florence vl 8b sft

Introduction
Florence-VL 8B SFT is a state-of-the-art model designed for advanced language and vision tasks. It leverages large-scale unsupervised and supervised data to enhance its performance across various applications.

Architecture
This model utilizes a transformer-based architecture with 8 billion parameters. The architecture is optimized for both language and visual understanding, making it suitable for multimodal tasks.

Training
Florence-VL 8B SFT is trained using a combination of self-supervised learning techniques and fine-tuning on specific datasets. This approach ensures the model is robust and capable of handling diverse input data.

Guide: Running Locally

1. Clone the Repository: Start by cloning the model repository from Hugging Face using Git.

   git clone https://huggingface.co/jiuhai/florence-vl-8b-sft
   

2. Install Dependencies: Ensure all necessary Python libraries and dependencies are installed. You may need packages such as transformers and torch.

   pip install transformers torch
   

3. Load the Model: Use the Hugging Face Transformers library to load the model.

   from transformers import AutoModelForCausalLM
   
   model = AutoModelForCausalLM.from_pretrained("jiuhai/florence-vl-8b-sft")
   

4. Cloud GPUs: For optimal performance, it is recommended to run this model on cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License
Florence-VL 8B SFT is distributed under a license that governs its usage and distribution. Please refer to the model card on Hugging Face for specific licensing information.