Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,537 Bytes
30ef1d4 |
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 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the pretrained model and tokenizer
MODEL_NAME = "atlasia/Al-Atlas-LLM"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto")
def generate_text(prompt, max_length=256, temperature=0.7, top_p=0.9, top_k=150, repetition_penalty=1.5):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
top_p=top_p,
do_sample=True,
repetition_penalty=repetition_penalty,
num_beams=8,
top_k= top_k,
early_stopping = True,
)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Create the Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Prompt: دخل النص بالدارجة"),
gr.Slider(50, 500, value=256, label="Max Length"),
gr.Slider(0.1, 1.5, value=0.7, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.9, label="Top-p"),
gr.Slider(1, 10000, value=150, label="Top-k"),
gr.Slider(0.0, 100.0, value=1.5, label="Repetition Penalty"),
],
outputs=gr.Textbox(label="Generated Text in Moroccan Darija"),
title="Moroccan Darija LLM",
description="Enter a prompt and get AI-generated text using our pretrained LLM on Moroccan Darija."
)
if __name__ == "__main__":
iface.launch()
|