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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
base_model_name = "chaseharmon/Rap-Mistral-Big"
@st.cache_resource
def load_model():
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=False,
bnb_4bit_compute_dtype="float16"
)
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map='auto',
quantization_config=nf4_config,
)
model.config.use_cache = False
model.config.pretraining_tp = 1
return model
@st.cache_resource
def load_tokenizer():
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
return tokenizer
def build_prompt(question):
prompt = f"[INST] {question} [/INST] "
return prompt
model = load_model()
model.eval()
tokenizer = load_tokenizer()
st.title("Rap Verse Generation V1 Demo")
st.header("Supported Artists")
st.write("Lupe Fiasco, Common, Jay-Z, Yasiin Bey, Ab-Soul, Rakim")
prompt_placeholder = st.empty()
display_placeholder = st.empty()
prompt_placeholder.write("Ask Rap-Mistral Something")
display_placeholder.write("")
# Temperature slider
temperature = st.slider('Temperature', min_value=0.0, max_value=1.0, value=0.5, step=0.01)
question = st.chat_input("Write a verse in the style of Lupe Fiasco")
if question:
display_placeholder.write("Loading...")
prompt_placeholder.write(question)
prompt = build_prompt(question)
inputs = tokenizer(prompt, return_tensors="pt")
model_inputs = inputs.to('cuda')
generated_ids = model.generate(**model_inputs, max_new_tokens=300, do_sample=True, pad_token_id=tokenizer.eos_token_id, temperature=temperature)
response = tokenizer.batch_decode(generated_ids)[0]
end_of_inst = response.find("[/INST]") + len("[/INST]")
if end_of_inst > -1:
actual_response = response[end_of_inst:].strip()
else:
actual_response = response
actual_response = actual_response.replace("\n", " \n")
display_placeholder.write(actual_response) |