Fix fp8 implementation which had bit-rotten a bit

I only tested with "on-the-fly" bf16 -> fp8 conversion, not the "load
from fp8" codepath.

YAML I tested with:

```
providers:
  - provider_id: quantized
    provider_type: meta-reference-quantized
    config:
      model: Llama3.1-8B-Instruct
      quantization:
        type: fp8
```

llama_stack/providers/impls/meta_reference/inference/generation.py

@@ -138,7 +138,7 @@ class Llama:
             else:
                 model = Transformer(model_args)
             model.load_state_dict(state_dict, strict=False)
-            model = convert_to_quantized_model(model, config)
+            model = convert_to_quantized_model(model, config, ckpt_dir)
         else:
             if torch.cuda.is_bf16_supported():
                 torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
@@ -228,8 +228,7 @@ class Llama:
                 ignore_index=pad_id,
             )
 
-        stop_tokens = torch.tensor(self.tokenizer.stop_tokens)
+        stop_tokens = torch.tensor(self.tokenizer.stop_tokens, device="cuda")
-
         for cur_pos in range(min_prompt_len, total_len):
             if is_vision:
                 position_ids = torch.arange(

llama_stack/providers/impls/meta_reference/inference/quantization/loader.py

@@ -13,9 +13,10 @@ from typing import Optional
 import torch
 
 from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
-
 from llama_models.datatypes import CheckpointQuantizationFormat
 from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock
+
+from llama_models.sku_list import resolve_model
 from termcolor import cprint
 from torch import Tensor
 
@@ -39,6 +40,7 @@ def swiglu_wrapper(
 def convert_to_quantized_model(
     model: Transformer,
     config: MetaReferenceQuantizedInferenceConfig,
+    checkpoint_dir: str,
     fp8_activation_scale_ub: Optional[float] = 1200.0,
 ) -> Transformer:
     if config.quantization.type == QuantizationType.bf16.value:
@@ -49,12 +51,14 @@ def convert_to_quantized_model(
 
     from .fp8_impls import Fp8ScaledWeights, load_fp8, quantize_fp8
 
-    checkpoint = config.checkpoint_config.checkpoint
+    llama_model = resolve_model(config.model)
+    assert llama_model is not None, f"Model {config.model} not found"
+
     # Move weights to GPU with quantization
-    if checkpoint.quantization_format == CheckpointQuantizationFormat.fp8_mixed.value:
+    if llama_model.quantization_format == CheckpointQuantizationFormat.fp8_mixed.value:
         cprint("Loading fp8 scales...", "yellow")
         fp8_scales_path = os.path.join(
-            checkpoint.checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
+            checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
         )
         assert os.path.isfile(
             fp8_scales_path