Update README.md

Updated model card with more infos and a more adapted example

README.md

@@ -19,18 +19,33 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 ```python
 from transformers import pipeline
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+prompt="""<bos><start_of_turn>human
+You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.You may call one or more functions to assist with the user query. 
+Don't make assumptions about what values to plug into functions.Here are the available tools:
+<tools> 
+[{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, 
+{'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}
+{'type': 'function', 'function': {'name': 'send_email', 'description': 'Send an email to a customer', 'parameters': {'type': 'object', 'properties': {'customer': {'type': 'string', 'description': 'The customer to send the email to'}, 'subject': {'type': 'string', 'description': 'The subject of the email'}, 'body': {'type': 'string', 'description': 'The body of the email'}}, 'required': ['customer', 'subject', 'body']}}}
+] 
+</tools>
+Use the following pydantic model json schema for each tool call you will make: 
+{'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
+For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
+<tool_call>
+{tool_call}
+</tool_call>Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>
+
+Hi, I need you to tell John@doe.com that I received his package ?<end_of_turn><eos>
+<start_of_turn>model
+<think>"""
 generator = pipeline("text-generation", model="Cotum/Qwen2.5-3B-Instruct-thinking-function_calling-V0", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0]
 print(output["generated_text"])
 ```
 
 ## Training procedure
 
- 
-
-
-This model was trained with SFT.
+This model was trained with SFT following the Bonus Unit 1 of the Agent Course of Hugging Face : https://huggingface.co/agents-course/notebooks/blob/main/bonus-unit1/bonus-unit1.ipynb
 
 ### Framework versions