Welcome to the world of Imp, where a very small man can certainly cast a very large shadow! Inspired by the potential of multimodal models, this project delivers robust performance through a compact design. In this guide, we will explore how to get started with the Imp model, its installation, and usage. Plus, we’ll cover some troubleshooting tips to ensure a smooth experience.

What is the Imp Model?

The Imp project aims to provide a family of strong multimodal small language models (MSLMs). Our flagship model, imp-v1-3b, boasts only **3 billion** parameters but performs exceptionally well compared to larger counterparts. It integrates a small yet powerful language model, Phi-2, with a sophisticated visual encoder, SigLIP, and is trained on the LLaVA-v1.5 dataset.

Installation Steps

To harness the power of the Imp model, follow these installation steps:

• Set up your Python environment.
• Install the necessary dependencies using the following commands:

bash
pip install transformers # latest version is ok, but we recommend v4.39.2
pip install -q pillow accelerate einops

Running the Model Inference

Once you have installed the dependencies, you can proceed to run the model. The following code snippet outlines the steps you need to execute:

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

torch.set_default_device('cuda')

# Create model
model = AutoModelForCausalLM.from_pretrained(
    'MILVLG/imp-v1-3b',
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained('MILVLG/imp-v1-3b', trust_remote_code=True)

# Set inputs
text = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
USER: imagenWhat are the colors of the bus in the image?
image = Image.open('images/bus.jpg')

input_ids = tokenizer(text, return_tensors='pt').input_ids
image_tensor = model.image_preprocess(image)

# Generate the answer
output_ids = model.generate(
    input_ids,
    max_new_tokens=100,
    images=image_tensor,
    use_cache=True
)[0]

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

Understanding the Code: An Analogy

Imagine you are setting up a magical library where every book (function) is neatly categorized, and every bookcase (model) can hold various genres (inputs). The first step is to choose a powerful librarian (model) who can read your books quickly and efficiently.

In our code:

• Library Setup: We first import the essential librarians (libraries) needed for reading.
• Choose Your Librarian: We create our model (librarian) using pre-trained parameters.
• Prepare Your Text and Image Books: You tell the librarian (model) what story (text) you want to uncover and provide the image book (image) for reference.
• A Task for the Librarian: We then instruct the librarian to find the answers (generate output) based on the text and the image. Finally, we read the conclusion from the librarian’s notes (output).

Model Evaluation Benchmarking

We evaluate the Imp model across various benchmarks to test its capabilities against other models. In 9 commonly-used benchmarks, the Imp model outperforms many similar-sized models, demonstrating its reliability and strength in MSLM performance.

Troubleshooting

If you encounter any issues while using the Imp model, consider the following troubleshooting tips:

• Ensure that your GPU is properly configured since the model requires GPU support for inference.
• Verify that all dependencies are correctly installed, especially if you encounter errors during import.
• Check the input paths for images and ensure they are correct to prevent file-not-found errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

The Imp project is set to shape the future of MSLMs with its efficient framework and robust performance. As we continue to evolve the model, we invite you to explore the capabilities of Imp in your applications.

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.