metadata
datasets:
- glaiveai/glaive-function-calling-v2
Tool Information
Define the tools and their functionalities as a list of dictionaries.
tools_info = [
{
"name": "cancel_reservation",
"description": "cancel a reservation",
"parameters": {
"type": "object",
"properties": {
"reservation_number": {
"type": "integer",
"description": "Reservation number"
}
},
"required": ["reservation_number"]
}
},
{
"name": "get_reservations",
"description": "get reservation numbers",
"parameters": {
"type": "object",
"properties": {
"user_id": {
"type": "integer",
"description": "User id"
}
},
"required": [
"user_id"
]
}
},
]
System Initialization
Initialize the system's interactive capabilities using the defined tools.
system = f"You are a helpful assistant with access to the following functions: \n {json.dumps(tools_info, indent=2)}."
Conversation Flow
Simulate a conversation flow where the user requests to cancel a reservation.
messages = [
{"role": "system", "content": system},
{"role": "user", "content": "Help me to cancel a reservation"},
{"role": "assistant", "content": "I can help with that. Could you please provide me with the reservation number?"},
{"role": "user", "content": "the reservation number is 1011"}
]
Or the user requests to display its reservations, note the use of "tool" role.
messages=[
{"role":"system","content": system},
{"role": "user","content": "Help me to find my reservations, my user id is 110"},
{"role": "assistant","content":'<func_call> {"name": "get_reservations", "arguments": {"user_id": 110}}'},
{"role": "tool","content":'["AB001","CD002","GG100"]'}
]
Model Loading
Load the causal language model and tokenizer.
model_id = "caldana/function_calling_llama3_8b_instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
Generating Response
Generate a response from the model based on the conversation context.
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))