app2.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/Unholy-v1-12L-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="main" 
model = AutoModelForCausalLM.from_pretrained( model_name_or_path,
                                              device_map="cuda:0",
                                             trust_remote_code=False,
                                             revision="main"
                                            
                                             
                                            )

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1 
)

print(pipe(prompt_template)[0]['generated_text'])

#t=prompt_template)[0]['generated_text']

st.json(pipe(prompt_template))