LlamaV-o1
| Property | Value |
|---|---|
| Parameter Count | 11 billion |
| Developer | MBZUAI |
| Release Date | January 13, 2025 |
| Framework | PyTorch |
| Paper | arXiv:2501.06186 |
What is LlamaV-o1?
LlamaV-o1 is an advanced multimodal large language model specifically designed for complex visual reasoning tasks. Built on the Llama architecture, this 11B parameter model excels in step-by-step reasoning across various domains including visual perception, mathematical reasoning, and document understanding. The model achieves impressive performance metrics, outperforming many open-source alternatives with 56.49% accuracy on final answers and 68.93% on reasoning steps.
Implementation Details
The model is implemented using PyTorch and can be easily accessed through the Hugging Face Transformers library. It utilizes advanced techniques like Beam Search and curriculum learning, with training conducted on the LLaVA-CoT-100k dataset. The architecture is optimized for both performance and computational efficiency.
- Fine-tuned for instruction-following and chain-of-thought reasoning
- Optimized inference scaling for balanced performance
- Includes over 4,000 manually verified reasoning steps
- Built on the established Llama architecture
Core Capabilities
- Complex visual reasoning and perception
- Step-by-step explanation generation
- Mathematical reasoning
- Social and cultural context understanding
- Medical imaging analysis
- Document comprehension
Frequently Asked Questions
Q: What makes this model unique?
LlamaV-o1 stands out for its exceptional performance in visual reasoning tasks and its ability to provide detailed, step-by-step explanations for its decisions. It achieves competitive performance against closed-source models while maintaining transparency and interpretability.
Q: What are the recommended use cases?
The model is ideal for applications requiring sophisticated visual reasoning, including conversational agents, educational tools, and content creation. However, it should not be used for high-stakes decision-making in fields like healthcare or finance.
