hidden_layers=20, num_attention_heads=16, num_key_value_heads=4, hidden_act="silu", use_qkv_bias=False, # quasar legacy use_bias=False, # quasar legacy rms_norm_eps=1e-06, tie_word_embeddings=False, # PretrainedConfig key, here change default value. embedding_dropout=0.0, attention_dropout=0.0, output_dropout=0.0, initializer_range=0.02, max_position_embeddings=32768, rope_theta=600000.0, use_cache=True, max_window_layers=20, rope_scaling=None, pad_token_id=156892, eos_token_id=156892, num_experts=256, num_shared_experts=1, num_experts_per_tok=8, n_group=8, topk_group=4, moe_intermediate_size=512, first_k_dense_replace=1, head_dim=128, output_router_logits=False, use_qk_norm=True, num_nextn_predict_layers=0, mtp_loss_scaling_factor=0, moe_router_enable_expert_bias=True, routed_scaling_factor=1.0, hybrid_attention_layers=None, hybrid_alpha_init=-15.0, hybrid_gla_expand_k=1.0, hybrid_gla_expand_v=1.0, hybrid_use_short_conv=False, hybrid_quasar_enabled=True, hybrid_gla_enabled=True, hybrid_branch_layout="mixed", hybrid_layerwise_cycle=None, # ── Looped Transformer ──────────────────────────────────────────────── num_loops=1, use_looped_injection=False, # ── Engram Conditional Memory ───────────────────────────────────────── # engram_layers=[] → module disabled (zero overhead, backward-compatible). engram_layers=None, engram_dim=512, engram_slots=2_000_000, engram_num_heads=8, engram_ngram_orders=None, engram_lr_multiplier=5.0, use_nope=False, long_context_mode="rope_short_nope_long", nope_after_position=512, max_seq_length=None, max_sequence_length=None, **kwargs, ): self.num_hidden_layers = num_hidden_layers self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.use_qkv_bias = use_qkv_bias self.use_bias = use_bias self.rms_norm_eps = rms_norm_eps self.embedding_dropout = embedding_dropout self.attention_dropout = attention_dropout self.output_dropout = output_dropout self.num_nextn_predict_layers = num_nextn_predict_layers self.mtp_loss_scaling_factor = mtp_loss_scaling_factor self.initializer_range = initializer_range self.max_position_embeddings = max_position_embeddings self.rope_theta = rope_theta self.use_cache = use_cache self.max_window_layers = max_window_layers self.head_dim = head_dim or self.hidden_size // self.num_attention_heads self.rope_scaling = rope_scaling self.use_qk_norm = use_qk_norm self.moe_router_enable_expert_bias = moe_router_enable_expert_bias self.routed_scaling_factor = routed_scaling_factor self.hybrid_attention_layers = hybrid_attention_layers or [] self.hybrid_alpha_init = hybrid_alpha_init self.hybrid_gla_expand_k = hybrid_gla_expand_k self.hybrid_gla_expand_v = hybrid_gla_expand_v self.hybrid_use_short_conv = hybrid_use_short_conv self.hybrid_quasar_enabled = hybrid_quasar_enabled self.hybrid_gla_enabled = hybrid_gla_enabled self.hybrid_branch_layout = hybrid_branch_layout self.hybrid_layerwise_cycle = list(hybrid_layerwise_cycle) if hybrid_layerwise_cycle is not None else [ "quasar", "raven", "gla", ] # Looped Transformer self.num_loops = num_loops self.use_looped_injection = use_looped_injection # Engram Conditional Memory self.engram_layers = list(engram_layers) if engram_layers is not None else [] self.engram_dim = engram_dim self.engram_slots = engram_slots self.engram_num_heads = engram_num_heads self.engram_ngram_orders = list(engram_ngram_orders) if engram_ngram_orders is not None else [2, 3] self.engram_lr_multiplier = engram_lr_multiplier self.use_nope = use_nope self.long_context_mode = long_context_mode self.nope_after_position = int(nope_after_position) self.max_seq_length = int(max_seq_length) if max_seq_length is not None else None self.max_sequence_length = int(max_sequence_length) if max_sequence_length is not None else None # MoE configs self.num_experts = num_experts self.num_shared_experts = num_shared_experts self.num_experts_per_tok = num_experts_per_tok self.n_group = n_group self.topk_group = topk_group self.moe_intermediate_size = moe_intermediate_size self.first_k_dense_replace = first_k_dense_replace self.output_router_logits = output_router_logits super().__init__(pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)