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import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_name = "infly/OpenCoder-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
@spaces.GPU
# Define the text generation function
def generate_text(prompt):
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
outputs = model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"], # Add attention mask
max_length=50, # Reduce max_length to conserve memory
num_return_sequences=1
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Create the Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=gr.Textbox(label="Enter your prompt", placeholder="Start typing...", lines=5),
outputs="text",
title="OpenCoder 8B Instruct",
description="Generate text using the OpenCoder model. Input a prompt to generate responses.",
)
# Launch the Gradio app
iface.launch() |