初始化项目，由ModelHub XC社区提供模型

Model: veyra-ai/Veyra-30M-Base
Source: Original Platform

configuration_veyra.py

@@ -0,0 +1,57 @@
+from transformers import PretrainedConfig
+
+
+class VeyraConfig(PretrainedConfig):
+    model_type = "veyra"
+
+    def __init__(
+        self,
+        vocab_size=8192,
+        d_model=512,
+        hidden_size=512,
+        n_q_heads=8,
+        num_attention_heads=8,
+        n_kv_heads=2,
+        num_key_value_heads=2,
+        intermediate_size=2048,
+        max_seq_len=1024,
+        max_position_embeddings=1024,
+        layer_pattern=("A", "M", "A", "M", "A", "M", "A", "M"),
+        num_hidden_layers=8,
+        rms_norm_eps=1e-6,
+        rope_theta=10000.0,
+        tie_word_embeddings=True,
+        bos_token_id=0,
+        eos_token_id=1,
+        pad_token_id=2,
+        unk_token_id=3,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.hidden_size = hidden_size
+
+        self.n_q_heads = n_q_heads
+        self.num_attention_heads = num_attention_heads
+        self.n_kv_heads = n_kv_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        self.intermediate_size = intermediate_size
+        self.max_seq_len = max_seq_len
+        self.max_position_embeddings = max_position_embeddings
+
+        self.layer_pattern = list(layer_pattern)
+        self.num_hidden_layers = num_hidden_layers
+
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.tie_word_embeddings = tie_word_embeddings
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            unk_token_id=unk_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )