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import streamlit as st
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from huggingface_hub import snapshot_download
cwd = os.getcwd()
cachedir = cwd + '/cache'
local_folder = cachedir + "/model"
# Check if the directory exists before creating it
if not os.path.exists(cachedir):
os.mkdir(cachedir)
# Define pretrained and quantized model directories
pretrained_quantized_model_dir = "FPHam/Jackson_The_Formalizer_V2_13b_GPTQ"
quantized_model_dir = "opt-125m-4bit"
quantized_model_dir = "FPHam/Jackson_The_Formalizer_V2_13b_GPTQ"
# Check if the model has already been downloaded
model_path = os.path.join(local_folder, 'pytorch_model.bin')
if not os.path.isfile(model_path):
snapshot_download(repo_id=quantized_model_dir, local_dir=local_folder, local_dir_use_symlinks=True)
model_basename = cachedir + "/model/Jackson2-4bit-128g-GPTQ"
use_strict = False
use_triton = False
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(local_folder, use_fast=True)
quantize_config = BaseQuantizeConfig(
bits=4,
group_size=128,
desc_act=False
)
model = AutoGPTQForCausalLM.from_quantized(
local_folder,
use_safetensors=True,
strict=use_strict,
model_basename=model_basename,
device="cuda:0",
use_triton=use_triton,
quantize_config=quantize_config
)
st.write(model.hf_device_map)
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