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import spaces
import gradio as gr
import torch
# Use a pipeline as a high-level helper
from transformers import pipeline
torch.cuda.empty_cache()
print("RUNNING PIPE")
pipe = pipeline("text-generation", model="NousResearch/Hermes-3-Llama-3.1-8B", max_new_tokens=200, device=0)
print("PIPE DONE")
@spaces.GPU(duration=120)
def llama3_1_8B(question):
messages = [
{"role": "user", "content": question},
]
if torch.cuda.is_available():
num_devices = torch.cuda.device_count()
print(f"Number of CUDA devices: {num_devices}")
for i in range(num_devices):
print(f"Device {i}: {torch.cuda.get_device_name(i)}")
else:
print("CUDA is not available.")
print("GATHERING RESPONSES")
responses = pipe(messages)
return str(responses)
def greet(name):
return "Hello " + name + "!!???"
demo = gr.Interface(fn=llama3_1_8B, inputs="text", outputs="text")
demo.launch()
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