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import streamlit as st
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
from huggingface_hub import snapshot_download
import os
import threading
cwd = os.getcwd()
cachedir = cwd+'/cache'
# Check if the directory exists before creating it
if not os.path.exists(cachedir):
os.mkdir(cachedir)
os.environ['HF_HOME'] = cachedir
local_folder = cachedir + "/model"
quantized_model_dir = "FPHam/Jackson_The_Formalizer_V2_13b_GPTQ"
snapshot_download(repo_id=quantized_model_dir, local_dir=local_folder, local_dir_use_symlinks=True)
model_basename = cachedir + "/model/Jackson2-4bit-128g-GPTQ"
class QuantizedModel:
def __init__(self, model_dir):
self.tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
self.model = AutoGPTQForCausalLM.from_quantized(
model_dir,
use_safetensors=True,
strict=False,
device="cuda:0",
use_triton=False
)
def generate(self, prompt, max_new_tokens=512, temperature=0.1, top_p=0.95, repetition_penalty=1.15):
inputs = self.tokenizer(prompt, return_tensors="pt")
outputs = self.model.generate(
input_ids=inputs['input_ids'].to("cuda:0"),
attention_mask=inputs['attention_mask'].to("cuda:0"),
max_length=max_new_tokens + inputs['input_ids'].size(-1),
temperature=temperature,
top_p=top_p,
repetition_penalty=repetition_penalty
)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
quantized_model = QuantizedModel(local_folder)
user_input = st.text_input("Input a phrase")
prompt_template = f'USER: {user_input}\nASSISTANT:'
# Generate output when the "Generate" button is pressed
if st.button("Generate the prompt"):
output = quantized_model.generate(prompt_template)
st.text_area("Prompt", value=output)
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