Llama 3.2 11 B Vision Instruct bnb 4bit

Introduction

Llama-3.2-11B-Vision-Instruct-bnb-4bit is a model developed by UNSLOTH AI, based on Meta's Llama 3.2 architecture. It is designed for multimodal applications, specifically those involving vision and text, and utilizes a precision of 4-bits for optimized performance.

Architecture

The Llama 3.2 model is an auto-regressive language model that employs an optimized transformer architecture. It has been fine-tuned using supervised techniques and reinforcement learning with human feedback to align with preferences for helpfulness and safety. The model supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with potential for further fine-tuning in other languages as allowed by the Llama 3.2 Community License.

Training

Llama 3.2 models are pretrained and instruction-tuned for multilingual dialogue and generative tasks. They employ Grouped-Query Attention to enhance inference scalability. The models are trained on a diverse dataset, with future versions potentially expanding capabilities and safety features.

Guide: Running Locally

1. Set Up Environment: Ensure you have Python and PyTorch installed.
2. Install Dependencies: Use the transformers library for loading the model.
3. Download Model: Access the model via Hugging Face's Model Hub.
4. Run Inference: Use the provided scripts to test the model on your data.

For enhanced performance, consider using cloud GPUs like those offered by Google Colab, which provides free access to Tesla T4 GPUs.

License

The Llama-3.2-11B-Vision-Instruct-bnb-4bit model is governed by the Llama 3.2 Community License, a custom commercial license agreement. For more information, refer to the license document. Compliance with the Acceptable Use Policy is required, especially when fine-tuning the model for additional languages.