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# app.py
# =============
# This is a complete app.py file for a text generation app using the Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF model.
# The app is built using Gradio and runs on a CPU without video memory.
# Imports
# =======
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Constants
# =========
MODEL_NAME = "Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF"
DEVICE = "cpu" # Ensure the model runs on CPU
# Load Model and Tokenizer
# ========================
def load_model_and_tokenizer():
"""
Load the model and tokenizer from Hugging Face.
"""
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16, device_map=DEVICE)
return tokenizer, model
tokenizer, model = load_model_and_tokenizer()
# Generate Text
# =============
def generate_text(prompt, max_length=100):
"""
Generate text based on the given prompt.
Args:
prompt (str): The input prompt for text generation.
max_length (int): The maximum length of the generated text.
Returns:
str: The generated text.
"""
inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
outputs = model.generate(inputs.input_ids, max_length=max_length, num_return_sequences=1)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
# Gradio Interface
# =================
def gradio_interface():
"""
Create and launch the Gradio interface.
"""
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
gr.inputs.Slider(minimum=50, maximum=500, step=10, default=100, label="Max Length")
],
outputs="text",
title="Qwen2.5-Coder-0.5B-Instruct-GGUF Text Generation",
description="Generate text using the Qwen2.5-Coder-0.5B-Instruct-GGUF model."
)
iface.launch()
# Main
# ====
if __name__ == "__main__":
gradio_interface()
# Dependencies
# =============
# The following dependencies are required to run this app:
# - transformers
# - gradio
# - torch
#
# You can install these dependencies using pip:
# pip install transformers gradio torch |