diff --git a/Build_Dist/model_args.py b/Build_Dist/model_args.py
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+++ b/Build_Dist/model_args.py
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+from dataclasses import dataclass
+
+@dataclass
+class DeepSeekV3ModelArgs:
+
+"""
+    Data class for defining model arguments and hyperparameters.
+
+    Attributes:
+        max_batch_size (int): Maximum batch size.
+        max_seq_len (int): Maximum sequence length.
+        dtype (Literal["bf16", "fp8"]): Data type for computations.
+        scale_fmt (Optional[str]): Format for quantization scale.
+        vocab_size (int): Vocabulary size.
+        dim (int): Model dimension.
+        inter_dim (int): Intermediate dimension for MLP layers.
+        moe_inter_dim (int): Intermediate dimension for MoE layers.
+        n_layers (int): Number of transformer layers.
+        n_dense_layers (int): Number of dense layers in the model.
+        n_heads (int): Number of attention heads.
+        n_routed_experts (int): Number of routed experts for MoE layers.
+        n_shared_experts (int): Number of shared experts for MoE layers.
+        n_activated_experts (int): Number of activated experts in MoE layers.
+        n_expert_groups (int): Number of expert groups.
+        n_limited_groups (int): Number of limited groups for MoE routing.
+        score_func (Literal["softmax", "sigmoid"]): Scoring function for MoE routing.
+        route_scale (float): Scaling factor for routing scores.
+        q_lora_rank (int): LoRA rank for query projections.
+        kv_lora_rank (int): LoRA rank for key-value projections.
+        qk_nope_head_dim (int): Dimension for query-key projections without positional embeddings.
+        qk_rope_head_dim (int): Dimension for query-key projections with rotary embeddings.
+        v_head_dim (int): Dimension for value projections.
+        original_seq_len (int): Original sequence length.
+        rope_theta (float): Base for rotary positional encoding.
+        rope_factor (float): Scaling factor for extended sequence lengths.
+        beta_fast (int): Fast beta correction factor.
+        beta_slow (int): Slow beta correction factor.
+        mscale (float): Scaling factor for extended attention.
+    """
+    max_batch_size int = 8 //maximum batch size for inference
+    max_seq_len: int = 4096 * 4 
+    vocab_size: int = 102400
+    dim int = 2048
+    inter_dim int = 10944 //intermediate dimension for MLP layers
+    moe_inter_dim int = 1408 //intermediate dimension for MoE layers
+    n_layers int = 64 //number of transformer layers
+    n_dense_layers int = 1 //number of dense layers in the model
+    n_heads int = 16 //number of attention heads
+
+
+    #MoE layers
+    n_routed_experts int = 64 //number of routed experts for MoE layers
+    n_shared_experts int = 2 //number of shared experts for MoE layers
+    n_activated_experts int = 6 //number of activated experts in MoE layers
+    n_expert_groups int = 1 //number of expert groups
+    n_limited_groups int = 1 //number of limited groups for MoE routing
+    score_func Literal["softmax", "sigmoid"] = "softmax" //scoring function for MoE routing
+    route_scale float = 1.0 //scaling factor for routing scores
+    q_lora_rank int = 0 //LoRA rank for query projections
+    kv_lora_rank int = 512 //LoRA rank for key-value projections
+    qk_nope_head_dim int = 128 //dimension for query-key projections without positional embeddings
+    qk_rope_head_dim int = 64 //dimension for query-key projections with rotary embeddings
+    v_head_dim int = 128 //dimension for value projections
+
+    # Yarn
+    original_seq_len int = 4096 //original sequence length
+    rope_theta float = 10000.0 //base for rotary positional encoding
+    rope_factor float = 40 //scaling factor for extended sequence lengths
+    beta_fast int = 32 //fast beta correction factor
+    beta_slow int = 1 //slow beta correction factor
+    mscale float = 1.0 //scaling factor for extended attention
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