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from transformers import pipeline | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
import gradio as gr | |
from nltk.tokenize import sent_tokenize | |
import torch | |
model = "janny127/autotrain-pje3d-uvelc1" | |
tokenizer = AutoTokenizer.from_pretrained(model) | |
pipeline = pipeline( | |
"text-generation", | |
model=model, | |
torch_dtype=torch.float16, | |
device_map="auto", | |
) | |
def predict(prompt, history): | |
# Prompt | |
formatted_prompt = ( | |
f"### Human: {prompt}### Assistant:" | |
) | |
# Generate the Texts | |
sequences = pipeline( | |
formatted_prompt, | |
do_sample=True, | |
top_k=50, | |
top_p = 0.7, | |
num_return_sequences=1, | |
repetition_penalty=1.1, | |
max_new_tokens=500, | |
) | |
generated_text = sequences[0]['generated_text'] | |
final_result = generated_text.split("### Assistant:")[1] | |
if " Human: " in final_result: | |
final_result = final_result.split(" Human: ")[0] | |
if " #" in final_result: | |
final_result = final_result.split(" #")[0] | |
# return generated_text.strip() | |
return final_result.strip() | |
gr.ChatInterface(predict, | |
title="Tinyllama_chatBot", | |
description="Ask Tiny llama any questions", | |
examples=['How to cook a fish?', 'Who is the president of US now?'] | |
).launch() # Launching the web interface. | |