cogvlm2 llama3 caption

Introduction

CogVLM2-Caption is a video captioning model designed to generate textual descriptions from video data. This process is crucial for creating training data for text-to-video models like CogVideoX. Most video data lacks descriptive text, making this conversion essential for model training.

Architecture

CogVLM2-Caption uses the Meta Llama 3.1-8B-Instruct as its base model. It transforms video inputs into text using a video-text-to-text pipeline. The model employs transformers and safetensors for efficient processing.

Training

The model is trained to handle video data by extracting frames and converting them into text. It uses a strategy called 'chat' to select frames based on their timestamps and processes these frames with a transformer-based approach to generate descriptive text.

Guide: Running Locally

To run CogVLM2-Caption locally:

1. Environment Setup: Install the necessary libraries, such as transformers, torch, and decord.
2. Model Loading: Use AutoModelForCausalLM and AutoTokenizer from the Transformers library to load the model.
3. Video Processing: Load and process video data using Decord's VideoReader to extract frames.
4. Prediction: Use the predict function to generate text descriptions from the video data by providing a prompt and adjusting the temperature parameter for text generation.
5. Test: Run the test function to see an example of the model in action.

For optimal performance, consider using a cloud GPU service that supports CUDA, such as AWS EC2 with NVIDIA GPUs or Google Cloud Platform's AI Platform.

License

This model is released under the CogVLM2 License. Models built with Meta Llama 3 must also comply with the LLAMA3 License. For more detailed licensing information, refer to the provided links.