TobyYang7/MFFM-8B-finma-v8

Installation

1. Install Package

   conda create -n llava python=3.10 -y
   conda activate llava
   pip install --upgrade pip  # enable PEP 660 support
   pip install -e .
   

2. Install additional packages for training cases

   pip install -e ".[train]"
   pip install flash-attn --no-build-isolation
   

Interface

from llava_llama3.serve.cli import chat_llava
from llava_llama3.model.builder import load_pretrained_model
import argparse
import os
import glob
import pandas as pd
from tqdm import tqdm
import json

root_path = os.path.dirname(os.path.abspath(__file__))
print(f'\033[92m{root_path}\033[0m')

parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="TobyYang7/MFFM-8B-finma-v8")
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--conv-mode", type=str, default="llama_3")
parser.add_argument("--temperature", type=float, default=0)
parser.add_argument("--max-new-tokens", type=int, default=512)
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
args = parser.parse_args()

# load model
tokenizer, llava_model, image_processor, context_len = load_pretrained_model(args.model_path, None, 'llava_llama3', args.load_8bit, args.load_4bit, device=args.device)

print('\033[92mRunning chat\033[0m')
output = chat_llava(args=args,
                    image_file=root_path+'/data/llava_logo.png',
                    text='What is this?',
                    tokenizer=tokenizer,
                    model=llava_model,
                    image_processor=image_processor,  # todo: input model name or path
                    context_len=context_len)
print('\033[94m', output, '\033[0m')

If you encounter the error No module named 'llava_llama3', set the PYTHONPATH as follows:

export PYTHONPATH=$PYTHONPATH:{$your_dir}/llava_llama3