Welcome to the exciting world of TinyLLaVA! This blog will guide you through the usage of the TinyLLaVA models, including the best practices for implementing them in your projects. So, roll up your sleeves, and let’s dive into the details!

What is TinyLLaVA?

TinyLLaVA is a family of small-scale Large Multimodal Models (LMMs) designed for image-text processing. The models range from 0.55B to 3.1B parameters, and our star performer, the TinyLLaVA-Phi-2-SigLIP-3.1B, showcases superior performance compared to existing 7B models, such as LLaVA-1.5 and Qwen-VL.

Setting Up TinyLLaVA

Before we jump into the code, make sure you have the necessary libraries installed. You will need the transformers library from Hugging Face.

Installation

• Install the necessary library using pip:

pip install transformers

Executing the Test Code

Now, let’s write the code to execute a test with our TinyLLaVA model. Below is the step-by-step breakdown:

python
from transformers import AutoTokenizer, AutoModelForCausalLM

hf_path = "jiajunlong/TinyLLaVA-OpenELM-450M-SigLIP-0.89B"
model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
model.cuda()

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 = "What are these?"
image_url = "http://images.cocodataset.org/test-stuff/2017000000000001.jpg"
output_text, generation_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer)

print("Model output:", output_text)
print("Running time:", generation_time)

Understanding the Code: The Chef Analogy

Think of the TinyLLaVA model as a chef in a kitchen, with a well-stocked pantry (your libraries) and a detailed recipe (the code). The chef (model) takes ingredients (inputs) like images and text, follows the recipe meticulously, and serves a delicious dish (output). In this analogy:

• Chef: The TinyLLaVA model
• Ingredients: Input data, such as images and prompts
• Recipe: Code instructions that guide the chef on how to process the ingredients
• Delicious Dish: The output that answers queries based on the input data

Results

Once you execute the above code, you should see output similar to this:

Model output: 
Running time: 

Troubleshooting

If you encounter any issues while executing the code, don’t worry! Here are some troubleshooting tips:

• Ensure all libraries are correctly installed and up-to-date.
• Check that the image URL is accessible and valid.
• If you face any hardware or CUDA-related issues, verify your GPU setup.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.