Llama 3V: Multimodal Model 100x Smaller than GPT-4

Llama 3-V is a groundbreaking open-source multimodal AI model that delivers performance comparable to the much larger GPT4-V model at a fraction of the size and training cost. Developed by researchers Aksh Garg and Mustafa Aljadery, Llama 3-V combines the language model Llama3 8B from Meta with the vision model SigLIP-SO400M to enable joint understanding of images and text.

Its compact size sets Llama 3-V apart - it is 100 times smaller than GPT4-V yet achieves 10-20% better performance than popular multimodal models like Llava on benchmarks, costing only around $500 to train. This makes Llama 3-V a highly efficient and accessible alternative to large proprietary models.

In this article, you will learn:

• How Llama 3-V achieves impressive performance with a model size 100 times smaller than its counterpart, GPT4-V.
• The secrets behind its efficient training process, costing under $500.
• The innovative combination of SigLIP and Llama 3 powers its multimodal capabilities.
• Practical use cases for Llama 3-V, from image captioning to robotics.
• The implications of this model for the future of AI research and development.

Let’s get right into it 🚀

Curate Data for Multimodal AI Models with Encord

Learn more

Llama 3-V: Training Process and Methodology

The training of Llama 3-V involves a novel approach that uses precomputed embeddings from the SigLIP vision model and a two-stage process of pretraining and supervised fine-tuning on a large dataset of image-text pairs.

This training method allows the model to effectively align the visual and textual modalities while remaining computationally efficient.

Precomputed Embeddings from SigLIP

SigLIP, or Sigmoid Loss for Language Image Pre-training, is a multimodal model that associates images and text using contrastive training on a large dataset. It uses a sigmoid loss function that operates on image-text pairs without requiring global normalization, enabling better performance at various batch sizes.

Llama 3-V uses the precomputed image embeddings from a SigLIP model with the Shape-Optimized 400M parameter vision transformer (ViT) architecture, SigLIP-SO400M. These embeddings capture rich visual features that can be aligned with the language model.

Illustration of how SigLIP embeddings work. Image from Twitter post by Merve. | Source: Llama 3-V: Matching GPT4-V with a 100x smaller model and 500 dollars.

Supervised Fine Tuning

The training of Llama 3-V occurs in two main stages:

1. In the pretraining stage, a projection layer is added to map the SigLIP image embeddings to the embedding space of the Llama3 language model. All weights except this projection layer are frozen, and the model is trained on around 600K image-text pairs. This allows the model to learn the alignment between the visual and textual features.
2. In the fine-tuning stage, the weights of the Llama3 model are updated while the SigLIP vision model and projection layer remain frozen. The model is trained on a larger dataset of approximately 1M images. Additionally, synthetic image-text data generated using the YI model family further improves the model's multimodal understanding.

This two-stage training process combines pretraining and supervised fine-tuning so Llama 3-V can effectively learn the joint representation of images and text while maintaining a compact size.

Cost Efficiency: Building Llama 3V for Under $500

One of the most remarkable aspects of Llama 3-V is its cost efficiency. Despite delivering performance comparable to models like GPT4-V, which are over 100 times larger, Llama 3-V was trained for only around $500. This is a significant breakthrough in making high-performance multimodal AI accessible to a wider range of researchers and developers.

The low training cost was achieved using pre-trained components, efficient training techniques, and a focus on open-source resources. With the training process, the creators of Llama 3-V avoided the massive computational costs associated with training the visual encoder (SigLIP) and language decoder (Llama 3) from scratch. The training techniques and publicly available datasets kept the overall training cost minimal.

The cost efficiency of Llama 3-V has important implications for the AI community. It demonstrates that state-of-the-art performance is achievable without massive proprietary datasets or computational budgets. This has leveled the playing field and empowered more developers and organizations to participate in cutting-edge AI research and development.

Technical Specifications of Llama 3V

Structural Overview of Llama 3-V

Llama 3-V’s architecture allows the model to understand jointly and reason about visual and textual information.

The Llama3 8B component is an LLM that excels at natural language understanding and generation. It has been trained on a massive corpus of text data and can handle various language tasks. The SigLIP-SO400M component is a vision transformer model optimized for efficient visual feature extraction.

Llama3-V Architecture: The researchers use SigLIP to embed our input image in patches. Then, they train a projection block with two self-attention blocks to align textual and visual tokens. | Source: Llama 3-V: Matching GPT4-V with a 100x smaller model and 500 dollars.

To integrate these two components, Llama 3-V introduces a projection layer that maps the visual features from SigLIP into the embedding space of Llama3. This allows the language model to incorporate visual information into its processing directly. 

The result is a unified model capable of tasks like image captioning, visual question answering, and multimodal reasoning.

Accessibility: Where to Find Llama 3-V?

A key aspect of Llama 3-V is its open-source nature. The model weights, code, and training datasets have been publicly available on platforms like Hugging Face and GitHub. This aligns with the growing trend of democratizing AI by enabling researchers and developers worldwide to access, use, and build upon state-of-the-art models.

By open-sourcing Llama 3-V, the creators aim to spur further innovation in multimodal AI and make the technology more widely accessible for various applications. The AI community has received the initiative well, with many already experimenting with the model and sharing their results.

Llama 3-V: Performance Benchmarks

Llama 3-V has demonstrated impressive performance across various benchmarks, rivaling and surpassing significantly larger models. Despite being 100 times smaller than GPT4-V, Llama 3-V achieves comparable results in most metrics.

Benchmarks show that Llama 3-V delivers 10-20% better performance than the popular multimodal model Llava. In all indicators except MMMU, it exhibits performance on par with competing closed-source models over 100 times larger.

VLLM Vision Benchmarks for Llama3v vs. GPT-4o and other multimodal AI models. | Source: Llama 3-V: Matching GPT4-V with a 100x smaller model and 500 dollars.

Performance Metrics: Llama 3-V vs. GPT-4

While GPT-4 still holds an edge in certain areas, Llama 3-V closes the gap significantly despite its compact size and lower training cost. Here are some key performance comparisons:

• MMMU (Multimodal Mulit-Task): GPT-4 outperforms Llama3v on this benchmark, indicating its superiority in handling multiple modalities simultaneously.
• MathVista: Although the GPT-4 models are ahead, Llama3v is not far off on this math-related visual reasoning task.
• AI2D Evals: Llama3v performs admirably well on this benchmark, which evaluates the models' ability to understand and reason about diagrams and visual information.
• ChartQA: Llama3v slightly outperforms GPT-4 turbo on this task, which involves answering questions based on chart and graph data.
• DocVQA: GPT-4 models perform better on this document visual question answering benchmark.

Overall, the benchmark results suggest that while GPT-4 maintains a significant edge in certain multimodal tasks, particularly those involving multiple modalities or document understanding, Llama3v matches or even exceeds GPT-4's performance in specific areas like chart comprehension and visual reasoning.

It's important to note that these benchmarks narrowly evaluate the models' capabilities, and their real-world performance may vary depending on the specific use case and data distribution.

Practical Applications of Llama 3-V

The versatility and efficiency of Llama 3-V open up a wide range of practical applications across various industries. Some notable use cases include:

1. Healthcare: Analyzing medical images and patient records to predict disease outbreaks and personalize treatment plans.
2. Agriculture: Assisting farmers in checking crops using satellite images, weather data, and soil information to decide on watering and fertilization.
3. Content Creation: Llama 3-V could generate creative content based on visual prompts, such as writing stories inspired by images or creating marketing materials.
4. Visual Question Answering: The model can answer questions about images' content, which could be applied in educational tools, search engines, or customer service chatbots.
5. Autonomous Vehicles: Equipping self-driving cars with multimodal AI to process information from multiple sensors, enabling them to make intelligent decisions in real-time.

These are just a few examples of the potential applications of Llama 3-V. As the model continues developing and refining, we expect more innovative and impactful use cases to emerge.

Llama 3v: Key Takeaways

Llama 3v is an impressive open-source multimodal AI model developed by researchers Aksh Garg and Mustafa Aljadery that delivers performance comparable to much larger models like GPT4-V at a fraction of the size and training cost. Here are some key takeaways about Llama 3v:

• Compact Size: Llama 3v is 100 times smaller than GPT4-V yet achieves 10-20% better performance on benchmarks than popular multimodal models like LlaVA. It costs only around $500 to train, making it a highly efficient and accessible alternative to large proprietary models.
• Open-Source: The model weights, code, and training datasets for Llama 3v have all been made publicly available, aligning with the trend of democratizing AI and enabling worldwide innovation. This open approach empowers a broader range of researchers and developers to access and build upon state-of-the-art multimodal AI.
• Novel Training Approach: Llama 3v leverages precomputed embeddings from the SigLIP vision model and a two-stage process of pretraining and supervised fine-tuning on a large dataset of image-text pairs. This methodology allows effective alignment of visual and textual modalities while remaining computationally efficient.
• Architectural Innovations: Key innovations in Llama 3v include integrating pretrained vision and language models, an efficient projection layer between modalities, an optimized training procedure, utilization of synthetic data, and an open-source foundation. These advancements enable high performance and efficiency.

Evaluate your models and build active learning pipelines with Encord

Try Encord today