Inference to use provider resource id to register and validate (#428)

This PR changes the way model id gets translated to the final model name
that gets passed through the provider.
Major changes include:
1) Providers are responsible for registering an object and as part of
the registration returning the object with the correct provider specific
name of the model provider_resource_id
2) To help with the common look ups different names a new ModelLookup
class is created.



Tested all inference providers including together, fireworks, vllm,
ollama, meta reference and bedrock

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

@@ -86,6 +86,7 @@ class Llama:
             and loads the pre-trained model and tokenizer.
         """
         model = resolve_model(config.model)
+        llama_model = model.core_model_id.value
 
         if not torch.distributed.is_initialized():
             torch.distributed.init_process_group("nccl")
@@ -186,13 +187,20 @@ class Llama:
             model.load_state_dict(state_dict, strict=False)
 
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
-        return Llama(model, tokenizer, model_args)
+        return Llama(model, tokenizer, model_args, llama_model)
 
-    def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs):
+    def __init__(
+        self,
+        model: Transformer,
+        tokenizer: Tokenizer,
+        args: ModelArgs,
+        llama_model: str,
+    ):
         self.args = args
         self.model = model
         self.tokenizer = tokenizer
         self.formatter = ChatFormat(tokenizer)
+        self.llama_model = llama_model
 
     @torch.inference_mode()
     def generate(
@@ -369,7 +377,7 @@ class Llama:
         self,
         request: ChatCompletionRequest,
     ) -> Generator:
-        messages = chat_completion_request_to_messages(request)
+        messages = chat_completion_request_to_messages(request, self.llama_model)
 
         sampling_params = request.sampling_params
         max_gen_len = sampling_params.max_tokens