If you’re diving into the world of multimodal AI models, TinyLLaVA is your ideal starting point. This friendly guide will take you through the process of installing, using, and troubleshooting TinyLLaVA, so you can harness its remarkable power with ease.

What is TinyLLaVA?

TinyLLaVA is an innovative framework designed for managing small-scale large multimodal models. These models integrate vision and text, allowing for exciting applications in AI development. Think of it as a Swiss army knife for developers – versatile, compact, but full of potential!

How to Install TinyLLaVA

• Clone the Repository: First, you need to clone the TinyLLaVA repository to your local environment. Open your terminal and run:

git clone https://github.com/DLCV-BUAATinyLLaVABench.git
cd TinyLLaVABench

• Install Packages: Set up your conda environment and install the necessary packages:

conda create -n tinyllava python=3.10 -y
conda activate tinyllava
pip install --upgrade pip
pip install -e .

• Install Additional Packages for Training: Use the following commands:

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

Running TinyLLaVA Models

Once installed, using TinyLLaVA is straightforward. It is comparable to preparing a meal with a recipe. The ingredients (elements of your model) are already measured out, you just need to follow the steps to whip up something delicious!

Here’s how to load and run inference with a model:

from tinyllava.model.builder import load_pretrained_model
from tinyllava.eval.run_tiny_llava import eval_model

model_path = "bczhouTinyLLaVA-3.1B"
tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path=model_path,
    model_base=None,
    model_name=get_model_name_from_path(model_path)
)

Running Inference

You can run inference using the preloaded model as follows:

prompt = "What are the things I should be cautious about when I visit here?" 
image_file = "https://llava-vl.github.io/static/images/view.jpg"

args = type(Args, (), {
    "model_path": model_path,
    "query": prompt,
    "image_file": image_file,
    "temperature": 0,
    "max_new_tokens": 512
})()

eval_model(args)

Evaluate TinyLLaVA

To ensure output reproducibility, models are evaluated with greedy decoding. You can refer to the Evaluation documentation for guidance.

Troubleshooting TinyLLaVA

It’s not uncommon to encounter hiccups when working with new frameworks. Here are some troubleshooting tips:

• Import Errors: If you see import errors after upgrading, run the following command to resolve potential dependency issues:

pip install flash-attn --no-build-isolation --no-cache-dir

• Model Loading Issues: Ensure that the model path is correctly specified and that you have downloaded all necessary files.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.