README.md

metadata

license: apache-2.0
pipeline_tag: image-text-to-text

TinyLLaVA

We trained a TinyLLaVA model with 3.1B parameters, employing the same training settings as TinyLLaVA. For the Language and Vision models, we chose Phi-2 and siglip-so400m-patch14-384, respectively. The Connector was configured with a 2-layer MLP. The dataset used for training is the ShareGPT4V dataset.

Usage

1. you need to download the generate file "generate_model.py".
2. running the following command:

python generate_model --model tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B --prompt 'you want to ask' --image '/path/to/related/image'

or execute the following test code:

from transformers import AutoTokenizer, AutoModelForCausalLM
from generate_model import *

hf_path = 'tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B'
model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
config = model.config
tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
prompt="you want to ask"
image="/path/to/related/image"
output_text, genertaion_time = generate(prompt=prompt, image=image, model=model, tokenizer=tokenizer)
print_txt = (
        f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
        '\033[1m Prompt + Generated Output\033[0m\r\n'
        f'{"-" * os.get_terminal_size().columns}\r\n'
        f'{output_text}\r\n'
        f'{"-" * os.get_terminal_size().columns}\r\n'
        '\r\nGeneration took'
        f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
        'seconds.\r\n'
    )
print(print_txt)

Result

model_name	vqav2	gqa	sqa	textvqa	MM-VET	POPE	MME	MMMU
bczhou/TinyLLaVA-3.1B	79.9	62.0	69.1	59.1	32.0	86.4	1464.9	-
tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B	80.1	62.1	73.0	60.3	37.5	87.2	1466.4	38.4