florence vl 3b sft

Introduction

Florence-VL 3B SFT is a sophisticated model designed for advanced visual-language tasks. This checkpoint provides optimized parameters for enhancing visual-language processing capabilities.

Architecture

The Florence-VL 3B SFT model architecture integrates extensive visual and language processing layers, enabling it to efficiently manage complex multi-modal data. The architecture is built to support large-scale data processing with a focus on scalability and performance.

Training

The model was trained using a diverse dataset that includes both visual and language inputs. It utilizes state-of-the-art machine learning techniques to refine its ability to understand and generate multi-modal content. The training process emphasizes fine-tuning to maximize the model's performance across various tasks.

Guide: Running Locally

1. Clone the Repository: Start by cloning the Florence-VL 3B SFT repository from the Hugging Face model hub.
2. Install Dependencies: Ensure all necessary dependencies are installed using the provided requirements.txt.
3. Download Model Weights: Obtain the model weights from the Hugging Face model page and place them in the appropriate directory.
4. Run the Model: Use the provided scripts to execute the model locally on your machine.

For enhanced performance, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure to handle the model's computational requirements.

License

The Florence-VL 3B SFT model is distributed under a specific license that governs its use and distribution. Users should review the license details before using the model to ensure compliance with its terms.