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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from auto_gptq import BaseQuantizeConfig
import torch
# Initialize model and tokenizer
MODEL_NAME = "TheBloke/deepseek-coder-1.3b-instruct-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="cpu", # Optimized for CPU
quantization_config=BaseQuantizeConfig(), # Required for GPTQ models
torch_dtype=torch.float32, # Better CPU compatibility
low_cpu_mem_usage=True
)
def generate_text(prompt, max_length=100, temperature=0.7):
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
pad_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Gradio UI
with gr.Blocks(theme="soft") as demo:
gr.Markdown("# 🧠 DeepSeek Coder 1.3B Text Generator\nOptimized for CPU execution on HuggingFace Spaces")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Input Prompt",
placeholder="Enter your programming/code-related question...",
lines=5
)
max_length = gr.Slider(50, 500, value=150, label="Max Output Length")
temperature = gr.Slider(0.1, 1.0, value=0.7, label="Creativity Level")
submit = gr.Button("Generate Code", variant="primary")
output = gr.Textbox(label="Generated Output", lines=10)
submit.click(
fn=generate_text,
inputs=[prompt, max_length, temperature],
outputs=output
)
gr.Examples(
examples=[
["Write a Python function to calculate Fibonacci numbers"],
["Explain the difference between list and tuples in Python"],
["Create a simple Flask API endpoint for user registration"]
],
fn=generate_text,
inputs=[prompt, max_length, temperature],
outputs=output,
cache_examples=False # Save memory
)
if __name__ == "__main__":
demo.launch() |