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configuration_recast_llama.py

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+from transformers import PretrainedConfig
+
+
+class RECAST1B_llama(PretrainedConfig):
+    model_type = "recast1b_llama"
+    attribute_map = {
+        "hidden_size": "hidden_size",
+        "num_attention_heads": "num_attention_heads",
+    }
+
+    def __init__(
+        self,
+        vocab_size=128256,
+        hidden_size=2048,
+        intermediate_size=8192,
+        num_hidden_layers=16,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=128000,
+        eos_token_id=128001,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=500000.0,
+        rope_scaling={
+            "factor": 32.0,
+            "low_freq_factor": 1.0,
+            "high_freq_factor": 4.0,
+            "original_max_position_embeddings": 8192,
+            "rope_type": "llama3",
+        },
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        # Template-specific configs
+        num_templates=2,
+        num_groups=8,
+        coef_height=4,
+        coef_width = 16,
+        num_cf=1,
+        torch_dtype="bfloat16",
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = 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.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.mlp_bias = mlp_bias
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.torch_dtype = torch_dtype
+
+        # Template-specific configs
+        self.num_templates = num_templates
+        self.num_groups = num_groups
+        self.coef_height = coef_height
+        self.coef_width = coef_width
+        self.num_cf = num_cf
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )