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4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 45586df 4edb4d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import gradio as gr
from transformers import pipeline
TITLE = "Qwen News Assistant"
DESCRIPTION = (
"Using Qwen2.5-0.5B-Instruct to generate news drafts. "
"This model is optimized for CPU efficiency and instruction following."
)
# Load the lightweight Qwen 0.5B model
generator = pipeline("text-generation", model="Qwen/Qwen2.5-0.5B-Instruct")
def generate_text(prompt: str, temperature: float, top_p: float, max_length: int) -> str:
if not prompt or not prompt.strip():
return "Please enter a prompt."
# Instruct models perform best when prompts are formatted correctly
# For Qwen, we wrap the prompt in a simple instruction format
messages = [
{"role": "system", "content": "You are a helpful news writing assistant."},
{"role": "user", "content": prompt},
]
# Apply the chat template
formatted_prompt = generator.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
temperature = max(0.01, float(temperature))
outputs = generator(
formatted_prompt,
do_sample=True,
temperature=temperature,
top_p=float(top_p),
max_new_tokens=int(max_length), # max_new_tokens is safer for instruct models
pad_token_id=generator.tokenizer.eos_token_id,
)
# Extract only the newly generated text
generated_text = outputs[0]["generated_text"]
return generated_text.split("<|im_start|>assistant\n")[-1].strip()
# Updated examples to be more "Instruction" focused
examples = [
["Write a breaking news headline about a discovery on Mars.", 0.7, 0.9, 100],
["Write the opening paragraph for a story about local students winning a robotics competition.", 0.5, 0.9, 150],
["Summarize the importance of artificial intelligence in 2024.", 0.3, 0.9, 200],
]
with gr.Blocks(title=TITLE) as demo:
gr.Markdown(f"# {TITLE}")
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt / Instruction",
placeholder="e.g., 'Write a news report about...'",
lines=4,
)
temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p")
max_length = gr.Slider(minimum=20, maximum=300, value=150, step=1, label="Max New Tokens")
generate_button = gr.Button("Generate", variant="primary")
with gr.Column():
output = gr.Textbox(label="Generated News Draft", lines=16)
generate_button.click(
fn=generate_text,
inputs=[prompt, temperature, top_p, max_length],
outputs=output,
)
gr.Examples(
examples=examples,
inputs=[prompt, temperature, top_p, max_length],
outputs=output,
fn=generate_text,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch() |