RAG-Generation / test_models.py
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feat: upgrade RAG conversational intent classification to multi-lingual LLM
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from gradio_client import Client
import time
def test_hy3():
try:
print("Testing Tencent/Hy3...")
client = Client("tencent/Hy3")
result = client.predict(
message="Hello",
system_prompt="",
history=None,
think_level="high",
temperature=0.7,
max_tokens=100,
top_p=0.8,
functions_json_str="",
api_name="/chat"
)
print("Hy3 Response:", str(result)[:200])
return True
except Exception as e:
print("Hy3 Error:", e)
return False
def test_qwen_omni():
try:
print("\nTesting Qwen3.5-Omni-Offline-Demo...")
client = Client("Qwen/Qwen3.5-Omni-Offline-Demo")
client.predict(api_name="/clear_history_offline")
result = client.predict(
text="Hello",
audio=None,
image=None,
video=None,
history=[],
system_prompt="",
temperature=0.7,
top_p=0.8,
top_k=20,
api_name="/chat_predict"
)
print("Qwen Omni Response:", str(result)[:200])
return True
except Exception as e:
print("Qwen Omni Error:", e)
return False
if __name__ == "__main__":
test_hy3()
test_qwen_omni()