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#!/usr/bin/env python3
"""
Quick start example for Qwen3-4B Tool Calling
"""
import json
import re
from llama_cpp import Llama
def extract_tool_calls(text):
"""Extract tool calls from model response"""
tool_calls = []
json_pattern = r'\[.*?\]'
matches = re.findall(json_pattern, text)
for match in matches:
try:
parsed = json.loads(match)
if isinstance(parsed, list):
for item in parsed:
if isinstance(item, dict) and 'name' in item:
tool_calls.append(item)
except json.JSONDecodeError:
continue
return tool_calls
def main():
"""Quick start demo"""
print("🚀 Qwen3-4B Tool Calling - Quick Start")
print("=" * 50)
# Load the model
print("Loading model...")
llm = Llama(
model_path="Qwen3-4B-Function-Calling-Pro.gguf",
n_ctx=1024, # Smaller context for quick demo
n_threads=4,
temperature=0.7,
verbose=False
)
print("✅ Model loaded!")
# Interactive demo
print("\n💬 Interactive Demo (type 'quit' to exit)")
print("-" * 50)
while True:
try:
user_input = input("\nYou: ").strip()
if user_input.lower() in ['quit', 'exit', 'q']:
break
if not user_input:
continue
# Format prompt
formatted_prompt = f"<|im_start|>user\n{user_input}<|im_end|>\n<|im_start|>assistant\n"
# Generate response
response = llm(
formatted_prompt,
max_tokens=200,
stop=["<|im_end|>", "<|im_start|>"],
temperature=0.7
)
response_text = response['choices'][0]['text']
print(f"\nAssistant: {response_text}")
# Check for tool calls
tool_calls = extract_tool_calls(response_text)
if tool_calls:
print(f"\n🔧 Tool Calls ({len(tool_calls)}):")
for i, tool_call in enumerate(tool_calls, 1):
print(f" {i}. {tool_call['name']}")
print(f" Arguments: {tool_call.get('arguments', {})}")
except KeyboardInterrupt:
print("\n\nGoodbye! 👋")
break
except Exception as e:
print(f"Error: {e}")
if __name__ == "__main__":
main()
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