Upload README.md with huggingface_hub

README.md

@@ -13,6 +13,8 @@ datasets:
 ## Quickstart
 
 ### utils for user content.
+- replace [TRIPLE_BACKTICK] to ```
+
 ```python
 xlam_system = (
     "You are an AI assistant for function calling. "
@@ -39,17 +41,29 @@ If the given question lacks the parameters required by the function, fill the pa
 
 FORMAT_INSTRUCTION = '''The output MUST strictly adhere to the following JSON format, and NO other text MUST be included.
 The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make tool_calls an empty list '[]'.
-```
+[TRIPLE_BACKTICK]
 { "tool_calls": [
     {"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
     ... (more tool calls as required)
 ] }
-```
+[TRIPLE_BACKTICK]
 '''
+
+def get_prompt(xlam_tools:list|dict, query:str ):
+    if not isinstance(xlam_tools, str): xlam_tools = json.dumps(xlam_tools)
+    prompt = f"<instruction>\n{TASK_INSTRUCTION}\n</instruction>\n\n"
+    prompt += f"<available tools>\n{xlam_tools}\n</available tools>\n\n"
+    prompt += f"<tool format>\n{FORMAT_INSTRUCTION}\n</tool format>\n\n"
+    prompt += f"<query>\n{query.strip()}\n<query>\n\n"
+    return prompt
+
 ```
 
 ### inference
+- replace [TRIPLE_BACKTICK] to ```
+
 ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 user_msg = '''<instruction>
 You are an expert in composing functions. You are given a question and a set of possible functions.
 Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
@@ -64,12 +78,12 @@ If the given question lacks the parameters required by the function, fill the pa
 <tool format>
 The output MUST strictly adhere to the following JSON format, and NO other text MUST be included.
 The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make tool_calls an empty list '[]'.
-```
+[TRIPLE_BACKTICK]
 { "tool_calls": [
     {"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
     ... (more tool calls as required)
 ] }
-```
+[TRIPLE_BACKTICK]
 </tool format>
 
 <query>
@@ -78,10 +92,7 @@ Check if the username 'ShopMaster123' is available on Shopify.
 
 messages = [dict(role='user', content=user_msg)]
 label = { "tool_calls": [{"name": "shopify", "arguments": {"username": "ShopMaster123"}}] }
-```
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained(
     "objects76/qwen2-xlam", trust_remote_code=True)