Llama3.2 Vision 90B Instruct - Details

Llama3.2 Vision 90B Instruct is a large language model developed by Meta Llama Enterprise with 90b parameters. It operates under the Llama 32 Community License Agreement (LLAMA-32-COMMUNITY) and is designed for instruct tasks. The model specializes in multimodal tasks, particularly visual recognition and image reasoning, making it suitable for applications requiring advanced understanding of both text and visual data.

The Llama 3.2-Vision collection of multimodal large language models (LLMs) includes pretrained and instruction-tuned models available in 11B and 90B parameter sizes. These models are designed to process text and images and generate text-based outputs. They are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The instruction-tuned variants outperform many open-source and closed multimodal models on common industry benchmarks, demonstrating strong capabilities in image understanding and multimodal interaction.

The Llama3.2 Vision 90B Instruct model features 90b parameters, placing it in the very large models category, which enables it to handle complex tasks but requires significant computational resources. Its 128k context length allows for very long text processing, making it suitable for tasks involving extensive input, though it demands high resource allocation. This combination of large parameters and extended context positions the model as a powerful tool for advanced multimodal reasoning but necessitates careful management of hardware and efficiency.

• Parameter Size: 90b
• Context Length: 128k

The Llama3.2 Vision 90B Instruct model offers possible applications in tasks requiring visual understanding and multimodal interaction, such as visual question answering (VQA) and visual reasoning, where it can analyze images and generate text-based responses. Its possible use in document visual question answering (DocVQA) suggests potential for processing complex visual documents, while image captioning could generate descriptive text for images. Image-text retrieval might enable matching of text queries to relevant visual content, and visual grounding could help identify objects or regions within images. Synthetic data generation and distillation are possible areas for leveraging the model to create or refine datasets. The model’s multilingual support in English, Italian, French, Portuguese, Thai, Hindi, German, and Spanish expands its possible utility across diverse linguistic contexts. These possible uses require further exploration to determine their effectiveness and suitability for specific tasks.

• visual question answering (vqa) and visual reasoning
• document visual question answering (docvqa)
• image captioning
• image-text retrieval
• visual grounding
• synthetic data generation and distillation

The Llama3.2 Vision 90B Instruct model presents possible applications in tasks like visual question answering (VQA) and visual reasoning, where it could analyze images and generate contextually relevant responses. Possible uses include image captioning, enabling the model to describe visual content in text, and image-text retrieval, where it might match textual queries to corresponding visual data. Possible applications in synthetic data generation could allow the model to create training datasets for other systems. These possible uses require thorough evaluation to ensure alignment with specific requirements and constraints. Each application must be thoroughly evaluated and tested before use.

• visual question answering (vqa) and visual reasoning
• image captioning
• image-text retrieval
• synthetic data generation

The Llama3.2 Vision 90B Instruct model's medium q4 version requires significant VRAM to operate efficiently, likely necessitating high-end GPUs with at least 24GB-40GB VRAM or multiple GPUs for optimal performance. This quantized version balances precision and performance, making it suitable for systems with robust hardware capabilities. However, the exact requirements may vary depending on the workload and deployment setup.

• fp16
• q4
• q8

Llama3.2 Vision 90B Instruct is a large language model with 90b parameters and a 128k context length, optimized for multimodal tasks like visual recognition, image reasoning, and captioning. Its high parameter count and extended context enable advanced capabilities but require significant computational resources for deployment.

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