Llama 3.2 11B Vision Instruct
Instruction-tuned image reasoning model from Meta with 11B parameters. Optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The model can understand visual data, such as charts and graphs and also bridge the gap between vision and language by generating text to describe images details.
PixelFlow allows you to use all these features
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Llama 3.2-11B Vision-Instruct
The Llama 3.2-Vision-Instruct model is a multimodal large language model (LLM) that can process both text and images to generate text. This model is part of the Llama 3.2 family, developed by Meta, and is designed for commercial and research applications. Llama 3.2-11B Vision-Instruct is in 11B parameter size. It is optimized for visual recognition, image reasoning, captioning, and answering questions about images. The model is built upon the Llama 3.1 text-only model, incorporating a vision adapter for image processing. The model use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
Key Features of Llama 3.2-11B Vision-Instruct
-
Multimodal Input: Takes both text and images as input.
-
Output: Generates text as output.
-
Image Reasoning Tasks: Supports Visual Question Answering (VQA), Document Visual Question Answering (DocVQA), Image Captioning, Image-Text Retrieval, and Visual Grounding.
-
Language Support: For text-only tasks, it officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. For image+text, only English is supported.
-
Context Length: 128
Technical Specifications
-
Architecture: Uses an auto-regressive language model with an optimized transformer architecture.
-
Vision Adapter: Employs a separately trained vision adapter consisting of cross-attention layers to integrate image encoder representations into the core LLM.
-
Training Data: Trained on 6 billion (image, text) pairs, with a data cutoff of December 2023. Instruction tuning data includes public vision instruction datasets and over 3 million synthetically generated examples.
-
Inference Scalability: Uses Grouped-Query Attention (GQA).
Intended Use Cases
-
Commercial and research use
-
Visual recognition, image reasoning, captioning, and assistant-like chat with images.
-
Adaptable for a variety of image reasoning tasks.
-
Leveraging model outputs to improve other model.
Other Popular Models
sdxl-controlnet
SDXL ControlNet gives unprecedented control over text-to-image generation. SDXL ControlNet models Introduces the concept of conditioning inputs, which provide additional information to guide the image generation process
idm-vton
Best-in-class clothing virtual try on in the wild
illusion-diffusion-hq
Monster Labs QrCode ControlNet on top of SD Realistic Vision v5.1
instantid
InstantID aims to generate customized images with various poses or styles from only a single reference ID image while ensuring high fidelity
