Llava-Visionary-70B

The Big Picture (Brainproject.ai)

The human brain is an intricate puzzle that we're continually striving to decode. The aim is to replicate its complexity, functionality, and depth in a digital realm - exploring the convergence of neuroscience and artificial intelligence to glean insights into the mind's intricate workings and harness that knowledge into digital counterparts.

Mixture of Experts

Llava-Visionary-70B utilizes a Mixture of Experts (MoE) architecture, with different expert modules specializing in various aspects of visual and language understanding. A gating mechanism selectively activates the most relevant experts for each input. This provides computational efficiency and scalability.

Llava-Visionary-70B

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Llava-Visionary-70B is an artificial intelligence system designed for visual reasoning and multimodal understanding. It builds on top of the Llama-2 architecture using a Mixture of Experts approach.

The model has been further pretrained on a large dataset of YouTube videos and images to develop human-like visual comprehension abilities. This enables it to understand the semantics of images, videos, and multimodal content.

Model Description

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• Developed by: Priyanshu Pareek
• Model type: Transformer-based multimodal model
• License: wtfpl
• Finetuned from model [optional]: Llama-2-70B

Uses

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Llava-Visionary-70B is designed for tasks that involve:

• Visual understanding of images, videos, diagrams
• Multimodal reasoning with vision and language
• Answering questions about visual content
• Generating captions or descriptions of visual data

It can provide value for uses cases such as:

• Multimodal chatbots and digital assistants
• Image and video search/recommendation
• Automated alt-text generation
• Vision-based QA systems

Direct Use

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Llava-Visionary-70B can be used out-of-the-box without further training for zero-shot inference on downstream visual/multimodal tasks.

Recommendations

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How to Get Started with the Model

Want to take Chameleon-Llama-70B for a spin?

Load the model and tokenizer from HuggingFace:

from transformers import LlavaVisionary70BModel, LlavaVisionary70BTokenizer

tokenizer = LlavaVisionary70BTokenizer.from_pretrained("llava-visionary-70b")
model = LlavaVisionary70BModel.from_pretrained("llava-visionary-70b")```

Pass multimodal input and generate output:

text = "What type of animal is shown in this picture?"
image = Image.open("animal.jpg")

inputs = tokenizer(text, images=image, return_tensors="pt") 
outputs = model(**inputs)

Training Details

Training Data

Llava-Visionary-70B was further pretrained on a large dataset of YouTube videos and images.

Training Procedure

The model was trained using supervised pretraining on video-text pairs, leveraging the original Llama-2 model weights.

Training Hyperparameters

• Batch size: 256
• Learning rate: 5e-5
• Optimizer: AdamW
• Training epochs: 3