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nanoLLaVA - Sub 1B Vision-Language Model

Description

nanoLLaVA is a "small but mighty" 1B vision-language model designed to run efficiently on edge devices.

• Base LLM: Quyen-SE-v0.1 (Qwen1.5-0.5B)
• Vision Encoder: google/siglip-so400m-patch14-384

| Model | VQA v2 | TextVQA | ScienceQA | POPE | MMMU (Test) | MMMU (Eval) | GQA | MM-VET | |---------|--------|---------|-----------|------|-------------|-------------|------|--------| | Score | 70.84 | 46.71 | 58.97 | 84.1 | 28.6 | 30.4 | 54.79| 23.9 |

Training Data

Training Data will be released later as I am still writing a paper on this. Expect the final final to be much more powerful than the current one.

Finetuning Code

Coming Soon!!!

Usage

You can use with transformers with the following script:

pip install -U transformers accelerate flash_attn

import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings

# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

# set device
torch.set_default_device('cuda')  # or 'cpu'

# create model
model = AutoModelForCausalLM.from_pretrained(
    'qnguyen3/nanoLLaVA',
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    'qnguyen3/nanoLLaVA',
    trust_remote_code=True)

# text prompt
prompt = 'Describe this image in detail'

messages = [
    {"role": "user", "content": f'<image>\n{prompt}'}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

print(text)

text_chunks = [token

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Tags

Languages