Re-order Llama class params

llama_cpp/llama.py

@@ -205,8 +205,6 @@ class Llama:
         model_path: str,
         # NOTE: These parameters are likely to change in the future.
         n_ctx: int = 512,
-        rope_freq_base: float = 10000.0,
-        rope_freq_scale: float = 1.0,
         n_parts: int = -1,
         n_gpu_layers: int = 0,
         seed: int = 1337,
@@ -223,6 +221,8 @@ class Llama:
         lora_path: Optional[str] = None,
         low_vram: bool = False,
         tensor_split: Optional[List[float]] = None,
+        rope_freq_base: float = 10000.0,
+        rope_freq_scale: float = 1.0,
         verbose: bool = True,
     ):
         """Load a llama.cpp model from `model_path`.
@@ -230,8 +230,6 @@ class Llama:
         Args:
             model_path: Path to the model.
             n_ctx: Maximum context size.
-            rope_freq_base: RoPE base frequency.
-            rope_freq_scale: RoPE frequency scale.
             n_parts: Number of parts to split the model into. If -1, the number of parts is automatically determined.
             seed: Random seed. -1 for random.
             f16_kv: Use half-precision for key/value cache.
@@ -246,6 +244,8 @@ class Llama:
             lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
             lora_path: Path to a LoRA file to apply to the model.
             tensor_split: List of floats to split the model across multiple GPUs. If None, the model is not split.
+            rope_freq_base: Base frequency for rope sampling.
+            rope_freq_scale: Scale factor for rope sampling.
             verbose: Print verbose output to stderr.
 
         Raises:
@@ -260,8 +260,6 @@ class Llama:
 
         self.params = llama_cpp.llama_context_default_params()
         self.params.n_ctx = n_ctx
-        self.params.rope_freq_base = rope_freq_base
-        self.params.rope_freq_scale = rope_freq_scale
         self.params.n_gpu_layers = n_gpu_layers
         self.params.seed = seed
         self.params.f16_kv = f16_kv
@@ -281,6 +279,9 @@ class Llama:
             self._c_tensor_split = FloatArray(*tensor_split) # keep a reference to the array so it is not gc'd
             self.params.tensor_split = self._c_tensor_split
 
+        self.params.rope_freq_base = rope_freq_base
+        self.params.rope_freq_scale = rope_freq_scale
+
         self.last_n_tokens_size = last_n_tokens_size
         self.n_batch = min(n_ctx, n_batch)