metadata

language:
  - en
tags:
  - llava
  - multimodal
  - qwen
license: apache-2.0

nanoLLaVA-1.5 - Improved sub 1B Vision-Language Model

Logo

Description

nanoLLaVA-1.5 is a "small but mighty" 1B vision-language model designed to run efficiently on edge devices. This is an update from the v1.0 version qnguyen3/nanoLLaVA

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
nanoLLavA-1.0	70.84	46.71	58.97	84.1	28.6	30.4	54.79	23.9
nanoLLavA-1.5	TBD	TBD	TBD	TBD	TBD	TBD	TBD	TBD

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'

model_name = 'qnguyen3/nanoLLaVA-1.5'

# create model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    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 = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)

# image, sample images can be found in images folder
image = Image.open('/path/to/image.png')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)

# generate
output_ids = model.generate(
    input_ids,
    images=image_tensor,
    max_new_tokens=2048,
    use_cache=True)[0]

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())

Prompt Format

The model follow the ChatML standard, however, without \n at the end of <|im_end|>:

<|im_start|>system
Answer the question<|im_end|><|im_start|>user
<image>
What is the picture about?<|im_end|><|im_start|>assistant