refactor: move all llama code to models/llama out of meta reference (#1887)

# What does this PR do?

Move around bits. This makes the copies from llama-models _much_ easier
to maintain and ensures we don't entangle meta-reference specific
tidbits into llama-models code even by accident.

Also, kills the meta-reference-quantized-gpu distro and rolls
quantization deps into meta-reference-gpu.

## Test Plan

```
LLAMA_MODELS_DEBUG=1 \
  with-proxy llama stack run meta-reference-gpu \
  --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct \
   --env INFERENCE_CHECKPOINT_DIR=<DIR> \
   --env MODEL_PARALLEL_SIZE=4 \
   --env QUANTIZATION_TYPE=fp8_mixed
```

Start a server with and without quantization. Point integration tests to
it using:

```
pytest -s -v  tests/integration/inference/test_text_inference.py \
   --stack-config http://localhost:8321 --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```

llama_stack/models/llama/llama3/args.py

@@ -0,0 +1,75 @@
+# 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.
+
+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