Meta Llama 3.2 90b

Description: Meta Llama 3.2 90B is a highly advanced, multimodal large language model developed by Meta, designed for advanced performance in image reasoning applications, including document-level understanding, image captioning, and visual grounding tasks.

Key Features:

• Context Window: 128,000 tokens, enabling the processing of large volumes of data and long text passages.

• Multimodal Capabilities: Supports both text and image inputs, making it versatile for various applications that require understanding and reasoning about visual content.

• Performance: Excels in image understanding, visual reasoning, and text-image applications, outperforming many available open-source and closed multimodal models on common industry benchmarks.

• Training Data: Pre-trained on a corpus of over 6 billion (image, text) pairs, significantly improving both the quantity and quality of the data compared to previous versions.

• Model Architecture: Built on top of Llama 3.1 text-only model, with a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model through cross-attention layers.

Use Cases:

• Image Reasoning: Ideal for tasks such as document-level understanding, including charts and graphs, image captioning, and visual grounding tasks like directionally pinpointing objects in images based on natural language descriptions.

• Text-Image Applications: Suitable for applications that require bridging the gap between vision and language, such as extracting details from an image, understanding the scene, and crafting sentences that could be used as image captions.

• Visual Understanding: Can be used for tasks like visual question answering, image classification, and object detection.

Limitations:

• Safety and Bias: May generate outputs that reflect biases present in its training data, similar to other Large Language Models (LLMs).

• Common Sense Reasoning: May not possess the same level of common sense reasoning as humans, which can lead to misinterpretations of factual queries or the generation of responses that are factually correct but nonsensical in context.