Llava Llama 3 8b

🚀 LLaVA-llama-3-8B

llava-llama-3-8b is a large multimodal model (LMM) that combines a powerful language backbone with a vision encoder, enabling it to handle both text and image inputs.

🚀 Quick Start

This section provides a quick guide on how to start using the llava-llama-3-8b model. Note that we only offer the trained weights difference, and you need to download the base meta-llama/Meta-Llama-3-8B-Instruct model separately.

# Copyright 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForPreTraining
import transformers

def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result

def add_model_a_to_b(model_a, model_b):
    state_dict_a = model_a.state_dict()
    state_dict_b = model_b.state_dict()

    # Ensure keys match before subtraction
    if set(state_dict_a.keys()) != set(state_dict_b.keys()):
        raise ValueError("Model state dicts do not have the same keys.")

    for key in state_dict_a:
        if state_dict_a[key].shape != state_dict_b[key].shape:
            raise ValueError(f"Shape mismatch for key '{key}': {state_dict_a[key].shape} vs {state_dict_b[key].shape}")
        # Subtract model_a's weights from model_b for the matching key
        state_dict_b[key] = state_dict_b[key] + state_dict_a[key]

    # Update model_b with the new weights
    model_b.load_state_dict(state_dict_b)

output_checkpoint = "" # set if you don't want to merge every time
hf_checkpoint = "Intel/llava-llama-3-8b"
device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained(hf_checkpoint)
model = AutoModelForPreTraining.from_pretrained(hf_checkpoint)
if model.language_model.model.embed_tokens.weight[-1].sum() == 0:
    print("adding llama3 weights")
    model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
    pipeline = transformers.pipeline(
        "text-generation",
        model=model_id,
        model_kwargs={"torch_dtype": torch.bfloat16},
        device_map="cpu",
    )
    llama3 = pipeline.model
    add_model_a_to_b(llama3, model.language_model)
    if output_checkpoint:
        print("saving weights, so no adding is needed again")
        model.save_pretrained(output_checkpoint)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

prompt = processor.tokenizer.apply_chat_template(
    [{'role': 'user', 'content': "<image>\nWhat's the content of the image?"}],
    tokenize=False,
    add_generation_prompt=True
)

url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = Image.open(requests.get(url, stream=True).raw)

#original llava pads with mean, HF llava pads with zeros
image = expand2square(image, tuple(int(x*255) for x in processor.image_processor.image_mean)) 
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
# Generate
generate_ids = model.generate(**inputs, max_length=30)
output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(output)

✨ Features

• Multimodal Capability: The model can handle both text and image inputs, making it suitable for a wide range of multimodal tasks.
• Trained with LLaVA-v1.5: Utilizes the LLaVA-v1.5 framework and data mixture for training, enhancing its performance.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

# The code above in the Quick Start section shows the basic usage of the model.

Advanced Usage

There is no advanced usage code provided in the original document.

📚 Documentation

Model Details

Intended Use

Factors

Training Data

The model was trained using the LLaVA-v1.5 data mixture, which includes:

• 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
• 158K GPT-generated multimodal instruction-following data.
• 450K academic-task-oriented VQA data mixture.
• 40K ShareGPT data.

Ethical Considerations

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. This model has not been assessed for harm or biases, and should not be used for sensitive applications where it may cause harm.

🔧 Technical Details

There are no specific technical details provided in the original document.

📄 License

The model is under the Intel Research Use License. All usage code is licensed Apache 2.0.

Results