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from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr
# Load the pre-trained model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("sberbank-ai/mGPT")
model = GPT2LMHeadModel.from_pretrained("sberbank-ai/mGPT")
def eval_aguila(text):
# Encode the input text
input_ids = tokenizer.encode(text, return_tensors="pt")
# Generate text
out = model.generate(
input_ids,
min_length=100,
max_length=100,
eos_token_id=5,
pad_token_id=1,
top_k=10,
top_p=0.0,
no_repeat_ngram_size=5
)
# Decode the generated output
generated_text = list(map(tokenizer.decode, out))[0]
print(generated_text)
return(f"Result: {generation[0]['generated_text']}")
demo = gr.Interface(fn=lecturabilidad, inputs="text", outputs="text", title="Mixtral")
demo.launch(share=True) |