Simplified model loading (#32)

This PR simplifies the model loading taking advantage of the new
functionality of `get_model()` from `fms.models`.
The current implementation automatically infers `architecture` and
`variant` from a given `model_path` pointing to directory with weights
in **hf** (hugging face) format.

### Changes:

- replacing as_fms_model() by get_model() for **hf** models. 
- removing if condition for **meta** weights

Note: make sure to use the **hf** format of the weights for model
**7B-F** (checkpoint trained by meta) from now on...

vllm/model_executor/model_loader/sendnn.py

@@ -182,34 +182,9 @@ def sample(
     def load_weights(self, model_name_or_path: str, device_type, 
                      max_prompt_length, max_decode_length, **kwargs):
 
-        # check model source: hf or meta
-        files = os.listdir(model_name_or_path)
-        model_source = 'hf' 
-        # default huggingface, but if .pth file in model directory, then it is meta weights
-        for f in files:
-            if f.endswith('.pth'):
-                model_source = 'meta' 
-                break
-
-        if model_source == 'hf': # hugging face
-            # load hugging face model
-            self.model = as_fms_model(model_name_or_path)
-        else: # meta
-            variant = "7b"
-            architecture = "llama"
-            distr_param = None
-
-            # Load the weights from the cached or downloaded files.
-            self.model = get_model(
-                architecture=architecture,
-                variant=variant,
-                model_path=model_name_or_path,
-                source=model_source,
-                device_type=device_type,
-                distributed_strategy=distr_param, 
-                group=dist.group.WORLD,
-            )
-
+        # function will infer architecture and variant for hf models based on model_name_or_path
+        self.model = get_model("hf_pretrained", model_name_or_path)
+
         compile_mode = "default"
         dynamo_backend = DYN_BACKEND