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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
MODEL_ID = "Qwen/Qwen2.5-Coder-1.5B-Instruct" # or your model
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
)
model.to(device)
def generate(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.2)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
demo = gr.Interface(
fn=generate,
inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
outputs=gr.Textbox(lines=2, max_lines=30) # auto-expands up to 30 lines
)
demo.launch()
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