refactor: move llama3 impl to meta_reference provider (#1364)

Just moving bits to a better place

## Test Plan

```bash
torchrun $CONDA_PREFIX/bin/pytest -s -v test_text_inference.py
```

llama_stack/models/llama/llama3/args.py

@@ -1,82 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# top-level folder for each specific model found within the models/ directory at
-# the top-level of this source tree.
-
-from dataclasses import dataclass
-from enum import Enum
-from typing import Optional
-
-
-class QuantizationScheme(Enum):
-    int4_weight_int8_dynamic_activation = "int4_weight_int8_dynamic_activation"
-
-
-@dataclass
-class QuantizationArgs:
-    scheme: Optional[QuantizationScheme] = None
-    group_size: Optional[int] = None
-    spinquant: bool = False
-
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if k == "scheme":
-                setattr(self, k, QuantizationScheme(v))
-            else:
-                if hasattr(self, k):
-                    setattr(self, k, v)
-
-
-@dataclass
-class LoRAArgs:
-    rank: int
-    scale: float
-
-
-@dataclass
-class ModelArgs:
-    dim: int = 4096
-    n_layers: int = 32
-    n_heads: int = 32
-    n_kv_heads: Optional[int] = None
-    vocab_size: int = -1
-    multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
-    ffn_dim_multiplier: Optional[float] = None
-    norm_eps: float = 1e-5
-    rope_theta: float = 500000
-    use_scaled_rope: bool = False
-
-    max_batch_size: int = 32
-    max_seq_len: int = 2048
-
-    # vision model params
-    vision_chunk_size: int = -1  # image resolution for image models
-    vision_max_num_chunks: int = 4
-    vision_num_cross_attention_layers: int = -1
-
-    quantization_args: Optional[QuantizationArgs] = None
-    lora_args: Optional[LoRAArgs] = None
-
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if k == "lora_args":
-                setattr(self, k, LoRAArgs(**v))
-            elif k == "quantization_args":
-                setattr(self, k, QuantizationArgs(**v))
-            else:
-                if hasattr(self, k):
-                    setattr(self, k, v)
-
-        if self.n_kv_heads is None:
-            self.n_kv_heads = self.n_heads
-        assert self.n_kv_heads <= self.n_heads
-        assert self.n_heads % self.n_kv_heads == 0
-        assert self.dim % self.n_heads == 0