add alpaca prompt evaluation

eval_prompt.py

-from argparse import Namespace
 import argparse
 import logging
+from argparse import Namespace
+
 from evoprompt.models import LLMModel
 from evoprompt.task import get_task, tasks
-
 from evoprompt.utils import setup_console_logger
 
 logger = logging.getLogger(__name__)
 
 
-def evaluate_prompt(prompt: str, **kwargs):
+def evaluate_prompt(prompt: str, task_name: str, args: Namespace):
     logger.info(f'Evaluating prompt "{prompt}"')
 
-    evaluation_model = LLMModel.get_model(name="llamachat", options=options)
-
-    task = get_task(
-        "sst5",
-        evaluation_model,
-        **kwargs,
+    # evaluation_model = LLMModel.get_model(name="llamachat", options=args)
+    evaluation_model = LLMModel.get_model(
+        name="hfllamachat", options=args, model="PKU-Alignment/alpaca-7b-reproduced"
     )
 
+    task = get_task(task_name, evaluation_model, **vars(args))
+
     eval_score, eval_usage, _ = task.evaluate_validation(prompt)
     logger.info(f"Score on evaluation set: {eval_score}")
     test_score, test_usage, _ = task.evaluate_test(prompt)
@@ -38,16 +37,17 @@ if __name__ == "__main__":
 
     options = Namespace(
         llama_path=None,
-        llama_model="QuantFactory/Meta-Llama-3.1-8B-Instruct-GGUF",
-        llama_model_file="Meta-Llama-3.1-8B-Instruct.Q8_0.gguf",
         chat_format=None,
         chat_handler=None,
         verbose=False,
         llama_verbose=False,
+        llama_model="QuantFactory/Meta-Llama-3.1-8B-Instruct-GGUF",
+        llama_model_file="Meta-Llama-3.1-8B-Instruct.Q8_0.gguf",
         disable_cache=False,
         use_grammar=False,
         evaluation_strategy="simple",
         max_tokens=None,
+        **vars(args),
     )
 
-    evaluate_prompt(**vars(args), **vars(options))
+    evaluate_prompt(args.prompt, args.task, options)