Nanollava

🚀 nanoLLaVA - Sub 1B Vision-Language Model

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

IMPORTANT: nanoLLaVA-1.5 is out with a much better performance. Please find it here.

🚀 Quick Start

You can use nanoLLaVA with transformers using the following steps. First, install the necessary libraries:

pip install -U transformers accelerate flash_attn

Then, use the following Python script to interact with the model:

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 = [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())

✨ Features

• 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

📦 Installation

pip install -U transformers accelerate flash_attn

💻 Usage Examples

Basic Usage

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 = [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())

📚 Documentation

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

Image	Example
	What is the text saying? "Small but mighty". How does the text correlate to the context of the image? The text seems to be a playful or humorous representation of a small but mighty figure, possibly a mouse or a mouse toy, holding a weightlifting bar.

📄 License

This project is licensed under the apache-2.0 license.

Model is trained using a modified version from Bunny

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!!!