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import os
from transformers import AutoModelForCausalLM, AutoTokenizer
# Setze das Cache-Verzeichnis
os.environ['TRANSFORMERS_CACHE'] = 'cache'
model_name = "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Funktion zur Textgenerierung definieren
def generate_text(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=100)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
import gradio as gr
# Gradio-Interface erstellen
iface = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
title="WizardLM Uncensored SuperCOT StoryTelling"
)
# Interface mit öffentlichem Link starten
iface.launch(share=True)
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