Llama3.2 Vision 11B Instruct - Details

Llama3.2 Vision 11B Instruct is a large language model developed by Meta Llama Enterprise, featuring 11b parameters under the Llama 32 Community License Agreement. Designed for instruct tasks, it excels in multimodal applications, particularly visual recognition and image reasoning.

A multimodal large language model optimized for visual recognition, image reasoning, captioning, and answering questions about images. Combines a text-only Llama 3.1 model with a vision adapter for image processing. Supports both pretrained and instruction-tuned versions with parameter sizes of 11B and 90B. Trained on 6B image-text pairs with a knowledge cutoff of December 2023. Designed to handle complex multimodal tasks requiring both textual and visual understanding.

The Llama3.2 Vision 11B Instruct model has 11b parameters, placing it in the mid-scale range for open-source LLMs, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 128k context length falls into the very long context category, enabling it to handle extensive text sequences but requiring significant computational resources. This combination makes it suitable for complex multimodal applications that demand both robust language understanding and the ability to process lengthy inputs.

• Parameter Size: 11b
• Context Length: 128k

The Llama3.2 Vision 11B Instruct model has possible applications in tasks like visual question answering (VQA) and visual reasoning, where it could analyze images and generate responses based on textual queries. Its possible use in document visual question answering (DocVQA) might enable it to extract information from complex visual documents, while possible roles in image captioning and image-text retrieval could involve describing visual content or matching text to relevant images. The model’s multilingual support for languages like English, Italian, French, and others suggests possible utility in cross-lingual tasks, though these possible uses would require validation in specific contexts. The model’s design emphasizes flexibility, but further investigation is needed to confirm its effectiveness for these possible applications.

• visual question answering (vqa) and visual reasoning
• document visual question answering (docvqa)
• image captioning and image-text retrieval

The Llama3.2 Vision 11B Instruct model has possible applications in visual question answering (VQA) and visual reasoning, where it could analyze images and generate responses based on textual queries. It might also be possible to use it for document visual question answering (DocVQA), enabling the extraction of information from complex visual documents. Possible roles in image captioning and image-text retrieval could involve describing visual content or matching text to relevant images. These possible uses, while aligned with the model’s design, would require thorough evaluation to ensure effectiveness in specific contexts.

• visual question answering (vqa) and visual reasoning
• document visual question answering (docvqa)
• image captioning and image-text retrieval

The Llama3.2 Vision 11B Instruct model’s medium q4 version requires a GPU with at least 20GB VRAM (e.g., RTX 3090) and 32GB system memory for optimal performance, balancing precision and efficiency. This configuration ensures the model can handle its 11b parameters while maintaining responsiveness. Possible applications may demand additional resources depending on workload, but this setup is suitable for most standard tasks.

• Quantizations: fp16, q4, q8

The Llama3.2 Vision 11B Instruct model, developed by Meta Llama Enterprise, features 11b parameters and a 128k context length, optimized for multimodal tasks like visual recognition and image reasoning. It operates under the Llama 32 Community License Agreement and supports multiple languages, making it suitable for diverse applications requiring both text and visual understanding.

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