VideoLLaMA3-7B is a state-of-the-art multimodal foundation model designed for comprehensive image and video understanding. It is developed by researchers at Alibaba Group, the framework addresses the previous challenges of video comprehension by introducing innovative features such as Any-resolution Vision Tokenization (AVT) and Differential Frame Pruner (DiffFP). These enhancements enable efficient processing of variable-resolution visual data and dynamic token reduction, which significantly improves both representation and computational efficiency. The model excels in integrating textual and visual information, extracting insights from sequential video data, and performing sophisticated reasoning across dynamic visual scenarios.
This guide will walk you through the step-by-step process of installing and running VideoLLaMA3-7B locally, helping you to test and use its advanced video analysis capabilities.
Prerequisites
The minimum system requirements for this use case are:
GPUs: A100 or RTX 4090 (for smooth execution).
Disk Space: 100 GB
RAM: At least 8 GB.
Jupyter Notebook installed.
Note: The prerequisites for this are highly variable across use cases. A high-end configuration could be used for a large-scale deployment.
Step-by-step process to install & run VideoLLaMA3-7B
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
1) Navigate to the menu on the left side.
2) Click on the GPU Nodes option.
3) Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
1) For this tutorial, we’ll be using the RTX 4090 GPU; however, you can choose any GPU of your choice based on your needs.
2) Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
1) After selecting your required configuration options, you'll see the available VMs in your region and according to (or very close to) your configuration. In our case, we'll choose a 1x RTX 4090 GPU node with 12 vCPUs/96GB RAM/200 GB SSD.
2) Next, you'll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step would be to choose an image for the VM, which in our case is Jupyter Notebook, where we’ll deploy and run the inference of our model.
That's it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
1) As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
2) Once your GPU shows this status, navigate to the three dots on the right and click on Connect with SSH. This will open a new tab with a Jupyter Notebook session in which we can run our model.
Step 7: Setting up Python Notebook
Start by creating a .ipynb notebook by clicking on Python 3 (ipykernel).
Next, If you want to check the GPU details, run the following command in the Jupyter Notebook cell:
!nvidia-smi
Step 8: Install Dependencies
We'll need PyTorch, Hugging Face, and other dependencies to run this model. Paste the below command in the code cell to install the packages one by one:
!pip install torch torchvision torchaudio einops timm pillow
!pip install git+https://github.com/huggingface/transformers
!pip install git+https://github.com/huggingface/accelerate
!pip install git+https://github.com/huggingface/diffusers
!pip install huggingface_hub
!pip install sentencepiece bitsandbytes protobuf decord ffmpeg-python imageio opencv-python
Output:
2) Install some other dependencies.
!pip install packaging
!pip uninstall -y ninja && pip install ninja
!pip install flash_attn --no-build-isolation
Output:
Step 9: Load and Run the model for inference
Once the installation is done, we'll load the model to take the inference.
1) Load the model with the following code snippet.
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, AutoModel, AutoImageProcessor
model_name = "DAMO-NLP-SG/VideoLLaMA3-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
Output:
2) Run the model.
video_path = "./video_test.mp4"
question = "Describe this video in detail."
# Video conversation
conversation = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{"type": "video", "video": {"video_path": video_path, "fps": 1, "max_frames": 128}},
{"type": "text", "text": question},
]
},
]
inputs = processor(conversation=conversation, return_tensors="pt")
inputs = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
output_ids = model.generate(**inputs, max_new_tokens=128)
response = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(response)
We'll use the following prompt and video to test our model. We have downloaded this video in the project directory with the filename "video_test.mp4".
Prompt: Describe this video in detail.
Video: https://www.pexels.com/video/teacher-giving-test-results-to-his-students-7092235/
Here's the output generated by the model:
As you may see in the output above, the model nicely describes the video, detailing its overall environment.
Conclusion
In this guide, we've explored the process of installing and running VideoLLaMA3-7B locally, highlighting its advanced features for efficient video analysis and multimodal understanding. By leveraging innovative features like AVT and DiffFP, the model sets a new benchmark for video comprehension tasks. With NodeShift's robust cloud platform, developers can seamlessly deploy and run resource-intensive models like VideoLLaMA3-7B, benefiting from scalable compute environments and optimized performance.
For more information about NodeShift:
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