Update README.md

README.md

@@ -102,16 +102,13 @@ vllm serve scb10x/llama3.1-typhoon2-70b-instruct --tensor-parallel-size 2
 
 ## Function-Call Example
 ```python
-import json
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-import os
 import ast
-
 model_name = "scb10x/llama3.1-typhoon2-70b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
-    model_name, torch_dtype=torch.bfloat16
+    model_name, torch_dtype=torch.bfloat16, device_map='auto'
 )
 
 get_weather_api = {
@@ -172,10 +169,6 @@ messages = [
     {"role": "user", "content": "ขอราคาหุ้น Tasla (TLS) และ Amazon (AMZ) ?"},
 ]
 
-final_prompt = tokenizer.apply_chat_template(
-    messages, tools=openai_format_tools, add_generation_prompt=True, tokenize=False
-)
-
 inputs = tokenizer.apply_chat_template(
     messages, tools=openai_format_tools, add_generation_prompt=True, return_tensors="pt"
 ).to(model.device)
@@ -188,7 +181,7 @@ outputs = model.generate(
     num_return_sequences=1,
     eos_token_id=[tokenizer.eos_token_id, 128009],
 )
-response = outputs[0][input_ids.shape[-1]:]
+response = outputs[0][inputs.shape[-1]:]
 
 print("Here Output:", tokenizer.decode(response, skip_special_tokens=True))
 
@@ -300,27 +293,6 @@ def parse_nested_value(value):
             )
     return repr(value)
 
-
-def decoded_output_to_execution_list(decoded_output):
-    """
-    Convert decoded output to a list of executable function calls.
-
-    Args:
-        decoded_output (list): A list of dictionaries representing function calls.
-
-    Returns:
-        list: A list of strings, each representing an executable function call.
-    """
-    execution_list = []
-    for function_call in decoded_output:
-        for key, value in function_call.items():
-            args_str = ", ".join(
-                f"{k}={parse_nested_value(v)}" for k, v in value.items()
-            )
-            execution_list.append(f"{key}({args_str})")
-    return execution_list
-
-
 def default_decode_ast_prompting(result, language="Python"):
     result = result.strip("`\n ")
     if not result.startswith("["):