app.py

import torch
import streamlit as st
from transformers import AutoTokenizer, T5Tokenizer, T5ForConditionalGeneration, GenerationConfig, AutoModelForCausalLM

st.title('How do LLM choose their words?')

col1, col2 = st.columns(2)

with col1:
    model_checkpoint = st.selectbox(
        "Model:",
        ("google/flan-t5-base", "google/flan-t5-xl")
    )

with col2:
    temperature = st.number_input('Temperature: ', min_value=0.0, max_value=1.0, value=0.5, format='%f')

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

model = T5ForConditionalGeneration.from_pretrained(  
    model_checkpoint,
    load_in_8bit=False,
    device_map="auto"
)

instruction = st.text_area('Write an instruction:')
max_tokens = st.number_input('Max output length: ', min_value=1, max_value=64, format='%i')

prompts = [
    f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
    ### Instruction: {instruction}
    ### Response:"""
]

inputs = tokenizer(
    prompts[0],
    return_tensors="pt",
)
input_ids = inputs["input_ids"]#.to("cuda")

generation_config = GenerationConfig(
    do_sample=True,
    temperature=temperature,
    top_p=0.995,      # default 0.75
    top_k=100,        # default 80
    repetition_penalty=1.5,
    max_new_tokens=max_tokens,
)

if instruction:
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            attention_mask=torch.ones_like(input_ids),
            generation_config=generation_config,
            return_dict_in_generate=True, 
            output_scores=True
        )
    
    output_text = tokenizer.decode(
        outputs['sequences'][0],#.cuda(), 
        skip_special_tokens=False
    ).strip()
    
    st.write(output_text)

    st.write(model.compute_transition_scores(outputs.sequences, outputs.scores, normalize_logits=False))