Fixes for phi4 mini (#1305)

llms/mlx_lm/models/phi3.py

@@ -23,8 +23,10 @@ class ModelArgs(BaseModelArgs):
     rope_theta: float = 10000
     rope_traditional: bool = False
     rope_scaling: Optional[Dict[str, Union[float, List[float]]]] = None
+    partial_rotary_factor: float = 1.0
     max_position_embeddings: int = 131072
     original_max_position_embeddings: int = 4096
+    tie_word_embeddings: bool = False
 
     def __post_init__(self):
         if self.num_key_value_heads is None:
@@ -59,9 +61,10 @@ class Attention(nn.Module):
         self.qkv_proj = nn.Linear(dim, op_size, bias=False)
         self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
 
+        rope_dim = int(head_dim * args.partial_rotary_factor)
         if args.rope_scaling and args.rope_scaling["type"] in ["longrope", "su"]:
             self.rope = SuScaledRotaryEmbedding(
-                head_dim,
+                rope_dim,
                 base=args.rope_theta,
                 max_position_embeddings=args.max_position_embeddings,
                 original_max_position_embeddings=args.original_max_position_embeddings,
@@ -74,7 +77,7 @@ class Attention(nn.Module):
                 assert isinstance(args.rope_scaling["factor"], float)
                 rope_scale = 1 / args.rope_scaling["factor"]
             self.rope = nn.RoPE(
-                head_dim,
+                rope_dim,
                 traditional=args.rope_traditional,
                 base=args.rope_theta,
                 scale=rope_scale,
@@ -190,7 +193,8 @@ class Model(nn.Module):
         super().__init__()
         self.model_type = args.model_type
         self.model = Phi3Model(args)
-        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
         self.args = args
 
     def __call__(
@@ -200,7 +204,11 @@ class Model(nn.Module):
         cache=None,
     ):
         out = self.model(inputs, mask, cache)
-        return self.lm_head(out)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
 
     @property
     def layers(self):