Llama-3.2-11B-Vision-Instruct: Image Chat Assistant

1. Tutorial Introduction

The Llama 3.2-Vision multimodal Large Language Model (LLM) collection is a set of pre-trained and instruction-tuned image reasoning generative models developed by Meta in 2024, with sizes of 11B and 90B (text+image input/text output) respectively. Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about images. These models outperform many available open source and closed multimodal models on common industry benchmarks. Supported languages: For text-only tasks, English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a wider range of languages than these 8 supported languages.

Llama 3.2-Vision is intended for commercial and research use. Instructions for tuning models are used for visual recognition, image reasoning, captioning, and assistant-like image chat, while pre-trained models can be adapted to a variety of image reasoning tasks. In addition, since Llama 3.2-Vision is able to take images and text as input, other use cases may include:

• Visual Question Answering (VQA) and Visual Reasoning: Imagine a machine that can look at an image and understand the question you ask about it.
• Document Visual Question Answering (DocVQA): Imagine a computer being able to understand the text and layout of a document (like a map or a contract), and then answer questions about it directly from the image.
• Image Captioning: Image captioning bridges the gap between vision and language, extracting details, understanding the scene, and then writing a sentence or two to tell the story.
• Image-text retrieval: Image-text retrieval is like a matchmaker between images and their descriptions. It is similar to a search engine, but it can understand both pictures and text.
• Visual Grounding: Visual grounding is like connecting what we see with what is said. It is about understanding how language refers to specific parts of an image, thus enabling AI models to accurately locate objects or regions based on natural language descriptions.

2. Operation steps

1. 启动容器后点击 API 地址即可进入 Web 界面

2. 进入网页后，即可与模型展开图像对话！
虽然官方语言中并没有中文，但仍可指定中文使模型输出为中文，例如 “请使用中文回答【问题】” 、 “请使用中文描述这张图”

3. 点击提交即可看到模型输出结果