In my earlier post we explored uncensored LLM like Dolphin. Today, we shall look into the intersection of visual and language understanding what happens when a marriage takes place between Vision & LLM. One such innovation is LLaVA (Large Language and Visual Assistant), an open-source generative AI model that combines the strengths of vision encoders and large language models to create a powerful tool for general-purpose visual and language understanding. In this blog post, we’ll delve into the details of LLaVA, its underlying models, and how you can harness its capabilities using LMStudio.
What is LLaVA?
🖼️ LLaVA is a novel, end-to-end trained large multimodal model that integrates a pre-trained CLIP ViT-L/14 visual encoder with the Vicuna large language model. The integration is achieved through a projection matrix, enabling seamless interaction between visual and language data. LLaVA is designed to excel in both daily user-oriented applications and specialized domains such as science, offering a versatile tool for multimodal reasoning and instruction-following tasks.
What is LLaMA 3?
🧠 LLaMA 3 is the third iteration of the Large Language Model from Meta AI, known for its remarkable language understanding and generation capabilities. LLaMA 3 builds upon its predecessors with improved architecture, enhanced training techniques, and a broader dataset, making it one of the most advanced language models available. In the context of LLaVA, LLaMA 3 serves as the foundation for the language model component, providing robust support for complex conversational and reasoning tasks.
How to Run the Model Locally Using LMStudio
💻 Running LLaVA locally using LMStudio is a straightforward process that allows you to leverage the model’s capabilities on your own hardware. Here’s a step-by-step guide to get you started:
- Setup Your Environment
- Install LMStudio: The software its available on (Windows, Mac & Linux). This software allows you to manage and deploy local LLMs without you having to setup Python, Machine Learning, Transformers etc. libraries. Link to Download the Windows Bits – LM Studio – Discover, download, and run local LLMs
- Download the Model and Dependencies
- The best space to keep a track on models is Hugging Face – Models – Hugging Face. You can keep a track of the model releases and updates here.
- Copy the model name from Hugging Face – xtuner/llava-llama-3-8b-v1_1-gguf
- Paste this name in LM Studio and it will list out all the quantized models
- In my case due to the configurations I selected int4 model. Please note lower the quantized version less accurate the model is.
- Obtain the LLaVA model files, including the quantized GGUF version and MMProj files, from the official repository.
- Download of the model will take time depending upon your internet connection.
- Prepare the Model for Running:
- Within LMStudio click on the Chat interface to configure model settings.
- Select the model from the drop down list – llava llama 3 v int4 GGUF
- You will be able to run it stock but I like to configure the Advanced Configurations
- Adjust the model settings to match your hardware capabilities and specific requirements.
- Based on your system set the GPU to 50/50 or max. I have setup for max
- Click Relod model to apply configuration
- Run Inference: Start the model and begin running inference tasks, whether for visual chat, science QA, or other applications.
Note – If there is enough interest, I can also do a extended blogpost on Dockerized version of this model. Leave comments down below.
What are MMProj Files?
📂 MMProj files are a key component in the LLaVA ecosystem, representing multimodal projection matrices that facilitate the alignment between visual and language features. These files are crucial for the seamless integration of visual encoders and language models, enabling LLaVA to effectively interpret and generate content that spans both modalities. MMProj files are fine-tuned during the model’s training process to ensure optimal performance in various applications.
What is the Quantized GGUF Version of LLaVA?
💾 The quantized GGUF (GPT-Generated Unified Format) version of LLaVA represents a compressed and optimized variant of the model, enabling efficient deployment on consumer-grade hardware. Quantization reduces the precision of the model’s weights, significantly decreasing the memory footprint and computational requirements while maintaining a high level of performance. This makes the quantized GGUF version ideal for applications where resource constraints are a concern.
Testing the Model
🧪 Testing showcases the beauty of the LLaVA model look at the details its providing in the example images.
Example 1
Example 2
Through rigorous testing and validation, LLaVA continues to demonstrate its potential as a versatile and powerful multimodal model.
Reference Links
Following are the list of helpful links:
| Description | Link |
|---|---|
| LLaVA Github Page | LLaVA (llava-vl.github.io) |
| Microsoft Research Paper | LLaVA: Large Language and Vision Assistant – Microsoft Research |
| Hugging Face GGUF model | xtuner/llava-llama-3-8b-v1_1-gguf · Hugging Face |
| Visual Instruction Tuning (arxiv) | [2304.08485] Visual Instruction Tuning (arxiv.org) |
🌐 LLaVA represents a significant advancement in the field of multimodal AI, combining powerful visual and language understanding capabilities in a single, efficient model. By leveraging the strengths of LLaMA 3 and innovative techniques like quantization and multimodal projection, LLaVA offers a robust tool for a wide range of applications. Whether you’re a researcher, developer, or enthusiast, exploring the potential of LLaVA can open up new possibilities in the realm of AI-driven interaction and understanding.
By following the steps outlined in this post, you can get started with LLaVA and begin harnessing its capabilities for your own projects. Please let me know if I’ve missed any steps or details, and I’ll be happy to update the post.
Thanks,
Aresh Sarkari