chore: remove default rope_scaling dict and use get to access type and factor to avoid key error

llms/hf_llm/models.py

@@ -3,7 +3,7 @@
 import glob
 import inspect
 import json
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Dict, Optional, Tuple, Union
 
@@ -26,20 +26,19 @@ class ModelArgs:
     rope_theta: float = 10000
     rope_traditional: bool = False
     model_type: str = None
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = field(default_factory=dict)
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
 
     def __post_init__(self):
         if self.num_key_value_heads is None:
             self.num_key_value_heads = self.num_attention_heads
-        
+
         if self.rope_scaling:
             required_keys = {"factor", "type"}
             if not all(key in self.rope_scaling for key in required_keys):
                 raise ValueError(f"rope_scaling must contain keys {required_keys}")
-            
+
             if self.rope_scaling["type"] != "linear":
                 raise ValueError("rope_scaling 'type' currently only supports 'linear'")
-          
 
     @classmethod
     def from_dict(cls, params):
@@ -83,9 +82,17 @@ class Attention(nn.Module):
         self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
         self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
         self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
-        rope_scale = 1 / args.rope_scaling['factor'] if args.rope_scaling is not None and args.rope_scaling['type'] == 'linear' else 1
+        rope_scale = (
+            1 / args.rope_scaling.get("factor", 1.0)
+            if args.rope_scaling is not None
+            and args.rope_scaling.get("type") == "linear"
+            else 1
+        )
         self.rope = nn.RoPE(
-            head_dim, traditional=args.rope_traditional, base=args.rope_theta, scale= rope_scale
+            head_dim,
+            traditional=args.rope_traditional,
+            base=args.rope_theta,
+            scale=rope_scale,
         )
 
     def __call__(