Model Card for Qwen2.5-3B-Instruct-thinking-function_calling-V0

This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

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 [email protected] 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": prompt}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

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

• TRL: 0.15.1
• Transformers: 4.49.0
• Pytorch: 2.6.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}